“COVID has accelerated a phenomenon that was happening anyway.”
—James Duez, CEO Rainbird
In the economic maelstrom of COVID, many companies have increased the urgency with which they consider adoption of AI and automation. Their CTO’s and CIO’s once long-term boardroom agenda items are now integral components of business recovery strategies.
And urgent it is. In autumn, as Europe put swathes of land under new lockdowns, businesses across the insurance sector and financial and professional services abandoned return-to-office plans. Now in winter, the promising news of the COVID vaccines has given way to the reality of awaiting their roll-out. “Companies are realising they need to move forward in coming up with answers to the questions that COVID has brought,” says our CEO, James Duez.
But, when we entered 2020, some companies could be understood for not rushing their AI and automation adoption. High-profile failures of AI transparency, such as Apple’s credit card algorithm’s purported discriminatory bias, have shown how AI debacles can not only cause serious reputational harm but also mean being investigated by regulators.
As businesses head into the potentially powerful economic currents of a new year, few can afford a debacle like Apple’s.
Fortunately, it’s easily avoidable.
Why? For Apple, the problem was not just that its algorithm appeared to show bias. It was also that the technique of AI used—machine learning—meant Apple couldn’t explain how the AI’s biased judgements had been reached. “The problem is, machine learning is not easily interpretable,” explains James. “Firstly, you can’t know for sure if it’s right. Secondly, it can’t give you a reason; it’s always a mathematical judgement.”
Any truly effective digital strategy must take into account compliance and transparency. Which is why so many companies are turning to intelligent automation (IA). IA is based on a human-down structure—that is, it starts with human knowledge and applies it to data—so that humans can always understand and explain what the machines are doing.
For businesses, IA can play many roles in COVID recovery—from the financial to the operational to business growth. Here are five roles.
1. Unlock thinking-time for employees
Just like the cattle drawn ploughs that turned the soils of Ancient Egypt, IA is designed to reduce heavy lifting. Building visual models of the thought processes of highly effective employees means businesses can automate decision making, helping to make judgement-heavy work—such as assessing suspect payments or valuing stocks—100 times faster.
This means businesses can make the workloads of those staff who are best placed to help with new solutions far more manageable, and let them think ahead. A London Business School study found that reducing employee time spent on repetitive tasks makes business innovation more likely. “If you can liberate people from time-heavy work, they can do the things that they are good at,” says James. “You can help people innovate.”
2. Protect cash flow by focusing on operational efficiency
Businesses who focus on operational efficiency during a recession have the highest chance of thriving after it, argued Ranjay Gulati et al. following their 2010 HBR study. Gulati et al. write: “Companies that master the delicate balance between cutting costs to survive today and investing to grow tomorrow do well after a recession.
10 years on, things are different. “Now, the IA sector is more mature,” says James. “Today, it’s the other way round. You invest in order to cut costs.” This is critical. While we’ve seen many organisations rush to make deep cuts and protect their bottom line, investment in IA can help operational efficiency while retaining (or indeed increasing) work output.
3. Support employee mental wellbeing to boost productivity and make work quality reliable
A snap poll held in April this year by the Institute of Employment Studies found that 48 per cent of home workers are working longer hours and irregular patterns. Studies in The Lancet have already shown that COVID has had a significant impact on mental health.
In times of crisis, you need your employees’ judgement at its best. Paradoxically, crises catalyse poor decision making. According to NCBI, poor mental health can markedly impair a business’ productivity and profits, with work-related stress being a major contributor to poor decision making.
IA can take the pressure off employees to perform on time-heavy tasks. This can help employers more easily reduce employee stress and therefore be better able to meet their duty of care to employee wellbeing.
4. Simplify workforce office usage to minimise risk of coronavirus transmission
IA can take off HR and office managers’ hands the pressure of making high-stakes judgements about simple questions, like whether an employee can go to the office. For instance, in June, we developed a risk assessment tool for Norfolk and Norwich University Hospital that manages risk of virus exposure to vulnerable front-line workforce with digital, one-to-one automated reports. The tool took manual effort out of workforce management for the NHS hospital at a truly critical time.
If their employees do need to use the office, businesses can use IA effectively to tether the burden of risk to an accurate, user-friendly management process.
5. Compete and scale in the new economy
As much of the world heads into recession, law firms anticipate that their clients will see a rise in litigation and disputes, and auditors expect to see an increase in restructuring and insolvencies. The kinds of bursts of work output that may be required from the professional services could be intense.
IA can give businesses the tools to create new revenue streams to help manage high volumes of work while staying cost-effective for clients.
“This isn’t the pandemic, it’s a pandemic. People now know they can work in different ways.” —James Duez
With predictions like PwC’s that AI will add $16trn to the world economy by 2030, there is no question that, in their path to COVID recovery, businesses will continue to fast track the adoption of AI and automation. For those that do, the real question will be whether the AI techniques on which they based their recoveries were right for the digital transformations they needed.