Category: Technology

Developing market-creating innovations in emerging markets

For over a decade I have been thinking about doing further study whether a masters, MBA, LLM or even a PhD. For various reasons I haven’t pressed the button on anything, although in 2019/20 I did get close.

I had just read a brilliant book called the “Prosperity Paradox” by Clayton Christensen which discusses why so many investments in economic development fail to generate sustainable prosperity, and how investing in market-creating innovations can create lasting change.

I was immediately hooked, although I was biased. I had focused my undergraduate business honours thesis on a former book by the same author called “The Innovators Dilemma”.

I found a few universities with suitable programmes and sent off applications. In the end I didn’t proceed with the offers but I thought it would be worthwhile to show the summary application and proposed research topic, approach and key areas to investigate. I still may look to explore this topic in the future albeit in a different way e.g. research, articles, consulting etc.

Proposed PhD Research title

“Developing Market-Creating Innovations That Drive Prosperity in Emerging Markets”

Background

The historic approach to improving outcomes and prosperity in emerging economies has typically focused around ‘poverty alleviation’ whereby private-sector companies and start-ups exploit existing markets at the top or ‘bottom of the pyramid’ (Prahalad 2004), or other initiatives which ‘push’ international aid, grants, loans, outsourcing, or incremental (‘sustaining’) improvements to existing offers for established customer bases. More recently, a number of leading management researchers led by Clayton Christensen (2019) argue that more successful approaches may lie in creating or ‘pulling in’ new market innovations that enable significant numbers of non-consumers to easily and affordably find a product or service to help them overcome daily struggles or solve an important problem. Pursuing this strategy (distinct from other types of innovation including ‘sustaining’ and ‘efficiency’ innovations), established firms and founders[1] typically see opportunity in the struggles of their respective frontier markets by targeting non-consumption in the broader market, creating not just products and services, but entire ecosystems, enabling infrastructure, networks and jobs to promote stability, prosperity and sustainable economic growth. Despite this opportunity, in 2016 alone, the OECD estimated that $143 billion was spent on official development approaches. Christensen (2019) however asks that what if this was instead channelled to support direct market-creation efforts in developing countries, even when those circumstances seemed unlikely? Some examples of market-creating innovations (MCI) are listed below:

  • M-PESA: A mobile money platform that enables the storage, transfer and saving of money without owning a bank account;
  • MicroEnsure: Affordable insurance for millions of people living on less than $3 a day;
  • Celtel: A pay-as-you-go mobile phone service that enables customers to purchase cell phone minutes from as little as 25 cents;
  • Galanz: An inexpensive microwave oven for the average Chinese citizen;
  • Tolaram: A tasty, inexpensive, easy-to-cook meal in Nigeria that can be prepared in less than three minutes;
  • Grupo Bimbo: Affordable, quality bread for Mexicans;
  • Ford Model T: An affordable car for the average American in the 1900s;

Topic

My PhD research will seek to build on these themes and the work of Christensen (2019) and others (Prahalad 2006; Auerswald 2012; Quadir 2014) to better understand the following key questions: How do established firms and start-ups successfully build market-creating innovations (“MCIs”) in emerging markets? Why are some firms successful, and others are not? The research will address gaps in understanding highlighted by Christensen (2019) in terms of further defining the process by which new markets are created, the characteristics that set market-creating innovators apart, and more details into the role of non-consumers (‘non-consumption economy’) in this process. In addition, my research will improve understanding of the relative importance of external factors which facilitate (or inhibit) success, including government, ecosystems, NGOs, investors, skilled labour, infrastructure, networks, and partners. The extent of benefits that MCIs deliver for society in terms of driving inclusive, sustainable and prosperous development across sectors including education, health, financial services, energy, and communications will also be analysed. Finally, the findings will deliver practical guidance, frameworks and insight for a wide range of international companies, entrepreneurs, governments, investors, thinktanks, and NGOs who pursue (or are looking to pursue) strategies and investments in emerging markets, or alternatively use the learnings to apply in more developed contexts

References

C.K. Prahalad, The Fortune at the Base of the Pyramid: Eradicating Poverty Through Profits (Upper Saddle River, NJ: Prentice Hall, 2006)

Philip Auerswald, The Coming Prosperity: How Entrepreneurs Are Transforming The Global Economy (Oxford University Press, 2012), 58

Iqbal Quadir, “Inclusive Prosperity in Low-Income Countries,” Innovations 9, no. 1-2 (2014): 65-66

Provide a statement of your research interests and intended research topics:

Research interests:

My research interests focus on how organisations innovate (across processes, practices, products, partnerships) in various contexts, including geographical (e.g. emerging or developed markets), new markets (e.g. non-consumption economy, consumer insight, go-to-market), operational (e.g. outsourcing, resource allocation, incentives, portfolio management, projects, change), offerings (e.g. new product development), technological (e.g. emerging technology), competitive (e.g. start-ups, business models), strategic (e.g. organic, M&A, JVs), human (e.g. leadership, culture, talent, skills), ecosystems (e.g. networks, partnerships, knowledge, public-sector), and sectoral (e.g. education, health, financial, energy).

I will use my many years of relevant professional experience working across most of the above topics (whether as an academic, lawyer, consultant, or founder) to ensure that the PhD research makes a substantial contribution to the academic research (see research questions), and provides practical insight for critical strategic and investment challenges for industry stakeholders (e.g. multi-national companies, investors, public sector, NGOs, etc).

Research topic:

My PhD research will seek to build on the themes of my research interests, and the work of Christensen and others to help answer the following question: How do established firms and start-ups successfully build market-creating innovations (“MCIs”) in emerging markets?

Core research questions include[2]:

  • What is the process by which these new markets are created?
  • What is the MCI development process within established and new (start-up) firms? For example, opportunity identification, development, investment, launch and scaling;
  • Why are some firms and efforts successful, and others are not?
  • What is the role non-consumers (‘non-consumption economy’) play in this process?
  • What are the qualities that set market-creating innovators and firms apart? For example, the ability to identify possibilities where there seem to be no customers;
  • What are the characteristics of the most successful (and unsuccessful) MCIs?  For example, business models, attributes, targeting non-consumption, value networks, ecosystems, partnering;
  • What are the most important internal and external conditions which facilitate or inhibit this process?
  • What commonalities exist across nations, sectors, firm size, age, or other variables?
  • What is the role of other key stakeholders in MCI development? For example, government, NGOs, investors, ecosystems, networks;
  • What are the key benefits for society, sectors (e.g. education) and stakeholders (e.g. government) from MCIs which deliver inclusive, sustainable and prosperous development?
  • What are the future implications for private and public sector organisations (e.g. companies, government, investors, NGOs etc) who wish to facilitate the future development of MCIs, or take the learnings into other developing (or developed) markets?

The below diagram describes the research focus areas and questions relevant to be asked:

Some anticipated research parameters may include a focus on:

  • Products/services and ventures which create new markets (“MCIs”) and benefits for large segments of the population, as opposed to product improvements (“sustaining innovations”) or efficiency gains (“efficiency innovations”).
  • Sectors that play key roles in prosperity development including education, health, financial services, communications, food and water, energy, and technology;
  • Data collection in a wide selection of geographies including BRIC nations, developing and developed nations (e.g. US), although the feasibility of this may prove problematic thereby requiring a more vertical approach (e.g. narrow to a few nations);
  • A time horizon of MCIs created post-2000 to capture more recent examples of MCI development;
  • An inter-disciplinary research approach given the wide-ranging research topic, building on academic researchers in fields including strategic management, strategic marketing, disruptive innovation, new product development, consumer insight, technology and operations management, innovation, organisational behaviour, leadership, emerging market strategy, international and economic development, and public policy;
  • Hybrid data collection strategy: whilst the research scope (e.g. companies, countries, sectors etc) and data collection strategy has yet to be defined, it is expected that a hybrid approach which mixes both qualitative and quantitative methods with primary and secondary research might be the most appropriate.  For example, face-to-face interviews, online surveys and case studies can help collect primary data to define firm MCI development processes. However, firm performance and development benefits (e.g. social, economic, and sectoral) will require quantitative analysis of public records and databases, as well as any additional internal data from private companies or government agencies.

[1] Examples of successful market-creating companies include Celtel (Africa), GrameenBank (Bangladesh), M-Pesa (Kenya), MicroEnsure (Africa), Jio (India) and Ford Motors (US) in the 1920s

[2] I have a range of sub-research questions but in the interests of brevity I have not included here.

Trends and issues of AI-enabled legal and compliance services

As AI continues to transform many industries[1], including the legal service industry, many experts are unanimous in predicting exponential growth in AI as a paramount technology to bring new tools and features to improve legal services and access to justice. Already, many aspects of the estimated $786B[2] market for legal services are being digitised, automated and AI-enabled whether discovery in litigation (e.g. RelativityAI), divorce (e.g. HelloDivorce), dispute resolution (e.g. DoNotPay) or contract management (e.g. IronClad).

As with many disruptive technologies, there are many experts who believe that AI will significantly disrupt (rather than extend) the legal market:

“AI will impact the availability of legal sector jobs, the business models of many law firms, and how in-house counsel leverage technology. According to Deloitte, about 100,000 legal sector jobs are likely to be automated in the next twenty years. Deloitte claims 39% of legal jobs can be automated; McKinsey estimates that 23% of a lawyer’s job could be automated. Some estimates suggest that adopting all legal technology (including AI) already available now would reduce lawyers’ hours by 13%”[3]

The real impact will be more nuanced over the long-term as whilst AI will eliminate certain tasks and some legal jobs, it will also augment and extend the way legal services are provided and consumed. In doing so, it will drive new ways of working and operating for both established and new entrant firms who will need to invest in new capabilities and skills to support the opening up new markets, new business models and new service innovations. In the past few decades, we have seen the impact of emerging and disruptive technologies on established players across many sectors, including banking (e.g. FinTechs), media and entertainment (e.g. music, movies, gambling), publishing (e.g. news), travel (e.g. Airbnb) and transportation (e.g. Uber). It is very likely traditional legal providers will be faced with the same disruptive challenges from AI and AI-enabled innovations bundling automation, analytics, and cloud with new business models including subscription, transaction or freemium.

Although AI and AI-enabled solutions present tremendous opportunities to support, disrupt or extend traditional legal services, they also present extremely difficult ethical questions for society, policy-makers and legal bodies (e.g. Law Society) to decide.

This is the focus of this article which sets out a summary of these issues, and is structured into two parts:

  1. Current and future use cases and trends of AI in legal and compliance services;
  2. Key issues for stakeholders including legal practitioners, society, organisations, AI vendors, and policy-makers.

A few notes:

  • This article is not designed to be exhaustive, comprehensive or academically detailed review and analysis of the existing AI and legal services literature. It is a blog post first and foremost (albeit a detailed one) on a topic of personal and professional interest to me, and should be read within this context;
  • Sources are referenced within the footnotes and acknowledged where possible, with any errors or omissions are my own.
  • Practical solutions and future research areas of focus is lightly touched on in the conclusion, however is not a focus for this article.

Part 1 – Current and future use cases of AI in legal and compliance services

Historically, AI in legal services has focused on automating tasks via software to achieve the same outcome as if a law practitioner had done the work. However, increasing innovation in AI and experimentation within the legal and broader ecosystem have allowed solutions to accelerate beyond this historical perspective.

The graphic below provides a helpful segmentation of four main use cases of how AI tools are being used in legal services[4]:

A wider view of use cases, which links to existing legal and business processes, is provided below:

  • e-discovery;
  • document and contract management
  • expertise automation;
  • legal research and insight
  • contract management
  • predictive analytics
  • dispute resolution
  • practice automation
  • transactions and deals
  • access to justice

Further context on a selection of these uses is summarised below (note, there is overlap between many of these areas):

