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
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?
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.
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:
Current and future use cases and trends of AI in legal and compliance services;
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:
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.
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.
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;
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:
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.
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).
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.
The business world is famous for jargon and phrases that become so overused they soon become meaningless. ‘Disruptors’ is one of these words, and is explained from different perspectives in this great podcast episode from BBC called Behind The Buzzwords.
I came across the term ‘disruptive technologies’ in the early 2000s after reading The Innovator’s Dilemma by the late Professional Clayton Christensen. I ended up using some of his theories to help me with major research project analysing what was going on with the record labels and retailers in the Australian music-industry as online P2P file-sharing emerged and began to ‘disrupt’ the physical music business model.
Interestingly, the perception of ‘disruption’ was subjective. For the small indie labels and artists, digital music was a new opportunity to reach new audiences. For the major labels, it was perceived negatively as a threat, especially as no legal services existed. It was the same with music retailers.
This podcast explores these fascinating issues further in the context of 20 years on. It is well worth a listen.
The House Judiciary Committee’s Democratic chairman, Rhode Island Rep. David Cicilline, concluded today’s daylong hearing by hinting at what might lie ahead as lawmakers ponder federal regulations to hold the four companies — worth nearly a combined $5 trillion — to account.
In summary, Rep. David Cicilline, D-R.I., says Amazon, Facebook, Google and Apple operate like monopolies and need to be broken up or regulated.
“These companies as they exist today have monopoly power. Some need to be broken up. All need to be properly regulated and held accountable,” said Cicilline, adding that antitrust laws written a century ago need to be updated for the digital age.
“When these laws were written, the monopolists were men named Rockefeller and Carnegie,” he said. “Today the men are named Zuckerberg, Cook, Pichai and Bezos. Once again, their control of the marketplace allows them to do whatever it takes to crush independent business and expand their own power. This must end.”
This power has been obvious for many years (and accelerated in 2020 due to COVID) however the political will has never been there until now, and agreeing the exact nature of the ‘stick’ or remedies to sort out the issues is never an easy task.
According to NPR, 4 key takeaways from today include the following:
Bezos “can’t guarantee” Amazon never used seller data to make its own products
Hurting the competition emerges as Democrats’ primary charge against Big Tech
Republicans sidetrack hearing to air complaints over anti-conservative bias
Missing from view? Zuckerberg’s reaction (when Bezos described social media as a “nuance destruction machine”)
NPR do a great job filling out the details and you can read the full article here
Recently I posted here about how organisations can go back to basics and understand what digital really means. In the context of today’s rapid acceleration of digital and IT investments to support remote or new ways of working – from cloud to SaaS tools to desktop VC solutions – this is critical to understand.
Another key fact to consider is that some of the most successful companies ever were started during or just after times of crisis (e.g. GE, GM, IBM, Disney, Facebook).
For leaders who can seize the ‘re-set’ opportunity this crisis provides – and start to engage with more long-term, future-focused, and exploratory strategic planning with digital at the core – this presents a potentially game-changing moment.
This presents a critical question: how should firm’s approach and organise to make digital or innovation investments and transformations successful?
Whilst there is no playbook, below I pull together a number of perspectives from some of the world’s leading management thinkers and practitioners on strategy, digital, innovation and change.
The Challenge
Digital transformation is extremely complex and requires new ways of approaching strategy. Starting big, spending a lot, and assuming you have all the information is likely to produce a full-on attack from corporate antibodies—everything from risk aversion and resentment of your project to simple resistance to change.
Start Small, Think Big
Professor Rita McGrath calls this ongoing learning approach to strategy: discovery-driven planning (DDP). It was developed in the 1990s as a product innovation methodology, and it was later incorporated into the popular “lean start-up” tool kit for launching businesses in an environment of high uncertainty. At its center is a low-cost process for quickly testing assumptions about what works, obtaining new information, and minimizing risks. According to Rita:
By starting small, spending a little on an ongoing portfolio of experiments, and learning a lot, you can win early supporters and early adopters. By then moving quickly and demonstrating clear impact on financial performance indicators, you can build a case for and learn your way into a digital strategy. You can also use your digitization projects to begin an organizational transformation. As people become more comfortable with the horizontal communications and activities that digital technologies enable, they will also embrace new ways of working.
