Unprompted: How to AI

Unprompted is an occasional opinion column from Kunal Gupta for Pivot 5 readers

Last week, I was surprised to receive such different responses to a talk on AI trends I was giving to a small room full of business leaders. 

One person sat frozen, visibly uncomfortable as the demo I showed reconstructed her industry before her eyes. Another leaned back with arms crossed, confidently explaining why this would never replace the "human element" in their business. And another leaned forward, asking questions that revealed neither blind acceptance nor dismissal—just genuine, insatiable curiosity.

That moment crystallized something I've been observing across hundreds of interactions with business leaders: there exists a clear spectrum of AI adoption, but it doesn't follow the linear progression we might expect.

Most frameworks present technology adoption as a journey from novice to master—a marathon with a finish line. But what if I told you that the goal isn't to "master" AI at all, but something different?

I've come to recognize three distinct states along the AI learning journey: the AI confused, the AI confident, and the AI curious. And contrary to what you might expect, it’s not the AI confident where significant competitive advantage lies.

The "AI Confused" aren't necessarily technophobes. Often they're thoughtful professionals who simply haven't found their bearings in this rapidly shifting landscape. They recognize AI's importance but remain paralyzed by its complexity, ethical considerations, or implementation challenges.

You'll recognize them by their behaviors:

What the confused don't realize is that hesitation carries a compounding cost. While they wait for clarity, they're accruing what I call an "AI lag"—a growing gap between their capabilities and those of their forward-moving competitors that becomes exponentially harder to close with each passing quarter.

A manufacturing CEO I know spent two years in this confused state, demanding more case studies and ROI projections while competitors implemented AI throughout their operations. By the time he was "ready to act," his company was facing a 30% efficiency gap and struggling to retain talent frustrated by outdated workflows.

The cost of confusion isn't just opportunity loss—it's existential risk.

More insidious than confusion is the false sense of confidence that often emerges next—not confidence in AI's capabilities, but confidence in its limitations.

The "AI Confident" have formed rigid conclusions about what AI can and cannot do, what it will and will not become. They've mentally "solved" the AI question with statements like:

"AI will never capture the nuances required in our industry." "We've already implemented AI in our data analytics, so we're covered." "The creative aspects of our work will always require human intelligence."

This confidence manifests as selective engagement—implementing AI in peripheral areas while trying to protect the "core" functions from disruption. It results in incremental improvements rather than transformative change.

The danger lies in how today's rapid advances repeatedly invalidate yesterday's certainties. The executive who confidently declared that "AI will never write compelling marketing copy" in 2022 now watches competitors deploy AI-generated campaigns that outperform their own.

I've witnessed this confidence trap spring in industry after industry. Legal partners dismissed AI contract review until smaller firms used it to underbid them. Radiologists insisted AI couldn't match their diagnostic accuracy until it began outperforming them in controlled studies. Creative directors laughed at AI-generated designs until their clients started asking why concepts took months instead of minutes.

The confidently wrong pay the highest price of all, because by the time they recognize their error, they've invested heavily in soon-to-be-obsolete approaches and a culture of “I can do it better”.

The third state—perpetual curiosity—emerges not as a midpoint between confusion and confidence, but as an entirely different approach to navigating technological change.

The "AI Curious" hold seemingly contradictory positions. They have enough knowledge to try AI effectively but remain profoundly aware of how much they don't know. They form working hypotheses rather than rigid conclusions. They experiment with conviction but iterate with humility.

You'll recognize them by distinctive behaviors:

What makes curiosity so powerful in the AI era is the sheer velocity of change. When breakthroughs emerge monthly rather than annually, confidence becomes a liability while curiosity becomes an asset.

Consider how in many verticals, there’s one large company that has already transformed from a struggling legacy company to an AI powerhouse. Not through a single strategic pivot, but through institutionalized curiosity—establishing ongoing relationships with research organizations, creating feedback loops between customer needs and AI capabilities, and maintaining a learning stance despite deep expertise.

The curious aren't paralyzed by complexity like the confused, nor blinded by premature conclusions like the confident. They maintain the nimbleness to capitalize on breakthroughs while avoiding investment in dead ends.

The habitat metaphor matters because it shifts our goal from "winning the race" to "finding our sustainable niche" within an evolving AI ecosystem.

If you recognize yourself in the AI confused state, start by acknowledging that perfect clarity will never arrive. Begin with small experiments that generate organizational learning without requiring massive commitments. Build networks that expose you to practical applications rather than theoretical possibilities.

If you've settled into AI confident conclusions about AI's boundaries, challenge yourself to revisit those assumptions regularly. Develop mechanisms to track how previously "impossible" AI capabilities become not just possible but commonplace. Protect your organization from over-investing in approaches that assume the technological landscape will remain static.

And if you're among the perpetually AI curious, focus on institutionalizing that curiosity. Individual learning isn't enough—build systems that allow your organization to sense, evaluate, and respond to emerging capabilities as a collective intelligence.

The greatest risk in this AI pivot isn't moving too slowly or too quickly—it's failing to develop the ongoing adaptability that only curiosity can sustain.

Your sustainable competitive advantage isn't going to come from having arrived at some mythical destination of AI mastery. It will emerge from having found your place in its ever-changing AI ecosystem—a place where confused hesitation gives way not to confident certainty, but to enduring, productive curiosity.

Unprompted is an occasional opinion column from Kunal Gupta for Pivot 5 readers. Follow Kunal on LinkedIn.