  • E-Discovery – Over the past few years, the market for e-discovery services has accelerated beyond the historical litigation use case and into other enterprise processes and requirements (e.g. AML remediation, compliance, cybersecurity, document management). This has allowed for the development of more powerful and integrated business solutions enabled by the convergence of technologies including cloud, AI, automation, data and analytics. Players in the legal e-discovery space include Relativity, DISCO, and Everlaw.
  • Document and contract management – The rapid adoption of cloud technologies have accelerated the ability of organisations across all sectors to invest in solutions to better solve, integrate and automate business processes challenges, such as document and contract lifecycle management. For contracts, they need to be initiated (e.g. templates, precedents), shared, stored, monitored (e.g. renewals) or searched and tracked for legal, regulatory or dispute reasons (e.g. AI legaltech start-ups like Kira, LawGeex, and eBrevia). In terms of drafting and collaboration, the power of Microsoft Word, Power Automate and G-Suite solutions has expanded along with a significant number of  AI-powered tools or sites (e.g. LegalZoom) that help lawyers (and businesses or consumers) to find, draft and share the right documents whether for commercial needs, transactions or litigation. New ‘alternative legal service’ entrants have combined these sorts of powerful solutions (and others in this list) with lower-cost labour models (with non-legal talent and/or lower-cost legal talent) to provide a more integrated offering for Fortune500 legal, risk and compliance teams (e.g. Ontra, Axiom, UnitedLex, Elevate, Integreon);
  • Expertise Automation –In the access to justice context, there are AI-powered services that automate contentious or bureaucratic situations for individuals such as utility bill disputes, small claims, immigration filing, or fighting traffic tickets (e.g. DoNotPay). Other examples include workflow automation software to enable consumers to draft a will (for a fixed fee or subscription) or chatbots in businesses to give employees access to answers to common questions in a specific area, such as employment law. It is forseeable that extending this at scale in a B2C context (using AI-voice assistants Siri or Alexa) with a trusted brand (e.g. Amazon Legal perhaps?) – and bundled into your Prime subscription alongside music, videos and same-day delivery – will be as easy as checking the weather or ordering an Uber.
  • Legal Research – New technologies (e.g. AI, automation, analytics, e-commerce) and business models (e.g. SaaS) have enabled the democratisation of legal knowledge beyond the historic use cases (e.g. find me an IT contract precedent or Canadian case law on limitation of liability). New solutions make it easy for clients and consumers (as well as lawyers) to find answers or solutions to legal or business challenges without interacting with a lawyer. In more recent times, legal publishing companies (e.g. LexisNexis, PLC, Westlaw) have leveraged legal sector relationships and huge databases of information including laws and regulations in multiple jurisdictions to build different AI-enabled solutions and business models for clients (or lawyers). These offerings promise fast, accurate (and therefore cost-effective) research with a variety of analytical and predictive capabilities. In the IP context, intellectual property lawyers can use AI-based software from companies like TrademarkNow and Anaqua to perform IP research, brand protection and risk assessment;
  • Legal and predictive analytics – This area aims to generate insights from unstructured, fragmented and other types of data sets to improve future decision-making.  A key use case are the tools that will analyse all the decisions in a domain (e.g. software patent litigation cases), input the specific issues in a case including factors (e.g. region, judge, parties etc) and provide a prediction of likely outcomes. This may significantly impact how the insurance and medical industry operate in terms of risk, pricing, and business models. For example, Intraspexion leverages deep learning to predict and warn users of their litigation risks, and predictive analytical company CourtQuant has partnered with two litigation financing companies to help evaluate litigation funding opportunities using AI. Another kind of analytics will review a given piece of legal research or legal submission to a court and help judges (or barristers) identify missing precedents In addition, there is a growing group of AI providers that provide what are essentially do-it-yourself tool kits to law firms and corporations to create their own analytics programs customized to their specific needs;
  • Transactions and deals – Although no two deals are the same, similar deals do require similar processes of pricing, project management, document due diligence and contract management. However, for various reasons, many firms will start each transaction with a blank sheet of paper (or sale and purchase agreement) or a sparsely populated one. However, AI-enabled document and contract automation solutions – and other M&A/transaction tools – are providing efficiencies during each stage of the process. In more advanced cases, data room vendors in partnership with law firms or end clients are using AI to analyse large amounts of data created by lawyers from previous deals. This data set is capable of acting as an enormous data bank for future deals where the AI has the ability to learn from these data sets in order to then:
    • Make clause recommendations to lawyers based on previous drafting and best practice.
    • Identify “market” standards for contentious clauses.
    • Spot patterns and make deal predictions.
    • Benchmark clauses and documents against given criteria.
    • Support pricing decisions based on key variables
  • Access to justice – Despite more lawyers in the market than ever before, the law has arguably never been more inaccessible. From a small consumer perspective, there are thousands of easy-to-use and free or low cost apps or online services which solve many simple or challenging aspects of life, whether buying properties, consulting with a doctor, making payments, finding on-demand transport, or booking household services. However, escalating costs and increasing complexity (both in terms of the law itself and the institutions that apply and enforce it) mean that justice is often out of reach for many, especially the most vulnerable members of society. With the accelerating convergence of various technologies and business models, it is starting to play a role in opening up the (i) provision of legal services to a greater segment of the population and (ii) replacing or augmenting the role of legal experts. From providing quick on-demand access to a lawyer via VC, accelerating time to key evidence, to bringing the courtroom to even the most remote corners of the world and digitizing many court processes, AI, augmented intelligence, and automation is dramatically improving the accessibility and affordability of legal representation. Examples include:
    • VC tools e.g. Zoom, FaceTime
    • Document and knowledge automation e.g. LegalZoom
    • ADR to ODR (online dispute resolution) e.g. eBay, Alibaba
    • Speed to evidence – Cloud-based, AI-powered technology e.g. DISCO

2. Key issues for the future of AI-power legal and compliance services  

There are many significant issues and challenges for the legal sector when adopting AI and AI-powered solutions. Whilst every use case of AI-deployment is unique, there are some overarching issues to be explored by key stakeholders including the legal profession, regulators, society, programmers, vendors and government.  

A sample of key questions include the following:

  • Will AI in the future make lawyers obsolete?
  • How does AI impact the duty of competence and related professional responsibilities?
  • How do lawyers, users and clients and stakeholders navigate the ‘black box’ challenge?
  • Do the users (e.g. lawyers, legal operations, individuals) and clients trust the data and the insights the systems generate?
  • How will liability be managed and apportioned in a balanced, fair and equitable way?
  • How do organisations identify, procure, implement and govern the ‘right’ AI-solution for their organisation?
  • Are individuals, lawyers or clients prepared to let data drive decision outcomes?
  • What is the role of ethics in developing AI systems?

Other important questions include:

  • How do AI users (e.g. lawyers), clients or regulators ‘audit’ an AI system?
  • How can AI systems be safeguarded from cybercriminals?
  • To what extent do AI-legal services need to be regulated and consumers be protected?
  • Have leaders in businesses identified the talent/skills needed to realise the business benefits (and manage risks) from AI?
  • To what extent is client consent to use data an issue in the development and scaling of AI systems?
  • Are lawyers, law students, or legal service professionals receiving relevant training to prepare for how they need to approach the use of AI in their jobs?
  • Are senior management and employees open to working with or alongside AI systems in their decisions and decision-making?

Below we further explore a selection of the above questions:

  • Obsolescence – When technology performs better than humans at certain tasks, job losses for those tasks are inevitable. However, the dynamic role of a lawyer — one that involves strategy, negotiation, empathy, creativity, judgement, and persuasion — can’t be replaced by one or several AI programs. As such, the impact of AI on lawyers in the profession may not be as dire as some like to predict. In his book Online Courts and the Future of Justice, author Richard Susskind discusses the ‘AI fallacy’ which is the mistaken impression that machines mimic the way humans work. For example, many current AI systems review data using machine learning, or algorithms, rather than cognitive processes. AI is adept at processing data, but it can’t think abstractly or apply common sense as humans can. Thus, AI in the legal sector enhances the work of lawyers, but it can’t replace them (see chart below[5]).
  • Professional Responsibility – Lawyers in all jurisdictions have specific professional responsibilities to consider and uphold in the delivery of legal and client services. Sample questions include:
    • Can a lawyer discharge professional duties of competence if they do not understand how the technology works?
    • Is a legal chatbot practicing law?
    • How does a lawyer provide adequate supervision where the lawyer does not understand how the work is being done or even ‘who’ is doing it?
    • How will a lawyer explain decisions made if they do not even know how those decisions were derived?

To better understand these complex questions, the below summaries some of the key professional duties and how they are being navigated by various jurisdictions:

Duty of Competence: The principal ethical obligation of lawyers when they are developing or assisting clients is the duty of competence. Over the past decade, many jurisdictions are specifically requiring lawyers to understand how (and why) new technologies such as AI, impact that duty (and related duties). This includes the requirement for lawyers to develop and maintain competence in ‘relevant technologies’. In 2012, in the US the American Bar Association (the “ABA”) explicitly included the obligation of “technological competence” as falling within the general duty of competence which exists within Rule 1.1 of its Model Rules of Professional Conduct (“Model Rules”)[6]. To date, 38 states have adopted some version of this revised comment to Rule 1.1. In Australia, most state solicitor and barrister regulators have incorporated this principle into their rules. In the future, jurisdictions may consider it unethical for lawyers or legal service professionals to avoid technologies that could benefit one’s clients. A key challenge is that there is no easy way to provide objective and independent analysis of the efficacy of any given AI solution, so that neither lawyers nor clients can easily determine which of several products or services actually achieve either the results they promise. In the long-term, it will very likely be one of the tasks of the future lawyer to assist clients in making those determinations and in selecting the most appropriate solution for a given problem. At a minimum, lawyers will need to be able to identify and access the expertise to make those judgments if they do not have it themselves.

Duty to Supervise – This supervisory duty assumes that lawyers are competent to select and oversee team members and the proper use of third parties (e.g. law firms) in the delivery of legal services[7]. However, the types of third parties used has expanded in recent times due to liberalisation of legal practice in some markets (e.g. UK due to the ABS laws allowing non-lawyers to operate legal services businesses). For example, alternative service providers, legal process outsourcers, tech vendors, and AI vendors have historically been outside of the remit of the solicitor or lawyer regulators (this is changing in various jurisdictions as discussed in below sections). By extension, to what extent is this more than just a matter of the duty to supervise what goes on with third parties, but how those third-parties provide services especially if technologies and tools are used? In such a case, potential liability issues arise if client outcomes are not successful: did the lawyer appropriately select the vendor, and did the lawyers properly manage the use of the solution?

The Duty to Communicate – In the US, lawyers also have an explicit duty to communicate to material matters to clients in connection with the lawyers’ services. This duty is set out in ABA Model Rue 1.4 and other jurisdictions have adopted similar rules[8]. Thus, not only must lawyers be competent in the use of AI, but they will need to understand its use sufficiently to explain to clients the question of the selection, use, and supervision of AI tools.

Black Box Challenge  

  • Transparency – A basic principle of justice is transparency – the requirement to explain and justify the reasons for a decision. As AI algorithms grow more advanced and rely on increasing volumes of structured and unstructured data sets, it becomes more difficult to make sense of their inner workings or how outcomes have been derived. For example, Michael Kearns and Aaron Roth report in Ethical Algorithm Design Should Guide Technology Regulation[9]:

“Nearly every week, a new report of algorithmic misbehaviour emerges. Recent examples include an algorithm for targeting medical interventions that systematically led to inferior outcomes for black patients, a resume-screening tool that explicitly discounted resumes containing the word “women” (as in “women’s chess club captain”), and a set of supposedly anonymized MRI scans that could be reverse-engineered to match to patient faces and names”.

Part of the problem is that many of these types of AI systems are ‘self-organising’ so they are inherently without external supervision or guidance. The ‘secrecy’ of AI vendors – especially those in a B2B and legal services context – regarding the inner workings of the AI algorithms and data sets doesn’t make the transparency and trust issue difficult for customers, regulators and other stakeholders. For lawyers, to what extent must they know the inner workings of that black box to ensure that she meets her ethical duties of competence and diligence? Without addressing this, these problems will likely continue as the legal sector increases its reliance on technology increases and injustices, in all likelihood, continue to arise. Over time, many organisations will need to have a robust and integrated AI business strategy designed at the board and management level to guide the wider organisation on these AI issues across areas including governance, policy, risk, HR and more. For example, during procurement of AI solutions, buyers, stakeholders and users (e.g. lawyers) must consider broader AI policies and mitigate these risk factors during vendor evaluation and procurement.

  • Algorithms – There are many concerns that AI algorithms are inherently limited in their accuracy, reliability and impartiality[10]. These limitations may be the direct result of biased data, but they may also stem from how the algorithms are created. For example, how software engineers choose a set of variables to include in an algorithm, deciding how to use variables, whether to maximize profit margins or maximize loan repayments, can lead to a biased algorithm. Programmers may also struggle to understand how an AI algorithm generates its outputs—the algorithm may be unpredictable, thus validating “correctness” or accuracy of those outputs when piloting a new AI system. This brings up the challenge of auditing algorithms:

“More systematic, ongoing, and legal ways of auditing algorithms are needed. . . . It should be based on what we have come to call ethical algorithm design, which begins with a precise understanding of what kinds of behaviours we want algorithms to avoid (so that we know what to audit for), and proceeds to design and deploy algorithms that avoid those behaviours (so that auditing does not simply become a game of whack-a-mole).”[11]

In terms of AI applications, most AI algorithms within legal services are currently able to perform only a very specific set of tasks based on data patterns and definitive answers. Conversely, it performs poorly when applied to the abstract or open-ended situations requiring judgment, such as the situations that lawyers often operate in[12]. In these circumstances, human expertise and intelligence are still critical to the development of AI solutions. Many are not sophisticated enough to understand and adapt to nuances, and to respond to expectations and layered meaning, and comprehend the practicalities of human experience. Thus, AI still a long way from the ‘obsolescence’ issue for lawyers raised above, and further research is necessary on programmers’ and product managers’ decision-making processes and methodologies when ideating, designing, coding, testing and training an AI algorithm[13]:

  • Data – Large volumes of data is a critical part of AI algorithm development as training material and input material. However, data sets may be of poor quality for a variety of reasons. For example, the data an AI system is ‘trained’ on may well include systemic ‘human’ bias, such as recruiters’ gender or racial discrimination of job candidates. In terms of data quality in law firms, most are slow at adopting new technologies and tend to be “document rich, and data poor” due, in large part, to legacy on-premise systems (or hybrid cloud) which do not integrate with each other. As more firms and enterprises transition to the cloud, this will accelerate the automation of business processes (e.g. contract management) with more advanced data and analytics capabilities to enable and facilitate AI system adoption (in theory, however there are many constraints within traditional law firm business and operating models which makes the adoption of AI-enabled solutions at scale unlikely). However, 3rd party vendors within the legal sector including e-discovery, data rooms, and legal process outsourcers – or new tech-powered entrants from outside of the legal sector – do not have such constraints and are able to innovate more effectively using AI, cloud, automation and analytics in these contexts (however other constants exist such as client consent and security). In the court context, public data such as judicial decisions and opinions are either not available or so varied in format as to be difficult to use effectively[14]. Beyond data quality issues, significant data privacy, client confidentiality and cybersecurity concerns exist which raises the need to define and implement standards (including safeguards) to build confidence in the use of algorithmic systems – and especially in legal contexts. As AI becomes more pervasive within law firms, legal departments, legal vendors (including managed services) and new entrants outside of legal, a foundation with strong guidelines for ethical use, transparency, privacy, cross-department sharing and more becomes even crucial[15].
  • Implementation – Within the legal sector, law firms and legal departments are laggards when it comes to adopting new technologies, transforming operations, and implementing change. With business models based on hours billed (e.g. law firms), this may not incentivize the efficiency improvements that AI systems can provide.  In addition:

“Effective deployment of AI requires a clearly defined use case and work process, strong technical expertise, extensive personnel and algorithm training, well-executed change management processes, an appetite for change and a willingness to work with the new technologies. Potential AI users should recognize that effectively deploying the technology may be harder than they would expect. Indeed, the greatest challenge may be simply getting potential users to understand and to trust the technology, not necessarily deploying it[16].

However, enterprises (e.g. Fortune500), start-ups, alternative service providers (e.g. UnitedLex) and new entrants from outside of legal do not suffer from these constraints, and are likely to be more successful – from a business model and innovation perspective – in adopting new AI-enabled solutions for use with clients (although AI-enabled providers must work to overcome client concerns as discussed above).   