2. Soft and Hard Facts About Change
Managing change is tough, but part of the problem is that there is little agreement on what factors most influence transformation initiatives. Ask five executives to name the one factor critical for the success of these programs, and you’ll probably get five different answers.
In recent years, many change management gurus have focused on soft issues, such as culture, leadership, and motivation. Such elements are important for success, but managing these aspects alone isn’t sufficient to implement transformation projects.
What’s missing, we believe, is a focus on the not-so-fashionable aspects of change management: the hard factors. These factors bear three distinct characteristics. First, companies are able to measure them in direct or indirect ways. Second, companies can easily communicate their importance, both within and outside organizations. Third, and perhaps most important, businesses are capable of influencing those elements quickly. Some of the hard factors that affect a transformation initiative are the time necessary to complete it, the number of people required to execute it, and the financial results that intended actions are expected to achieve. Our research shows that change projects fail to get off the ground when companies neglect the hard factors. That doesn’t mean that executives can ignore the soft elements; that would be a grave mistake. However, if companies don’t pay attention to the hard issues first, transformation programs will break down before the soft elements come into play.
3. Breaking Down the Barriers
According to a 2019 article from the partners from Innosight, a critical reason for businesses failing to get the impact they want is because they’ve failed to address a huge underlying obstacle: the day-to-day routines and rituals that stifle innovation.
Innosight Partner Scott Anthony talks further about this below:
4. A Systematic Approach
A study by McKinsey here of leaders post-transformation has shown there are 21 best practices for organisation’s to implement to improve the chances of success.
These characteristics fall into five categories: leadership, capability building, empowering workers, upgrading tools, and communication. Specifically:
having the right, digital-savvy leaders in place
building capabilities for the workforce of the future
empowering people to work in new ways
giving day-to-day tools a digital upgrade
communicating frequently via traditional and digital methods
One interesting best practice was that firm’s who deploy multiple forms of technologies, tools and methods tended to have a great success rate with transformation (see below).
This might seem counterintuitive, given that a broader suite of technologies could result in more complex execution of transformation initiatives and, therefore, more opportunities to fail. But the organizations with successful transformations are likelier than others to use more sophisticated technologies, such as artificial intelligence, the Internet of Things, and advanced neural machine-learning techniques.
The full list of success tactics are below with the full article is here.
Out of 83 practices that were tested in the survey, the following are those that best explain the success of an organization’s digital transformation:
Implement digital tools to make information more accessible across the organization.
Engage initiative leaders (leaders of either digital or nondigital initiatives that are part of the transformation) to support the transformation.
Modify standard operating procedures to include new digital technologies.
Establish a clear change story (description of and case for the changes being made) for the digital transformation.
Add one or more people who are familiar or very familiar with digital technologies to the top team.
Leaders engaged in transformation-specific roles encourage employees to challenge old ways of working (processes and procedures).
Senior managers encourage employees to challenge old ways of working (processes and procedures).
Redefine individuals’ roles and responsibilities so they align with the transformation’s goals.
Provide employees with opportunities to generate ideas of where digitization might support the business.
Establish one or more practices related to new ways of working (such as continuous learning, open physical and virtual work environments, and role mobility).
Engage employees in integrator roles (employees who translate and integrate new digital methods and processes into existing ways of working to help connect traditional and digital parts of the business) to support the transformation.
Implement digital self-serve technology for employees’ and business partners’ use.
Engage the leader of a program-management office or transformation office (full-time leader of the team or office dedicated to transformation-related activities) to support the transformation.
Leaders in transformation-specific roles get more involved in developing the digital transformation’s initiatives than they were in past change efforts.
