Charting AI's Path Forward In The Historical Context Of Disruptive Technologies That Came Before

*The views and opinions expressed by Anil Kumar are their own and do not necessarily represent those of any former or current employers.

Behind the promise of AI lies a surprising reality for banks. While most race to adopt it, their efforts remain largely fragmented. Now, disconnected pilot programs are creating more organizational friction than strategic advantage. The result is a significant challenge for banking leaders: turning scattered AI experiments into a unified strategy.

For an expert's perspective, we spoke with Anil Kumar, Senior Technology Lead at a Tier 1 Financial Institution. An ITIL-certified financial services IT professional with over 18 years of experience, including senior roles at Morgan Stanley, Kumar is deeply familiar with how large enterprises navigate technological disruption.

"The core challenge for leadership is aligning the fragmented efforts that define this inception stage. It's about moving beyond scattered pilots to build a unified strategy," Kumar said. But today’s chaotic environment is not unprecedented, he continued. Instead, it follows a familiar "crawl, walk, run" pattern seen in previous technology shifts.

• A familiar path: Rather than viewing AI as a unique crisis, financial institutions should see it as another chapter in a well-known story of technological disruption, Kumar said. "AI will follow a similar path to other disruptive technologies. We saw Bitcoin face initial skepticism before evolving with regulation. And just as most companies landed on hybrid models for the cloud, we'll likely see the same with AI."

Now, the industry is squarely in the “crawl” stage, he explained. One defining feature of this phase is the temptation to “bolt on” vendor tools to existing systems, a practice that weakens infrastructure and collects technical debt.

• The meaning of maturity: The goal is more than AI implementation, Kumar said. For him, success is measured by tangible business value, not technical sophistication. "Real maturity isn't about having the most advanced model. It’s about when AI consistently improves efficiency, reduces operating costs, and protects company data. That’s the goal, and we’re still about two to three years away from that being the norm."