  • Liability – There are a number of issues to consider on the topic of liability. Key questions are set out below:
    • Who is responsible when things do go wrong? Although AI might be more efficient than a human lawyer at performing these tasks, if the AI system misses clauses, mis-references definitions, or provides incorrect outcome/price predictions caused by AI software, all parties risk claims depending on how the parties apportioned liability. The role of contract and insurance is key, however this assumes that law firms have the contractual means of passing liability (in terms of professional duties) onto third parties. In addition, when determining relative liability between the provider of the defective solution and the lawyer, should a court consider the steps the lawyer took to determine whether the solution was the appropriate one for use in the particular client’s matter?
    • Should AI developers be liable for damage caused by their product? In most other fields, product liability is an established principle. But if the product is performing in ways no-one could have predicted, is it still reasonable to assign blame to the developer? AI systems also often interact with other systems so assigning liability becomes difficult. AI solutions are also fundamentally reliant on the data they were trained on, so liability may exists with the data sources.  Equally, there are risks of AI systems that are vulnerable to hacking.
    • To what extent are, or will, lawyers be liable when and how they use, or fail to use, AI solutions to address client needs? One example explained above is whether a lawyer or law firm will be liable for malpractice if the judge in a matter accesses software that identifies guiding principles or precedents that the lawyer failed to find or use. It does not seem to be a stretch to believe that liability should attach if the consequence of the lawyer’s failure to use that kind of tool is a bad outcome for the client and the client suffers injury as a result.
  • Regulatory Issues – As discussed above, addressing the significant issues of bias and transparency in AI tools, and, in addition, advertising standards, will grow in importance as the use of AI itself grows. Whilst the wider landscape for regulating AI is fragmented across industry and political spheres, there are signs the UK, EU and US are starting to align.[17] Within the legal services sector, some jurisdictions (e.g. England, Wales, Australia and certain Canadian provinces) are in the process of adopting and implementing a broader regulatory framework. This approach enables the legal regulators to oversee all providers of legal services, not just traditional law firms and/or lawyers. However, in the interim the implications of this regulatory imbalance will become more pronounced as alternative legal service providers play an increasing role in providing clients with legal services, often without any direct involvement of lawyers. In the long run, a broader regulatory approach is going to be critically important in establishing appropriate standards for all providers of AI-based legal services.
  • Ethics – The ethics of AI and data uses remains a high concern and key topic for debate in terms of the moral implications or unintended consequences that result from the coming together of technology and humans. Even proponents of AI, such as Elon Musk’s OpenAI group, recognise the need to police AI that could be used for ‘nefarious’ means. A sample of current ethical challenges in this area include:
    • Big data, cloud and autonomous systems provoke questions around security, privacy, identify, and fundamental rights and freedoms;
    • AI and social media challenge us to define how we connect with each other, source news, facts and information, and understand truth in the world;
    • Global data centres, data sources and intelligent systems means there is limited control of the data outside our borders (although regimes including GDPR is addressing this);
    • Is society content with AI that kills? Military applications including lethal autonomous weapons are already here;
    • Facial recognition, sentiment analysis, and data mining algorithms could be used to discriminate against disfavoured groups, or invade people’s privacy, or enable oppressive regimes to more effectively target political dissidents;
    • It may be necessary to develop AI systems that disobey human orders, subject to some higher-order principles of safety and protection of life;

Over the years, the private and public sectors have attempted to provide various frameworks and standards to ensure ethical AI development. For example, the Aletheia Framework[18] (developed by Rolls-Royce in an open partnership with industry) is a recent, practical one-page toolkit that guides developers, executives and boards both prior to deploying an AI, and during its use. It asks system designs and relevant AI business managers to consider 32 facets of social impact, governance and trust and transparency and to provide evidence which can then be used to engage with approvers, stakeholders or auditors. A new module added in December 2021 is a tried and tested way to identify and help mitigate the risk of bias in training data and AIs. This complements the existing five-step continuous automated checking process, which, if comprehensively applied, tracks the decisions the AI is making to detect bias in service or malfunction and allow human intervention to control and correct it.

Within the practice of law, while AI offers cutting-edge advantages and benefits, it also raises complicated questions for lawyers around professional ethics. Lawyers must be aware of the ethical issues involved in using (and not using) AI, and they must have an awareness of how AI may be flawed or biased. In 2016, The House of Commons Science and Technology Committee (UK Parliament) recognised the issue:

“While it is too soon to set down sector-wide regulations for this nascent field, it is vital that careful scrutiny of the ethical, legal and societal dimensions of artificially intelligent systems begins now”.

In a 2016 article in the Georgetown Journal of Legal Ethics, the authors Remus and Levy were concerned that:

“…the core values of legal professionalism meant that it might not always be desirable, even if feasible, to replace humans with computers because of the different way they perform the task. This assertion raises questions about what the core values of the legal profession are and what they should or could be in the future. What is the core value of a solicitor beyond reserved activities? And should we define the limit of what being a solicitor or lawyer is?[19]

These are all extremely nuanced, complex and dynamic issues for lawyers, society, developers and regulators at large. How the law itself may need to change to deal with these issues will be a hot topic of debate in the coming years.

Conclusion

Over the next few years there can be little doubt that AI will begin to have a noticeable impact on the legal profession and consumers of legal services. Law firms, in-house legal departments and alternative legal services firms and vendors – plus new entrants outside of legal perhaps unencumbered by the constraints of established legal sector firms – have opportunities to explore and challenges to address, but it is clear that there will be significant change ahead. What is required of a future ‘lawyer’ (this term may mean something different in the future) or legal graduate today – let alone in 2025 or 2030 versus new lawyers of a few decades ago, will likely be transformed in many ways. There are also many difficult ethical questions for society to decide, for which the legal practice regulators (e.g. Law Society in England and Wales) may be in a unique position to grasp the opportunity of ‘innovating the profession’ and lead the debate. On the other hand, as the businesses of the future become more AI-enabled at their core (e.g. Netflix, Facebook, Google, Amazon etc), the risk that many legal services become commoditised or a ‘feature set’ within a broader business or service model is a real possibility in the near future.

At the same time, AI itself poses significant legal and ethical questions across all sorts of sectors and priority global challenges, from health, to climate change, to war, to cybersecurity. Further analysis on the legal and ethical implications of AI for society, legal practitioners, organisations, AI vendors, and policy-makers, plus what practical solutions can be employed to navigate the safe and ethical deployment of AI in the legal and other sectors, will be critical.

[1] AI could contribute up to $15.7 trillion1 to the global economy in 2030, more than the current output of China and India combined. Of this, $6.6 trillion is likely to come from increased productivity and $9.1 trillion is likely to come from consumption side effects.

[2] https://www.statista.com/statistics/605125/size-of-the-global-legal-services-market/

[3] https://jolt.law.harvard.edu/digest/a-primer-on-using-artificial-intelligence-in-the-legal-profession

[4] https://www.morganlewis.com/-/media/files/publication/presentation/webinar/2020/session-11_the-ethics-of-artificial-intelligence-for-the-legal-profession_18june20.pdf

[5] https://kirasystems.com/learn/can-ai-be-problematic-in-legal-sector/

[6] https://www.americanbar.org/groups/professional_responsibility/publications/professional_lawyer/27/1/the-future-law-firms-and-lawyers-the-age-artificial-intelligence

[7] Australian Solicitors Conduct Rules 2012, Rule 37 Supervision of Legal Services.

[8] https://lawcat.berkeley.edu/record/1164159?ln=en

[9] https://www.brookings.edu/research/ethical-algorithm-design-should-guide-technology-regulation/

[10] https://hbr.org/2019/05/addressing-the-biases-plaguing-algorithms

[11] https://www.brookings.edu/research/ethical-algorithm-design-should-guide-technology-regulation/

[12] https://hbr.org/2019/05/addressing-the-biases-plaguing-algorithms

[13] https://bostonreview.net/articles/annette-zimmermann-algorithmic-political/

[14] https://www.law.com/legaltechnews/2019/10/29/uninformed-or-underwhelming-most-lawyers-arent-seeing-ais-value/

[15] https://www.crowell.com/NewsEvents/Publications/Articles/A-Tangled-Web-How-the-Internet-of-Things-and-AI-Expose-Companies-to-Increased-Tort-Privacy-and-Cybersecurity-Litigation

[16] https://www.lexisnexis.co.uk/pdf/lawyers-and-robots.pdf

[17] https://www.brookings.edu/blog/techtank/2022/02/01/the-eu-and-u-s-are-starting-to-align-on-ai-regulation/

[18] https://www.rolls-royce.com/sustainability/ethics-and-compliance/the-aletheia-framework.aspx

[19]https://go.gale.com/ps/i.do?id=GALE%7CA514460996&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=10415548&p=AONE&sw=w&userGroupName=anon%7E138c97cd

Top ten traps which cause GC’s to fail around technology, automation and change

I recently came across a brilliant guide from Plexus, a legal software and services provider based in Australia. Although most GCs will rate rate ‘investing in technology and automation’ as their top priority, many initiatives will never get past the starting line.

There are many reasons for this and everyone’s context is different. It is certainly not for lack of intent, ambition, need, or interest.

According to Plexus, the biggest challenge functional leaders have is when they are required to rapidly work and operate in a cross-functional way. These skills are a new ‘core’ capability for a legal function or executive:

If you ask a GC how they select and sign up a new law firm to spend $100,000 - the answer is often as simple as a few emails and signed engagement letter. Ask them how they will spend half that amount on technology, and they will scratch their head… even though - because of the ‘sunk cost’ nature of professional services spend’ it is far more likely that it will not generate value

Plexus.co

I have summarised the top ten traps below:

You let risk aversion get the better of you.

You approach the project like a contract negotiation.

You frame the business case around ‘what legal needs’

You get hung up in ‘integrations’

You get too many people involved.

You try to go too big too early.

You delegate responsibility to others.

You allow other functions to drive the agenda.

You fall for the ‘tomorrow fallacy’.

You work with vendors with the wrong orientation.

I highly recommend reading the full version here which contains more detail on the above traps plus extremely practical ‘do this’ and ‘do not do this’ strategies and tactics.

The 12 Core Principles for Legal Operational Excellence

Management consultants whether McKinsey, BCG or Accenture have made an industry out of identifying best practices and applying to specific company challenges.

Although most in-house legal and compliance departments have remained immune from this for many decades, the tide has been turning for some years now with many legal departments building out higher-performing teams, operations, and services. Leveraging best practice insight – from across all sectors not just legal teams – has been a key ways to support this.

One of these tools is the ‘Core 12’ from the Corporate Legal Operations Consortium:

“While every company and team has its own unique needs, the guidance in these functional areas – known as the “Core 12” – applies to many environments and requirements towards operational excellence”.

The Core 12 can be seen below:

Essentially these are the operations, services or capabilities which define the legal function. CLOC provide more context below:

“Legal operations” (or legal ops) describes a set of business processes, activities, and the professionals who enable legal departments to serve their clients more effectively by applying business and technical practices to the delivery of legal services. Legal ops provides the strategic planning, financial management, project management, and technology expertise that enables legal professionals to focus on providing legal advice.

The Core 12 allows any legal department leader or 3rd party consultant to assess their current state of performance maturity, map it to the ideal state, and then decide and plan what are steps they wish to take to improve which makes sense for their specific context and constraints.

The last aspect is critical as the Head of Legal in a Series A-funded start-up will have completely different challenges, requirements and objectives to a Fortune 100 legal team.

When selecting one of the 12, you can deep-dive further into that area of competence. For example, with Technology, CLOC provide the following high-level (and non-exhaustive) detail to help understand what good generally looks like:

TECHNOLOGY: Innovate, automate, and solve problems with technology.

Current reality: Teams often rely on manual, time-consuming, and fragmented point solutions. They may lack an overall technology vision and are deploying costly applications that are underused and disconnected from the team’s workflow.

Desired state: Create a clear technology vision that spans all of the needs of your organization. Automate manual processes, digitize physical tasks, and improve speed and quality through the strategic deployment of technology solutions.

  • Create and implement a long-term technology roadmap
  • Incorporate connected tools for e-billing, matter management, contact management, IP management, e-signature, and more
  • Automate repetitive or time-consuming manual processes
  • Determine where to build and where to buy
  • Evaluate new vendors, suppliers, and solutions
  • Assess emerging technology capabilities and incorporate into your long-term strategic planning
  • Structure an effective partnership with your corporate IT team

Although the CLOC 12 isn’t of itself a useable tool as far as detailed diagnostic, business analysis or benchmarking is concerned, it does provide a helpful introduction for legal leaders looking to learn more about what good looks like in terms of legal operations and capabilities.

CLOC have a download guide with more information on the Core 12 which you can access here

BBC’s Reith Lectures – Living with AI

Last Wednesday BBC R4 hosted the first of 4 weekly lectures hosted by Professor Stuart Russell, a world-renowned AI expert at UCLA. The talks (followed by Q&A) examine the impact of AI on our lives and discuss how we can retain power over machines more powerful than ourselves.

I think this area (e.g. AI commercialisation, AI governance, AI safety, AI ethics, AI regulation etc) is going to be one of the hot topics of the next decade alongside trends including climate change, fintech (crypto), AR/VR, quantum computing etc. Accordingly I couldn’t wait to hear Professor Russell speak.

The event blurb states the following:

The lectures will examine what Russell will argue is the most profound change in human history as the world becomes increasingly reliant on super-powerful AI. Examining the impact of AI on jobs, military conflict and human behaviour, Russell will argue that our current approach to AI is wrong and that if we continue down this path, we will have less and less control over AI at the same time as it has an increasing impact on our lives. How can we ensure machines do the right thing? The lectures will suggest a way forward based on a new model for AI, one based on machines that learn about and defer to human preferences.

As I write, I have heard 2 talks both of which have been absolutely fascinating (and quite honestly, scary. Especially regarding military applications of AI which is already here). I didn’t take notes however the BBC interviewed Professor Russell ahead of the talks. I have provided a summary of the Q&A below which is well worth a read:

How have you shaped the lectures?

The first drafts that I sent them were much too pointy-headed, much too focused on the intellectual roots of AI and the various definitions of rationality and how they emerged over history and things like that.

So I readjusted – and we have one lecture that introduces AI and the future prospects both good and bad.

And then, we talk about weapons and we talk about jobs.

And then, the fourth one will be: “OK, here’s how we avoid losing control over AI systems in the future.”

Do you have a formula, a definition, for what artificial intelligence is?

Yes, it’s machines that perceive and act and hopefully choose actions that will achieve their objectives.

All these other things that you read about, like deep learning and so on, they’re all just special cases of that.

But could a dishwasher not fit into that definition?

It’s a continuum.

Thermostats perceive and act and, in a sense, they have one little rule that says: “If the temperature is below this, turn on the heat.

“If the temperature is above this, turn off the heat.”

So that’s a trivial program and it’s a program that was completely written by a person, so there was no learning involved.

All the way up the other end – you have the self-driving cars, where the decision-making is much more complicated, where a lot of learning was involved in achieving that quality of decision-making.

But there’s no hard-and-fast line.

We can’t say anything below this doesn’t count as AI and anything above this does count.

And is it fair to say there have been great advances in the past decade in particular?