Leaders in transformation-specific roles encourage their employees to experiment with new ideas (such as rapid prototyping and allowing employees to learn from their failures).
Senior managers get more involved in digital initiatives than they were in past change efforts.
Leaders in transformation-specific roles ensure collaboration between their units and others across the organization when employees are working on transformation initiatives.
Senior managers ensure collaboration between their units and others across the organization.
Engage technology-innovation managers (managers with specialized technical skills who lead work on digital innovations, such as development of new digital products or services) to support the transformation.
Senior managers encourage their employees to experiment with new ideas.
Senior managers foster a sense of urgency within their units for making the transformation’s changes.
4. Execute AND Innovate
For any followers of the work of the late Professor Clayton Christensen on Disruptive Innovation (view his HBR collection of popular articles here), this is a fundamental challenge for almost every established firm which often becomes a matter of survival during industry, business model, technology or other shifts.
According to Alex Osterwalder:
This continues to be one of the biggest challenges we see companies face: to create two parallel cultures of world-class execution and world class innovation that collaborate harmoniously.
Watch this video here to read more about how leaders can overcome this challenge.
5. Constant Learning
In a constantly evolving landscape, it is critical for leaders to keep learning and evolving their thinking and tool-sets for strategic planning, crisis management, innovation, problem solving, and other business processes.
There are lots of sources out there (including those mentioned above), but below I provide a few great ways I do this (NB I am not formally affiliated with any of these organisations):
There are a number ways to improve the thinking and planning of the approach to digital investment strategy. This not only improves the chances of benefit realisation and achievement of strategic goals, but in the current time of crisis, can set up the firm for long-term success.
Whilst there is no playbook for leaders, these initial set of resources can help organisations to refocus current thinking and initiatives during these extraordinary times.
“Everyone wants to go digital. The first step is truly understanding what that means” – McKinsey
I was talking to a COO of an off-shore investment bank yesterday and he mentioned something which gave me the impression that his bank did not understand what ‘digital’ really meant. According to McKinsey:
For some executives, it’s about technology. For others, digital is a new way of engaging with customers. And for others still, it represents an entirely new way of doing business. None of these definitions is necessarily incorrect. But such diverse perspectives often trip up leadership teams because they reflect a lack of alignment and common vision about where the business needs to go. This often results in piecemeal initiatives or misguided efforts that lead to missed opportunities, sluggish performance, or false starts.
As COVID-19 continues to rapidly accelerates the shift to building more digital capabilities within organisations, it is a critical time to take a step back and reevaluate existing efforts in light of the new challenges ahead. This means properly understanding what digital means, assessment of existing efforts, aligning to future strategy, and identifying what capabilities are needed across leadership, culture, and execution.
Whilst extremely hard, now is the best time to refocus efforts toward accelerating digitisation as the case for such change is for some a matter of survival. Think about how many food and other retailers are rapidly shifting to e-commerce models requiring new skills, software, tools and mindsets.
“COVID-19 will produce a thinning of the herd and a reimagined legal industry” – Mark Cohen
Last Thursday I watched a great online webinar run by LegalGeek entitled ‘An Uncertain Decade’. Legal sector experts Mark Cohen and Richard Susskind ran the sessions. Whilst I have read various thought leadership from each expert, it was the first time I had seen either speak. Not surprisingly, they both were very impressive in both domain expertise and thoughtfulness around their points of view.
Here are some key takeaways (along with my thoughts):
Disruption: COVID-19 will dramatically turbocharge legal industry transformation which has been slowly accelerating since the 2008 Financial Crisis. This may not be that surprising to many outsiders, however many lawyers – including those in Generation X – still tend to be conservative when thinking about competition, new technologies, business models, and structural market changes. Transformation and disruptive models and services will continue to come from outside traditional law firms. Whilst Disruptive Innovation theories of Professor Clayton Christensen were not referenced, his work on how established firms often are disrupted by low-end entrants who move up-market over time, will provide insight as to why and how this is happening within the legal sector (click here for his articles from Harvard Business Review).
