New research: 500 product leaders reveal the biggest blockers to scaling AI

AI is no longer a side experiment for product teams and has become an essential part of how the work gets done.

In a new airfocus survey of 500 product professionals across the UK and US, 71% of respondents said their output would be less productive if AI was removed from their workflows tomorrow. That finding sits alongside a broader adoption story where 45% said AI is already embedded in multiple core workflows, while another 20% said AI tools are foundational to how their product organization operates.

In other words, product teams have crossed the AI Rubicon and can’t simply go back to the way they worked before.

But AI adoption is not the same as maturity. The survey also reveals a more complicated picture in which product teams are using AI, relying on AI, and seeing value from AI, while still struggling with the trust, strategy, and context they need to scale it confidently.

The AI strategy paradox

One of the sharpest tensions in the research is around the manner in which product organizations are using AI strategically.

Some 80% of respondents agreed that their product organization has a clearly defined AI strategy, but more than half – 57% – also agreed that their AI strategy is only informal.

Those statements can overlap. A product organization may have executive buy-in, approved tools, and a broad ambition for AI, while still lacking a formal operating model for how AI should be used in day-to-day product work.

The data suggests many teams may be mistaking AI activity for AI maturity. They may have tools, experiments, pilots, and individual champions, but not always a shared answer to the more difficult questions: 

• Where should AI make the biggest impact? 

• What data should it use? 

• Who is responsible for its outputs? 

• How do teams know whether AI is improving product decisions, or simply accelerating work?

AI adoption means AI is present in the workflow, but AI maturity means teams understand where it creates the most value, where it introduces risk, and what context it needs to produce reliable outputs.

Trust is the biggest blocker to scaling AI

The survey also shows that the biggest barriers to scaling AI are not purely technical.

When asked about the biggest blockers to scaling AI in their product organization, respondents pointed first to trust in AI outputs, selected by 40%. Security, privacy, or legal concerns followed closely at 39%, while lack of training or time dedicated to upskilling came in at 35%. Poor data quality or lack of access to data was selected by 32%, and lack of clear use cases or defined goals by 29%.

That hierarchy tells us that the ceiling on AI maturity isn’t simply what the model can do, but whether product teams trust the output, understand where it came from, and know whether it is grounded in the right business context.

More AI does not automatically mean more signal

Product teams are already using AI in many of the areas where clarity matters most. According to the survey, 57% use AI for analytics and experimentation, 52% use it for user research and insight synthesis, and 47% use it for customer feedback and analysis.

Yet 48% also said their product organization struggles to separate signal from noise.

And this contradiction is the heart of the challenge that product teams are now facing. AI can summarize faster than humans. It can cluster themes, draft documents, and surface patterns across large volumes of information. But if the context it draws on is fragmented, incomplete, or disconnected from product strategy, it can still leave teams with more output to interpret.

In other words, as Spencer Cowley, Product Manager at airfocus, explains, “AI agents are only as useful as the context they can access.”

For product teams, the next step isn’t just using AI more often but making sure your AI tools can see the right information, understand how that information connects, and act inside the workflows where product decisions happen.

The next phase is context-aware AI

Moving from generic AI assistance to context-aware product intelligence is where the next phase of AI maturity begins.

AI is already part of the product operating mode, delivering value, changing expectations, and raising the bar for speed, but our survey shows that product organizations are still working through the harder questions of maturity around trust and strategy, data quality, skills, signal, and context.

Product teams need AI that can access the strategy, roadmap data, feedback, insights, and decisions already shaping their product work.

The product teams that win the next phase will be the ones using AI with the clearest strategy and the greatest confidence in their outputs.

Download the full report and learn how airfocus helps product teams move to more purposeful use of AI.

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The AI maturity gap:
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