By Rohit Talwar

In the coming decades, the planet’s most heavily concentrated populations may occupy city environments where a digital blanket of sensors, devices, and cloud connected data are orchestrated to enhance humanity’s living experience. A variety of smart concepts are forming key elements of what enable city ecosystems to function effectively – from traffic control and environmental protection to the management of energy, sanitation, healthcare, security, and buildings. In this article, we reflect on the potential personal impacts of the smart city, and its technologies, on the individuals residing there.

Eyes on the Prize

In the race to attract ideas, business, talent and money, the world’s premier cities are competing to build highly interconnected smart environments where people, government, and business operate in symbiosis with spectacular, exponentially improving technologies. These include big data, the Internet of Things (IoT), cloud computing, hyperconnectivity, artificial intelligence (AI), robots, drones, autonomous green vehicles, 3D/4D printing, and renewable energy. The trick will be to ensure that this array of technological goodies is harnessed in service of the humans that make cities what they are.

Purpose, Engagement, and Vision

Despite the inevitable focus on technology, experience to date suggests that the most effective smart city programs are driven by a clear understanding of the forces shaping the future that will impact city life and a compelling vision of what type of city we aim to deliver. They also have a high level of citizen engagement around the core requirements the city must serve and the behavioural changes required from the population to ensure the vision is brought to life.

With an emphasis on civic engagement, education will be a key priority. It will be important to ensure that information overload doesn’t wreak havoc, as information will be abundant in future smart cities. To balance the flow of data, it will be essential that lifelong learning, critical thinking, and mindfulness techniques are taught from a young age. Since the notion of “sustainable life” in a smart city would by definition need to be purposeful and abundant, the people living within one may find it valuable to focus on some vision of the future that they wish for, and plan how to create it with the resources and privileges they are afforded – the smart city will be a place where all “needs” and most “wants” are easily met.

Observation and Surveillance

The blanketing of cities with sensors and cameras can provide the underlying data to enable continuous monitoring of everything from traffic, pollution, people movement, and security risks. The downside here is the potential for continual and deep surveillance which may butt up against concerns over personal privacy. On the other hand, as the young digital natives who were born immersed in technology, perhaps societal norms on personal privacy will shift. Continuous monitoring may simply fade into the background of everyday life. The advantages of monitoring – no crime, better traffic flows, and fewer unpredicted life events – might overshadow the negatives. Predictability might be boring, but good – for most of us. Smart cities offer an image of the future where we make trade-offs: So while our comings and goings each day would be monitored continually, the by-product is that crimes like mugging could become almost non-existent.

Always Connected

The ultimate vision of a fully connected and always on city implies every activity configured for and in service of the individual. Imagine an individual being monitored by a collection of body sensors. These feed the individual’s mobile phone, which detects a pattern that suggests that person may be building up to a cardiac arrest. The phone might notify the relevant GP and call an ambulance. A driverless medical vehicle then arrives with a robot capable of carrying the patient to the vehicle and undertaking a range of medical tasks under guidance from a doctor observing remotely via video. On the journey to the hospital, the patient’s phone notifies the relevant next of kin, reschedules meetings and controls all of the pre-programmed domestic heating and cooking activities that had been scheduled to take place.

Mobility

The Holy Grail of the smart city is a fully interconnected, multi-modal transport system, where a combination of rail, metro services, buses, taxis, personal vehicles, rickshaws, and bicycles can be used to interconnect journeys. The driver is to save time, money, reduce environmental impacts, and allow passengers to purchase and complete end-to-end journeys seamlessly. As an added bonus, the entire transportation system would ideally run on a backbone of clean, renewable, and electric power.

Ecological Impact

In the idealised vision, the smart city dweller of the future would leave behind a small ecological footprint compared to their early 21st century counterparts. By 2030 and beyond, living in a city might be the lifestyle in which society falls into symbiosis with nature. It would be ironic to find that countercultural rebels who’ve moved out of cities and “off-grid” form a greater burden to society than a taxi-hailing urbanite. Built-in ecological efficiencies in smart cities could range from use of vertical farms to lock cities in to strictly local food production, or the use of zero-waste regimes and local circular economies, where the life cycle of things becomes stretched longer through reuse, recycling and repurposing. Eventually, cities could become self-sustaining, giving back more than they take from the ecosystem.

Cities of the Future

Cities are powerful symbols of modernity. People are drawn to cities for numerous reasons but sometimes it is simply to try their luck at making money, meeting a spouse, or learning something new. Smart cities in the future might make luck obsolete through exquisite planning and monitoring systems which predict every detail of what happens day to day. Some fear that such extreme social planning would make serendipity extinct. Nothing would happen by chance in a smart city, or at least that is the idea: no burst pipes flooding apartment buildings, no debris blocking the trains, no traffic accidents or broken-down cars. On the other hand, it means no chance meetings, no getting lost in a strange neighbourhood, and no more anonymity. The vision of smart cities can be idyllic, but it also implies living under the shadow of Big Brother. Ultimately the future is what we create, so there is no absolute path determined. But our choices regarding technology today and tomorrow will play a major role in shaping which future emerges, which is something to think about for planners and city leaders as they map their future strategies and investment plans.

Image:https://www.lifeder.com/wp-content/uploads/2017/12/cd-cover-2969102_640.jpg

By Rohit Talwar

Image: https://pixabay.com/illustrations/eye-web-detail-macro-face-4470679/

By Rohit Talwar, Steve Wells and Alexandra Whittington

It has been fascinating to watch the rhetoric of technology companies evolve in the last 18 months. From heralding the potential of technologies such as Artificial Intelligence (AI) to automate processes and reduce staff costs, they have rapidly evolved to a new story line of “augmented intelligence” – arguing that the machines are here to serve not replace us. Why? The reality is that throughout history, turkeys have steadfastly refused to vote for an early Christmas. So, from business leaders to front line employees, the penny is dropping with an ever-louder and more resonant thud. The realization is growing that – unlike previous industrial revolutions and waves of technological change – this time it’s different. Hence, in the face of rising concerns about the impact on jobs and society, the technologists are in damage limitation mode pretending that their Ferraris are just pushbikes with the addition of a shopping basket and mud guards.

So, what’s really going on? Whilst it is far too early to know or predict the true potential and resulting impact of AI, it is clear that it will have widespread applications and deep ramifications across society. These include scenarios such as the end of jobs as a mainstream income-generating activity, the rise of state guaranteed incomes, the emergence of fully automated Decentralized Autonomous Organizations (DAOs) with no employees, and a massive shift in societal norms and culture if leisure becomes the prime use of our time. To help explore these next waves of change, below we address six key questions on the rise of these disruptive exponential technologies, the impact on humanity and how we can ensure a very human future in the age of superintelligent machines.

Q1: How might we prepare for changing relationship between humans and machines over the next 10-20 years?

As we look at the changes shaping our world and the pace of technological advancement, some very big questions start to arise:

• Are humans irrelevant to the future of business?
• What role should humans play when machines can outperform them in most tasks?
• How should society prepare for an unknowable future?

We see five important dimensions that we must address as part of securing humanity’s future in an automated world and ensuring that the advances in technology are used to serve humanity – not replace it.