Innovation: Enterprise clients and consumers will continue to drive the shift away from bundled services toward a more productised, customer-centric mode of consumption at scale which leverage new technologies, business models, and regulatory changes. This has already been happening to some extent ‘around the edges’, and facilitated by ABS models in the UK (whereby retailers (e.g. Co-Op), real estate agents, insurers and other firms can compete head-on with traditional legal practices with their own legal services ventures). DoNotPay in the US was cited as a recent example of a start-up which has over 100 use cases of dispute resolution services (e.g. fight a parking ticket). A new and current use case in the US for them has been to make it easy, cheap and convenient to file unemployment and other worker claims.
Unbundling: The impending depression driven by COVID-19 and resultant cost pressures for clients will accelerate the shifting of lower value, high-volume work to more flexible, alternative providers (e.g. LPOs, ABS Licensed Firms, Big4, Axiom, UnitedLex etc) and digital platforms (e.g. DoNotPay). This will continue to enable these players to move further up-scale into higher-value, more complex work and jobs. This is how the Indian IT outsourcing firms managed to make significant in-roads against Accenture, IBM and others in the 2000s, and how Toyota managed to become a US car manufacturing leader with its low-cost model US market strategy. Over the coming years, the legal industry will continue to rapidly fragment beyond traditional structural boundaries to incorporate a much bigger share for alternative providers (which will grow rapidly at the expense of incumbents), but significant new markets will be created especially for those consumers (i.e. non-consumers) who historically have never able to access low-cost, convenient legal services;
Business Models: A next generation of technology-enabled service providers (e.g. FisherBroyles) will gain rapid scale over the next decade in the same way as FinTechs have within Retail Banking. Continued experimentation by established law firms (e.g. non-legal services diversification, in-sourcing IT, new product development etc) and further consolidation within and amongst traditional law firms and alternative services providers, vendors and legaltechs unable to re-capitalise or scale. Large traditional law firms with the foresight and capital to invest over the coming years will likely continue to struggle to properly allocate resources and organise these innovative models efficiently and effectively within the established firm.
Online Dispute Resolution: COVID-19 has provided an MVP to as legal systems and courts globally have had to re-think how to deliver this. According to Mark Cohen:
The inaccessibility, cost, formality, abstruse rules, and protracted processes of courts in their present guise is misaligned with life in the digital age. The urgency of modernization is unprecedented. Courts around the world have ground to a halt when demand for accessible, efficient, and widespread administration of justice is desperately needed.
Education: Law Schools have not really changed their content, formats or approach to skills in over 40 years. Combined with EdTech disruption, providers will be under significant pressure to change in line with industry and client demands. Traditional JD/LLB’s offered by mid-market schools in the short-term will see massive disruption and closures, whilst the degree as it stand may become a more commoditised requirement, augmented by other specialty courses run by others or industry. Clearly, now is the time for online law ‘degree’ or course models, assuming the solicitor/lawyer regulatory boards provide approval (if they haven’t already)
Training: Traditional insistence of a junior being trained up or supervised by a senior lawyer/partner will be turned on its head. Assuming a longer-term shift to more remote working for a large number of the workforce and demand for more multi-skilled lawyers (e.g. project management), training for juniors (and all staff) will need to be redesigned.
In a recent Forbes article here, Mark Cohen concludes the following:
The old guard will cling to the hope these are temporary changes. They will point to the recession precipitated by the 2008 global economic crisis and suggest the current one will take a similar course. This time is different. Technology and new delivery models are far more advanced than they were in 2008. Consumers have a different mindset and a greater urgency to solve a growing list of complex challenges. The potential of technology and its ability to support new models, processes, and paradigms is already on display. The genie is out of law’s bottle, and it will not return.
