Читать книгу Artificial Intelligence for Asset Management and Investment - Al Naqvi - Страница 20

While the battles between the first two eras were being fought, machine learning silently rose to become widespread in other departments and functions in firms. It was no longer the exclusive domain of quants, and functional areas such as marketing, customer services management, regulatory and compliance, governance, and other departments and capability areas began embracing machine learning.

With the widespread adoption came the problem of unmanageable proliferation. I was involved in guiding one of the world's largest financial services firms on how to architect their machine learning and intelligent automation programs. That is where I was able to see firsthand the chaos unleashed by unplanned adoption of machine learning and intelligent automation. I realized that the firm had hundreds of projects going on all over the world. There was little to no coordination between the heads of departments leading these projects. Each department head had architected his or her own vision of automation—which was limited to their own political interests, capabilities, outlook, experience, and understanding of machine learning. Political fiefdoms developed, and departments began competing for AI talent. While all of that was going on, several consulting firms and suppliers jumped into the mix—each with its own angle, methodology, understanding, and interests. Given that no broad platform-centric capability set was available, each group, with the help from its own supplier and consulting firm, developed its customized expression of what needed to get done. Besides politics and self-interest (promotions, bonus, impressing higher executives, resume building), psychologies of various leaders also influenced their decisions. The ones with more aggressive personalities launched more aggressive programs. The more risk averse settled for developing some cute chatbots. In a meeting held with representation from across the firm, it was discovered that there were literally thousands of parallel, but uncoordinated, efforts going on in the company. Everyone had their own ideas of what AI and cognitive revolution is all about. The firm had become a weedy overgrown garden of AI artifacts. As you would have expected, the overall performance of the firm had not improved.

To appreciate the gravity of the situation, let us take a step back and evaluate the third era with the backdrop of the first two. As I mentioned before, shifting from the first era to the second era will be a monumental change. Realigning practices, rebuilding process streams, redefining incentive structures, and managing cultural change will take years and require organizational commitment, leadership vision, and execution excellence. It is not something that can be decoupled and reconfigured instantaneously. Now add to that companywide sporadic and unplanned adoption of machine learning point solutions, and you have a perfect storm. The third stage is a current reality for many firms—but it is a recipe for failure and a death spiral.

Now let us review the desired stage - Stage 4: