In the past year, AI investment has surged across industries, but the results have not kept pace. 

Many leaders are seeing the same pattern: pilots multiply, demos impress, but day-to-day adoption still feels limited.

INSEAD describes this as an “AI clarity gap,” where early pilots look promising in controlled conditions but stall when they hit the realities of the enterprise: messy workflows, unclear decision rights, and poor collaboration. 

In most cases, it’s not because of the technology. It’s because the organization isn’t prepared to manage what AI really changes: the way people work.

We addressed this phenomenon in our recent webinar, Make Your People AI-Ready, where our clients unpacked three adoption challenges that routinely weaken AI’s impact and what leaders can do to overcome them. The five tips below translate those organizational AI success stories into practical moves leaders can make now to get more out of their AI investments: 

Tip 1: Treat AI adoption as a capability problem

It’s tempting to treat AI adoption like a tech rollout: provision access, run a short how-to session, then move on. But access doesn’t change work. Capability does. 

That gap came through clearly in our webinar. Nearly a third of leaders named “lack of practical, role-specific capabilities” as the #1 barrier to AI confidence in their organizations. 

Where we’ve consistently seen results is when organizations help people build that capability through role-specific, hands-on practice, shared learning that establishes what “good” looks like, and regular checkpoints to surface what’s working (and what isn’t) as the tools keep evolving.

The bottom line, as ExperiencePoint’s own VP of Organizational Innovation, Andrew Webster, said in the webinar, “treat AI as a capability to build, not a tool to deploy."

Tip 2: Build AI confidence before scaling

Confidence can sound like a soft metric, but in AI adoption, it’s foundational. People won’t scale what they don’t trust themselves to use, especially when their work is high-stakes.

Our guest speaker, Chris Clarke from Economist Impact, saw this firsthand while designing a global summit for Finance and Treasury leaders who were “by nature quite risk averse.” Many arrived, understandably, hesitant to use AI in daily work since their decisions carry real consequences.

It wasn’t until they had a safe environment to experiment, collaborate with peers, and practice applying AI to real-work challenges that adoption began to move. As a result, daily AI usage jumped from 13% to 59%.

The takeaway for leaders: build a “safe-to-try” path before you push for scale. Clear guardrails, approved tools, and explicit permission to learn without reputational risk are often the difference between AI enthusiasm and AI adoption.

Tip 3: Create AI use cases from first principles

When AI use cases feel scattered, it’s usually because teams start with what the technology can do instead of what value the organization was built to deliver. Andrew Webster offered a simple first-principles framework in the webinar to reset that conversation. Here’s how it goes:

AI Use Case Discovery Framework

STEP 1 - Create Value Statements 
Start by defining the value your team is here to deliver:
a) What is the work of our team?
b) If we do it poorly (or not at all), what breaks for our users or the business?
c) If we do it brilliantly, what is possible for our users or the business? 

STEP 2 - Map AI Abundance vs. Scarcity
Once you have those value statements, pressure-test where AI can genuinely help by asking: 
a) What has AI made easy or abundant (and should be automated)?
b) What remains scarce or has become more valuable (and should stay human-led)? 

STEP 3 - Choose AI Use Cases
Use that map to choose beacon projects with clearer purpose, clearer boundaries, and a much higher chance of traction.

Tip 4: Don’t patch the old with AI. Reimagine it.

A common AI adoption trap is using AI as a band-aid, layering features on top of the way work already gets done. It can look like progress, but it rarely changes outcomes or adds real value.

In the webinar, Azadeh Pak, VP of Product at Pipedrive, described this exact tension. As the team worked toward an AI-native CRM, their early efforts validated several promising ideas, but leadership wanted to go above and beyond. Rather than patching AI onto an existing product, they chose to redesign the experience itself around customer value, starting with one discrete, high-impact problem.

What this means for leaders: Don’t try to add AI to everything at once. Start narrow with a “beacon project” to help your team reimagine their ways of working with AI, then scale that approach across teams.