In object recognition, for example, which was one of the things we’ve been trying to do since the 1960s, we’ve gone from completely pathetic to superhuman, according to some measures.

And in machine translation, again we’ve gone from completely pathetic to really pretty good.

So what is the destination for AI?

If you look at what the founders of the field said their goal was, general-purpose AI, which means not a program that’s really good at playing Go or a program that’s really good at machine translation but something that can do pretty much anything a human could do and probably a lot more besides because machines have huge bandwidth and memory advantages over humans.

Just say we need a new school.

The robots would show up.

The robot trucks, the construction robots, the construction management software would know how to build it, knows how to get permits, knows how to talk to the school district and the principal to figure out the right design for the school and so on so forth – and a week later, you have a school.

And where are we in terms of that journey?

I’d say we’re a fair bit of the way.

Clearly, there are some major breakthroughs that still have to happen.

And I think the biggest one is around complex decision-making.

So if you think about the example of building a school – how do we start from the goal that we want a school, and then all the conversations happen, and then all the construction happens, how do humans do that?

Well, humans have an ability to think at multiple scales of abstraction.

So we might say: “OK, well the first thing we need to figure out is where we’re going to put it. And how big should it be?”

We don’t start thinking about should I move my left finger first or my right foot first, we focus on the high-level decisions that need to be made.

You’ve painted a picture showing AI has made quite a lot of progress – but not as much as it thinks. Are we at a point, though, of extreme danger?

I think so, yes.

There are two arguments as to why we should pay attention.

One is that even though our algorithms right now are nowhere close to general human capabilities, when you have billions of them running they can still have a very big effect on the world.

The other reason to worry is that it’s entirely plausible – and most experts think very likely – that we will have general-purpose AI within either our lifetimes or in the lifetimes of our children.

I think if general-purpose AI is created in the current context of superpower rivalry – you know, whoever rules AI rules the world, that kind of mentality – then I think the outcomes could be the worst possible.

Your second lecture is about military use of AI and the dangers there. Why does that deserve a whole lecture?

Because I think it’s really important and really urgent.

And the reason it’s urgent is because the weapons that we have been talking about for the last six years or seven years are now starting to be manufactured and sold.

So in 2017, for example, we produced a movie called Slaughterbots about a small quadcopter about 3in [8cm] in diameter that carries an explosive charge and can kill people by getting close enough to them to blow up.

We showed this first at diplomatic meetings in Geneva and I remember the Russian ambassador basically sneering and sniffing and saying: “Well, you know, this is just science fiction, we don’t have to worry about these things for 25 or 30 years.”

I explained what my robotics colleagues had said, which is that no, they could put a weapon like this together in a few months with a few graduate students.

And in the following month, so three weeks later, the Turkish manufacturer STM [Savunma Teknolojileri Mühendislik ve Ticaret AŞ] actually announced the Kargu drone, which is basically a slightly larger version of the Slaughterbot.

What are you hoping for in terms of the reaction to these lectures – that people will come away scared, inspired, determined to see a path forward with this technology?

All of the above – I think a little bit of fear is appropriate, not fear when you get up tomorrow morning and think my laptop is going to murder me or something, but thinking about the future – I would say the same kind of fear we have about the climate or, rather, we should have about the climate.

I think some people just say: “Well, it looks like a nice day today,” and they don’t think about the longer timescale or the broader picture.

And I think a little bit of fear is necessary, because that’s what makes you act now rather than acting when it’s too late, which is, in fact, what we have done with the climate.

The Reith Lectures will be on BBC Radio 4, BBC World Service and BBC Sounds.

Legaltech Venture Investment

This week Crunchbase produced some numbers covering Legal tech investments in 2021.

Legal tech companies have already seen more than $1 billion in venture capital investments so far this calendar year, according to Crunchbase data. That number smashes the $510 million invested last year and the all-time high of $989 million in 2019.

While dollars are higher, deal flow is a little behind previous years, with 85 funding rounds being announced so far in 2021, well behind the pace of 129 deals last year and 147 in 2019.

Some of the largest rounds in the sector this year include:

  • San Francisco-based Checkr, a platform that helps employers screen job seekers through initiating background checks, raised a $250 million Series E at a $4.6 billion valuation earlier this month;
  • San Francisco-based legal services provider Rocket Lawyer closed a $223 million venture round in April; and
  • Boston-based on-demand remote electronic notary service Notarize raised a $130 million Series D in March at a reported $760 million valuation.

According to various start-up founders:

“This mainly is a paper-based industry. However, COVID exposed inefficiencies and it forced people to look at everything you do and explore new ways.”- Patrick Kinsel, founder and CEO at Notarize

“There’s no doubt COVID provided huge tailwinds for legal tech growth,” said Jack Newton, co-founder and CEO at Vancouver-based legal tools platform Clio, which raised a $110 million Series E at a $1.6 billion valuation. “It was the forcing factor for firms that had put off their transformation.”

“Since the midpoint of last year, we’ve seen an acceleration of our business,” said Vishal Sunak, co-founder and CEO at Boston-based management tool developer LinkSquares, which used that increased interest to help raise a $40 million Series B in July.

Here are a few observations on what is going on:

  1. Impact of the Cloud: Just as in many industries, the cloud and other new tech had been slowly changing the legal world for more than a decade. However, after COVID caused offices to close and legal processes and documents to go virtual, adoption of those technologies skyrocketed. Investors started to eye technologies that took many firms “in-house” processes and moved them to the cloud—many involving documentations and filings as well as tools to help better communicate with clients.

2. Cloud-first generation: Many general counsels are now coming from a “cloud-first” generation and know the importance of things such as data insights that can help predict outcomes. Just as data and AI has changed marketing, sales and finance, the legal community is now catching on, and many don’t just want to be a cost centre

3. Increasing investor knowledge: The increasing market and scaling legaltech start-ups are causing VCs to take note. While many investors eyed the space in the past, more investors have knowledge about contracts and legal tech, and founders do not tend to have to explain the market

However, the market is still small albeit growing and no ‘goliaths’ exist in the space. With no large incumbents, how investors see returns remains a popular question.

This may chance if, for example, horizontal software companies like Microsoft or Salesforce could become interested in the space—as legal tech has data and analytics those types of companies find useful, Wedler said.

Some companies in the space also have found private equity a viable exit, with films like Providence Equity rolling up players such as HotDocs and Amicus Attorney several years ago.

However, perhaps more interesting to some startups is the legal tech space even saw an IPO this year, with Austin, Texas-based Disco going public on the New York Stock Exchange in July. The company’s market cap now sits at $2.8 billion.

One thing most seem certain about is that while the legal world’s tech revolution may have been brought on by a once-in-a-century event—there is no turning back.

25 Legal Tech Stats for 2020/21

This week I came across a blog post from ImpactMyBiz which compiled a list of great statistics, use cases and market data pertaining to the current state of technology in the legal sector.

In sum, there’s a lot of good progress but the sector is still subject to a lot of hype and extremely slow adoption when compared to other sectors. This is moreso in the B2B space with B2C innovation moving at a faster rate of adoption in improvement over time.

Perhaps the continued challenges presented by COVID around the world, increasing regulatory complexity, competitive pressures from alternative legal service providers (ALSP) and new entrants, remote working, client cost pressures, access to justice, and other key drivers will continue to move the needle forward.

25 legal tech stats to shed light on where where the industry is heading for in the new decade:

1.  In 2018, legal tech investments broke the $1 billion mark. That figure was topped in 2019, with $1.23 billion in funding by the end of the third quarter alone.

2. With the help of AI, a contract can be reviewed in less than an hour, saving 20-90% of the time needed to perform this work manually without sacrificing accuracy.

3. AI legal technology offerings for businesses increased nearly two-thirds in 2020 compared to 2019.

4. JP Morgan launched their in-house program, COIN, which extracts 150 attributes from 12,000 commercial credit agreements and contracts in a few seconds. This is equivalent to 360,000 hours of legal work by lawyers and loan officers per year.

5. Cloud usage among firms is 58%, with smaller firms and solos leading the way.

6. Security measures are lacking, with no more than 35% of firms using precautionary cybersecurity measures to protect their businesses. A staggering 7% of firms have no security measures at all.

7. Despite some reservations, lawyers continue to use popular consumer cloud services like Google Apps, iCloud and Evernote at higher rates than dedicated legal cloud services. Clio and NetDocuments ranked the highest among the legal cloud services.

8. The percentage of the ABA 2019 Legal Technology Survey participants answering “Yes” to the basic question of whether they had used web-based software services or solutions grew slightly, from 55% to 58%. 31% said “No”, a small decrease. 

9. When asked what prevented their law firms from adopting the cloud, 50% cited confidentiality/security concerns, 36% cited the loss of control and 19% cited the cost of switching.

10. 26% of respondents in a 2019 survey report that their law firms have experienced some sort of security breach

11. In 2018, just 25% of law firms reported having an incident response plan. In 2019, this figure had risen to 31%, and we expect the same for 2020.

12. Interest in cloud services from law firms is high, but expectations of adoption among them remain low, with just 8% of firms indicating they will replace existing legacy software with cloud tools.

13. Only one-third of lawyers (34%) believe their organizations are very prepared to keep up with technology changes in the legal market.

14. Firms described as “technology leading” fared better, with 50% prepared to meet digital technology demands in the industry.

15. 49% of law firms report that they are effectively using technology today, and 47% say they can improve technology adoption and plan to do so.

16. Over half (53%) of lawyers in the US and Europe say their organizations will increase technology investment over the next three years.

17. While over half of lawyers expect to see transformational change in their firms from technology like AI, big data and analytics, fewer than one quarter say they understand them.

18. The biggest trends cited by lawyers that are driving legal tech adoption are “Coping with increased volume and complexity of information” and “Emphasis on improved productivity and efficiency.”

19. It is estimated that 23% of work done by lawyers can be automated by existing technology.

20. 27% of the senior executives at firms believe that using digital transformation is not a choice, but a matter of survival.

21. The top challenges for corporate legal departments today include reducing and controlling outside legal costs; improving case and contract management; and automating routine tasks and leveraging technology in work processes.

22. 60% of lawyers believe their legal firm is ready to adopt new technology for routine tasks.

23. According to research conducted by Gartner, only 19% of law firms’ in-house teams are ready to move forward with enterprise-level digital strategies.

24. A recent study uncovered that 70% of consumers would rather use an automated online system or “lawbot” to handle their legal affairs instead of a human lawyer because of three important factors—cost, speed, and ease of use.

25. 70% of businesses indicated that “using tech to simplify workflow and manual processes” to cut costs was a top priority going forward.

“New Law” Opportunities for Law Firms

I recently came across a presentation I gave in April 2015 to senior partners at Eversheds LLP in London. At the time, Eversheds were proactive in starting to diversify their professional services offerings away from traditional legal and transactional work into ‘alternative’ services areas, such as business improvement consulting for in-house legal teams, and flexible resourcing solutions.

You can view the deck here: Download

At the time, it was unusual for a major corporate firm to be experimenting into different areas.

The question for the presentation was as follows:

Downward cost pressure, deregulation and new technology are transforming the legal industry, as ‘New Law’ providers compete with traditional law firms.  What are the opportunities for large law firms in this evolving marketplace? 

I focused on 2 main themes of (a) Changing the mind-set and (b) Managing innovation.

Since then, in six years a lot of innovation has been introduced into the legal sector. However, it has been a fairly low-bar for many years with the legal sector ‘glacial’ when it comes to change and technology.

Certainly the ‘legaltech’ and/or ‘lawtech’ markets have received significant injections of VC to build next generation B2C and B2B solutions. Most large firms are now experimenting with different AI and automation solutions, running incubators, offering flexible resourcing arrangements, investing in start-ups, and so on.

To better support Fortune500 General Counsels with their efficiency challenges, the Big4 are building services and capability at scale, as are legal process outsourcers and ALSP’s.

Many of these ideas were referenced in the presentation.

However, the critical question is has anything really changed in how legal services are delivered, bought and sold? How much of this is ‘innovation theatre’ and nibbling around the edges versus real change?

For example:

  • Does the partner in the Freshfields office in HK work any differently then they did as a trainee 20 years ago?
  • Are the skills and requirements of a newly qualified lawyer any different?
  • Does the single lawyer law office in Bristol run their practice any differently?
  • Does the COO of a regional law firm run the business any differently?
  • Do consumers who need a family lawyer do this any differently?
  • Does the barrister or judge involved in a trial do this any differently?

The short answer I think is not a great deal of change across the industry as a whole. However there has been a tonne of experimentation and innovation in some fragmented areas, especially in B2C (e.g. DoNotPay). COVID-19 has certainly accelerated this, and that can only be a good thing.

I think what we are seeing is a marathon, not a sprint. In fact, it is more like the start of a triathlon where there’s a washing-machine effect as participants fight their way forward before a steadier state emerges.

We see this with most new technologies, where things often take much longer to truly disrupt. In retail and e-Commerce, it is only recently that the Internet is causing significant challenges for traditional players, almost 20 years after the Dot.Com crash in 2001.

One thing is for sure – the next 10 to 15 years in the legal sector will be fascinating.

A Stronger Science, Technology + Innovation Agenda: 6 Areas of Focus

“Science, technology and innovation (STI) are universally recognized as key drivers for economic growth, improving prosperity, and essential components for achieving the Sustainable Development Goals (SDGs)” –  UN Conference on Trade and Development (2019)

A few months ago I wrote down some thoughts and questions after being inspired by political events where I live (Guernsey) and internationally (e.g. US). In both jurisdictions, the balance of power has dramatically shifted for different reasons but both against a backdrop of major crises including health (COVID), rising inequality, and skills gaps.

In essence, I was trying to think through answering 2 key questions for the new Government and ecosystem players (e.g.businesses, investors, educators etc): what are some key STI areas of focus, and what questions would I ask?