1. Reframing Society – We are reaching a truly dramatic point in human history where a number of exponential technologies are being combined to deliver radical performance improvements. A powerful mix of unleashed imaginations applied to disruptive technologies is catalyzing a possibility revolution across every aspect of human life, society, government, and business. As a result, in the next few years, society will be challenged to confront fundamental issues that go to the core of what it means to be human. Advances in science and technology will test every assumption we have about how our world works and the purpose of humans within it. For example, AI already outperforms humans in many domains, and the possibility of “artificial superintelligence”, or constantly learning and evolving systems, could result in machines capable of overtaking human capabilities – ultimately even in so-called soft skills such as empathy, intuition, and creativity.
2. Humanity 2.0 – Advances in cognitive enhancement drugs and “nootropic” supplements, electronic brain stimulation techniques, genetics, age extension treatments, 3D printed limbs and organs, and body worn exoskeletons, have given rise to the notion of enhancing the human brain and body well beyond the limits of natural evolutionary processes. Indeed, many leaders in the field of AI are fierce advocates of “Transhumanism” as the next stage of human evolution. They argue that if humans want to keep up with AI, we ourselves will have to become machines – embedding technology in our brains and bodies to give us similar levels of processing power. So, is there a meaningful future for version 1.0 humans in this brave new and enhanced world that the techno-progressives would have us believe is the only viable way forward for humanity? Will we have to enhance ourselves if we want to be considered for one of the potentially declining number of jobs that might be available?
3. The Risks of Automation – The challenge here lies in our choices as decision makers and the value we place on human attributes that machines cannot as yet replicate. Clearly, automation has many benefits such as cost efficiency, consistency, speed, and accuracy. Many firms will inevitably choose to place their faith in computer systems, automating wherever possible. Such a philosophy is common in new technology ventures where the heart of the business is embedded in its code. Some are already creating DAO – entities that have no employees and exist entirely in software. The potential rewards of widespread digitization of an enterprise are well-covered in the business media, but what isn’t talked about enough is the spectrum of risks presented by automation, especially to well-established organizations. Companies run the risk of dehumanizing and becoming identical to others in their industry – losing whatever their distinctive edge might be and commoditizing themselves in the process. Furthermore, the more we choose to embed all that we do in software, the easier it becomes for competitors to replicate our offering and go a step further at a slightly lower price – locking us into deadly race to the bottom on prices, revenues, and profitability.
4. What Differentiates Humans from Machines? – The challenge is to harness AI and other disruptive technologies such as robotics, cloud computing, the Internet of Things (IoT), blockchain, and hyperconnectivity as power tools to support and unleash human potential. At least for some time to come, what differentiates a company will be very human characteristics – the quality of its ideas, strategies and business models; its community relations; the ability to spot and exploit opportunities or address changing situations, problems and risks quickly; handling exceptional customer needs; creating new products and services; building deep connections within and outside the organization; how it navigates external developments such as regulatory requirements; and how well it manages change. These still remain very human traits which machines cannot as yet replicate. New technologies can play a powerful role in supporting the people performing these tasks and automating the more routine work to free up time for us to undertake these higher level human functions. Organizations that see AI as simply a way to cut back on staffing are missing the point and potentially short-changing their future.
5. Unleashing Human Potential – Artificial intelligence is increasing business productivity, knowledge, and efficiency, but humans cannot be written off just yet. For example, In the insurance industry, whilst chatbots are emerging at the customer interface, there is a concern that AI is not yet at the point where machines can respond appropriately to distressed customers, an unfortunately common emotional state due to the nature of matters insurance companies deal with. Artificial intelligence offers a chance to re-humanize the workforce by providing more time to use our talents and softer skills and emotional intelligence while offloading less sensitive tasks to machines. Obviously, we will need training and support to help us step into these intellectually more demanding roles and to develop our capacity for empathy, sensitivity, compassion, creativity, and intuitive listening.

Q2: How might the new technologies impact the workplace and what new job opportunities might arise?

As individuals, managers, leaders, investors, and politicians we crave certainty and predictability. We want the future served up to us on a plate with the timelines, impacts, and solutions clearly defined. Reality is far messier and changes constantly – the only certainties are that i) ignoring the emerging future will store up problems; and ii) trying to apply yesterday’s or today’s solutions to the future’s challenges will almost certainly fail. What we do know is that the situation will evolve rapidly as the pace of technology development and adoption quickens and businesses seek to act faster to take advantage of what’s on offer and respond to potential competitive threats. A wide range of professions from sales person and school teacher to investment banker, risk assessor, claims analyst, plumber, and bus driver will see technology emerge that can enhance or even replace their roles.

Within five years, it is reasonable to foresee quite significant shifts in the types of jobs available, the skills levels required, and a shortening duration for those roles. On a ten-year timeframe, we could reasonably expect to see widespread automation, a dramatic reduction of the jobs that exist today, new roles emerging in new firms and in existing businesses as they seek to stay competitive. Educationally, a degree could become a minimum entry requirement for 80 per cent or more of all new jobs.

So, what about the messy middle between here and the end of the next decade? In the short term, the picture will be confused – certain firms and industries will accelerate rapidly towards an “employee light” model. Other sectors will see temporary skills shortages until the processes become more automated and the machines learn to code themselves. In professions ranging from machine learning specialists to quantity surveyors – we can see a near-term skills shortage with supply lagging demand. This represents a relatively short window of opportunity to retrain people for these in-demand roles. However, as the process of automation accelerates, and the way we work evolves over the next 5-10 years, we might see these skill shortages erode and the emergence of very different ways of achieving an outcome.

Within a few years, an autonomous vehicle might automatically fine its driver should they choose to take the wheel while drunk, or override the speed limit. The vehicle might also self-insure – sharing the risk across the pool of autonomous cars on the road. These smart cars might also drive themselves to the shop for repairs – carried out by a team of robots and drones. These changes wouldn’t so much re-engineer the work of solicitors, courtrooms, garages, and insurance firms – rather the activities, associated tasks, and related jobs might be eliminated completely.

New jobs will arise with the emergence of new activities, businesses, and sectors. Human augmentation will require a range of new skills, possibly combined into hybrid roles that draw on chemistry, biology, electro-mechanical engineering, psychology, and counselling. Highly trained workers will also be required in sectors such as smart materials, 3D/4D printing, autonomous car manufacture, superfast construction, environmental protection and remediation, renewable energy, and care of the elderly. In insurance, the skills of the next generation risk assessor will need to encompass a wider range of disciplines to handle the new fields of science and technology coming to market. At a more fundamental level, we could see a rise in teacher numbers if countries see education as a priority. In parallel, the opportunities in basic and applied R&D could blossom if nations and firms increase their research investments in search of future growth. We could also see a massive growth in small businesses and mentoring roles as people seek to take control of their own destiny. Finally, the stress associated with job displacement due to technology could result in a growing need both for mental health support for people whilst still in the job and for care in the community for those with mental health issues resulting from the loss of their job.

One of the most important things to keep in mind is that there could be many new definitions of the term “job” in the next 5-20 years. A job today is still a fundamental assumption and organizing principle in most Western nations – even if it is being eroded, governments still plan on that basis. A job today is a means to earn money by achieving a set of given tasks. For some it is more – a calling to fulfill one’s purpose and give meaning and structure to our lives. For others, it is a means to an end – be that paying for our next meal or providing the money to realize our materialistic, experiential, or spiritual desires.

So, as work is gradually and then more rapidly automated away, what becomes of the job? What might a job look like in 2027? Will it still be a “production” role delivering measurable daily outputs, or will a job imply a more creative human activity? Will it still be what people do all day? Conceivably, AI could remove aspects of jobs that tend to be considered “work” while emphasizing the parts of a job that make it a social and enriching activity. Will we have moved to a guaranteed or universal basic income (UBI) – with people having the choice over where they spend their time, from being a server in a restaurant to taking part in community building restoration projects? The link between how we spend our time and the income we receive might be broken in less than a decade, meaning people could have more autonomy over how they use their time and energy than ever before.

The technology we adopt today will also allow companies to increase their options in terms of achieving outcomes. While Company A might use AI to reduce the size and budget of their legal department, they might in turn boost their investment in the IT and HR departments to ensure they have the right technological capacities and that the lawyers and others they hire are absolutely the right fit. Company B might implement AI to reduce the number of customer service calls routed to human operators, but they could re-invest the salary savings in bringing in trainers and facilitators to raise digital literacy, emotional intelligence, critical thinking ability, and communication skills across the firm. New training curricula would require new positions to run the programs, e.g. “Director of Life-long Learning”. In this case, a job might be more akin to an education: you would leave it smarter and better-prepared than when you arrived.

The technologies coming through will also enable and require new professions and a raft of new roles might emerge across the globe in every sector as we wrestle with the ethical, legal and societal implications of machine decision making. For example, as driverless vehicles get closer to becoming a market reality, we may see the rise of the “autonomous ethicist” – specialists who attempt to work out the ethics necessary to program autonomous vehicles. This is going to be a social, moral, ethical, political, economic, and – ultimately – legal minefield. Every country, city, region, regulator, insurer, religion, civil rights group, and car manufacturer will want to contribute to the debate. The goal is to try and establish the rules and assumptions that will underpin the decision making within an autonomous vehicle as it becomes aware that it is about to have an accident.

Should a self-driving vehicle prioritize the safety of its passenger, the pedestrian who stepped in front of it, or the pregnant mother on the pavement behind them? Should it put the interests of the taxi owner over those of the driver? How will it make those choices? In making those decision, will it use facial recognition to identify individuals, and pull our tax records and other public information to work out what our net worth is to society or what our future contribution might be? How will it assess the contribution of a writer/journalist versus a baker, doctor, or actuary? What if it chooses to run down an irreplaceable hundred-year-old tree instead of a human? In a Hindu village in India, for example, running over a cow to save a passenger might be viewed as the worst possible outcome, and therefore the ethics programmed into the vehicle may prioritize the safety of the sacred animal over that of the human. Our ethicists will have to take account of all these different perspectives in constructing their guidance, and this could vary dramatically even within a country.

