Navigating The AI Platform Shift: How Leaders Actually Keep Up

Vivek Bhaskaran is founder & CEO of QuestionPro, a global leader in survey and research, data and insights services.

gettyAI is not just another technology trend. It is a platform shift. In every true platform shift, the way value is created and delivered fundamentally changes, and the leaders who treat it like a routine upgrade usually experience the hardest surprise. In 2005, when I started my company, the shift was from shrink-wrapped software to cloud. Then came mobile. Today it is AI, and the speed and range of outcomes are unlike anything we have seen before.

What keeps many leaders up at night is not whether AI will change everything. It is whether they, their teams and their companies are keeping up. The good news is you do not need a 70-page AI readiness framework to respond. What you need are a few concrete behaviors you can put in place this quarter.​

Every platform shift introduces fog. You know something big is happening, but you cannot see exactly where it will land or how long the uncertainty will last. When the iPhone launched in 2007, BlackBerry’s revenue actually doubled over the next two years. If you were the CFO in 2009, the numbers would have told you everything was fine. We know how that story ended.

With AI, the fog is even thicker. The range of possible outcomes runs from “we all lose our jobs” to “we all sit on a beach drinking margaritas while agents do the work.” Smart people argue for both extremes. As a founder and CEO, worrying about outcomes I cannot personally influence does not help me run a better company. What matters is how my team and I navigate the next days, weeks and quarters.

One simple metaphor guides my thinking: do not sail into the wind, sail along with it. You do not need the wind perfectly behind you, but you cannot stubbornly fight it either.

There is a romantic narrative in tech that you must be an early adopter of everything or you are a laggard. I do not buy it. Blockchain and Bitcoin are a good example. Years ago, people told me we should put surveys on the blockchain. We could have. But there was no real movement, no real value creation at scale. Lots of froth, very little traction.

AI and mobile have been different. You can see real movement, real value, real users. This is where an “early majority” mindset pays off: wait just long enough to see real traction, then lean in hard.

• Has this technology produced measurable value in use cases similar to ours?

• Are customers asking for it, not just vendors pitching it?

• Can we test it in a six-week project with limited downside?

When the answers start trending yes, that is your signal to move from watching to doing. Any leader can borrow this checklist tomorrow.

We never rolled out a formal AI readiness model. We run a company with a few hundred people, not a consulting case study. Instead we built around three behaviors any organization can adopt: awareness, selective adoption and adaptability.

We run a weekly all-hands town hall. One of our leaders suggested a 10-minute “AI hacks” segment. Every Thursday, teams demo how they are using AI in marketing, support, product or operations. Skeptics realize their peers are already using it. Informal champions emerge. Experimentation gets normalized.

We picked a bounded project: a chat-based interface for survey creation instead of the traditional point-and-click builder. Low stakes, fast to deploy. It taught us how AI actually behaves with our data and users, what skills our team had and lacked and where the experience needed to change to feel natural rather than gimmicky.

This is where most organizations fail. They run a pilot, write a slide deck and move on. The value comes from deliberately scaling what you learned. In our case, early experiments using AI to structure and interpret survey data gave us the confidence to lean into synthetic data as a strategic bet, extending the usefulness of the feedback we collect while protecting privacy. That move only made sense because of the credibility we had already built with smaller wins.​

You cannot jump straight from confusion to moonshots. You earn that right by building awareness, practicing selective adoption and proving adaptability. Once that foundation is in place, you can ask bolder questions. For us, that is agentic workflows for customer experience: imagine every piece of customer feedback triggering an end-to-end, semi-autonomous workflow that diagnoses the issue, takes action in billing or support systems and closes the loop with the customer. With unlimited people, that is what you would build anyway. AI agents make a version of it feasible.

For your organization, a moonshot should feel slightly unreasonable but directionally aligned with where you already see value. Anchor it in real problems customers care about, constrain it enough that you can iterate and keep humans in the loop wherever trust and brand are on the line.

The lesson from this platform shift is simple. Accept that you are in the fog and decide to move anyway. Adopt an early-majority mindset. Institutionalize awareness, selective adoption and adaptability. Do that, and you give your teams permission to evolve with the technology instead of freezing in place.

The question is not “Is AI overrated or overhyped?” It is “What concrete behavior will you change inside your company in the next 90 days to sail with the wind instead of into it?” That answer will determine whether this platform shift becomes your opportunity or your post-mortem.​

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