Ridley Scott’s 1982 cult film Blade Runner, based on Philip K. Dick’s science-fiction classic Do Androids Dream of Electric Sheep?, came of age five months ago: its dystopian futurescape was Los Angeles ablaze in November 2019. 

While some elements accurately hit today’s world, now stricken by the coronavirus pandemic, the planet is dangerously warm and computers can be commanded by a human voice for instance, other predictions fall short. High-collar, full-length trench coats are unfashionable, flying cars have failed to take off and, most pertinently, so-called general artificial intelligence (AI) does not exist.

Sci-fi is increasingly becoming sci-fact, admittedly, but a technology that can replicate a range of highly advanced human characteristics – the basic definition of general AI – does not walk among us, yet. Moreover, the so-called singularity, when machines achieve sentience and technological growth becomes uncontrollable and irreversible, is some distance away, most experts say.

“Think of general AI as HAL from 2001: A Space Odyssey, or Skynet in the Terminator series,” suggests Bernd Greifeneder, founder and chief technology officer of leading automated-software organisation Dynatrace. “We’re currently nowhere near that becoming a reality, with estimates ranging from it being five years to a century away. Some even believe we’ll never see general AI step out of sci-fi and into the real world.”

Arguably that conclusion is good for the longevity of the human race, though not everyone agrees. “Unless humanity takes a wrong turn, general AI is likely to arrive around 2050, perhaps sooner,” says David Wood, chair of London Futurists. “General AI, handled wisely, can enable humanity to enter a profound new era that I call ‘sustainable superabundance’, in which we can transcend many of the cruel limitations of the human condition that we have inherited from our evolutionary background.”

Wael Elrifai, global vice president of solution engineering at Hitachi Vantara, pleads for greater caution. “When we achieve general AI, it will drastically transform our economy and society in ways we can’t even predict,” he says. “We’ll be faced with what Dr Stuart Russell, a pre-eminent thinker in the field, dubs ‘the gorilla problem’. Namely, human beings will be outmoded by machines in the same way we evolved to dominate our gorilla kin.

“Finding our place in that future isn’t a decision that can be left in the hands of a few. Technologists, educators, psychologists, policymakers and testing experts must put their heads together to consider how we measure human capital, improve human performance and ensure equity in a world where machine intelligence surpasses human capabilities.”

For the moment, though, narrow AI, which is programmed by humans to focus on a niche task, will have to suffice. The hype around AI has calmed recently, in part because business leaders have realised it is neither akin to the general AI of Blade Runner or Terminator nor a silver bullet. Narrow AI, however, is potent if pointed the right way; those who work out what direction to aim at will triumph.

Besides, as Dr Iain Brown, head of data science at SAS in the UK and Ireland, posits: “The machines have already taken over, to some extent, and with little resistance.” Our smartphones, smart speakers and driverless cars all rely on AI. “Self-learning machines are embedded in services or devices used by three quarters of global consumers,” says Brown, “and algorithms choose what news we read and the entertainment we consume.”

Canny members of the C-suite are beginning to realise the true potential of narrow AI. “General AI isn’t a pipe dream, but it is irrelevant,” says leading futurist Tom Cheesewright. “Focusing on it as a business leader is like seeing the wheel for the first time and spending your time dreaming about a Tesla. Make use of the wheel.”

Indeed, according to Microsoft’s Accelerating Competitive Advantage with AI report, published in October, businesses in the UK already using AI at scale are performing 11.5 per cent better than those who are not, up from 5 per cent in 2018. Further, the study calculates the number of UK companies with an AI strategy has more than doubled, from 11 per cent two years ago to 24 per cent in 2019. The report also finds that more than half of organisations in the UK (56 per cent) are using AI to some extent, including a rise of 11 per cent in machine-learning from the previous year.

Focusing on general AI as a business leader is like seeing the wheel for the first time and spending your time dreaming about a Tesla. Make use of the wheel

“Narrow AI is certainly a more rewarding prospect for businesses in the short term, as it has more specific applications and so can help to overcome the clearly defined challenges that exist today,” says Greifeneder. “It’s also easier to manage the risks and ethical implications associated with it.” As an example of granting too much autonomy to a machine, he points to Microsoft’s infamous AI chatbot Tay, which began tweeting racist and inflammatory remarks in March 2016, after just 24 hours of exposure to users on Twitter. And, like any tool, AI can be used for good or bad. 