Q3: What are the implications for how we lead and manage tomorrow’s organizations?

Aside from jobs, bringing AI into the workplace successfully will require new workplace leadership styles. The leaders of AI-powered organizations will face unprecedented challenges which will test their people skills and emotional intelligence. “Warm” and highly relatable individuals might be in demand to offset the extent of “cold” automation within an organization. Of course, this won’t be universally true – for some, the ultimate goal is to create the DAO, and so for them the pursuit of automation and a workforce led by “robot overlords” is just a stepping stone to the employee-free business of the future. However, at present, humanity seems to be prevailing to some degree, and total digitization seems unlikely to become a genuine threat for the majority of larger global businesses in the near term.

Indeed, in a world where there’s a risk of automation, dehumanization, and commoditization proceeding hand in hand, those who put people first could find themselves better positioned to create, innovate, adapt, evolve, stand out, and outperform the market. Hence, leaders could become more important than ever, raising their own digital literacy, investing heavily in people development, and demonstrating the kind of extraordinary leadership required in an ever-evolving landscape. In many ways, the real opportunity is being ready to stand up for the longer term with this investment in people, going against a strong near-term focused, pro-AI trend that prioritizes immediate profits over humanity and future business sustainability. The emphasis on machines, processes and structures plays into – and perhaps emanates from – the dominant masculine culture in many firms. In contrast, the pursuit of a unique, distinctive, people-centered brand and culture means there could be a greater need for leading from the feminine, with an emphasis on traits such as empathy, social awareness, sensitivity, and collaborative working. Feminine might be just one word for it, but ultimately it is a perspective that puts people, relationships, and the long-term above efficiency and short-term cost savings.

Q4: What might the implications be for large and mature global companies?

Over the next decade, if things follow the “preferred future” that most nations and businesses are pursuing, the global economy could grow from about US$78Tn today to around US$120Tn. More than half of that is likely to come from businesses that have emerged recently or don’t yet exist, and over 80% will almost certainly be from products and services we don’t have today. For most, this represents a massive opportunity to innovate and evolve to ensure they maintain or attain a leadership position in their sector. The challenge is to embrace innovation at speed across the company and conduct accelerated experiments with a range of new ideas that could help generate near and long-term opportunities. So, for example, let us consider the insurance sector as a case study. Technological disruption might mean rethinking the entire approach to designing and developing policies. The speed at which markets, products, and services emerge and evolve means that increasingly we may see a shift to simple collective insurance pools, peer-to-peer, and crowdfunding models where the members of a sector ecosystem (customers and suppliers) effectively self-insure. Equally we could see per second policies where objects such as industrial machinery and domestic power tools are connected via the Internet of Things (IoT) and only insured when actually in use.

With so many sectors, products, and services emerging – from driverless cars to self-administered neural stimulation drugs and personal genetic enhancement kits – no insurance company could keep up using current approaches to identifying opportunities, assessing risks and defining appropriate coverage solutions. The responsibility will need to be handed to the sector participants to create self-service and customizable products. There will be an opportunity here for firms to become the provider of the platforms for sectors and businesses to design their own tailored insurance solutions. Rather than assume responsibility for product development, insurers could provide the software infrastructure, risk assessment models, and investment management tools and let the industry participants bear the risk.

Industries will also change, meaning a lower risk profile. Smart farms might mean fewer crop-failures, the IoT could enable smart cities with better hazard prevention, and self-driving cars should theoretically never have accidents and hence the notions of self-owning and self-insuring vehicles becomes a possibility. A range of equally dramatic developments across a range of other sectors could have potentially serious implications for insurance. Furthermore, changing lifestyles, potentially lower real-term incomes, and smart tracking technology are all driving growth of the sharing economy and scenarios where ownership is rather obsolete and most possessions are shared, not owned by one individual. This goes along with the shrinking value of owning something and instead purchasing access to it. Shared items could come insured as part of the deal, thus negating any need for buying individual policies. The risks might be borne by the users and reflected in the price. Again, the opportunity for individual firms might be to become leaders in designing customizable sharing economy policies for customers as diverse as power tool manufacturers or community ownership schemes.

The growing experience economy also creates opportunities. For the developed world and middle classes everywhere, we are at a time in history where experiences are starting to matter more than things – whilst tricky to insure, these products could take a similar form to trip insurance. Infinitely flexible policies could be designed to protect people against bad dates or wasting their time on a movie they didn’t enjoy. The payout could vary from a ticket refund through to the cost of counselling and treatment should the experience be truly traumatizing.

To enable these kinds of shifts, firms needs to ensure an effective ‘innovation architecture’ that supports a wide range of creative thought and action across all its employees globally. Key components would include ensuring leadership and management truly understand both the technologies reshaping our world and the associated mindsets that are creating new and disruptive concepts, strategies, business models, products, and services. At the local level, the freedom and capacity to conduct rapid market facing experiments is critical – as is the need to have people across the organization seeking out and connecting with emerging businesses and sectors and their respective associations. These market focused conversations are critical to understanding how current and future sectors and opportunities might evolve. The goal is to gain early access to what might become important future revenues streams. Firms might also want to consider following the path being pursued by many large organizations that are creating exponential or 10x growth and improvement programs to identify breakthrough ideas that could deliver step change gains internally and in the marketplace. The key is to let ideas blossom and see which ones create the quickest and/or biggest opportunities to take us into the future.

A final piece of the innovation mix is developing a culture of organizational foresight spread across each department and country. This involves consistently scanning the horizon to identify new and emerging societal, technological, commercial, political, and economic developments which could impact our current and future markets, products, services, and customers – or the way the firm itself operates. Embedding the importance of foresight and long-term thinking could be critical in ensuring the next 5, 50 or 100 years of existence and success.

Q5: How might emerging technologies impact the future products, services and business models of these large global players?

Over the next decade, pretty much every major business on the planet will probably evolve into a technology company almost indistinguishable from the likes of IBM or Google in its capabilities. The ability to master technologies such as AI, blockchain, the IoT, cloud computing, hyperconnectivity, and big data will simply be a ticket to the game. Success lies in the firm’s ability to leverage that portfolio of smart technologies to help unleash human potential. Let’s look at some of the possibilities – again using insurance as a case study.

Artificial intelligence – particularly when combined with other technologies – offers potentially the largest disruption. Internally the technology could transform literally every process within the business. In the marketplace, embedding AI could create wholly new concepts at the boundaries of current insurance thinking. Imagine the smart building that monitors data from tens of thousands of sensors to predict a failure somewhere in its ecosystem and calls in the appropriate inspection or repair; the smart vehicle that fines the driver for speeding and disables itself if the sensors detect alcohol on our breath; the medical monitoring device that manages drug delivery to ensure constant flow of medication. The applications are literally limitless – some may create insurance opportunities; others may open up possibilities that are currently outside our focus and comfort zone.

Sensors and their associated data are enabling the IoT which could reorient the relationship we have with our natural and physical environments and a whole range of “smart” objects. As technology becomes increasingly observant of us, and more and more human behavior is captured, stored, and analyzed, will our regulators and personal privacy preferences allow monitoring to continue along this path? There are still a lot of uncertainties in this area regarding who owns the data. Does it belong to government, like a public resource? To the people? What new insurance product and service opportunities might arise? What role might social media play in accumulating data related to insurance matters – could analysis of a person’s social media habits provide a better method of insurance risk assessment than current approaches?

Blockchain, which is the secure ledger technology underlying cryptocurrencies like Bitcoin, could help determine ownership and prevent fraud. Smart and open peer-to-peer technologies like blockchain could increase the transparency of risks and force the development of taxes to cover social risks of future technologies. In this case, is it possible that insurance companies based on inevitable surprises could emerge, highlighting and protecting against the inherent biases in technologies?

With the risk of rising economic inequality, clients may increasingly opt to share more personal data to reduce insurance rates, which might also curb their more unsafe activities and behaviors. However, in the smart city context, this may not be required as AI could take on such a large role in our lives as to advise us 24/7 – to the extent that poor decisions are automated away and eliminating the inherent biases to make risk-taking more attractive. Technology can enable more customer-centric solutions such as per day mobile phone insurance while travelling. Furthermore, the needs for insurance could be reduced through the emergence of “self-repairing assets” using 4D printed shape-shifting materials for example. Furthermore, 3D printing could allow the cost of manufactured goods to fall so much that people simply insure fewer assets. Finally, life-extension technology emerging from the marriage of Big Pharma and Silicon Valley could create a demographic, economic, and societal tidal wave. How will insurance companies respond if one future perk of wealth is the ability to buy high-tech drugs that allow lifespans of 120 or more years?

