Debunking myths that might be holding back AI adoption

By 2030, Bloomberg  expects artificial intelligence (AI) will account for as much as $15.7 trillion of the world economy. Of that figure, an estimated $6.6 trillion will come from businesses’ ability to be more efficient via automation and freeing their work forces to work on other tasks. But the lion’s share ($9.1 trillion) will come from the goods and services these companies are able to produce as a result, and which consumers will purchase.

 Even though the opportunity is vast and seemingly irrefutable, and although most firms are investing in AI initiatives, a mere 14.6% of financial services firms have actually deployed AI capabilities in production, according to Forbes. What accounts for this disparity between opportunity and execution? One of the key problems is the number of myths that surround AI.

Managing myths

AI is surfacing in all sorts of sectors, from personal digital assistants to chatbots on websites, and  self-driving cars. Each of these systems, however, are reliant on carefully curated sources of information and algorithms that help transform data into actionable insights. In many cases, industry practitioners who seek to implement AI look only at the outcomes of other instances of it, and don’t consider how much has to happen to enable them to reach those outcomes successfully and consistently, leading in turn to unrealistic expectations and despondency.

AI is inevitably going to impact the jobs market, but not in the manner which many people fear. Pessimists argue that AI will destroy entire segments and render people redundant, but what’s more likely — as we’ve seen in the risk management space — is that AI will free up people’s time by assuming repetitive tasks. These tasks are time consuming  for humans, but are easy for computers, for example, analysing past expenditure patterns to unearth and flag any anomalies. While tedious for a human, pattern recognition is something AI excels at, and by handing it the duty, people can focus on higher-level aspects of risk assessment that require more nuanced judgment.

Contrary to many people’s perception, AI will create new roles, some of which don’t even exist yet. As AI evolves, it becomes easier to implement, and the more applicable harnessing data becomes to a wide range of industries, new jobs will be created. These employment opportunities will revolve around the design, distribution, deployment and management of AI systems.

Interoperability and interdependence

Like data literacy, an understanding of AI’s possibilities and pitfalls is going to become increasingly important for all members of an organisation and not only those working in IT. To this end, good governance of AI is about evaluating and monitoring its biases, effectiveness, risk and return on investment. Another consequence which data literacy may bring is ensuring AI projects are socialised and coordinated across the company and not siloed.

Working across divisions implies that security becomes even more important. It’s not only about ensuring that competitors don’t gain access to trade secrets, but about securing the processes by which users interact with AI. Good systems will include strong authentication, follow the principles of least privilege, and be resistant to modifications like data injections, deletions and poisoning.

Measure to evaluate Another potential point of resistance to AI adoption comes when trying to measure its success or failure, and the value it adds (or removes) from a business (or individual business units). When undertaking an AI project, it’s thus essential to set realistic intentions for what you want to achieve with it and then consider how to measure whether or not those goals have been met. If, for instance, the goal is to reduce instances of fraudulent transactions, it’s important to have a baseline at the outset against which the AI’s performance can be compared. This measurement and evaluation process may vary between projects, divisions, or organisations, and it may require extended testing periods for assessments to even be possible, but it’s as essential to do as it is to temper expectations. AI can’t solve every problem or alleviate every pain point.