I have since shared the memo with various stakeholders in the ecosystem, and now I thought it would make sense to post it publicly here. If you have any feedback, be sure to let me know

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Research, analysis and policy development opportunities and questions for the new Government and ecosystem players (e.g. businesses, investors, educators etc)

Business case for an STI economy: the importance of ‘science, technology and innovation’ for Guernsey’s future in driving economic growth and improved prosperity for all citizens 

  • Key STI trends, opportunities and challenges 
  • What is STI/digital, why important, global best practices
  • Why important for Gsy?
  • Defining and measuring Guernsey’s existing STI/digital economy 
  • Benefits and impacts to economy, society, prosperity and infrastructure 
  • Jobs, skills, human capital and education
  • Role of stakeholders e.g. education, govt, business, people etc
  • Building blocks, what is needed? E.g. 
    • Policy and regulatory frameworks
    • Institutional setting and governance
    • Entrepreneurial ecosystems and access to finance
    • Human capital
    • Technical/ICT & R&D infrastructure
  • Relevance of Sustainability, Green Finance, Solar/Wind, FinTech, RiskTech, RegTech, GovTech
  • Role of tax policy, skills, FDI, govt, business etc 
  • Strategic options for Guernsey 

Resources – A FRAMEWORK for Science, Technology and Innovation Policy Reviews: UN Conference on Trade and Development 

http://www.oecd.org/innovation/inno/

OECD (2020) A Common Framework for Measuring The Digital Economy 

ICT Infrastructure: Reshaping SURE Telecoms as a strategic asset to benefit the future of Guernsey and investigating the promise of new tech e.g. fibre, 5G etc

  • What is SURE’s current investment model, business strategy etc with regard to infrastructure, network performance and speeds, pricing and tariffs etc
  • How does it fit with Guernsey’s requirements, strategy and vision for the future? 
  • To what extent does the existing relationship with Sure/Cicra/others need to be reshaped?
  • What is the overall technology vision of Guernsey? E.g. the most digitally-enabled small island economy in the world? KPIs? 
  • What are the existing telco infrastructure challenges and market opportunities? E.g. connectivity, fibre, 5G etc
  • What are the key ICT indicators/KPIs for Guernsey?
  • How good or bad is the current network/asset performance? Where is the evidence? 
  • To what extent does Sure need to be incentivised to improve performance?
  • How will that benefit Guernsey? E.g. access, education, WFH, FDI, economic growth etc
  • What are the different levers to pull that can assist that?
  • What are the roles of the key stakeholders and to what extent does this need reshaping? E.g. Cicra, 
  • What are some example ownership models from around the world that should be considered?

Resource – 14 Key ICT Indicators

Smarter e-Government: Transforming public sector services to improve efficiency and effectiveness

  • Agilisys IT procurement: What was the promise vs reality, where is the accountability and island benefit (e.g. jobs, knowledge etc), what is the road ahead, and what needs to change 
    • What is the current status of the Agilysys IT procurement, what are the benefits (e.g. local jobs) vs costs, what is the roadmap
    • Current status and how successful has it been, why?
    • What was the scope of the original deal that was signed? How did that change over time? 
    • How much has been spent by the SOG?
    • What have been the benefits? E.g. local jobs, tax payer savings etc
    • How is the vendor managed, programme governed, quality assurance provided, risks/issues etc 
    • Strategic options and recommendations 
    • Role of new ways of working and thinking e.g. agile, design, lean
  • e-Gov/Future Digital Services
    • What is the latest vision and roadmap forward? Is it good enough?
    • In 2017 FDS was championed with a delayed and time-consuming procurement process which say Agilysys hired – what is the status?
    • What are the roadblocks, challenges vs opportunities 
      • E.g. SOG IT procurement decision-making and processes
    • What can we learn from other e-gov national leaders e.g. Estonia
    • What are the big opportunities/challenges?
    • What are the areas of focus?
    • What is required to move forward?
    • What are the costs/benefits?

Digital skills: how to re/upskill the population to be fit for the future 

  • What digital skills does Guernsey need? 
    • E.g. Data Analysis, Business Analysis, CS/Software Engineering, Product Development, Agile, PM, UX/UI, Google etc
  • What courses should be created?
    • For which groups e.g. school-leavers vs mid-level vs later stage 
  • How to deliver this?
  • Best practice models from similar jurisdictions 
  • Who to deliver this?
  • How much to deliver this?

Entrepreneurship and innovation ecosystems: what is the Innovation & Growth vision for Guernsey PLC? How to create a more efficient, attractive and collaborative system: 

  • What new tax policies, incentives and regulatory changes are needed to drive the captial/FDI and other behaviours? E.g. EIS
  • How to encourage businesses to invest in R&D?
  • How to encourage angels/HNWI/funds/businesses/VC etc reallocate investments into start-ups?
  • Build on my article here – https://andrewessa.com/2020/08/28/digital-ecosystems-tzars-puzzle-pieces-the-halo-effect/
  • What is the Innovation vision for Guernsey PLC
  • Baselining and measurement
    • What is Guernsey’s approach and how effective is it?
    • Strengths/weaknesses
    • Challenges/opportunities
    • Actors in the ecosystem
  • What is best practice in small or island communities and competing off-shore jurisdictions?
    • What have Jersey done? cost/benefit?
    • What can we learn from them and other nations?
    • What could we improve?
    • What needs to happen?
  • Ecosystem pillars: how effective are the current actors and what needs to change 
    • Tax, finance and incentives 
      • The role of tax policy, tax credits, R&D, and other incentives 
      • Access to capital, finance
    • Guernsey Innovation Fund
      • What has been invested in to date?
      • What type of investments and how much?
      • What mix of businesses e.g. local vs overseas, maturity etc
      • What returns, benefits to date?
      • Who is involved in the fund, what governance etc
      • What is success? 
      • How does it compare to other small community or island ‘sovereign’ investment funds?
      • What is ‘best practice’ in this space?
    • Human capital strategies – from cradle to grave 
      • Understanding the digital/skills crisis e.g. PwC report
      • Practical solutions to solve it 
      • Alignment with Guernsey PLC strategic vision 
      • Baselining, what new skills, how to up/reskill, what incentives for businesses and people 
      • Life-long learning 
      • More flexible access to skills and talent 
      • Immigration policies 
    • Digital Greenhouse
      • Current vs future state
      • Cost/benefit
      • Challenges/opportunities
      • Recommendations 
    • Governance 
      • Effectiveness of current system
      • What changes are needed
      • What models, what structure, what responsibilities etc e.g. Guernsey Innovation 
    • Corporate innovation
      • How to incentivise investments in skills and new ventures 
    • International cooperation
      • Role of collaboration including within the Bailiwick 
    • New business opportunities 
      • The role of new ‘market creation innovation’ (MCI) policies- see below
      • Relevance of Sustainability, Green Finance, Solar/Wind, FinTech, RiskTech, RegTech, GovTech
      • Regulatory innovation models e.g. sandboxes 

Resource – OECD Review of Innovation Policy – New Zealand (2007)

Other sample areas of ‘innovation policy’ to explore:

  • Environment: Sustainability, ESG and climate change  
    • What is best practice around the world in small island or communities 
    • What are some potential or viable new business opportunities
    • Assess current state of initiatives (e.g. Green Funds)
    • Evaluate new initiatives e.g. Wind, solar etc
  • International collaboration and trade
    • How important is it to be more market-focused and rethink and prioritise international partnerships/affairs? E.g. Jersey
    • A colleague and partner Chris Brock covers some of this topic in a recent report here
  • Regulatory, governance and risk innovation: to what extent do the various regulatory bodies and related private/public sector organisations (e.g. GFSC, Cicra, TISE, DPO etc) need to adopt a more balanced and innovative approach to regulation and new business? How to accelerate existing initiatives and opportunities? e.g. Green Finance
    • What is the nature of the current approach? 
    • How to balance bureaucracy/risk-adversity in the Guernsey ecosystem but at same time encourage innovation, FDI and new businesses?
    • What are best practice examples of innovative regulatory/risk models from competing or similar jurisdictions or around the world?
    • To what extent could this be useful in Guernsey?
    • How is the wider market evolving and how will this impact Guernsey?
    • What are the pros/cons and opportunities/threats?
    • What new business opportunities a more innovative approaches enable? E.g. FinTech, RegTec
    • What are practical recommendations forward and for which actors 
  • Role of market-creating innovations to drive prosperity AND economic growth (MCI): What is the opportunity for Guernsey to incubate market-creating innovations for local use and export? And how can Guernsey facilitate the development of MCIs across different sectors – e.g. FS, Infrastructure, Transport, Environment etc – for local use and export to improve income inequality and other social/economic benefits? 

Resources:

https://hbr.org/2019/01/cracking-frontier-markets

Digital Ecosystems, Tzars, Puzzle Pieces, & The Halo Effect

This week I have had numerous informal discussions with different business leaders about the digital potential of Guernsey in the context of a COVID world. It got me thinking.

What are the key ingredients of an efficient digital and innovation ecosystem? What are the key pillars? If I was Digital Tzar for a day, what would I focus on?

I immediately thought back to my own entrepreneurial journey starting in 2011 in Shoreditch (London) when I left Accenture Consulting & co-founded The Social Experiences Club, one of the first European experiences and activities marketplaces. Along the way and following an exit I have advised, mentored, coached and consulted to many other entrepreneurs, VCs and corporates on everything from new venture development to business models to fundraising to hiring and firing.

Below I have provided a list of some key ‘ingredients’ to an efficient innovation and digital ecosystem. They are like pieces of a puzzle. There can’t be one without the other. Whilst there are wider factors required for success (e.g. smart, collaborative and decisive government), these are not the focus here.

Key Ingredients Of An Efficient Digital and Innovation Ecosystem:

  • Innovation-I think the focus on ‘digital’ is too narrow. Perhaps the better conversation is around how to foster new ways of thinking, working and investing (in technologies, skills, institutions etc), and how to provide the right infrastructure for anyone or any organisation to be able to build new solutions and deliver benefit, value and prosperity for consumers/citizens.
  • Commitment + Vision – As with anything in business or life, a strong vision and commitment to that vision is required to create impact and make change happen. For the public-sector, having a strong technology and innovation policy is critical, and was the foundation of Estonia’s e-Government transformation  Even with such intent and will execution will be hard enough, but without this and appropriate support, resources and political capital, nothing will change.
  • IT Infrastructure – The pandemic has shown how strategic this asset class is to the future prosperity of nations – and will continue to be – which may require regulators to rethink approaches to regulation and competition. Without reliable and quality connectivity and access for all people at a fair price today or in the near future (e.g. 5G, fibre etc), economic and social growth could suffer and could lead to catastrophic long-term consequences. On regulation, balancing the strategic interests of nations and the telecom providers (who all have very different corporate strategies, business models and operating structures) is no doubt a difficult but critical balancing act, especially in light of COVID’s acceleration of digital services, access and inequality issues, and continued and future investments in next generation infrastructure (e.g. 5G). 
  • Centralised Governance – A centralised market-focused unit as the knowledge and resource ‘hub’ responsible for digital activity can provide benefits for an emerging innovation ecosystem, especially where aspects of the infrastructure might be lacking. London had TechCity, although it was arguably overshadowed by the power of the entrenched historic networks of the wider ecosystem in terms of universities, commerce, government, and investment community. 
  • Incentives – Smart technology and innovation tax policies is critical to facilitate a more efficient and attractive market to build the wider entrepreneurship and corporate innovation ecosystem.

Support for business R&D can help to foster innovation and boost productivity. Investment in new technologies can also be supported through more generous depreciation deductions or immediate expensing – OECD Report (2018) – Tax Policies for Inclusive Growth in a Changing World

Incentives (whether EIS, SEIS, tax-breaks or otherwise) can encourage and unlock local (and overseas) private and corporate capital flows into start-ups/scale-ups. In 2011 when I was raising funds for a start-up in London in 2011, everywhere we went investors, accountants and lawyers would immediately ask the same question: are you EIS compliant? Clearly the years following the 2009/09 Financial Crisis was a massive boon for innovation with a huge supply of entrepreneurs choosing new paths and supported by an abundance of capital. 

Since its inception in 1993 the Enterprise Investment Scheme (EIS) has enabled UK companies to raise over £16 billion in investments. Of the 3,470 companies benefitting from the EIS Scheme in 2015/16 alone, 1,645 companies were raising funds for the first time, between them generating £997 million of investment – Thomas Jenner LLP 

On the supply-side, facilitating a more efficient is needed to generate an increasing supply of entrepreneurs able to access capital (plus ‘smart’ capital) especially at early stages. For companies, encouraging the development of in-house IP via R&D tax credits (or similar) (UK HMRC policy is here) could also have downstream benefits such as up skilling (depending on the policy), and can be aligned with any national Digital Vision.

  • e-Government – For smaller nations, it is especially critical to invest in citizen-facing automation (e.g. paper-less) and improved customer experience opportunities across social security, ID, e-voting, e-health, data, e-signatures, and EdTech. Often government is the largest employer in smaller communities hence these investments can have outsized impacts and benefits. It also ‘opens’ the government up to being more accessible, transparent, and helpful in working with and facilitating the wider ecosystem.
  • Ecosystem – One of the key reasons why London has been able to become a global leader in innovation (especially FinTech) has been due to the infrastructure and network effects facilitated by a number of key factors. In particular, within a 1hour train ride you have leading universities (e.g. Oxbridge, LSE, UCL, Imperial etc), commerce, and government. It creates enormous opportunities for creativity and collaboration to flourish, share knowledge, and build relationships with every piece of the start-up puzzle, from enterprise clients, to talent, to regulators and so on. As a start-up co-founder in Shoreditch in 2011, you could easily do nothing but network and attend amazing events, meet ups, hackathons, talks, pitch competitions etc  every night. Whilst not every city or small community can replicate that, the principles and practices are there to be examined and implemented within whatever your specific context is.

“We are witnessing a rapid changing of the guard for global investment in innovation centers. The US and Europe have traditionally been viewed as dominant forces in innovation and technology but Asia could soon surpass the US for number of innovation centers built and operated. Moreover it is clear that funding alone is not enough — the success or failure of any innovation center hinges on how effectively it taps into the surrounding ecosystem, and the role it plays in driving a broader corporate innovation strategy – Eric Turkington, Director at Fahrenheit 212, part of the Capgemini Group

  • Talent/Skills – Education is critical for the future of innovation in a society. At K-12, schools need to be offering introductory (and advanced) knowledge-based and/or practical courses on digital topics whether entrepreneurship, digital marketing, Excel/Google Spreadsheets, coding, design thinking, or analytics. This creates opportunities for ‘start-up clubs’ and business idea/pitch competitions aligned with industry, which can provide pathways for hiring and investors. Businesses should also prioritise up skilling which includes investing in softer skills (e.g. communication, creativity, collaboration, empathy).

“Twenty years from now, if you are a coder, you might be out of a job,” Cuban predicted. “Because it’s just math and so, whatever we’re defining the A.I. to do, someone’s got to know the topic. If you’re doing an A.I. to emulate Shakespeare, somebody better know Shakespeare”. – Mark Cuban

In addition, it is critical to learn new ways of working and thinking (e.g. agile, lean, design), and how to significantly improve inclusivity and diversity initiatives for existing talent (and future hires). At the higher education level, it is no surprise that some of the best known ecosystems (from Hollywood to Silicon Valley) have top-tier universities in close proximity. A centralised knowledge, teaching and research centre for technology and related skills and excellence must be a high-priority for any region without this. Also, making it easier or more flexible to hire overseas talent and plug skill-gaps in high-priority areas – whether software, analytics, UX or engineering – should also be considered, especially as this removes the friction for individuals or companies to pursue innovation.