Q6: What characteristics should organizations seek to enhance or develop to ensure they have a very human future?

For organizations to navigate the decades ahead they need to see themselves as a living, breathing, constantly evolving, and very human organization – designed for and by people. This means a culture that embraces continuous innovation and experimentation on both an incremental and a dramatic scale, and a willingness to pursue exponential improvements. Such a journey requires a highly empathetic, trusting, and nurturing relationship with employees where technology is seen as a means of allowing them the time to be creative, innovative, experimental, and customer-centric. In parallel it means being seen to be supportive of those whose jobs are displaced. If one was to look at any successful brand in a decade’s time, we hope it would stand out as forward thinking, opportunity seeking, risk-aware, and far sighted in service of its customers. We would be commending its capacity to anticipate changing societal needs and risks and its willingness to adapt and evolve to deliver solutions and surprises that meet the needs of a rapidly changing reality or create new sources of delight for the customer.

Image: Alex Andreyev

By Rohit Talwar, Steve Wells and Alexandra Whittington

By Rohit Talwar and Steve Wells

Legal circles are abuzz with talk of artificial intelligence and its potential to transform the sector, generate market opportunities, replace lawyers with machines, or simply be the next over-egged technology that fails to deliver on its early promise and marketing hype.

The sad but honest truth is that much of the debate is ill informed – it’s very hard to discuss something if you know nothing about it. While no self-respecting legal professional would give advice on a domain without making sure they had researched the relevant legal factors, many have no such qualms about entering fact-free debates on AI’s potential impact. It’s time to deepen our learning about what’s here and what’s next.

The goal of AI is to replicate a range of functions associated with human intelligence like planning, reasoning, deduction, inference, prediction, language processing, and image recognition. At the core of the most powerful AI systems are machine-learning and deep-learning algorithms. These are effectively trained by observing humans performing specific tasks such as drafting a contract or preparing a court submission.

Once trained, the systems can be deployed and they continue to update their knowledge and rule base as new instances arise. Some argue that there are literally no bounds to what such systems could achieve, and that if AI could surpass human capabilities and become superintelligent, it may well be man’s last invention.

Our view is that superintelligence is some way off and may never happen, but a humbler form of AI is already in use across legal and other sectors and the applications will become ever more sophisticated. The tools will also get cheaper and be available over the internet using the software as a service (SaaS) model – enabling even individual practitioners to experiment by paying a monthly rental fee rather than purchasing the software outright. For example, in marketing, ReFUEL Spark lets organisations create targeted online adverts and marketing campaigns from $99.

The true opportunity with AI lies beyond just what we can do internally. In the client environment, we see industries being transformed and created through the application of AI and a range of other fantastic technological beasts such as mobile, wearable, and embedded devices, big data analytics, the Internet of Things, cloud computing, drones, robotics, blockchain, 3D printing, and high-speed communications. These technologies are taking clients into unknown territory, where risks are often unknown, regulations have yet to be written, and insurance frameworks don’t exist.

Indeed, such innovations could help grow the global economy from $78tn to around $120tn within a decade. Further, well over half of this future GDP will come from emerging industries like driverless vehicles, or yet-to-be-launched sectors such as high-street body shops offering chemical, physical, and genetic augmentations of the human brain and body. In this rapidly changing reality, exponential rates of growth will accrue to those lawyers who invest the time to understand the nature of AI and the potential legal implications of this and other emerging technologies.

AI opportunities

Within the legal sector right now, we see seven key areas in which a range of firms are starting to deploy AI:

• Automation of legal tasks and processes: Berwin Leighton Paisner uses AI to accelerate mass data processing, while a Linklaters application checks client names on 14 European regulatory registers;
• Decision support and outcome prediction: A Pinsent Masons AI tool reads and analyses loan agreement clauses, guides lawyers through transactions, and suggests precedents. DLA Piper deploys AI for M&A document review, extracting and analysing key contract provisions and providing rapid summaries of key aspects of the transaction;
• Creation of new offerings: Fenwick and West’s AI tool enables online document generation for startup formation. Brexit Connect from Dentons’ Nextlaw Labs provides clients with online education and impact analysis regarding the UK’s withdrawal from the European Union. LawPath’s online AI chatbot advises on privacy law and automatically generates client-specific compliance policies;
• Development of tools for in-house legal teams: Kennedys’ KLAiM online litigation platform helps clients manage entire claims processes, from serving court proceedings through to settlement. Riverview Law’s lawyer advisory app Kim helps with tasks like ordering corporate contract negotiations;
• Process design and matter management: Several firms are deploying tools to automate generation of process flows and project plans, with real-time impact assessment of process changes on timeframes, resources, and costs;
• Practice management: AI tools will increasingly help with benchmarking across practice areas for comparable tasks from document production through to completion of key matter stages and dynamic modelling of alternative billing approaches; and
• Fully automated online services: Several direct access AI applications are emerging like DoNotPay and Case Hub for class actions, allowing cases to be conducted online.

Immersing yourself in AI and other emerging technologies should highlight the marketplace opportunities they create and their potential to transform the way you do business.

Small firms have the potential to be nimble and fast moving when going after new market opportunities, and the price of the technology is falling so rapidly that they can also achieve many of the benefits that were previously the sole preserve of larger and better-funded firms.

In both cases, and for large and small firms alike, the key to success is a willingness to venture into new territory and start having some fun evolving the next iteration of your practices.

Image: https://apollo.imgix.net/content/uploads/2017/10/Paul_Signac_Opus_217_Against_the_Enamel_of_a_Backg.jpg?auto=compress,enhance,format&crop=faces,entropy,edges&fit=crop&w=900&h=600 – Opus 217. Against the Enamel of a Background Rhythmic with Beats and Angles, Tones, and Tints, Portrait of M. Félix Fénéon in 1890 (1980), Paul Signac. Courtesy of the Museum of Modern Art, New York

By Rohit Talwar and Alexandra Whittington

Bold predictions and skeptical challenges abound regarding the speed with which autonomous vehicles could emerge. The evangelists believe we could see a rapid evolution from ‘level 1’ function specific automation of tasks – such as stability control – through to ‘level 4’ fully self-driving vehicles. Indeed, in early 2017 Elon Musk, the founder of Tesla, claimed that full autonomy could be less than six months away. Whilst development to date has been relatively slow, as with most current fields of technology, we are likely to see a rapid, if not exponential, acceleration of the self-driving sector. Hence, for the vehicle recovery and repair sector, now is the time to start thinking about the potential evolution of autonomous cars and the resulting implications and opportunities.

Dramatic claims are being made about the potential for autonomous vehicles to cut accident rates, drastically reduce the need for vehicle repairs, improve fuel management, increase traffic flows, and lower the number of taxis required in a city. This could transform the automotive sector, enhance the productivity of human ‘drivers’, make car journeys fun again, render cities more livable, and reinvent auto insurance. Here, we take a look at where some of these claims stand, and bring a futurist’s professional perspective to assess the cultural and consumer shifts that could arise from a move toward self-driving cars.

Accident rates: An op-ed written by Barack Obama in 2016 cited the fact that 94% of car accident deaths are caused by human error. Making driving safer is a key assumption when it comes to promoting the adoption of autonomous vehicles. But the actual record of autonomous vehicle deaths and injuries so far suggests there is work still to be done, nor does the available data provide a very complete picture. Far too many road conditions are as yet untested, and the high-profile fatality involving a Tesla on autopilot has generated some negative sentiment. The evidence base will of course improve as the quantity of trips and miles driven rises, and the number of manufacturers of semi- or fully-autonomous vehicles increases. The cars will also get safer with continuous improvement in the underlying autonomous management systems powered by artificial intelligence (AI).

One of the main ways that accidents and casualties will be addressed in self-driving cars is via the AI systems on board. These systems are complex ‘machine learning’ software applications that draw data from a range of vehicle sensors, learning from experience and an ever-increasing awareness of their surroundings – which are used to update the initial knowledge base they were ‘trained’ with. Not only will the system tap into its own elaborate internal sensor network for information, it will also interact with and learn from other vehicles using v2v (vehicle to vehicle) as well as v2i (vehicle to infrastructure) and v2x (vehicle to everything) communications. The automated car will be a communications hub for a number of in- and on-vehicle devices (e.g. cameras) and objects (e.g. seats), of which the human occupant is just one. The converging influx of information is assumed to provide a safety net, although this is still an unproven hypothesis.