Opportunities for Law (and other) Firms
Interestingly, there wasn’t too much discussion on this during the webinar. For me, Disruptive Innovation theory might provide some guidance for any progressive law firms who wish to take on the inevitable structural and business model disruptions described above. I’ll save this analysis for another post soon.
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.
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.
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:
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.
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
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.
Government: local, state, national, and international governing bodies, their planning cycles, their elections, and the regulatory decisions they make.
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.
Economy: Standard macroeconomic and microeconomic factors, including interest rates, inflation, exchange rates, taxation
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.
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
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.
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.
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.
Legal: Privacy, health and safety, labour, consumer rights, product safety
Industry: Suppliers, buyers, non-buyers (e.g. non-consumption), competitors (current and new), substitutes, distribution channels, partners, ecosystems and value-networks
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.
Just about every business large and small across the world has been in crisis mode for some time dealing with the catastrophic consequences of the Corona Virus pandemic. Almost daily now there are announcements from companies shedding employees or going into administration. One interesting trend however are those CEOs who are pledging not to lay-off workers. For example, on March 26th in Techcrunch Marc Benioff (Salesforce CEO) announced a 90 day pledge.
This has got me thinking lately at a more macro-level: who are those business leaders who have successfully led companies through crises?
Netflix CEO Reed Hastings in pivoting and transforming the business when confronted by an emerging disruptive technology, business models, and established well-funded competition;
Steve Jobs in the late 1990s when he returned to the company he founded (and was ousted from);
Richard Branson in the 1990s at Virgin Atlantic during the ‘dirty tricks war’ with British Airways;
Bob Iger as CEO of Disney when, whilst he was opening Shanghai Disney, a toddler was killed by an alligator at Disney World Florida;
Jørgen Vig Knudstorp as the new CEO of Lego Group in 2004 and embarking on a transformational turnaround;
Angela Ahrendts as the new CEO of Burberry and transforming its brand and performance from ‘chavvy’ to high-end luxury;
Amex CEO Kenneth Chenault following the September 11, 2001 terrorist attacks;
CEO Howard Schultz in 1997 when a robbery killed 3 employees at a US store;
Toyota CEO Jim Lentz during a 2.3 million car recall in the US
The way these leaders responded to their various crises – whether caused by macro-factors (e.g. terrorism, recession, disruptive technologies) or internal (e.g. mismanagement) – were certainly ‘make or break’ situations. In other words, without strong leadership and executing on relevant crisis management principles, the outcomes could have been catastrophic and/or bankrupted their respective companies.
Such leaders deployed various business strategies and tactics but professionally exhibited courage, decisiveness, emotional intelligence, transparency, ownership, clear communication, and many other characteristics. It will be interesting to see how many current organisations and brands respond in a way that also gives them the best chance to survive short-term, as well as in a post-Corona world.
I came across an article this week here which asked whether Corona Virus (CV) could present a tipping point for virtual events. This reminded me of a pre-CV experience at the end of 2019. I attended a virtual conference hosted by marketing guru Seth Godin.
Afterward I was amazed at how far VC technology has come in being able to easily manage large numbers of people in an interesting and organised way which adds-value to both sides. He ran it using Zoom, had over 300 attendees from 50 countries, used self-managed break-out rooms over the course of the 2hrs, and created interactivity (which game them market research) with Q&A into the chat boxes.
Whilst it wasn’t perfect, it was impressive. I would certainly attend more of these, and reconsider in-person ones. Until this time, I had only ever used VC tech for standard corporate meeting use cases with just a few people. Now, I am recommending my parents to set up Zoom as an alternative for traditional B2C options (Skype, FaceTime). Although that may change if the firm can’t get a handle on Zoom-bombing.
Despite some negative scaling side-effects and challenges, Zoom’s stock has post-IPO gone through the roof (actually, through the atmosphere). This gives them a massive window to place new bets and scale-up new products, value-added services, and M&A.