  • Specialism – It certainly helps to be known and famous for a certain speciality. London has done well to intentionally (or accidentally) carve out a ‘brand’ around FinTech which leverages the reputation, expertise and talent in that sector, although it is still active in many other sectors. This helps with the halo effect to build an ecosystem around that which then flows out into other areas. 
  • Examples – The halo effect above also extends to when there has been one or more successful start-ups and entrepreneurs who have moved though the start-up stages i.e. idea to exit. In a similar way that we celebrate sports stars and use them as aspirational icons for children and others, this can be used to inspire the next generation of entrepreneurs. If the right examples exist, we need to profile them and start holding them up examples of what can be possible (and using them as mentors).
  • Intellectual Property – Historically patents have been used a measure of R&D and innovation – and hence subject to tax breaks – but since 2000s software development has become a critical focus. Incentivising corporate investment into building out in-house IP vs using an overseas agency/service provider may provide local benefits and stimulate the local digital skills ecosystem.
  • Pathways – Programmes for potential entrepreneurs whether at school or higher-education or post-university to educate prospective entrepreneurs. To be effective it requires all of these initiatives to be in place or in-flight
  • Collaboration – A critical digital ‘soft-skill’, without a collaborative approach and mindset amongst key participants – coupled with the strongest of commitments from smart government – attempts to develop and execute on a digital vision will struggle. This needs to be baked into any refreshed governance supported by strong top-down commitment.
  • Experimentation – Modern start-up development relies on many small experiments: start with a small hypothesis, test, learn, iterate, build, repeat. Government therefore needs to be more comfortable with this way of working to ensure progress is made versus spending years analysing and/or smothering creativity with bureaucratic processes which ultimately delivers nothing or very little. In the midst of an ongoing pandemic, unprecedented government spending, and a reduction in tax revenues, the Government must work differently and smarter in order to be more accountable to taxpayers and deliver benefit, value and sustainable progress for citizens.

 

 

BigTech Power, Regulation, And The Early Days Of The Internet

I recently came across a Guardian article looking at the winners and losers from last month’s US Congressional hearings into the power, practices and conduct of various ‘Big Tech’ companies. It got me thinking.

BigTech’s power and urgent need for regulation reminds me of a hot topic back in the early days of the Internet being….the urgent need for regulation.

In Australia during the early 2000s, the approach of business and government to the emerging Internet and associated applications tended to be driven by fear and uncertainty (“let’s sue them, shut them down, and take control of the IP” – major records labels in the music industry) as traditional legal and regulatory frameworks struggled to adapt to the new paradigm and business models began to creak.

Between 2000-2004, I was entrenched in these issues as I wrote and delivered a brand new undergraduate and post-graduate course at Queensland University of Technology called ‘e-Commerce law’.

At the same time, I was in private practice advising Australia’s biggest casino, media and other operators on how to navigate the emerging world of online gaming and meet the increasing demand of Australian consumers (who love to gamble).

Most topics in the course and in practice grappled with the issue of how do the traditional legal frameworks apply to this new technology and applications, from payments and money, copyright (e.g. music file-sharing), privacy (e.g. data protection), and reputation (e.g. defamation).

In 2004, I analysed the Governments prohibition of online casinos in my first academic article published in QUT’s law journal, titled ‘The Prohibition of Online Casinos in Australia: Is It Working?’.

I’ve pasted the introduction here as in the context of the BigTech Congressional Hearings, a few points are still interesting:

Preliminary online research of consumer gaming activity was utilised to develop an assumption that [after 2 years of prohibition] prohibition is not working. A key reason for this is the futility of prohibition given the unique nature of Internet technology. This article will also critique Government motives for prohibition, as arguably, the best approach to deal with interactive gaming was not implemented. The relevant question for public policy appears to be not whether online gambling can be controlled, but the extent to which it can be controlled.

Obviously, 16 years on you can apply this principle to the other areas which BigTech have completely dominated including social media, search, video, browsing, advertising, e-commerce, web services, app stores, personal data, and so on. In the early 2000s, it was a nascent and emerging industry and overall regulation policy needed to be ‘light-touch’ (although exceptions existed especially where consumer harm risk was high, such as gambling, payments).

As converging technologies penetrated (Internet, broadband, OS software, mobile, apps, cloud etc), limited regulation has allowed a handful of companies control the majority of our online data, purchases, browsing habits etc. This will only accelerate given the impact of COVID on our behaviour, and soon that will extend in the last frontier of growth for such firms including health, education, government services, and so on.

Whilst regulation (and disposals or break-up) is clearly required for many different reasons (competition, national security, business and consumer harm etc), it is unclear what will play out given the power of these firms, how politicised the issues have become, and the nature of US anti-trust enforcement and law which historically focused on pricing practices and consumer harm.

In Chairman Cicilline’s wrap-up:

This hearing has made one fact clear to me. These companies as they exist today have monopoly power. Some need to be broken up. All need to be properly regulated and held accountable … their control of the marketplace allows them to do whatever it takes to crush independent business and expand their own power. This must end.

Something needs to be done. But we will have to see what happens after the Nov elections.

OneTrust: How A Privacy-Law-Compliance Tech Start-Up Became America’s Fastest Growing Company

Today I came across an incredible story of OneTrust, a privacy-law-compliance start-up based in Atlanta.

OneTrust landed at No. 1 on this year’s Inc. 5000, with more than $70 million in 2019 revenue and a staggering 48,337.2 percent three-year growth rate. It is among the global leaders in privacy-law-compliance technology with a suite of digital tools that gives companies a clearer view of all the user data they accumulate.

This enables them to comply with privacy laws, like the European Union’s General Data Protection Regulation (GDPR) which gives consumers greater control of how com­panies can use their data.

Whilst Enterprise B2B SaaS and analytics isn’t the most sexiest space, in many cases, firms that play there can be the fastest-growing, most scaleable and profitable (and do good things at the same time). 

In an age of Big Tech monopolies, increasingly intelligent AI and API-powered platform business models, growing regulatory oversight and appetite, and increasing consumer-awareness, OneTrust and others are clearly riding a tidal wave. 

Read the full story here 

 

 

Digital Playbook: How And Where to Focus to Maximise Opportunities In a COVID World

In summary, this article provides:

  • An 8-point playbook of strategies which leaders can use to focus time and resources to build digital capabilities and navigate business change
  • A useful framework to compare or evaluate existing digital investment and innovation initiatives to improve quality and impact
  • A useful article to share or use for internal discussions with non-digitally native executives, Board members and cross-functional teams
  • A set of practical strategies to guide implementation following on from the key insight and findings in the REIGNITE 2020 Report authored by Andrew Essa
  • A playbook to evaluate your digital progress and help plan for the future. Get in touch with any questions, comments or help to implement these perspectives here andrew@rocketandcommerce.com or at ROCKET + COMMERCE

The 8 strategies include:

  1. Understand current digital usage, productivity, value and benefits
  2. Diagnose and benchmark digital performance and opportunities
  3. Scale digital capacity for increasing demand but manage complexity
  4. Review and upgrade cybersecurity measures
  5. Move from ‘good’ to ‘great’ across 4 key areas
  6. Prioritise resource reallocation to digital initiatives (with a crisis mindset)
  7. Improve the digital acumen of the Board (and workforce)
  8. Organise to build digital capabilities

8 Strategies For Leaders to Navigate Digital Acceleration

Although some organisations are thriving on the back of tailwinds in this environment, many more are struggling. In many cases, the difference between the former and the latter is an organisation’s ability to rapidly adapt and chart a sustainable and differentiated path forward, especially through maximising Digital opportunities across areas including Customer Experience, Growth Strategy, Workforce Productivity, and Organisational Adaptability (I posted recently here about the 3 Big Digital Opportunities for Organisations)

Below are 8 playbook strategies for leaders to now consider:

#1 Understand productivity, value and benefits 

For most organisations, the critical first step has been to safeguard employees by enabling them to work remotely using the full suit of available tools (see below). 

hub---digital-workplace

As this continues alongside partial or even full reintegrations, firms should continuously engage or ‘pulse check’ with workers, customers and key stakeholders. It is critical to evaluate what is working well (e.g. feedback, analytics, usage), what is missing (e.g. cybersecurity, training, IT hardware), lessons learned, and where low-hanging fruit is for further digitisation opportunities and benefits (e.g. customer service and experience).

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A challenge to overcome is that most firms typically fail to realise the full value from their technology investments for a variety of reasons (e.g. budgets, skills, governance, change, training etc). What tends to happen is some efficiency and cost reduction, but limited revenue generation, improved customer experiences and new products/services. The firms who out-perform their peers are the ones who prioritise and maximise the full potential of digital and are laser-focused on benefits realisation across the organisation. 

“The crisis has sped up the utilisation of tools such as Microsoft Teams for meetings, e-signature software and other tech which will assist both with internal and external customers moving forward. Typically face to face meetings or travel has been a big part of how we’ve conducted business particularly in my role in the past – Client Director, Private Investment Bank (interviewed in the REIGNITE! 2020 Report)

#2 Diagnose digital performance and opportunities 

For some SMEs, the current state of digital maturity involves a combination of accelerated back-end cloud, front-end software tools (e.g. MS 365), and new ways of working. Other larger, established firms however continue to have core (or hybrid) infrastructure set-ups based on outdated tools, processes, and assumptions combined poor digital acumen at leadership level and limited workforce training or up skilling.

This makes it increasingly difficult to adapt to new challenges (e.g. remote work, new services, cybersecurity), manage complexity, and properly reap the benefits of digital technologies. In some cases, the lack of agility will drag down the business which might be fighting to to rescue declining margins, compete, or even survive.

The challenge for leaders is to build on the momentum of change (‘it can be done!’) and increased adoption by leveraging the potential of digital across the entire organisation (not merely in pockets) for improved efficiency, productivity, customer experiences and new products/services.

To get started, leaders need to know what they are dealing with today.  If strategic planning around digital opportunities are to be robust and there is leadership intent to focus time and resources on the digital agenda, data and insight about the current digital state of the organisation will be needed.

Diagnostic surveys tools and assessments can help to evaluate an organisation’s digital and analytics maturity to discover digital growth, operational  improvement and worker productivity opportunities now, with recommendations on where to focus efforts for longer-term growth, change or productivity. 

At ROCKET + COMMERCE our Digital Performance Index (DPI) focuses on areas including Strategy, Customers, Analytics, Technology, Operations, Marketing, Offerings, People, Culture, and Automation. This data-driven, diagnostic approach helps CxOs and functional leadership teams to shape, refresh and align around a common vision and strategy across key digital and innovation dimensions.

We also critically incorporate human-centric approaches (see below) to our diagnostic tools which also provides people-focused data of digital change on users, customers, experiences, productivity, collaboration, skills, behaviours, trust, safety, belonging, health and well-being. 

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Read these brief case studies on how  at ROCKET + COMMERCE we have helped organisations do this and find new ways to go-to-market, become more customer-centric, launch new ventures, or pilot new up skilling programmes. 

This exercise also allows leaders to identify gaps between current capabilities and those of digital leaders (or the desired future state of the organisation), and plan a prioritised road map of tactical improvements or new strategic initiatives. This data-driven, diagnostic approach can also help CxOs and functional leadership teams align around a common vision and strategy across key digital dimensions. 

DMM_Model_Overview_2020

#3 Scale digital capacity for increasing demand but manage complexity 

Many IT teams are now grappling with providing sufficient capacity to serve the increased (and varying) volumes of traffic flowing through digital channels. One respondent to the survey (a provider of web-based collaboration tools), experienced a surge in demand from all of the newly remote workers and had to rapidly build out new infrastructure capacity to ensure availability.

This transition to digital channels will likely continue beyond the current health crisis as customers and organisations adopt fundamentally different ways of working. Recent research from Gartner indicates that about 41% of employees are likely to work remotely for some of the time post-pandemic. 

RemoteWorkStatisticsSource: Blackfog

The accelerated capacity build-out in H1 2020 has taken many forms beyond physical infrastructure deployment. In many cases, it has pushed organisations to adopt different architectural solutions for expansion, such as cloud bursting and augmenting on-premises deployments with virtual appliances and software-based deployments in the public cloud.

According to Mike Pelliccia, head of worldwide financial services technology solutions at Amazon Web Services (AWS), on-premises infrastructure no longer meets the business needs of today:

On-premises data infrastructures do not scale to meet variable and increasing volumes of data. Multiple disconnected data silos with inconsistent formats obscure data lineage and prevent a consolidated view of activity. Rigid data schemas prevent access to source data and limit the use of advanced analytics and machine learning. The high costs of legacy data warehouses also limit access to historical data.

The cloud helps organisations to harness the value of their data and aggregate it at speed and scale so that they can achieve their business goals. Traditional data solutions cannot keep up with the volumes and variety of data that is being collected today by financial players.

Pelliccia adds that a cloud-based data lake allows organisations – from banks to SMEs – to store all data in one central repository where it can be more readily available for the application of other technologies such as machine learning, “to support security and compliance priorities, realise cost efficiencies, perform forecasts, execute risk assessments, improve understanding of customer behaviour, and drive innovation.”

This enables organisations to maintain a holistic view of their business, while identifying risks and opportunities. For instance, analyses can help to detect fraud, surface market trends and mine for deeper customer insights to deliver tailored products and personalised experiences.

#4 Review and upgrade cybersecurity measures

Whilst many organisations will have robust cybersecurity processes and culture, for many others this will represent a new capability and massive learning curve. What was good just a few months or weeks ago may not be adequate today.

The urgency and impact of the shift away from office working will mean most organisations may have introduced new levels and types of cybersecurity risk not previously seen before at this scale (see below for leading causes of cyber risks).

bakerhostetler-causes-graph

Source: PropertyCasualty360

While allowing the workforce to be flexible is only a small part of digital transformation, it carries with it the need to ensure that new hardware (laptops, home printers, smartphones) and services have been, and continue to be, implemented securely (e.g. full disk encryption, enabling strong multi-factor authentication, and using VPN      technology).  