This represents an interesting challenge for those involved in vehicle maintenance. Reductions in accident rates would lead to lower demand for recovery and repair services. The upside is that there are an increasing number of upgrade kits becoming available to add varying degrees of autonomy to a conventional vehicle. Fitting these add-ons could become a major revenue stream for repair garages.

Fuel Management: Autonomous vehicles would, by definition, use fossil fuels more efficiently and reliably. They would be programmed for efficiency in a way that would completely unburden the driver from making such decisions. The intention is that the car would always take the most effective routes and waste almost no resources whatsoever. They would also be very well-maintained by virtue of the vehicle’s own ‘smart’ internal systems, so any issues that would cause excessive fuel use could be identified and addressed immediately.

There is an even more promising trend in self-driving vehicles, which is that they are being built with electric or hybrid engines under the hood. Chrysler’s new minivan is one example of this EV design. Self-driving cars aren’t just changing the way we drive, but offer a huge opportunity to fundamentally transform how mobility is powered.

Traffic Flows: The majority of the earth’s people now live in cities, and that is good news for the autonomous car industry. Cities are ‘on the grid’, they have an increasingly connected and ‘intelligent’ traffic management set-up – which is what self-driving cars need; data, connectivity and infrastructure only happen in big, urban areas. Cities also have the greatest demand for moving people around efficiently. The country highways and back roads aren’t equipped for self-driving cars, so they are likely to be an urban phenomenon for the time being.

The other source of demand for these vehicles will come from developing urban areas in the emergent global economies like China and India. Urban growth in these areas are prime for growing ‘smart,’ that is, taking advantage of all the technology at their disposal including smartphones and IT for gathering big data about commuter patterns, for example. Building out developing areas with a self-driving mindset would prevent the construction of wasteful infrastructure—like parking lots and multi-lane highways—and allow emergent cities to build smart.

Taxis: According to CB Insights there are 33 companies working on autonomous vehicles, and about half of those are already permitted to use roads in California. Testing these new waters, Uber rolled out and then withdrew a self-driving fleet in California, due to a chorus of controversy and issues. Even more futuristic visions include a self-steering cruise ship, self-flying planes and self-flying cars. Individual car ownership might be on its way out and pretty soon all passenger cars become, essentially, taxis. This in turn leads to the possibility of self-owning assets.

Along these lines, regulation is a key uncertainty, but a necessity. Previously the driver’s license formed the social compact to enforce norms for those behind the wheel. Now, the responsibility for applying such ethics lies with technology. Do we trust AI in the same way we trust perfect strangers not to run into us and to drive us where we need to go? Maybe we can give them as much, or more trust than a human stranger. Other than the fact that – over time – driving jobs would become rare or extinct, in the near term, input is needed from policy makers, consumers and industry insiders alike, so that good judgement is exercised to identify and address emerging challenges.

Ultimately, the self-driving vehicle is not about cars or even mobility, but about information. The car is transforming from an impersonal analog machine to a smart and responsive interactive personalized gadget on wheels – more like a smartphone in the way we think about its utility and how we might pay for it. While there has always been a lot of personal identity wrapped up in automobiles, there was also a clear hierarchy between man and machine; the car was nothing without a driver. This is now changing.

If the current prevailing vision of the future of self-driving cars comes about, we can expect a few key changes. First, an automobile’s own awareness and knowledge—about the driver, the places it drives, and other vehicles—will become a valuable new commercial resource in and of itself. Will getting into a car be like signing into Facebook?

Second, the car industry will no longer be owner-oriented, but sharing-oriented and based on renewable energy instead of fossil fuels. A lot of CO2 emissions, and all kinds of consumer waste, will be prevented. Could automobiles also enter a new phase of no ownership, but instead be leased or pay-per-use forms of mobility?

Third, mobility will live up to its name (mobility, not gridlock) and cities will manage a new source of vulnerability—complete trust in data and technology—in exchange for no traffic. Cities should become more efficient and well-managed, and presumably safer, and offer a higher overall quality-of-life. They will also be more automated and closely surveilled. The discussion about the future development path, take-up and social impact of self-driving cars will continue for some time to come and we are at a very early point in the sector’s evolution. The journey will continue with many possible routes to the future.

Image: https://pixabay.com/illustrations/ufo-alien-alie-futuristic-science-1622863/

By Rohit Talwar, Steve Wells and Katharine Barnett

Artificial Intelligence (AI) is a computer science discipline working towards replicating critical human mental faculties, and it has reached a level of capability and maturity where it could have a transformative impact on healthcare and other knowledge intensive sectors. The power, scope and scale of AI applications are increasing exponentially. Here, we draw on key insights from our book on The Future of Business and our upcoming publication on The Future of AI in Business to explore the current uses, emerging applications and future possible impact of AI in healthcare. Finally, to offer a glimpse of the sheer scale of what is over the horizon, we present a scenario outlining the revolutionary impact AI could have on the central tenets and functioning of the UK healthcare system.

Technological Disruption

The technological revolution is already disrupting the healthcare industry across the globe. Incredible developments in the power and precision of new and emerging technologies are driving radical changes in diagnosis, drug development, treatment plans, surgery techniques, monitoring devices, estates, and facility management. Already, there are remote patient monitoring devices that feed back their readings in real time; blood pressure, glucose levels, heart rates and more can all be tracked. Sub-miniature electronics has given rise to a ‘lab-on-a-chip’ diagnostic tool – only a few square millimetres in size – that can replicate many of the functions of an entire laboratory.

So, what’s next? Exponentially accelerating advances in science and technology are now taking us to the next level and blurring the boundaries between science fiction and reality. For example, with a wry eye to the future, Qualcomm offered a US$10 million X-prize for the invention that best replicates the Star Trek Tricorder medical instrument – the small hand held device that can diagnose all illnesses. With only a slight scaling back on the functionality of the Start Trek solution, Qualcomm expected their entrants to diagnose over a dozen medical conditions – with two winners announced on April 12th 2017 – Final Frontier Medical Devices and Dynamical Biomarkers Group.

In this era of groundbreaking technological innovations, special attention should be paid to the revolutionary developments enabled and promised by AI. Some are calling it mankind’s last invention because of its potential to reach a super intelligent status that far exceeds mankind’s capabilities. While that ultimate invention may be some way off, right now AI has the potential to disrupt the healthcare industry from the inside; to become embedded in every aspect of healthcare provision – bringing about monumental changes for patient and provider.

Artificial Intelligence

The goal of AI is to create intelligent machines that can replicate critical human mental faculties. Key applications include speech recognition, language translation, visual perception, learning, reasoning, inference, strategising, planning, decision-making, and intuition. There are several underlying disciplines within the field of AI which include data mining, big data, rules-based (expert) systems, neural networks, fuzzy logic, machine learning (ML), deep learning (DL), cognitive computing, natural language processing (NLP), robotics and recognition of images, speech and video. Currently the power and scale of AI technology is starting to drive significant developments within the healthcare sector. Already, AI is being used for diagnosis and prediction, drug development, healthcare management – applications that are improving efficiency and cutting costs. Indeed, we are already seeing staggering results in the use of AI technology in medical diagnosis and prediction. For example, the IBM Watson Health application can process, analyse and extrapolate inferences from huge amounts of data, and is already outperforming cancer experts in patient diagnosis.

In another development, Google DeepMind has launched DeepMind Health; in a five-year deal with the UK’s National Health Service (NHS), DeepMind Health will access NHS patient data to help develop and deploy its Streams healthcare app. This app will alert healthcare professionals to patients in most need, will allow them to securely assign tasks and communicate about workload decisions. It aims to provide mobile viewing of results, patient medical histories and messaging; the alerts should speed up delivery of care and the hope is that the application will revolutionise the outdated model of task allocation by paper memos and fax machines. These are examples of what’s happening now – so what else can we expect?

What’s on the horizon?

Predictive Diagnosis

The rapidly increasing processing power and functionality of healthcare AI applications will enable huge amounts of data from multiple sources to be aggregated, analysed and extrapolated – allowing ever more sophisticated and comprehensive insights, inferences and causal patterns to be identified. This underlying process may, in the very near future, change diagnosis and guide all subsequent patient interactions throughout the treatment process. These applications can draw input from a far more diverse range of sources that most humans would have the time, inclination – and possibly the capacity – to gather, analyse and interpret. For example, data can be combined from wearable devices, healthcare records, genetic information, family histories, food diaries, shopping purchases, patient income statements, public health sources and local authority databases.

