AI has become a standard part of product development, with just 4% of product teams reporting they don’t use AI at all. Yet, beneath this uptake lies a more complex reality – while AI is everywhere, confidence in its outputs is not. This broader trend is reflected in airfocus by Lucid’s AI Maturity Gap Report, which highlights that while AI adoption is accelerating, many organizations still lack the operational foundations needed to realize its full potential.
A growing number of organisations are discovering that, after deploying AI tools, the real challenge is generating insights teams can trust, interpret and act upon consistently. This emerging disconnect signals a new phase in AI maturity, where success depends whether teams are aligned around the data, priorities and decision-making frameworks that shape AI recommendations.
Why alignment matters more than adoption
For the past few years, the focus has been on embedding AI into workflows as quickly as possible. Whether in customer research, analytics or feedback processing, often treating adoption itself as a marker of progress.
Despite this adoption, nearly half (48%) of product teams say they struggle to separate meaningful insights from the noise, according to the AI Maturity Gap Report. When combined with the fact from Bain’s research that shows writing and testing code now accounts for just 25-35% of the idea-to-launch journey, it is clear the primary bottleneck in product development has moved upstream towards prioritisation, alignment and strategic judgement. As AI speeds up execution, the decisions that happen before development begin to carry even greater weight.
Teams that consistently make better product decisions share the same understanding of customer needs, business priorities and success metrics. AI strengthens those organisations because it operates on a common foundation rather than disconnected information.
The trust gap in AI outputs
Despite the sophistication of modern AI tools, 40% of product leaders still cite a lack of trust in AI outputs as a top blocker for scaling its use effectively. Similarly, concerns around security (39%) and data quality (32%) both hinder AI use across enterprises.
AI systems can aggregate vast amounts of data, from user feedback to behavioural analytics, but they do not inherently provide context. When that data is fragmented across systems or poorly structured, the outputs become harder to interpret and verify.
The consequence is ‘analysis paralysis’, where more data does not lead to better decisions but instead creates uncertainty. Teams may hesitate to act, or default to instinct over insight, limiting the value AI can deliver.
Building trust in AI starts with creating reliable foundations. Think: connected data, shared product context and transparency into how recommendations are generated. When teams can trace outputs back to a clear source of truth and evaluate them against shared criteria, they are far more likely to trust and act on AI-driven recommendations.
Turning insights into action
One of the most persistent barriers organisations face in maturing their use of AI is disconnected data sources and fragmented workflows preventing them from turning insights into action.
This is where strong product management becomes increasingly valuable. Product management principles provide the structure that gives AI the context it needs to generate reliable recommendations. As execution becomes faster, the real competitive advantage lies in how effectively organisations prioritise and align around what to build next.
Rather than treating AI outputs as standalone recommendations, teams can integrate them into structured decision-making processes. This includes linking insights directly to business objectives and strategic initiatives, applying consistent prioritisation frameworks and creating feedback loops that continuously refine outputs.
Crucially, this approach shifts AI from being a source of isolated recommendations to a tool that supports repeatable, scalable decision-making systems. It also reduces reliance on individual judgement, replacing it with shared processes that can be applied consistently across teams.
Closing the gap
As AI adoption reaches saturation, closing the gap between using AI and trusting it will require deliberate effort. Organisations must focus on building trust, improving data foundations and creating systems that turn insights into action. Those that succeed will be those that have the greatest clarity and alignment needed to turn AI into a trusted partner for decision-making.