“We don’t need to wait for general AI to experience elements of AI utopia or dystopia,” says Peter van der Putten, assistant professor of AI at Leiden University in the Netherlands and director of decisioning solutions for cloud software company Pegasystems. “AI is used successfully to understand the structure and function of COVID-19 and to mine COVID-19 research articles. But bias has been creeping into models to determine credit card limits, decide who needs to await a court case in jail or who gets selected for preventive care programmes.”

There may be justified concerns about algorithmic biases, how the associated technologies might develop and AI displacing human jobs. But it is critical for business leaders to understand what AI can achieve and it’s certainly not for every organisation.

“If you don’t understand what you are trying to solve first, you are carrying a hammer looking for a nail and AI is going to be of no real use,” says Nick Wise, chief executive of OceanMind, a not-for-profit organisation using AI to protect the world’s fisheries.

For now, the realm of sentient computers seems a long way off. And if we humans are prudent, if or perhaps when general AI becomes a reality, man and machine will augment one another. As Brown concludes: “The future belongs to the cyborg: humans working hand in glove with AI, rather than the android alone.”

The COVID-19 pandemic may have locked down the world, but paradoxically it has liberated our thinking. As we all become more appreciative of the preciousness of life, there is a growing sense that this is a unique opportunity to regroup, reboot and revamp. 

Moreover, it is the perfect time for organisations to establish teams, processes and technology that are better placed to respond to the immediate situation and can shore up their defences. How can technology be applied and how can the focus be shifted so businesses plan for the future and not just the day-to-day operation?

Now is a great chance to look further ahead than tomorrow and work towards improving performance across the organisation. Some legal and procurement teams, scrambling to reorganise and adjust to home working, have struggled to respond to the extreme level of contract disruption. When budgets are being cut, deploying armies of professionals and externalising work is not a feasible long-term solution. 

Now is a great chance to look further ahead than tomorrow and work towards improving performance across the organisation

Many of our clients, though, have been able to make the switch to remote working quickly as they have had the fundamentals in place, such as modern document management. It is  evident that it is simply not possible to deliver the speed, accuracy and agility required to support business-critical, decision-making processes without leveraging data using the appropriate technology, including artificial intelligence (AI).

Indeed, an International Association for Contract and Commercial Management (IACCM) report highlights that in the month from March 9, to April 9, the percentage of organisations reporting moderate to severe impact on contract performance rose from 37 per cent to 78 per cent.

Furthermore, according to the IACCM data, more than half the respondents (55 per cent) regard the importance of contract management automation as being either a four or five out of five, in terms of business criticality (with 5 being business critical). Of those respondents who don’t currently use automation in their contract management, just over 50 per cent said the current situation with COVID-19 makes deployment of such capabilities imminent.

At iManage, a leading technology supplier to the legal and professional services market for the last 25 years, we can help. With iManage RAVN, a cutting-edge legal AI engine, we can transform how organisations review and manage their contracts, supercharging levels of efficiency and getting an accurate understanding of the risks and opportunities across their contract portfolio. 

AI offers speed, scale and accuracy, and frees people to focus more on aspects of their job that they relish and provide value. I’m excited we are giving them access to data insights that drive better decision-making and in turn improve overall satisfaction. AI should not be a reactionary measure to one-off events. It should be incorporated into core business strategy and made part of overall business processes.

At iManage, which has attracted more than 3,500 customers, we have a global team of lawyers who have spent many years working at top law firms. We are also data savvy, so know how best to utilise the RAVN engine. There is such a massive opportunity to augment our lives with tech and I urge decision-makers to let AI solutions reduce the pressure piled on by the COVID-19 fallout.

Many tech providers and law firms are pointing to force majeure, but the response to COVID-19 needs to be more than that. Organisations need tools at their fingertips which are adaptable to the changing landscape within which we operate. 