By using a wide array of relevant information in a predictive and pre-emptive manner, we may be able to eradicate the notion of diagnosis as a clinical practice that only takes place once symptoms have manifested. Instead, the predictive power of AI can be applied to these data sets to assess or predict the likelihood of conditions, diseases or illnesses that a patient might develop in coming years. This hyper-early prediction will allow sufficient time for interventions to take place, and with increasing success. Lifestyles can be altered, prophylaxis administered and special attention paid to the manifestation of indicative symptoms – enabling treatment to be initiated immediately if a health risk does indeed become apparent. This should deliver significantly better patient outcomes, and lower rates of admission to secondary care will put less burden on the public health budget.

Patient Pathway Management

Enhanced patient pathway management is another key AI development we may see in the near future – which will have particular resonance in the UK from both a patient care and a funding perspective. As we know, the UK NHS is split into Clinical Commissioning Groups (CCGs) – clinically led statutory bodies responsible for the planning and delivery of healthcare within their local area. As patients and their records transition through the healthcare system, there is the potential for administrative errors and issues. To help address this, an AI embedded in a patient’s smart device can use data collected from multiple sources; patient records, interactions, social media, and transactions can all be aggregated and processed to draw out potential patient behaviours.

In this scenario, AI systems would alert patients to take their medications, turn up to appointments, and provide them with information. This will be radically different from the current text update service that the NHS provides. Instead of a standard text to remind patients of appointment times, these nudges will be unique, personalised to the individual, use language they are accustomed to, and present information in a format that will be most easily understandable to that individual. In the near future, we may see this evolve from a text message to a GIF, image or animation that appears on a young person’s phone reminding them of their appointment. Equally, a robot generated voice memo might be sent to a lawyer to remind them to take their medication, which they can listen to in between seeing clients.

Alongside helping the individual take action, the AI could also manage their time and appointments without them ever knowing. If a consultation needs to be booked and a patient allows access to the ever-smarter intelligent assistants (e.g. future generations of Siri) on their smartphones, then the AI could look at the patient’s calendar and upcoming commitments, extrapolate data from their movements and lifestyle choices, and book a convenient appointment time. So, knowing a particular student habitually misses morning classes, the AI would schedule an afternoon consultation. Similarly, a parent who works part time can automatically have their appointments booked for mid-morning after the school run. There will be no need to enter this information, the AI will ‘know’ it from the deep inferences drawn from data analysis.

Such AI enabled applications in early diagnosis and patient management systems could radically improve outcomes, increase efficiency and cut costs significantly. The savings could be directed to more labour-intensive practises such as surgery and to funding critical research projects. Early successes with clear benefits from the adoption of AI applications could lead to a rapid and potentially exponential increase in the usage, reach, scope, and impact of such smart tools across the health service.

Future innovation – radical convergence

Looking slightly further into the future, we may see AI embedded in all aspects of the healthcare process. For example, significant change could come by linking AI to blockchain technology. Blockchain is a form of distributed ledger technology, that is used to provide a super secure time stamped immutable ‘block’ or record for each transaction, and which includes information from the previous record in the chain. This means transactions cannot be deleted or amended. Originally developed to record transactions undertaken in the bitcoin cryptocurrency, increasingly applications are being seen in tasks like transaction tracking in financial services. The combined application of AI and blockchain could radically alter the flow of funds through the entire healthcare system.

The existing funding system lacks the flexibility and sufficiently sophisticated technology solutions to allow for the concept of having the money actually following the patient throughout the system. In contrast, as the AI is planning pathways, directing patients and guiding medical intervention, the transactions and interactions can be tracked and time stamped using a blockchain. Such a blockchain will provide an immutable healthcare transaction record – allowing for instant identification of the funds available and the amount spent on a particular patient. So, this system would offer the seemingly impossible and revolutionary benefit of tracking both the cost of each intervention and – possibly – the value of its outcome With the immutable time stamped record that blockchain offers, the impact of each individual intervention could be tracked across the whole of the healthcare system thereafter. This would provide a concrete basis for a system of value based pricing to be implemented; the value being drawn out of an assessment of the balance between the total cost and resources used for an intervention and the resulting increase of healthy years of life as an outcome.

The furthest reaches of AI

On a 10 to 20 year horizon, it is possible that AI could actually protect us from any knowledge of our condition – allowing us to go about our daily lives blissfully unaware of all the technological magic that is happening behind the scenes to help us stay fit, healthy and happy. The emerging applications explored above – hyper-early diagnosis, smart management of patient pathways, coordination between departments, guidance through consultations and alerts to take medication – can all be done with minimal knowledge on the part of the patient. So, in the first generation of such systems, NHS patients could sign a digital consent form, stating the different levels of knowledge they would like.

For some patients who see healthcare worries as an undue burden on their lives, they may agree to receive no factual information on their condition at all. Alerts may read something like ‘We have booked you an appointment with your doctor to receive some medication they may be beneficial to you’. To reduce stress and in turn maximise outcomes, each stage of their treatment could be conducted using friendly, neutral, or disinterested language depending on the patient’s preference and what the system has learnt from past interactions. For those less worried – or indeed intrigued – by their potential medical conditions, more direct language could be used. They could be informed of the biological processes that may occur, told what symptoms may arise, what to expect and when to expect it. Some may prefer to go further and receive in-depth biomedical information, review charts tracking their progress, see other patient group statistics, be offered links to useful website, and view videos of procedures – all as selectable options to be included in their guidance and support package.

In the second generation version, patients would have no need to even sign a consent form. The AI would have learned about the patient by gleaning data and insight from multiple sources, and simply ‘know’ what information would be necessary for each person to be made aware of and when to provide it. The system could determine salient personality traits from your data and interactions to discern who should be told what, when and how much. It will also be able to differentiate when – if you are usually highly inquisitive and stable individual – a recent high intensity workload has induced a stressful period in your life. The AI will recognise this from analysing your information and interacting with your intelligent assistant behind the scenes, and tailor its interactions with you to ensure minimal stress and maximum efficiency.

Taking this a step further, it is possible that an AI will be able to build an effective medicine regime tailoring the size and timing of dosage to have maximum effect given your personal physiology. By analysing your responses and condition over time it could start to make decisions about adapting or completely changing your medication regime. The AI could send information directly to a regional pharmacy, where complex multi-drug ingredients would be combined in 3D printed multi-pills. Using all the data about an individual, including their unique genetic code, these drugs will be designed specifically for each patient. We can anticipate that with a convergence of applications and technologies, the AI could take over many such aspects of healthcare delivery.

The Revolution is Underway

While we are at the very early stages of the adoption of AI in healthcare, the take up is likely to accelerate – driven both by the proven benefits of AI technology and the potential to improve outcomes, increase efficient use of scarce resources and drive down the cost of delivering care.

As we look to the future, an increasingly sophisticated range of AI applications promise to deliver timely and completely individualised medical intervention with minimal resource wastage and to potentially revolutionise the funding structure of our healthcare system. This could herald a new age of intelligent care, one that takes a holistic view of every aspect of a human and directs advice, action and intervention to help ensure their total wellbeing. The ultimate goal here is not to become subservient to the machine but, rather to harness its immense potential in service of humanity.

Image https://usbeketrica.com/article/de-quoi-allez-vous-mourir-en-2050

By Rohit Talwar and Alexandra Whittington

Over the last 18 months, we have seen exponential growth in the discussion of and experimentation with the use of artificial intelligence in legal – with an increasing focus on the project management domain. From determining process flows and project plans for conducting a matter and estimating the cost using different billing approaches through to determining optimum resource allocation, managing workflows, maintaining a dynamic document hierarchy, budget monitoring, time tracking, variation analysis, and client reporting – AI has clear applications throughout the legal project management lifecycle. Given that such applications have already been discussed and assessed quite widely elsewhere, we decided to wind the clock forward to explore how the potential applications of AI in legal project management could evolve over the next three to five years. To this end, presented below is a scenario of what might be possible by 2020 in the more advanced adopters of AI in legal.

9:00 am Monday June 1st 2020, Janet reports to work as a legal project manager at the shared office building where her law firm leases co-working space. The start time to her day is the only traditional thing about her job. Janet works for NFW, a top 20 global law firm, which relocated in 2019 from a plush city centre office building to a practical and economical co-working cooperative. The legal team shares conference rooms and some support staff with the other tenants, which include technology startups, graphic designers and consulting firms. The working space is diverse and a bit chaotic, but always occupied.

