From Awareness to Action: How Phase 2 of AI Literacy Turns Strategy into Measurable Impact

Let’s be honest, most organizations today are talking about AI more than they’re doing anything meaningful with it. There are strategy decks, leadership offsites, and even AI literacy sessions popping up across departments. Everyone’s “exploring possibilities.”

But ask a simple question — “Can you show me one area where AI has actually improved business outcomes?” You’ll often get silence.

That’s the gap between awareness and action. And that’s exactly where Phase 2 of AI Literacy, Pilot Design and Proof of Value, steps in.

This phase is not about running flashy experiments. It’s about proving, in real terms, how AI can solve business problems, make people more capable, and build confidence across the enterprise.

Phase 1 of AI literacy is about leadership alignment, awareness, setting vision and building comfort with AI. But Phase 2 is the inflection point where you move from intention to impact.

Research by MIT Center for Information Systems Research (CISR) shows the greatest financial returns come when organisations progress from the “pilot & capability-building” stage (Stage 2) into scaling (Stage 3) of AI maturity. Additionally, a report found only around 5% of AI pilots are achieving rapid revenue acceleration, highlighting how easily pilot efforts can stall without the right design.

In other words: Phase 2 is where proof replaces promise — and if you skip this or do it poorly, the whole enterprise-wide transformation stalls.

Phase 2 typically runs over a few months, focusing on targeted, high-value pilots. The goal is not to “try AI everywhere” — it’s to choose a few areas where AI can make visible, measurable impact.

Key features:

For example: Launch a pilot to increase global compliance-training completion from 60% to 75% via AI-powered microlearning recommendations.

Such tangible outcomes demonstrate not only the power of AI but the organization’s readiness to apply it. These small-scale successes become the stories that inspire enterprise-wide confidence.

Moreover, in the context of L&D, embracing AI literacy means moving beyond tool demos and exploring how AI can personalise learning, automate rote work, serve as just-in-time support, and surface analytics for continuous improvement.

If you’re leading AI literacy in your organization, especially from an L&D seat, here’s what works (and what doesn’t) when you step into Phase 2.

The four foundational principles that can make or break Phase 2 success:

Choose pilots where the pain is real and the data accessible. For L&D, this could be non-optional compliance modules, onboarding ramp-up time, or learner-support workflows. Research shows many organisations fail because they picked overly broad or ill-defined use cases.

Without baseline data and clear targets, you cannot prove value. L&D metrics might include: completion rate, time-to-competency, learner satisfaction, or reduction in manual admin time. As one framework noted: organisations that define role-based AI metrics and track adoption are 5× more likely to succeed.

Pilots succeed when they’re not siloed. L&D must work with IT/data, business units, and change-management. The MIT CISR research emphasises the challenge of “synchronisation: creating AI-ready people, roles and teams” as a key barrier from stage 2→3.

Pilots without stories rarely translate into broader adoption. Use visuals, case snapshots, data points and internal ambassadors. In L&D, share learner narratives, manager feedback, and business leader quotes. This builds the trust needed to scale.

For senior leaders, Phase 2 is not just an experiment — it’s a low-risk way to validate AI investment. You’re not gambling millions on hype. You’re investing in evidence.

This is your moment to find out:

According to Forbes (2025), 95% of AI pilots fail because they chase technology first and business value second. Phase 2 reverses that — it starts with value, not algorithms.

For L&D teams especially, Phase 2 is a moment of opportunity. You’re no longer just “learning providers”—you become change partners in enterprise transformation.

Given that training programs in 2025 must focus on future-oriented skills (AI literacy, data analytics, digital transformation) to stay relevant.

So, L&D leaders should ask: “Which pilot can we lead that demonstrates value and paves the way for wider transformation?”

Every successful pilot you lead becomes a proof point for how L&D can drive enterprise transformation — not just support it. You’re not a bystander in this story. You’re the architect of AI readiness.

Even well-intentioned pilots can stumble. Here’s what derails most AI pilots — and how to stay clear:

The MIT research shows that the failure is rarely about algorithms—it’s about implementation, context, governance and workforce readiness. Remember, the goal isn’t to finish a pilot. It’s to learn from one — visibly, collaboratively, and strategically.

Once you’ve proven value, you’re ready for Phase 3 — scaling and institutionalizing AI literacy. This is where the lessons, playbooks, and frameworks from Phase 2 evolve into a formal AI Center of Excellence. As the World Economic Forum notes, “Scattered AI pilots are noise; scaled capability is strategy.” Phase 2 is the bridge between the two.

Every organization is somewhere on its AI journey. Some are still exploring, others are already optimizing. But the real differentiator? Who can prove impact.

Phase 2 is that proof. It’s where your teams move from saying “we’re learning about AI” to showing “we’re delivering value with it.” And if you’re in L&D, HR, or business leadership — this is your moment to lead that shift.

Because in the age of AI, it’s not the most informed organizations that win. It’s the most applied. So, stop just talking about AI literacy. Design your pilot. Measure your impact. And show your enterprise what AI can really do.

—RK Prasad (@RKPrasad)