Those seeking pragmatic solutions with customers will be interested in change control provisions and payment terms. Those looking to diversify their supply chain will want to know the position on exclusivity and intellectual property. 

Organisations are also seeking to complete contract portfolio analysis without necessarily diving into a particular contract, for example aggregate liability exposure. The RAVN engine provides the flexibility to change the scope of the contract review and build ever-increasing contract intelligence. 

Above all else, it’s imperative to acknowledge the entire world is suffering. We need to think about pragmatic solutions that can heal and help everyone continue to do better business. Now, more than ever, it is crucial to collaborate and drive improvements. At iManage we are committed to supporting our customers in working through the current situation and developing paths for the future.

For more information please visit www.imanage.com/contract-intelligence

Let’s talk about fried chicken, in particular, the finger lickin’ variety coated in a blend of 11 herbs and spices. This may seem like a strange place to start an investigation into the convergence of artificial intelligence (AI) in business and employee knowledge, but that famous recipe is a prime example of how documenting corporate knowledge can ensure continued business success. 

After all, if the company’s famous founder hadn’t had the presence of mind to write down his original recipe on the sheet of notebook paper now locked away in a corporate safe in Louisville, who knows how the American company’s commercial history would have been rewritten or if they would have had a history at all. 

Every company has their own original recipe, their secret formulas and corporate secrets. In reality they have thousands of them, but rather than being locked away for perpetuity on paper, they are held in the minds and habits of employees, and this can be a significant limiter of growth. 

“The existence of this tribal knowledge also poses a significant risk, particularly as the rate of change increases exponentially,” says Charles Araujo, an independent digital analyst and founder of the Institute for Digital Transformation. “As organisations are forced to rapidly change course, these bits of undocumented, yet critical, information become a minefield of known unknowns that can threaten their ability to adapt.” 

However, AI can be leveraged against data from company intranets and collaboration tools such as Slack, Jive, Microsoft Office and Teams to extract hidden processes and uncodified knowledge, and turn it into known knowns that can then be made accessible through the mobiles and desktops of every employee. 

Questions can be asked by employees of the system as if they were asking a relevant colleague at the water cooler, or a subject matter expert in a formal meeting, and AI platforms can analyse the syntax of a question to reveal the intent, even if it’s asked in different ways, to provide advice and answers in real time. 

This reduces the time the average employee spends trying to find colleagues who can help with specific tasks, highlighted in research from McKinsey & Company, which revealed the average interaction worker spends nearly 20 per cent of their working week searching for internal information. 

Training AI to strategically forget what’s no longer relevant to make room for new intel will allow it to mirror the human brain’s decision-making process

The problem becomes even more acute during times of rapid change and remote working, as incidents become more frequent and the time to troubleshoot them increases, which is why using AI in business to digitise employee knowledge is now a priority.

“A key reason that incidents can take so long to resolve is the lack of timely access to the critical operational knowledge needed to validate, diagnose and resolve an issue,” says Larry Lien, chief product officer at Resolve Systems. 

This problem is only exacerbated in a crisis when there aren’t enough hours in the day for experts to get involved in all the problems they would usually have the time for. “While experts are pulled into high-priority incidents, other incidents go unaddressed and these ignored incidents can have serious consequences that hurt the business,” explains Lien. 

An organisation that is carrying too much undocumented employee knowledge can also suffer from a slowdown in innovation and stunted growth as company-wide experts are drawn into fire-fighting instead of driving innovation. 

The increasing sophistication of AI in business means that operational employee knowledge can be converted into mathematical models and critical decision flows digitised. 

Starmind is a Zurich-based company focused on making the collective human intelligence within an organisation accessible with the power of AI. Founder and chief technology officer Marc Vontobel believes the current increase in distributed teams and remote working means there has never been a better, more crucial time to optimise hidden organisational intelligence. 

“AI technology can help organisations sift through vast and growing data pools in real time to understand what information resides where and who knows what,” he says. Self-learning algorithms that replicate how the human brain works are the key to finding this frozen knowledge and allowing it to thaw so it can begin to flow throughout an organisation. 

