TL;DR

• You're not alone—but that's not comforting. 88% use AI, but only 39% report bottom-line impact. Just 6% report significant value.
• The problem isn't the technology. It's ambition, workflow approach, and leadership engagement.
• Ambition gap: Organizations seeing results are 3.6x more likely to aim for transformative change (50% vs. 14%). They pursue efficiency AND growth AND innovation—not just efficiency.
• Workflow gap: They're 2.8x more likely to have fundamentally redesigned workflows (55% vs. 20%).
• Leadership gap: They're 3x more likely to have senior leaders actively engaged—not just funding, but championing and role-modeling.
• What to do: Raise ambition, redesign (don't optimize) workflows, re-engage leadership, define human-in-the-loop processes, shore up foundations.

You've made real investments in AI. Chatbots, automation, pilots that showed promise. But when the board asks about ROI, the conversation gets uncomfortable. The experiments were interesting. The results? Less clear.

If this sounds familiar, you're in good company—but not the company you want to be in.

McKinsey's latest research (November 2025) puts a fine point on the problem: while 88% of organizations are using AI, only 39% report any measurable impact on their bottom line. And the bar for "high performance" is stark—just 6% of respondents say their organizations are seeing more than 5% of EBIT from AI and report "significant value" from their investments.

Six percent. That leaves 94% investing time, money, and energy without clear returns.

So what's going wrong? It's usually not the technology.

The Three Patterns That Kill AI Initiatives

When we work with organizations stuck in experimentation mode, the root cause is rarely the technology itself. It's usually one of three things.

Pattern 1: The ambition is too small

Most companies approach AI primarily as an efficiency play: reduce costs, automate manual work, do more with less. Those are legitimate objectives, but they're not enough.

The data here is striking:

And it's not just ambition—it's what they're ambitious about:

Organizations seeing results pursue efficiency, growth, AND innovation. Those that aren't mostly just pursue efficiency.

Pattern 2: AI is being bolted onto existing processes

The instinct is understandable. Take what you're already doing and make it faster or cheaper with AI. But this approach has a ceiling. You end up with incremental improvements at best, and often just added complexity.

Key finding: Organizations seeing results are 2.8x more likely to have fundamentally redesigned their workflows—55% versus just 20% of those that aren't.

Workflow redesign isn't just correlated with success—it's one of the strongest predictors of it. The difference isn't adding AI to existing processes. It's rethinking how work gets done from the ground up.

Pattern 3: Leadership engagement drops off after the kickoff

AI initiatives often start with executive enthusiasm and budget approval, then get handed off to IT or innovation teams to figure out. But sustained success requires ongoing leadership involvement—not just funding, but active championing, role modeling, and strategic guidance.

Organizations seeing results are 3x more likely to strongly agree that senior leaders demonstrate ownership of and commitment to AI initiatives.

These aren't passive sponsors—they're actively engaged in driving adoption, including using AI themselves.

The Practices That Correlate with Results

Beyond ambition and leadership, specific practices show up consistently in organizations seeing measurable impact. The research identified several that matter most:

Investment levels also diverge: Organizations seeing results commit more than 20% of their digital budgets to AI at 3x the rate of those that aren't (roughly one-third vs. 7%).

That investment correlates with scaling: about three-quarters of organizations seeing results have scaled or are scaling AI, compared with roughly one-third of those that aren't.

Getting Unstuck

If your AI initiatives have stalled, the path forward usually isn't "try harder at what you've been doing." It's stepping back to address the underlying issues.

Five moves to make:

1. Raise the ambition. If your AI strategy is purely about efficiency, expand it. What could AI make possible that wasn't possible before? How could it change your competitive position?
2. Identify workflows to redesign, not optimize. Pick one or two processes where AI could fundamentally change how work happens—not just speed up existing steps.
3. Get leadership re-engaged. Not just informed—actively involved in defining what success looks like and removing barriers. If leaders aren't using AI themselves, start there.
4. Define when humans stay in the loop. Build clear processes for validating AI outputs in high-stakes situations. This builds trust and prevents the costly errors that can derail adoption.
5. Shore up your foundations. Clean data, documented processes, flexible infrastructure. These aren't glamorous, but they determine what's possible.

The Hardest Question

Here's the counterintuitive finding from the research: organizations seeing the most value from AI actually report more negative consequences—issues with inaccuracy, IP concerns, regulatory compliance. That's not because they're doing it wrong. It's because they're using AI for consequential work with real stakes. They're also actively mitigating those risks.

But that finding points to something uncomfortable for organizations that have stalled: if you've been investing in AI for two years and can't point to clear business impact, more investment probably isn't the answer.

The question you need to ask isn't "how do we accelerate?" It's "should we keep going in this direction at all?"

That's not defeatism. It's honesty. Sometimes the right move is to stop, acknowledge that the current approach isn't working, and start over with fundamentally different assumptions:

• Different goals (transformation, not just efficiency)
• Different scope (workflow redesign, not bolt-on automation)
• Different leadership engagement (active involvement, not passive sponsorship)

Abandoning an approach that isn't working isn't failure. Continuing to invest in it is.

If you're stalled, the path forward probably isn't doing more of what you've been doing. It's having the harder conversation about whether to change direction entirely.