Why Did the CEO of a $13 Billion Dollar Company Put Their Entire AI Strategy in the Hands of a Non-Data Person (Who Doesn’t Really Know What AI Is)?
The race is on, and many companies need to catch up.
In case you haven’t noticed, the distance between what AI can do and what most people understand is widening at a frighteningly accelerated pace. As we watch leading AI developers launch new, previously unimaginable applications into uncharted territory, companies are feeling pressure to harness the benefits of this new frontier.
In one recent case, the CEO of a $13 Billion Dollar company assigned one of his operations VPs to develop their AI strategy. ?
Essentially, the CEO chose a reliable Cessna pilot to get him to outer space.
Within minutes of listening to the new “strategic plan for AI†it was clear to the company’s data science team that this strategic VP was not fully aware of what AI is, let alone what it can do.
As we might expect, those same data scientists – who DO know what AI can do – felt undervalued, excluded and betrayed.
The CEO could have asked for their input. Or, he could have hired an outside, true AI expert who would build a cutting-edge plan.
He didn’t.
Why?
One word.?
Trust.
Trust: to rely upon or place confidence in someone or something.
While it may seem counterintuitive to outsiders, the CEO deliberately chose a person he knew he could rely upon over someone more qualified to accomplish the actual task. This is not unusual. In difficult or uncertain situations, we often choose trust over know-how. (See this video about Navy Seals).
I do not know this CEO personally, so I can only speculate about his motives based on many similar experiences. For whatever reason, the CEO did not trust that an AI-expert will do things like:
·?????? Prioritize company objectives,
·?????? Clarify rather than complicate,
·?????? Find valuable solutions, not just interesting models,
·?????? Move quickly, when required,
·?????? Make him look good,
·?????? Help him understand,
·?????? NOT make him feel stupid,
·?????? NOT undermine his business decisions,
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·?????? Or some similar concern.
Notice these reasons include both business concerns and interaction/communication concerns. Both matter. ?Everyone, including CEOs, prefers to surround themselves with people they trust.
To be clear, I am not talking about surrounding oneself with people who always agree or never challenge them. ?Indeed, we are more likely to invite differing opinions and accept criticism when we trust that the other person shares our goals and has motives that align with ours.
For whatever reason, the CEO did not believe AI experts would be true partners.
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Why?
The underlying problem is not an unwillingness to embrace new technology. It is an epidemic of distrust between business professionals and advanced analytic professionals.
That distrust only widens the distance between what business leaders do understand about AI and what they need to understand.
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And the cycle gets worse.
From the data scientist’s perspective, the CEO’s choice seems ridiculous and short-sighted.? And one potential response I have witnessed many times – understandable although not helpful-- is to publicly challenge the new AI strategy owner in front of everyone else to show them just how much they don’t know.
The intention behind this makes sense: convince leaders that data scientists have something to contribute! ?If they are not consulted directly, how else can they demonstrate their abilities?
But public embarrassment simply reinforces to leaders that data scientists aren’t team players.
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Both sides need help.
Of course, leaders SHOULD consult their talent. Be open to new ideas. Of course, data scientists SHOULD prioritize business needs over advancing their craft or proving how smart they are. But too often, mistrust gets in the way. Under pressure, leaders tighten control, scientists assemble evidence.
This AI strategy decision is but one example of what happens every day, in thousands of companies, and millions of projects. Data science remains underused and misunderstood. AI’s potential remains unrealized.
Frustratingly, both teams – business and data science – want the same thing.? To harness and implement new, valuable solutions.? But because the dynamic is broken, companies waste time and energy. (See the report about what analytic leaders tell me about the magnitude of waste.)
The solution is straightforward: build a powerful, trusted partnership between these two essential teams. This takes processes and skills that maximize the contributions of both while interacting in new ways. ?If your company experiences this type of disconnect, consider hiring or training people who speak both languages.
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Terrific example of the importance of earning and building trust.