It is not often a newly public firm gets to invest for the long-term, but now is the certainly the time to accelerate investments into becoming a key player within the enterprise (and B2C?) ecosystem. Vertical, horizontal and hybrid platform solutions across many sectors and use cases will likely emerge as it has with IoT. It will be interesting to see how it plays out, how Zoom responds, and how long until its market capitalisation falls back down to earth.
In early January 2003 I embarked on a year-long academic research project at Queensland University Of Technology where I was studying and teaching. The work culminated in a 50,000 word thesis centred around applying Clayton Christensen’s theories on disruptive innovation to the Australian music industry. I was fascinated in trying to understand the competitive responses of key players in the Australian music industry as they battled a disruptive innovation – digital music distribution. At the time, the entire industry – from major record labels to retailers such as HMV – was in a state of chaos meaning it presented a fascinating ‘live’ research case study.
As part of the literature review, I had come across Clayton Christensen’s academic work and books in a comprehensive strategy and innovation theory review alongside management luminaries in Michael Porter. His work didn’t feature too widely in peer-reviewed journals as his seminal work (The Innovator’s Dilemma) had only recently been published (late 90s). However I distinctly remember that I was immediately captivated by how insightful and unique his work was in such a complicated area i.e. understanding why established companies often fail when confronted with emerging technologies. I felt that this represented a step-change from the traditional (i.e. manufacturing-driven) strategic management literature, but also drew relationships (and challenges) with research from various fields, including management, economics, finance, strategy, leadership, innovation, & organisational behaviour. As I sought to better understand what was happening, why, and the implications in the rapidly evolving music industry, I felt that his frameworks, models and case studies of other industries were highly relatable to analysing the challenge I faced.
Clayton was the reason why I subsequently pursued career paths loosely aligned with his work. I became a technology lecturer teaching university students in the early 2000s on the new field of e-Commerce law. I became a technology lawyer advising governments on emerging online gambling regulatory models. I became a technology management consultant helping global telcos with strategy, transformation, & operating models. I launched a start-up to gain the ‘innovators’ perspective on launching & scaling disruptive technologies (NB the start-up was too early and later failed, and as such was far from being disruptive). I even launched my own version of Clayton’s Innosight consulting firm called ROCKET + COMMERCE which helps CXOs to navigate and take advantage of new and emerging technologies (e.g. Digital, Internet Of Things, Digital, SaaS etc).
I had planned to make contact with Clayton and share my thesis in 2003/04, but I didn’t. I had planned to experience his teachings in Boston, but I never applied. I had once planned to convince Innosight to hire me, but I never pursued them. Upon hearing about Clayton’s recent passing, I immediately thought about these potential ‘missed’ opportunities to meet, engage, and express gratitude to someone who has had so much influence from afar. Whilst I now won’t ever have that opportunity, perhaps there are other ways. A crazy idea might be to build upon his work, like I aimed to do back in 2003. To do that properly may mean a radical career U-turn back to my academic roots. An easy idea would be to express gratitude to those who have helped me along the life journey so far, even if just a little. I don’t think I have thoughtfully done this, so right now would be a good time to start. To help provide additional inspiration, I’ve just ordered Clayton’s book from some years ago – How Will You Measure Your Life? (I didn’t realise he had written it). I’m sure it will have great ideas. And I wouldn’t be surprised if it also has a profound impact like his earlier works did on how I might spend the next 10-20 years. Watch this space (NB: I’ll provide a direct update to this post in 5 years on Jan 26 2025. Promise).
It feels as though people have been talking smart contracts for a long time. Like most new innovations, it will take a specific use case (i.e. business challenge important enough to justify adoption) to kick it out of the domain of academics & legal conferences, and into commerce & industry. Perhaps this has already happened. If it has, be sure to give me a shout.