“Human-inspired AI uses self-learning algorithms to create neural intelligence networks, which replicate how the human brain works, because it’s important to remember that for AI to function effectively, it needs to learn how to forget, not just overwrite information, just like the human brain,” explains Vontobel. “Training AI to strategically forget what’s no longer relevant to make room for new intel will allow it to mirror the human brain’s decision-making process.”

Starmind’s AI draws on data from more than 200 workplace apps to generate thousands of real-time employee skills profiles automatically and continues to evolve as the expertise develops.

This extracts the hidden processes and codifies employee knowledge, which can then be used to make any organisation more resilient and ready for the future. 

In doing so it can also gift a company another benefit, an important communal hub that can bring employees closer together. This is a crucial step towards longevity, especially in light of a Gallup survey, which revealed that for every two engaged workers – those who are involved in, enthusiastic about and committed to their work – there is one who is actively disengaged. 

However, just because an employee is disenfranchised doesn’t mean they have nothing to offer and often what they lack is access to a company camp fire where they can share knowledge and experience. AI has the potential to be that camp fire around which the true potential of any business can be unlocked.

1. Sales prioritisation

Sales and business development are suffering and AI-powered sales performance solutions can help. So-called propensity models can identify which customers are most likely to buy a product or service from a company, says Dr Tom Davenport, president's distinguished professor of information technology and management at Babson College, Massachusetts. These models can help those working in sales improve their productivity and effectiveness, by showing them which customers to prioritise. 

“For brands, having insight into what their customers think and want has always been a key priority, but the COVID-19 pandemic has made this understanding even more critical,” says Chris Colley, principal of customer experience at Medallia. But he notes, at the same time, collecting data on what customers think has become more challenging. 

As people stay at home, consumers have shifted from personal interactions, where they provide direct feedback, to digital interactions. “Instead of visiting a bank branch where they can speak to the cashier, they are more likely to be banking online,” says Colley. “As it’s no longer possible to eat out, they’ll be ordering online deliveries. The same pattern is being replicated in sectors across the board. This shift is creating a ton of new, unstructured data, which can be hard to make sense of.” That’s where AI solutions can cut through the noise and find out what consumers feel and need.

2. Matching demand and supply

“Companies are interested in matching demand and supply, and that's going to be really critical coming out of this crisis,” says Davenport at Babson College. “The good news is there’s more and more external data available on demand.” A big steel company, for example, has information about the various factors that might influence demand for steel, such as the demand for automobiles. These demand measures depend on external data that’s used to match up to what their supply chains can produce. “So that you're not producing more than you need to satisfy demand and you're not leaving unfulfilled demand out there,” he says.

AI solutions can analyse this external data. But, as Davenport points out, AI typically relies on data from the past, while the COVID-19 crisis is unprecedented. Therefore, companies have to ensure they use data that is representative. He says: “I suspect that in some industries, the past will be a better guide to present and future activity than it is in others.” 

Davenport notes that we do have data from the 2008 financial crash to go on, but the current crisis is happening at a faster rate and its consequences might differ. There are, however, some industries where spending patterns might continue at similar rates, such as groceries and consumer staples. “People have to buy detergent, no matter what,” he says. Conversely, expensive consumer goods might struggle. Data from the last financial crisis might give some indication of how much demand there might be.

3. Document and identity verification

AI can work on identity and document verification, says Dr Terence Tse, associate professor of finance at ESCP Business School. Think of a bank, for instance, that needs to verify its customers for onboarding and compliance. This is often done by human checkers, who check payslips or driving licences. “It's a very costly, inefficient process,” says Tse.

Instead, AI can be used to “quickly identify the type of ID document captured, determine if the security features of the ID are present, perform face-matching – comparing the picture in the ID to the person in the selfie – and even help determine whether the person is physically present”, says Robert Prigge, chief executive at Jumio. 

“For the past few years, digital account opening has been at the top of the list of technologies organisations intend to add or replace, but COVID-19 is pushing this element of digital transformation to the front of the line,” says Prigge.  