 #5 Move from ‘good’ to ‘great’ across 4 key areas 

Once solutions to immediate workforce and business priorities are in-flight, organisations should accelerate the exploring of different ways to use digital to work and operate, deliver innovative customer experiences, and create value in the new normal. For example, restaurants enabling entirely new in-home dining experiences, telemedicine becoming more of a norm, and different ways to shop with ubiquitous curb-side pick-up.

According to McKinsey, whilst many B2B companies have a general sense of what they need to do to become more digitally-enabled, it is the best B2B leaders who move beyond “accepted wisdom” to focus on being ‘great’ at 3 main differentiators of digital success:

  • Customer Insights
  • Process Improvement
  • Capability Building

To this list, I add a critical 4th dimension: Business Models 

The below provides further explanation:

Customer insights

  • Good: Focus on understanding their customer preferences and demographics.
  • Great: Ability to quickly translate into the most relevant value-creation strategies. Pick one or two high-value customer segments, then map decision journeys front-to-back to understand how customers buy, what channels they use, what turns them on—and off. More than 90 percent of B2B buyers use a mobile device at least once during the decision process, yet fewer than 10 percent of the B2B companies in the survey indicated that they have a compelling mobile strategy.

Process improvement

  • Good: Relentlessly improve existing processes.
  • Great: Use agile development techniques, automation, and design thinking to reengineer or reinvent supporting processes. Effective pre-sales activities—the steps that lead to qualifying, bidding on, winning, and renewing a deal—can help B2B companies achieve consistent win rates of 40 to 50 percent in new business and 80 to 90 percent in renewals. Incorporating agile techniques forces product development, marketing, sales, and IT to come together and use digital design practices, such as launching minimally viable products (MVP). That can ramp up the cultural changes needed as well.

Capability building

  • Good: Build important capabilities for digital initiatives
  • Great: Identify and augment the capabilities critical to achieving scale. B2B leaders create an organisational structure that supports their digital transformation. That involves identifying which skills need to be reallocated, what data and analytics resources are needed, and which customer opportunities require capabilities that need to be built, hired, or acquired. Systematic performance tracking needs to be in place to keep the efforts on track and make sure they having the desired impact (only one in five B2B companies systematically tracks digital performance indicators).

Business Models

  • Good: Optimise existing business model by digitising their traditional products, interfaces and distribution channels. 
  • Great: Take advantage of platform models and thinking leveraging network effects, intelligent AI-powered solutions, developer/API enablement and ecosystems, and customer-centric orchestration. As every sector digitises – accelerated by the COVID crisis – the imperative to incorporate new digital business models becomes more urgent. This underpins the ‘great’ executors. 

According to digital platforms expert Simon Torrence:

Platform thinking is about taking advantage of flexible software and digital  infrastructure to leverage, at scale, other economic actors (complementary third parties and/or developers) to create new value for customers and markets.Rather than trying to design and build everything yourself – which is the default for most companies today – platform thinking encourages you to act as a coordinator or enabling intermediary between the needs of your customers, your own expertise and the expertise of others.

Simon goes on to say that:

Incumbent leaders admire and fear the big tech giants, and would love to emulate or incorporate some of their ‘secret sauce’ into their own businesses, but don’t know how. They have been happy to invest large sums to digitise their existing business model and fund experiments, pilots and CVC investments in new areas, but have found it difficult to fully embrace the types of digital business models that work best in a hyper-connected world and to take bold steps in re-allocating meaningful levels of capital and resources towards them.

In summary, a commitment to “great” is really what allows companies to reap the rewards from digital and build digital and supporting capabilities. Without it, organisations will find their improvements provide only modest benefits that cannot be scaled.

#6 Prioritise resource reallocation to digital initiatives (with a crisis mindset)

As outlined above, the COVID crisis will accelerate the gap between digital laggards and transforming leaders requiring firms to now evaluate investments, baseline ‘digital maturity’, and in the short-term, secure a stronger, repositioned role for digital investments in 2021. 

In fact, in 2019 McKinsey believed a ‘crisis mindset’ was required. And that was before COVID….

1-920x1024

This is likely to require an urgent reallocation of resources. Although most senior executives understand the importance of strategically shifting resources (according to McKinsey research, 83 percent identify it as the top management lever for spurring growth— more important than operational excellence or M&A), only a third of companies surveyed reallocate a measly 1 percent of their capital from year to year; the average is 8 percent. 

This is a huge missed opportunity because the value-creation gap between dynamic and drowsy reallocators can be staggering. A company that actively reallocates delivers, on average, a 10 percent return to shareholders, versus 6 percent for a sluggish reallocator. Within 20 years, the dynamic reallocator will be worth twice as much as its less agile counterpart—a divide likely to increase as accelerating COVID impacts, digital disruptions, and growing geopolitical uncertainty boost the importance of nimble reallocation. 

The disconnect tends to be because managers struggle to figure out (and agree) where they should reallocate, how much they should reallocate, and how to execute successful reallocation. Additionally, disappointment with earlier reallocation efforts can push the issue off top management’s agenda.

Although these challenges can be overcome, feedback and data from employees, customers, and the maturity benchmarking should help to align senior management commitment to prioritising the short-term digital investment requirements, and at the same time laying the foundation for more detailed discussions and analysis for longer-term strategic planning. 

#7 Improve the digital acumen of the Board (and workforce)

 A UK government report published in 2016 found that the digital skills gap is costing the UK economy £63 billion a year in lost GDP. Similarly, a report from Amrop, a global executive search firm, reveals that just 5% of board members in non-tech organisations have digital competencies, and that the figure has barely moved in the last two years.

In the new COVID world requiring adaptability and digital adoption at a scale never seen before, boards must get to work in reassessing competencies, adopting new ways of working (e.g. continuous strategic planning, collaborating internally and with the wider ecosystem), and being open to hiring diverse backgrounds if needed. 

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In addition, since many new digital directors may have atypical perspectives (e.g. deep technical vs product vs strategy vs HR), companies must make sure that they have strong on-boarding processes in place, to capture and maximise the impact of their new board members.

A critical first step is to ensure a consistent understanding of what digital and innovation means amongst leaders and boards, what are the best practices of leading tech and non-tech organisations, and what are the big opportunities for digital (and threats) in a COVID world. As part of this, improving the board’s understanding of the external environment and how it is shifting, and how the big trends and signals might impact the immediate and longer-term future. 

In many cases, firms will need outside help across recruitment (e.g. diversity), training and education (e.g. research and insight, best practices, benchmarking), advisory, and briefings from experts, entrepreneurs, academics, and other ecosystem players. 

Once the above happens (which in theory can happen quickly with committed leadership), this should provide the intent and focus to refresh strategic plans and budgets, and then roll-out or accelerate digital and innovation upskilling throughout the wider workforce as a strategic priority.  

#8 Organise to build digital capabilities  

Put simply, digital capability can be defined as doing everything it takes to develop an organisation and workforce able to:

  • Maximise the potential of technology, data and talent to address business challenges; and
  • Ability to respond quickly to continual shifts in consumer behaviour and external environment in a fast-changing connected world.

According to recent study by Deloitte involving interviews with industry leaders, achieving this is not easy as the survey had a multi-faceted response. However, organisations that have successfully adapted to this new environment typically make delighting the customer their #1 priority, set bold goals to achieve factors of 10x impact, and challenge the status quo by looking for new ideas to solve.

3 core critical success factors to building digital capabilities:

Leadership:

In these times of significant change, leaders must understand, collaborate, and champion the exciting potential of technology from the very top of the organisation.

However, understanding the full suite of digital opportunities (e.g. API-based BaaS platforms) are often new and alien to leaders of incumbent firms. Teams and advisers need to help them to understand how digital can work, and the options in terms of where to play and how to win. This is critical to getting commitment to re-allocating sufficient capital and resources from other initiatives to support this market opportunity in a meaningful way.

Organisational Structure and Operating Models:

Organisations need to embed and build the right structures and models that allows them to drive digital change and execute in an agile way.  This requires clarity on the firm’s approach to digital strategy (e.g. build vs buy vs partner) as the implementation approaches to build digital capabilities will differ.

For example, many established firms will embark on dual-transformation or innovation portfolio approaches by

(i) executing process improvement and cultural change in the main firm (see ‘A’ below)

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(ii) creating separate legal entities, JVs and alliances to tackle new markets, exploit new business models, sometimes at the risk of cannibalising the main business (see ‘B’ above or ‘Exploit’ below)

82247d19-8db0-4050-8831-d4ec50b39f43.__CR0,0,300,300_PT0_SX300_V1___ (1)

03-Chart-ExploreExploitContinuum

Source: Strategyzer

PingAn has pursued the above approaches to become one of the best-performing transformer of the past decade (and become a much sough-after MBA case study subject). It typically kick-starts new ventures with partners as part of the ‘explore’ portfolio which is one of the most effective approaches to reducing risk and increasing chances of success.

Typically these are best managed away from the core in an ‘explore’ portfolio of businesses within a new organisational structure and P&L. 

Talent, Skills, Culture and Data:

Maximising digital opportunities require radically different skills, technologies, ways of working, and metrics. Organisations need to empower people to be creative, test and learn and challenge existing ways of working. They also need to cultivate diversity and a lifelong learning mindset, recognising that many will resist change. This was highlighted in PwC’s recent Skills Report.

In addition, whilst the focus of the ‘future workforce’ tends to focus on the technical and ‘hard’ skills (e.g. engineering, analytics, coding etc) it is the soft skills and humanities expertise which will gain increasing importance.

Screen Shot 2020-08-21 at 10.56.33

According to billionaire tech entrepreneur Mark Cuban:

“Twenty years from now, if you are a coder, you might be out of a job,” Cuban predicted. “Because it’s just math and so, whatever we’re defining the A.I. to do, someone’s got to know the topic. If you’re doing an A.I. to emulate Shakespeare, somebody better know Shakespeare.” Cuban acknowledged the importance of coding as a short-term opportunity. Long-term, however, the Shark Tank investor pointed out that A.I. is only as good as the data it’s given–meaning the highest-skilled workers in the future will be the ones who can identify “what is right and what is wrong and where biases are.”

Already today design thinking and human-centred design is a new differentiator in digital which complement technical mobile, cloud, AI, and other more technical digital skills.

“Creativity, collaboration, communication skills: Those things are super important and are going to be the difference between make or break” – Mark Cuban

In terms of data (the new ‘oil’) organisations need to capture, track, protect, analyse and maximise the business value of their data, as along with people, this is the most valuable asset.

Some further tactics might include:

  • Senior executive and board training, commitment and refreshed digital strategies 
  • Centralising digital business expertise (e.g. Centre of Excellence) using hub-and-spoke engagement model 
  • Hiring a Chief Digital Officer and team/function
  • New talent and up skilling (e.g. analytics, user experience)
  • Hiring external, flexible talent e.g. freelancers
  • Cross-functional governance
  • New incentives and behaviours
  • Collaborating with wider industry and ecosystem partners
  • Training will be integral which will also enable every C-level executive to be their own ‘Chief Digital and Innovation Officer’ for their functions.

Accenture summarise this using an 8 step ‘playbook’ below:

Accenture-Change-Leader-Digital-Economy-ThumbnailWhat’s next?

To better understand these issues further or explore our range of digital business advisory offerings, get in touch here andrew@rocketandcommerce.com or at ROCKET + COMMERCE

3 Big Digital Priorities for Leaders

After analysing the data of over 439 senior leaders at global organisations in the recent REIGNITE! 2020 Report, it was clear that the use of technology for 95% of the majority had been to maintain business operations, whether that was survival or business continuity in facilitating remote work. 

This is not surprising per se in response to a major emergency. Before the tectonic shifts caused by COVID-19, some organisations were executing on multi-year digital transformation plans, with others focused on fighting other fires with digital not even on the radar. 

The ongoing pandemic, economic, social and health crises continues to raise the stakes for leaders on digital priorities, underscored by three major opportunities:

#1 Increased digital adoption enables adaptability at speed and scale

For many firms this has involved a combination of accelerated back-end cloud, front-end software tools, and new ways of working. Many of those digital initiatives quickly became make or break—for example restaurants, cafes, and retailers enabling digital orders and connecting seamlessly with delivery services. 

Other firms however continue to have core (or hybrid) infrastructure set-ups based on outdated tools, processes, and assumptions which need to be re-envisioned for the evolving landscape, continuing remote workforce requirements and leadership appetite to maximise the full potential of digital across the firm.

The focus for leaders should be to build on the momentum of change the crisis has caused (‘it can be done!’) and adoption by moving beyond ‘getting back to business’ and understanding the full set of digital opportunities for customers, internal processes, workers, and organisational capabilities. 

#2 Digital acceleration increases the widening gap between the ‘laggards’ and transforming leaders 

COVID-19 has accelerated this trend and has firmly planted digital and innovation at the top of most CEO’s (and CXO’s) agenda. Whilst many of the worlds large and small companies went into tailspin or survival mode once the pandemic took hold, a handful of digital-powered and platform-enabled companies have instead added billions to their market capitalisation and top-line revenues. And they won’t stop (even likely break-up by the US government will not slow them down). 

In other words, if COVID crisis hasn’t shown you the burning platform (i.e. how fast change is moving, and how digital can help you adapt), then nothing will.

Here are the 3 rough categories of organisations today:

The Leaders: 

A business or brand, which has invested heavily (monetarily and otherwise) into a digital transformation strategy that goes far beyond ‘remote work facilitation’. Integrating key technologies and talent (up skilling existing and augmenting with external expertise) to elevate customer experiences, exploit new business models and ventures, and optimise business processes. Not to be confused with those who have attempted digital transformation, only to implement a new email system and hang up their hats.

The Laggards: 

Those who, for whatever reason, have failed to incorporate new technologies and/or invest in up skilling their talent, leaving their business to rely solely on manual or traditional forms of operations, business models, go-to-market, and communications. While you may be inclined to think of this group as pure traditionalists, grasping on to their old standards, assumptions and ways of working, this group has grown to include a much broader range of organisations. 

In talking with many leaders and employees across the world, it is surprising how many leaders have gone straight back to this way of working after Q2 2020 lockdown. In some cases, they have retreated even further. 

The Middle-Ground Mavens:

This may be the point in which you find yourself asking, but what about those in middle? Not quite a leader, but definitely not a laggard. In our post-COVID world and given the pace of change, the space taken up by these ‘middle ground mavens’ you could argue is increasingly dwindling, giving way to a landscape in which we can only find ourselves as laggards or leaders. Those who have mastered the art of transformation and innovation, and those who have not. 

(NB This is obviously hugely simplified and far from black or white, but the sentiment remains).