Yesterday, I came across an interesting analysis of smart contracts from Charles Kerrigan, a lawyer at big law firm CMS. It was compiled by Richard Troman of the blog Artificial Lawyer’s (must read for the legal techies out there). Mr Kerrigan was giving a speech as part of a panel giving evidence to the UK’s All Party Parliamentary Group on Blockchain at the end of last year. Whilst the speech is detailed, it provides an interesting deep dive into some of the pervasive questions out there on smart contracts. As Richard Troman’s points out:
…as the prospect of their use in day to day legal work draws ever closer, what should we be focusing on? How should we approach this subject and what really will be the key issues we need to grapple with before this quintessentially legal technology becomes mainstream?
The full extract is posted on Artificial Lawyer’s blog here. If you have any thoughts, or know of any live smart contract use cases in industry, be sure to let me know.
I came across an article today which talked about why IoT has fallen short of expectations (check it out here). In summary, the key themes were:
Optimism of prediction
Niche consumer value
Privacy concerns
Inconsistent standards across hardware/software
Costs and limitations
Slow promise of the smart home use case
Reading this reminded me of what tends to happen with the adoption of most disruptive (or new) technologies, whether the Internet, AR/VR, AI, blockchain, or cryptocurrency. It is best represented by the Hype Cycle for Emerging Technologies who shows the rise-fall-rise of how markets tend to adopt innovations.
Below I’ve pasted in a Hype Cycle dedicated to IoT:
The key takeaway from the above charts is time. People always overestimate how quickly the mass market will adopt new innovations. There’s an entire body of work dedicated to explaining the reasons and not for this post. But it’s just not easy to get technology to a cost/performance level that works beyond the early adopters. A lot of things have to go right. And that includes one of the biggest things beyond technology: changing human behaviour.
In a previous post I talked about how SoftBank recently announced that by 2035 1 Trillion devices would be connected. Whether or not that happens is not the point, as it’s about the ambition & not necessarily the result. But for that to happen, what needs to occur?
1. Market adoption by consumers and businesses of new products/services that help them solve most of their important daily problems & challenges;
2. Significant improvement in connectivity, cloud, data analytics & management, AI & other IoT solution & related technologies to help enterprises and the ecosystem handle all the real-time data being generated by the devices at such a significant scale;
3. Digital transformation of established enterprise & government to rapidly adapt to the new paradigm and compete with IoT focussed startups;
4. Deep ecosystem & cluster development with value-chain players working together & aligned in R&D and GTM within specific industry sectors or use cases.
5. Significant lowering of device manufacturing costs to enable business model innovations to drive market adoption, such as subscriptions, service models and so on.
There may be others but this is just a sample of my initial thoughts right now. If you have any others be sure to let me know
This post won’t be about what you might see on an African safari. Instead, today I’m thinking about where we are now, and where we are going in relation to the full potential of five disruptive technologies that get the most attention: AI, IoT, Blockchain, AR/VR, & Big Data.
Each technology is still in their infancy but fast maturing and gathering steam. We saw with cryptocurrencies in 2018 as a key use case for Blockchain which drove significant consumer & business adoption & later, government intervention
As I look ahead, the most interesting thing for me two-fold: (i) the timeframe(s) for the intersection of these technologies from a market adoption and technology maturity perspective, and (ii) the subsequent implications for established firms, and opportunities for new ones. If we think back to the late 2000s (over a decade since consumer internet introduction), it wasn’t until the launch of next generation mobile phones (via smartphones, tablets) that dramatically accelerated internet adoption driven largely by e-commerce. This opened up entirely new ecosystems (Apple, Google, Facebook, Uber, Amazon) whilst destroying others (Nokia, Motorola, Sears) in the process. For B2B/C, this enabled a new lawyer of applications & services for consumers & businesses alike, such as local discovery (e.g. restaurants), on-demand services (e.g. taxis, TV), & mobile (e.g. Amazon, eBay, banking). All designed to make life easier & better.
In 2018, such continued technology disruption – driven by the intersection of mobility & the internet – is only getting started (think retail, financial services, real estate etc). If we layer on top one or more the Big 5 technologies, it will be like pouring kerosene over an already burning fire. I can’t wait to see how it all plays out.
Andrew Essa 