As researchers who study AI and teach about AI transformation and technology, we believe that many leaders are making the same mistake they made a decade earlier with digital transformation: encouraging experimentation, which is good, but falling into the trap of letting experimentation run wild, which is counterproductive.
For context, in the previous wave of digital transformation, when many leaders felt confused about digital transformation and the path forward, they embraced innovation and experimentation. Leaders embraced a “let 10,000 flowers bloom” approach, hoping that a few experiments produced unicorn-level returns.
The lack of focus proved to be a blunder, however. Without a clear connection to the real business opportunity—the way to create meaningful value for users—the result was a morass of unfocused, under-resourced teams that produced few scalable results.
Facing such disappointing returns, many leaders naturally concluded that experimentation with digital was broken and shut down the experiments. In its place they either returned to business as usual or refocused on a few safer bets: perhaps replacing an aging IT system or a near-term payoff like a digital asset management system.
What went wrong? While experimentation is good, without a connection to the true business opportunity—e.g., transforming the core to serve existing and new customers—experiments inevitably fall short of hopes and expectations. It sounds obvious, but by framing AI as radical and disruptive we often lose sight of the connection to the most fundamental objective of business: to solve problems for customers.
The way out of this trap is to 1) understand this AI moment in the larger arc of transformation, 2) focus on AI’s potential to help better serve customers, 3) experiment with a focused set of opportunities to prove them out (with an eye toward scaling), and then 4) scale them up.
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