When not being used as office space, the building is used for pop-up adult education courses, retail shops and civic meetings. This saves NFW money and helps build a presence in the community, giving a local feel to their global firm. NFW’s previous location was a beautiful old building that used only fossil fuel energy, and was poorly retrofitted with the efficiency boosting detectors, beacons and transmitters that today’s smart offices rely on. Plus, it was very expensive real estate. The shared office is cheaper, more ecologically sound and much more high-tech. Furthermore, the reduced overhead allows the firm to spend more on IT and HR, two areas where the legal industry is finally starting to see significant returns on investment.

NFW was one of the first of the global law firms to pioneer a new client service model where an AI-enabled project manager is assigned to orchestrate the conduct of a matter from the outset. With an increasing technology component in many projects and the growing use of non-legal staff including business consultants, researchers and accountants, several law firms decided to put in place legal project managers to ensure the effective integration and co-ordination of all other elements involved to deliver the desired outcomes to the client. While some lawyers objected to what they felt was an undermining of their roles, others appreciated the time it freed up for them to focus on providing sound business-orientated legal advice and keeping their own expertise up to date. A key by-product of this team approach coupled with the use of AI billing tools has been a massive reduction in billing disputes, write-offs and payment delays –issues which many lawyers had traditionally found it uncomfortable raising with clients.

The workday begins with a chat with her personal digital legal assistant, Lawrence, an AI helper who has been with Janet for a year now. Although Janet has a very personal relationship with Lawrence, he is in fact a firm wide legal management environment that effectively supports all aspects of legal project management and provides personal assistant services to professional staff across the firm. Lawrence was programmed to respond to moods and promote the mental and emotional wellbeing of its human companion.

Today, Lawrence notices from Janet’s biometric data feeds that she is tired; she worked all weekend from her home office. Lawrence communicates to the smart coffee machine to make Janet’s cup extra strong so that she can get started on her busy day. He switches on some ambient music which relaxes her. Janet doesn’t even realise that Lawrence has embedded some productivity enhancing tracks alongside the soft music that welcomes her. The sounds were designed just for Janet, based on her biometric feeds. HR ensures that worker productivity at the firm is kept at a maximum by using personalised approaches to stress-reduction and efficiency. A biological and genetic profile of each employee is created for the purpose of customising their environment to enhance each individual worker’s potential. Janet is quickly revitalised and they begin to review the day’s activities.

Lawrence uses hologram displays to show Janet the daily briefing. The first thing on the agenda is to review the dossiers on the clients involved in a potentially big M&A case the firm is bidding on. Full profiles of the companies and the CEOs involved in the merger begin appearing in a 3D video with an amalgamation of data gleaned from the internet and their social media profiles. The presentation gives her a better understanding of the client’s history, motivations, and the context of the job. Above all, it will provide the information necessary for the team to communicate effectively with these clients about the context of the merger and NFWs understanding of the subtleties of the transaction. This also allows Janet and the lawyers she works with to provide a special finesse to the account management, billing and scheduling part of each working relationship.

To create the video, Lawrence trawled the internet, as well as several large databases, to pull in relevant, up to the minute transaction related details for the 10:00 am virtual meeting. The information is presented in a way that Janet is most comfortable with: visual and image-based. Lawrence confirms that the lead lawyer Sarah is happy with the content, and that she is ready to join Janet in this virtual call with the clients to offer the custom detailed legal service package Lawrence has prepared to the exact specifications of the various contributing team members – with oversight from Janet. While it took a while to get the teams working in this highly collaborative AI enabled manner, the model is working well now – with the personal team building, conflict resolution and co-ordination skills of the project manager being seen as the critical differentiator between the best performing projects and the rest.

With what Janet now knows about the client—their likes, dislikes, habits and history as analyzed by her AI assistant—she realizes they are possibly going to ask for alternative service options beyond the bid Lawrence has created. Lawrence can provide a set of delivery options with associated pricing, ranging from highly automated execution through to heavy personal involvement from the lawyers and other key members of the team. The more human involvement, the more expensive the job, but this has become a hallmark of the legal industry and Janet knows that clients appreciate having this range of choices to select from.

Often, particularly with more complex matters and new developments such as the introduction of autonomous vehicles and human enhancement centres, the clients involved are willing to spend more to get the input and oversight of human experts. However, there are an increasing number of situations where clients accept that, for the matter in hand, a ‘robolawyer’ will be just as good—and much more cost-effective—than a person. The firm is not bashful about this reality. Law firm automation is not just happening in the management and accounting functions, it is helping to redefine the entire firm and most other white-collar industries.

Lawrence performs several jobs but his most important contribution is the ability to analyse ever larger data sets to extract usable information for bids and proposals, spot interesting patterns, correlations and anomalies and help apply that knowledge to proposal development, project planning and workflow management. One of the main benefits of Lawrence’s software is the ability to predict case outcomes. Form the early 2010’s onwards, a range of innovative software tools such as Lex Machina started to emerge, predicting the outcome of court cases based on previous results. NFW believes there is still competitive advantage to be gained in this arena and has developed a proprietary algorithm within Lawrence which takes all the factors of a case into account, compares the information to previous cases in public databases, and then determines the probability, costs and likely financial impacts of a range of possible outcomes.

The ability to predict outcomes has technically lost the firm revenue – as clients won’t pursue a case if Lawrence believes the results won’t be in their favour. However, the resulting reduction in legal costs has won the loyalty of many important clients, and has shown the firm to be a source of solid, evidence-based advice. Seamlessly, this has become a new source of revenue for the firm: sales of the predictive outcome service to clients of other firms have more than made up for the loss of revenue. The algorithm can also predict the time and costs involved in each case down to the minute and penny. Hence clients that choose to take a case to court can do so confident in the likely outcome and associated costs. NFWs AI outcome prediction strategy has earned it an excellent reputation for putting client needs first, having never lost a case since its implementation. This implicit guarantee of customer satisfaction has helped drive exponential improvement in the rates of revenue growth, client attrition and new client acquisition.

After lunch Janet checks on the progress of new applicants to work with her in a (human) junior project manager role. The firm uses AI enabled gamification based on role playing real life client scenarios to identify and recruit job candidates, a trend that is really starting to take root in the sector. The idea is to identify those who have the technical knowledge, social skills and emotional intelligence to perform effectively in dynamic multi-disciplinary client facing teams. The firm’s website and social media pages include a link to the game, and high scorers are invited to submit CVs when there are new job openings.

Instead of having a huge file of résumés to read through, Janet checks the scores and team behaviours of the latest batch of applicants. She sees that one candidate, a graduate of an online university, has some very good point scores on the core technical and social interaction requirements, but is also doing well on the strategy and critical thinking criteria, which the gamers probably don’t even realize they are being tested on. Janet remembers when her children’s school had an unexpected closure and she had to bring her 10-year-old son to work with her one day. She let him play the recruitment game all afternoon and he had no idea that it was anything different from the video games he played at home—and Lawrence said his scores suggest he’d make a great lawyer!

Before the day’s end, there would be a big meeting related to the new hire. There would be several departments represented there, including IT, since the candidate needed to have some fairly strong coding experience. The person would be working alongside Janet and Lawrence to train a new piece of AI software, one which, for now, they jokingly call “Big Brother.” Essentially a surveillance technology, the new AI takes the form of a wearable device meant for attorneys. It would be placed on the person’s body, having the appearance of a badge or piece of jewellery, but it would actually use cameras, sensors and recording devices to monitor the entire workday’s activity to simplify the billing process. The device could even pick up on the attorney’s brain patterns to identify when he or she was thinking about a client, to capture all billable moments.

With Big Brother, the firm was hoping to pioneer a to-the-second billing structure, which would allow profits to grow while ensuring the highest possible accuracy in billing. The software also has the ability to recognize the difference between deep thought, reflection and analysis and more routine review and drafting tasks – allowing for truly differential pricing for tasks performed by the same person. This would also allow lawyers to focus more on the core aspects of their job, and made roles like Janet’s much easier. She and her staff (with their AI helpers, of course) should now be able to analyse and monitor the device output in real-time and prepare detailed financial reports at the request of the lawyers, accounting or the client—this is where the coding skills come in.

Another “as-a-service” niche the firm is hoping to fill is that of litigation data analysis, providing clients detailed insights into the time and resources spent on legal services both by their law firms and their own internal legal departments. Hence the new hybrid role would work with IT, HR and legal project management – so there would be a lot of cross-over involved, something Janet was seeing increasingly with the new project team approach. The sensitive nature of the data being handled by Big Brother meant HR needed to be involved to monitor both the human analyst’s adherence to privacy rules, and the evolving behaviour of the system itself, to make sure it didn’t start to cross ethical boundaries as it adapted and evolved.