4. Back-office tasks

AI-powered cognitive assistants can perform a company’s back-office tasks. This includes ordering new credit cards, issuing refunds or cancelling orders, says Faisal Abbasi, UK managing director at Ipsoft. He notes: “When the cognitive assistant is unable to handle a task due to its complexity, this can be seamlessly handed over to human agents to manage. This ensures the time of those team members is spent solving the most challenging problems and focused on value-add activities.”

This process is often referred to as robotic process automation (RPA) and is increasingly combined with machine-learning. It spans all sorts of back-office service operations, as long as they are structured tasks, such as automating the claims processes of insurance companies or banks.

“Almost all the companies that I talked to about RPA said, ‘Oh, we're just using it to free up people to do more creative, less structured work’,” says Davenport at Babson College. But he notes that if the current COVID-19 crisis leads to a severe recession, which seems likely, companies will use it to replace workers. “My guess is that it's going to contribute to substantial job losses or at least slower growth of employment after the recession because companies will have automated a fair amount of work,” he says.

5. Cash-flow forecasting

Over the next few months, cash flow is likely to continue to be a serious concern for smaller businesses as revenue streams dry up. But there are a number of forecasting AI solutions that can help. “Cash flow is always an issue in difficult economies,” says Babson College’s Davenport. AI solutions are already in place that analyse data for the purpose of cash-flow forecasting. 

One important caveat is “you have to make sure you have the right data period to create models that would be useful for this current environment”, he says. Once again, AI can only help if the data we feed it is representative. 

“You have to go back to recessionary environments to ask, what were your cash needs in the past? And again, it's difficult because this recession appears to be happening much faster,” says Davenport. Economic data comes in slowly and a recession is typically defined as two quarters of negative GDP growth. He adds: “We won’t have this data until the end of June. But I think there is not much doubt among economists that we're in a recession already.”

6. Medical support

The COVID-19 crisis has put unprecedented pressure on NHS staff as public health has taken centre stage. “Medical services have been terribly shaken and our beloved NHS may be near a coup de grâce,” says Dr Alex Ribeiro-Castro, data scientist and senior teaching fellow at Imperial College Business School in London. 

He says health tech may offer a temporary buffer to allow non-critical ailments to be treated, leaving clinics and hospitals free to focus on critical cases. An example is Doctorlink, which provides online doctor’s appointments and has algorithms that can provide medically endorsed diagnostics. Another is Babylon Health, which is building an AI-based health app that can help diagnose patients’ issues. It’s effectively a chatbot that can “translate layman’s language into medical terminology and deduce what may be causing the pain”, says Ribeiro-Castro. 

Dinesh Venugopal, president at Mphasis Direct & Digital, says: “AI-based chatbots and robot-advisory services can very well be useful in relieving the administrative burden on extremely busy and under-resourced healthcare staff, automating processes such as screening patients for symptoms and recording necessary information.” By reducing the amount of face-to-face interaction between patients and hospital staff, this goes a long way to lessening the risk of spreading infection, he says.

7. Staff demand, supply and infrastructure

Given that many employees may have to self-isolate during the COVID-19 outbreak, AI can analyse the number of staff needed. “AI companies get requests from their clients to identify if they are likely to even have enough workers to staff a railroad,” says Davenport at Babson College. In this case, AI can help to match demand and supply, but from a labour standpoint. “If companies are laying off people, they’d like to know it’s the right number of people. Making sure you have enough people to staff a particular train or a production shaft could be quite difficult.”

Transportation companies represent a significant component of a country’s infrastructure. “They are faced with an unfortunate Catch-22 situation: we, as a society, need to keep critical infrastructure and its employees healthy, however not all of them can manage critical infrastructure remotely,” says Ribeiro-Castro at Imperial College Business School.

What’s more, semi-automation is already implemented in certain forms of public transport. Ribeiro-Castro cites Navya, a company that designs and manufactures autonomous vehicles, such as shuttle buses at airports or theme parks. “AI is already being used more generally in the transportation sector to do things such as increase passenger safety, reduce traffic congestion and accidents, lessen carbon emissions, and also minimise overall financial expense.”