For non-tech large incumbents with some tailwinds and the appetite to transform, there is significant opportunity to use the scale and resources to digitise processes for efficiency, and at the same time, investing in future growth and innovation portfolios, new business models, and up skilling. PingAn’s transformation is a brilliant case in point. 

Whilst this is not easy and requires the right leadership, the alternative is arguably worse: a slow death-march toward extinction or significant value-destruction. 

 #3 Digital acceleration enables more advanced and integrated human and digital combinations

In other words, digital adoption will enable the workforce of today and tomorrow (e.g. remote, virtual, distributed, agile, flexible, gig etc) to become more productive, effective and efficient (‘smarter’) utilising automated workflows (enabled by cloud, analytics, AI, automation, software) of both repetitive and higher-order tasks.

The Boston Consulting Group call this the ‘Bionic Organisation’ which at its core will combine more advanced and integrated human/software combinations (see below):

The-Bionic-Company-of-the-Future_Exhibit_tcm-233419-1024x829

According to BCG, what the company of the future will look like is becoming clearer. At the centre is purpose and strategy: the reasons it is in business and how it brings those reasons to life. Four enablers allow companies to operate as bionic organizations: two have to do with technology and data, while the other two address talent and organisation.

What’s next?

To better understand these issues further or explore our range of digital business advisory offerings, get in touch here andrew@rocketandcommerce.com or at ROCKET + COMMERCE

Rethinking Education and Learning

“Direct to learner” (DTL) business models and start-ups that leverage online, mobile, AI and other technologies have been an area of much focus within the ‘Edtech’ sector for over a decade.

The late Professor Clayton Christensen had made the topic one of his core areas of focus in the last decade of his life with books including Disrupting Class and The Innovator’s University. 

Companies like Coursera, Udemy, Duolingo, Quizlet, Skillshare, Codecademy, Outschool and Lambda are just a few examples. 

Just this sample reaches hundreds of millions of learners all around the world each month. Many learners use these products for free. A small percentage of learners pay. And yet this portfolio will generate close to a half a billion dollars of revenue in 2020.

Another interesting thing about this portfolio is that none of these companies have spent a lot of capital building their businesses. They have all been very capital efficient and most are cash flow positive at this point.

So, what?

  • Direct to learner businesses are obviously very attractive for consumers and investors
  • They can serve a very large number of learners very efficiently
  • They can lightly monetize and yet produce massive revenues because of their scale
  • They don’t require a huge amount of capital to build

As they are competing with a sector which broadly, looks exactly the same as it did 100 years ago (schools, universities, training), the current pandemic will massively accelerate significant structural changes in the way people and companies learn, train and educate. 

The University segment in particular is in for a massive shock. I can’t see as much change happening in junior schooling (e.g. ages 3-7) mainly as the main job that these bodies do is child-care. I’m currently parenting a 3 and 4 year old and this is the main reason why I’m sweating on schools (safely) re-opening soon. 

I’ll share further thoughts on these topics in later posts.  

 

The Challenge for Video-Conferencing Vendors

Yesterday I participated in a 4hr virtual symposium called ‘Disruption 2020’ run by the MIT Sloan Management Review. If you are interested in disruptive innovation and strategic management, this was a brilliant session with experts including Scott Anthony (Innosight), Amy Webb (NYU Professor and Founder, Future Today Institute), and Rita Gunter McGrath (Professor, Columbia Business School).

They have an edition dedicated to it which you can see here.

The VC session was run on GoToWebinar, one of the traditional VC incumbent firms founded in 2004. Unfortunately, the technology didn’t work that well. If I had to rate the user experience of the technology on a scale of 0-10, with 10 being perfect, I would give it a 5.

Every speaker and host (there were 8 or so speakers and 2 hosts) had issues. One had to drop out then come back in. A few speakers could only speak as the VC wasn’t working. Some had constant cutting out or freezing throughout.

It is unclear why these issues were occurring, and who exactly is responsible. Whether it is GoToWebinar, internet bandwidth, home wifi, 4G, human error or something else, this shouldn’t be an issue in 2020. For me, this feels like what dial-up internet was in the early 2000s.

Clearly these are the fundamental CX issues which have enabled a young start-up (Zoom) to rapidly scale across B2B and B2C with a powerful value proposition focusing on the B2B SaaS Playbook: ease of use, integrations, free or flexible pricing, and better performance. I wrote a post recently on how impressed I was a few months ago in being able to easily host a global conference hosted by Seth Godin with many hundreds of people.

Whilst Zoom is facing other challenges right now (e.g. security, privacy etc), from what I have seen with competing vendors, it will be around for a long time to come (and the others, won’t be).

14 Sources of Disruption

As the COVID-19 pandemic continues to cause significant or catastrophic disruption to many organisations, it is almost crazy to think that COVID-19 represents one source of disruption. Obviously it is a major shock and is inter-related with other forces (e.g. economic). However, from a crisis response perspective and the need to re-set short and longer-term strategic plans, it is important for leaders to always look at the bigger picture.

Why? According to Amy Webb, founder of  The Future Today Institute:

If leaders think that they are aware of the forces that might disrupt their company, their lens’ may be far too narrow…

To support such analysis, I use a tool called The Strategic Forces Framework (SFFF) which Amy Webb discusses in detail here. 

MAG-webb-essay-s1

Clearly, the SFFF builds on long-standing (and less comprehensive) frameworks including PESTLE. Many forces will seem obvious, but others less so.  

Amy Webb provides context on using the tool:

The SFFF helps clients identify external uncertainties which broadly affect business, markets, and society across positive, neutral, or negative dimensions. In over a decade of strategy consulting and research, I have observed that all major or ‘disruptive changes’ are the result of one or more of the 11 forces. 

For leaders and executives, the critical skill is being able to look for areas of convergence, inflections, and contradictions, with emerging patterns especially important because they signal ‘transformation’ of some kind. People must connect the dots back to their industries and companies, and position teams to take incremental – or transformative – actions as required.

Whilst many of the 11 sources of disruption might seem obvious or onerous at first, taking a broader viewpoint provides perspective as the tool can help identify critical growth opportunities (e.g. market-creating innovations) or areas of potential disruption (e.g. new business models). For example, an established regional farming equipment firm tracking eco-friendly infrastructure trends could be a first mover into new or emerging markets, while a traditional electronics retailer (with online operations) monitoring 5G, IoT and AI plus segments of non-consumption, could be better positioned to compete against the big e-commerce platforms.

Whilst Amy uses 11 forces, I add 3 more to make 14. See below for details but I believe that Legal, Industry, and Business Models deserve their own line of enquiry. You only have to think about the music-industry in the early 2000s to understand why that matters.

Sources of macro change encompass the following:

  1. Prosperity: the distribution of income and wealth across a society; asset concentration; and the gap between the top and bottom of the pyramid in within an economy.
  2. Education: access to and quality of primary, secondary, and postsecondary education; workforce training; trade apprenticeships; certification programs; the ways in which people are learning and the tools they’re using
  3. Infrastructure: physical, organizational, and digital structures needed for society to operate (bridges, power grids, roads, Wi-Fi towers, closed-circuit security cameras); the ways in which the infrastructure of one city, state, or country might affect another’s.
  4. Government: local, state, national, and international governing bodies, their planning cycles, their elections, and the regulatory decisions they make.
  5. Geopolitics: the relationships between the leaders, militaries, and governments of different countries; the risk faced by investors, companies, and elected leaders in response to regulatory, economic, or military actions.
  6. Economy: Standard macroeconomic and microeconomic factors, including interest rates, inflation, exchange rates, taxation
  7. Public Health: changes occurring in the health and behaviour of a community’s population in response to lifestyles, disease, government regulation, warfare or conflict, and religious beliefs.
  8. Social: Life-style, trends, ethics, norms, religions, diversity and inclusion, culture, religion, demographics, population rates and density, human migration, and other dynamics are leading to shifts in communities, markets (including non-consumption) and societal needs
  9. Environment: changes to the natural world or specific geographic areas, including extreme weather events, climate fluctuations, rising sea levels, drought, high or low temperatures, and more. Agricultural production is included in this category.
  10. Communications: all of the ways in which we send and receive information and learn about the world, including social networks, news organizations, digital platforms, video streaming services, gaming and e-sports systems, 5G, and the boundless other ways in which we connect with each other.
  11. Technology: not as an isolated source of macro change, but as the connective tissue linking business, government, and society. We always look for emerging tech developments as well as tech signals within the other sources of change.
  12. Legal: Privacy, health and safety, labour, consumer rights, product safety 
  13. Industry: Suppliers, buyers, non-buyers (e.g. non-consumption), competitors (current and new), substitutes, distribution channels, partners, ecosystems and value-networks 
  14. Business Models: The incredible pace of technological change continues to open up more ways to make money and go-to-market. Combined with the tremendous disruptive impact business model innovation can have on traditional firms and industries, I believe it is critical to include it as a separate category for investigation e.g. Software-as-a-service, Direct-to-consumer, Pay-as-you-go

How best to use the SFF?

Most companies we encounter use the Strategic Forces Framework to help make sense of initial or deep uncertainty, optimise existing planning processes, or reinvent how that is typically performed. Some use it at the start of a strategic project at corporate levels, while others use it as a guiding principle throughout their functional or departmental work streams, processes, and planning. The key is to make a connection between each source of change and the organisation with questions such as: 

  • Who is funding new developments and experimentation in this source of change? 
  • Which populations will be directly or indirectly affected by shifts in this area? 
  • Could any changes in this source lead to future regulatory actions? 
  • How might a shift in this area lead to shifts in other sectors? 
  • Who would benefit if an advancement in this source of change winds up causing harm?

Here are some good examples of use in business as usual (BAU) provided by Amy Webb:

I have seen the most success in teams who use the macro change tool not just for a specific deliverable but to encourage ongoing signal scanning. One UK-based multinational professional services firm took the idea to an amazing extreme:

    • It built cross-functional cohorts made up of senior leaders and managers from every part of the organization all around the world.
    • Each cohort had 10 people, and each person is assigned one of the sources of macro change, along with a few more specific technology topics and topics related to their individual jobs. 
    • Cohort members are responsible for keeping up on their assigned coverage areas. A few times a month, each cohort has a 60-minute strategic conversation to share knowledge and talk about the implications of the weak signals they’re uncovering. 

Not only is this a great way to develop and build internal muscles for signal tracking, it has fostered better communication throughout the entire organization.

Whilst this process might go against the established culture of your organization, embracing uncertainty is the best way to confront external forces outside of your control. Seeking out weak signals by intentionally looking through the lenses of macro change is the best possible way to make sure your organization stays ahead of the next wave of disruption. Better yet, it’s how your team could find itself on the edge of that wave, leading your entire industry into the future.

Gig Work Acceleration

I came across an article today (here) in Forbes which summarised findings from recent PwC and BCG CEO surveys. Most of the findings were self-evident, especially around the acknowledgement of increased requirements for, and adoption of, independent consultants going forward during and beyond COVID 19:

  • More companies are likely to access freelancers both for cost efficiency and to supplement critical skill sets;
  • The 4th Industrial Revolution (4IR) offers interesting and important freelance work and lots of it;
  • Freelancers are a critical resource to more industries;
  • More areas of freelancing will grow in importance.
Source. BCG

 

It is hard to believe that a little over a decade ago, organisations weren’t able to easily access remote (or on-site) highly-skilled workers and teams using the internet (e.g. online marketplaces) or mobile apps.  Nor were such firms willing to do so, especially at scale and for a wider variety of higher-skilled freelance talent e.g. lawyers, management consultants.

As a lawyer in Australia and London in the mid-to-late 2000s, I remember that it was career suicide to turn your back on traditional law firms and pursue contracts with in-house legal departments. How things have changed!

 

Jeff Bezos

“We innovate by starting with the customer and working backwards. That becomes the touchstone for how we invent” – Jeff Bezos

Jeff Bezos is arguably one of the greatest – if not the greatest – founder/CEO’s of all time. What is more unbelievable is that he is only 58, and many believe that the company he founded (Amazon) is only just getting started.

Like many of the best leaders who have disrupted industries or successfully navigated disruptive events or crises, there are many unique leadership traits which characterise Jeff Bezos. However, when I think of one thing, it is this: Customer-obsession.

Below are some ways that Jeff executes this within Amazon:

Leadership principles: It is so important to Amazon that it is the first on their list of 14. Apparently all the other principles are interchangeable, but only one must be first – customer obsession.

Core value-driver: Jeff Bezos sees that there are 5 main ways of creating shareholder value:

  • Competitor obsession
  • Business model obsession
  • Product obsession
  • Technology obsession
  • Customer obsession

While he acknowledges merits of all the approaches he believes that Customer Obsession is the healthiest approach:

“Leaders start with the customer and work backwards. They work vigorously to earn and keep customer trust. Although leaders pay attention to competitors, they obsess over customers. They experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight. A customer-obsessed culture best creates the conditions where all of that can happen…”

Symbols: Early on in Amazon’s life, Jeff Bezos brought an empty chair into meetings so lieutenants would be forced to think about the crucial participant who wasn’t in the room: the customer. Now that ­surrogate’s role is played by specially trained employees, dubbed “Customer Experience Bar Raisers.” When they frown, vice ­presidents tremble.

Founding value: Amazon’s 1997 shareholder letter is the first documented account of the term Customer Obsession – in the heading Obsess over Customers.

While the whole letter makes an interesting read, not least the growth between 1996 and 1997 of 838% from $15.7 million to $147.8 million. It’s this paragraph discussing their relentless focus on delivering value for customers as the driver of their growth.

Customer obsessed growth has taken Amazon from a start-up in a garage to one of the leading companies in the world and disrupting multiple industries in its wake.

Customer focus vs customer obsession: Gibson Biddle, former VP of Product at Netflix, wrote an interesting blog post explaining how Netflix adopted ‘Customer Obsession’ in his time there. In the post Gibson uses an image to compare Customer Obsession with Customer Focus which properly distinguishes the strategies:

Screenshot 2019-05-17 at 21.39.05

The below is a great short video summarising Jeff’s approach to customer obsession and long-term thinking:

This video provides details on Amazon’s 14 Leadership Principles with footage from various interviews with Jeff:

Other useful articles:

Below I have captured a few must-read resources to gain insight on Jeff’s leadership philosophy:

Forbes article on seven things a highly agile CEO does: Jeff Bezos

Jeff Bezos (2017) on his management style and philosophy 

Havard Business review article on how Jeff Bezos makes decisions

Hal Gregersen on the one skill that made Jeff Bezos so successful: Experimentation

 

 

 

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