As Janet was leaving the office she saw that the conference room where they held their meeting today was being set up for a learn-to-code class. She thought of possibly taking the course herself, coding being something she never learned in her youth, when it was really cutting-edge. Maybe she could learn to reprogram Lawrence to order her a lower strength morning coffee and turn down the welcome music.

Image: Aykut Aydogdu

By Rohit Talwar

Image Credit: http://verix.com/wp-content/uploads/2015/05/patient-generated-health-data-670×300.jpg

By Rohit Talwar

Artificial Intelligence (AI) represents both the biggest opportunity and potentially the greatest threat to the legal profession since its formation. This is part of a bigger global revolution – where society, business and government are likely to experience more change in the next 20-30 years than in the last 500. This large scale disruption is being driven by the combinatorial effects of AI and a range of other disruptive technologies whose speed, power and capability is growing at an exponential rate or faster – and which both enable AI and are fed by it. These include quantum computing, blockchain technology, the internet of things (IoT), big data, cloud services, smart cities, and human augmentation – all of which could literally be hundreds or thousands of times more powerful and impactful within a decade. The resulting changes will literally lead to the total transformation of every business sector, the birth of new trillion dollar industries and a complete rethink of the law, regulation, legal infrastructures, and the supporting governance systems for literally every activity on the planet.

At present, the sheer scale of the opportunity is lost on all but a few genuinely forward thinking players across the legal ecosystem. The majority in the sector are either blissfully unaware of what impact AI could have or they are becoming obsessed with the internal applications of AI. In many cases, a natural tendency towards risk aversion is leaving firms paralysed by fears of declining revenues, commoditization, the depersonalisation of the sector, and the loss of professional roles. These fears have in turn driven reluctance to even understand let alone embrace the true opportunity presented by AI and its disruptive technology cousins.

I believe law firms can and should escape from conventional wisdom and look to drive exponential improvements in internal performance and market growth by exploiting the opportunities presented by AI and other emerging technologies. Indeed, some in the legal sector are already diving deep to understand what they are and their true commercial potential. However, many are still more worried about the potential negative impacts of AI on the US$650 Billion legal services market and are proceeding cautiously as a result. I would argue that the real exponential growth opportunity lies in helping the world respond to the transformative impact of AI on the ~US$78 trillion global economy.

Driving Internal Transformation

The pace of AI development is stunning – even to those working in the sector. Indeed, the resounding victory of Google DeepMind’s AlphaGo over the world GO champion in March 2016 demonstrated just how far machine learning – the core technology of AI – has evolved. With over 560 million possible moves, the system was not taught to play GO. Instead it was equipped with a sophisticated learning algorithm that allowed it to deduce the rules and possible moves from observing thousands of games. This same technology can now be used across the various datasets held by law firms. AI has truly transformative potential – with a wide range of legal applications emerging, such as:

• Inferring the likely outcome of a case
• Determining the best structure for a contract
• Suggesting how best to approach a new matter, or
• Making sense of literally billions of data points across the web to spot new and emerging risks and legal threats.

I envisage five broad categories where we will see increasing use of AI within law firms in the next three to five years:

• Automation of legal tasks and processes
• Decision support and outcome prediction
• Creation of new product and service offerings
• Process design and matter management
• Practice management.

In addition, we are likely to see the growing use of AI both by in-house legal teams and in a range of online platforms offering direct services to businesses and individuals. AI will also power developments using blockchain technology (the secure transaction encoding mechanisms that underpin most digital currencies such as Bitcoin) e.g.:

• Smart contracts encoded in software which require no human intervention
• Distributed autonomous organisations (DAOs) with no human employees that exist entirely in software
• Decentralized Arbitration and Mediation Networks – which effectively operate as ‘opt-in’ global justice systems for commercial transactions, and which sit outside the existing national and global mechanisms
• Algocracy (Algorithmic Democracy) – creating global codes of legal transacting by codifying and automating legal documents, including contracts, permits, organizational documents, and consents
• Rewriting and embedding the law in software – e.g. automatic fines, drawing evidence from the IoT, standardized open source legal documents, and automated judgements.

So How Might AI Evolve Within the Sector?

Here is a plausible timeline of AI developments in the legal sector over the next five years:

The Next 18 Months

• Growth of law firms establishing internal technology innovation labs, creating seed funds to invest in legal technology start-ups, and running joint experiments with technology providers and clients
• A number of firms and in-house teams will run AI trials and develop applications than create smarter internal processes
• A range of trials and applications of AI for lawyer decision support
• Launch of the first client facing AI applications and new AI-enabled products and services
• Growth of FinTech – rising pressure from financial services to embrace AI/ Blockchain technology – with legal cost reduction a key driver
• Emergence of Blockchain Smart Contracts and DAO’s

The Next Three Years

• Clear evidence of lawyer replacement by smart technologies
• Widespread and accelerating deployment of AI on core law firm processes
• Meaningful penetration of AI into in-house legal
• First truly AI-centric law firms
• Significant range of AI-based solutions offered direct to consumers and SMEs / Technology businesses
• Widespread adoption of Blockhain smart contracts in newer firms / Rise of DAOs in both the private and public sectors.

The Next Five Years

• Applications starting to emerge that display near-human levels of intelligence (Artificial General Intelligence) in certain domains
• First examples of true Algocracy – Countries moving to digitising / automating / embedding the law
• Blockchain / smart contracts / DAOs in widespread use in financial services and other sectors
• 20-50% of ‘routine’ legal work by sector fully automated by clients with no law firm involvement
• New technology-centric legal sector entrants from the last five years competing head on with Big Law
• AI in widespread use across law firms and frequently mandated by clients.

Going for the Bigger Prize

Whilst AI can clearly be disruptive within law firms, the real AI transformation opportunity lies in the broader marketplace. Indeed, by focusing almost exclusively on the internal impact on the ~US$650 Billion legal services market, the sector is missing the point. I believe that AI – combined with the other disruptive technologies mentioned – could redefine every existing business sector and drive the creation of new ones – leading to dramatic growth of the global economy to US$120Bn or more in the next decade.

AI and the technologies it enables such as robotics, blockchain, Medtech, Edtech, and FinTech will drive the reinvention of existing sectors from media, healthcare, education, and transport to retail, construction, and financial services. AI is already enabling the next wave of trillion dollar sectors and developments such as autonomous vehicles, DAOs, synthetic biology, smart materials, intelligent cities, blockchain data networks, and smart contracts. AI is also driving interest in new economic paradigms, new notions of money, and new legal models such as Algocracy.

All these developments will require the interpretation, reframing and redrafting of legal frameworks and the creation of new legal concepts and dispute resolution mechanisms to encompass new political, economic, social, and business paradigms. So while AI will undoubtedly have a transformative impact on how law firms work internally, the true exponential growth opportunity lies in helping, governments, businesses and civil society to understand, regulate for and adjust to the coming waves of AI-enabled disruption.

Here are a few examples of those new legal sector opportunities:

• Establishing the governing principles and regulations around the use and insurance of self-driving vehicles
• Rollback, recovery, contract review, and dispute arbitration for fully automated, blockchain based financial transaction systems
• Governance and ‘right of redress’ protocols where AI systems are replacing human decision makers in areas as diverse as healthcare, social security and legal dispute resolution
• Usage control and privacy protection within the AI systems that will manage and interpret the massive data flows arising from the IoT
• Creating regulatory frameworks to govern the conduct of and dispute resolution for DAOs
• Determining governance and monitoring frameworks for science research which is designed and conducted entirely by AI systems e.g. the creation of new lifeforms.

Over the next five to ten years we will see these and many more opportunities start to emerge as existing sectors are transformed and new ones emerge. AI and the related technologies will enable the creation of entirely new markets, commercial concepts, business models, and delivery mechanisms – ideas we couldn’t even begin to imagine or describe today. For forward thinking law firms, these developments offer the potential to drive exponential growth in revenues – if we give ourselves permission to invest the time understanding the brave new world technologies and their transformative potential. Whether firms seize the opportunity or become paralysed by fear and indecision will ultimately be a matter of choice and a function of our willingness to step into the unknown and start learning.

Image: ARTIST MANZEL BOWMAN http://68.media.tumblr.com/67883163b2e0d537892f1afe9bd7120a/tumblr_nztj0wO5yB1r1thfzo2_1280.jpg