When AI can do more, where do you add value?

Nearly four years ago, many of us were testing whether AI could write a decent email. Today, we are asking how much of an entire workflow it could handle.

That shift can feel personal. When you have spent years becoming good at research, writing, analysis or planning, watching a tool produce something similar in seconds can unsettle your sense of professional value.

Fear is understandable. It just cannot be our development plan.

Standing aside will not stop work from changing. We need to learn where AI helps and become more deliberate about where we put our attention.

If your contribution rests mainly on producing more emails, reports or presentations, it is time to broaden it.

The opportunity is to build stronger judgment: understanding the situation, connecting evidence, challenging assumptions and taking responsibility for what happens next.

Revenue teams make this shift tangible.

From preparing information to understanding the situation.

Before a discovery call, AI can help organise account research and develop hypotheses.

Your contribution is investigating what that research cannot establish. Why has the customer prioritised this problem now? Who benefits from a change? Who carries the implementation burden? What happened the last time they tried?

A prospect might ask for better reporting. Further conversation might reveal that their operations director cannot explain rising staffing costs or that a previous implementation damaged trust.

Those are different problems. They require different conversations. A polished pitch is useful. Knowing which conversation to have is more valuable.

From presenting dashboards to explaining business impact.

For a renewal or expansion conversation, AI can help organise usage data and prepare a review.

Your contribution is interpreting that evidence with the customer. Has the product reduced administrative work? Improved operational visibility? Where has adoption stalled, and why?

Sometimes the most valuable expansion recommendation is: “Before you buy more, let’s fix what is preventing your current team from getting value.” That takes commercial judgment and the confidence to look beyond this month’s target. It also builds trust through a concrete decision.

From producing launch materials to shaping a launch that matters.

For a product launch, AI can help draft messaging, create campaign variations and prepare sales materials.

But a launch can have excellent content and still give customers little reason to care.

Imagine launching training software like Parim Academy, which connects completed training to employee qualifications and scheduling.

“Create courses, assign training and track completion” describes functionality. Understanding the operational problem gives Marketing a stronger starting point.

Picture a manager preparing next week’s roster: Who has completed the required training? Whose qualification is expiring? Can this person work at that site? If answering means checking spreadsheets and chasing colleagues, the friction extends well beyond managing courses.

The message becomes concrete: know who is trained and ready before you assign the shift.

Marketing’s contribution is identifying which problem matters most to the buyer, testing whether that message resonates and working with Product and Revenue to make sure the promise holds up.

AI can help throughout. The team owns the choice of audience, the promise and the evidence behind it. The measure of a successful launch is whether the right customers understand the value and start using the product not how many assets the team produces.

From generating activity to improving the growth engine.

AI can help analyse pipeline movements and suggest explanations.

The team still needs to establish what the evidence supports. Are opportunities poorly qualified? Are deals stuck because buyers cannot justify the investment? Is one channel producing interest but little revenue?

More outreach will not resolve every pipeline problem. Someone needs to diagnose the constraint, decide what to test and check whether the change worked.

This is what higher-order thinking looks like in practice. It shows up in better questions, clearer choices and decisions grounded in evidence.

I would be careful, though, with a comforting division: basic tasks belong to machines; complex, creative work belongs to humans. AI can contribute to both, and its usefulness varies across tasks. Research has shown improvements in creative and analytical work, alongside situations where relying on AI worsened performance.

Professional mastery means learning how to work with AI while remaining accountable for the reasoning, the commitments and the outcome.

That requires practice. Ask AI to challenge your proposal, identify missing evidence or argue against your preferred explanation. Then examine its reasoning. Knowing your field still matters: it helps you recognise a plausible answer that would fail in reality.

From a People leadership perspective, this also changes our responsibility.

We cannot ask people to “be more strategic” while rewarding email volume, filling their calendars and giving them no space to learn. We need clear expectations, practical experimentation and coaching on real decisions.

And when AI saves time, decide where that time should go. Better customer conversations? Testing a market assumption? Resolving an adoption problem? The benefit needs somewhere useful to land.

Start with one recurring workflow. Use AI to reduce the preparation. Reinvest the time deliberately. Check whether understanding, decision quality or commercial results improved.

Confidence grows through that practice.