I wrote last week about how to break through the barriers that prevent many firms from fully realizing the promise of advanced tech like artificial intelligence (AI). The starting point is a solid data foundation. But you also need to embrace new ways of developing systems and commit the investment needed.
Address the concerns
Any change in your business process will demand a change in your workforce mindset. This is where change management comes in. When people suddenly go from reactive mode to predictive, new skillsets must be learned. The fear of losing your job to a robot is real. But if people are engaged in the process, they can weigh in on what parts of their job might be improved through AI and see first-hand the value in terms of personal productivity and outcomes.
People tend to think of AI as a black-box computer, working behind a veil, spitting forth outputs from thin air. Magic that can’t be explained is not necessarily good in an industry like post-trade capital markets that is so regulated and routinely audited. You must be able to explain how your systems arrived at your conclusions and demonstrate the linkages behind the technology that produced the results.
By documenting the mechanics behind a model, AI developers at financial firms can create models that are reliable and defensible – not only to auditors, but also to analysts who might eschew the results as “magic” and carry on in traditional fashion – only to arrive at the same conclusion.
So, the discussion comes full circle to change management, wherein users must be engaged in the process upfront. IT can no longer be the sole proprietor of AI solutions. They must be developed under the guidance of a cross-functional team, including IT, data, automation, business and operational principals.
Then the resulting applications can be explained and users can sleep at night knowing that their jobs are not only secure, but greatly enhanced by a machine that can do the dirty work 10 times faster and cheaper, while they perform more creative work.