Executives are overstating their AI maturity, and it’s slowing real progress

Executives believe AI is embedded across their product lifecycle. Managers aren’t so sure. In new research from Modus Create, 550 product and technology leaders share how AI is reshaping product development as it moves from experimentation to deeper integration. The data reveals a widening gap between perception and day-to-day reality. Read the full report here.

The signals of innovation change every few years.

A decade ago, having a smartphone app meant you were cutting-edge. Five years ago, blockchain dominated boardroom conversations. Today, the spotlight has moved again. No prizes for guessing where.

AI integration is quickly becoming a signal that an organization is keeping pace with the times. 95% of organizations plan to modernize their legacy infrastructure, with enabling AI and analytics emerging as the leading driver. But the research suggests many organizations may be earlier in their integration journey than they believe, meaning perception is already running ahead of reality.

The AI maturity mirage

84% of decision-makers say their organizations have either fully or partially integrated AI across the product development lifecycle. But dig a little deeper, and the picture begins to shift.

Surprisingly, only 28% report using AI for prototyping, 38% for coding features, and 49% for customer research. Many organizations may equate AI integration with tool usage, even when foundational workflows remain unchanged.

This isn’t deliberate misrepresentation. For organizations, “AI integration” can mean using generative assistants like ChatGPT, Claude, and Gemini, or isolated automation tools. But just because you’re using a generative AI tool to brainstorm ideas or get suggestions doesn’t mean you have integrated AI into your product workflows. You need to redesign your workflows so AI is embedded in how decisions are made, code is written, features are validated, and outcomes are measured.

Those closest to delivery see a different reality

AI maturity exhibits one of the starkest role-based perception gaps. Over half of executives believe AI is fully integrated across their product lifecycle, compared with just 18% of managers—a more than 3x difference.

While 56% of executives believe AI is embedded across the product lifecycle, only 31% of VPs and directors and just 18% of managers agree.

For the C-suite, AI progress is visible through investment decisions, approved roadmaps, and adoption metrics. If it has declared AI a priority, funded initiatives, and integrated tools into parts of the lifecycle, maturity indeed appears real.

The picture changes as you move closer to delivery. Managers are not evaluating AI based on announcements or budgets; they are evaluating it based on friction. So, when governance slows down AI integration, or AI-generated insights can’t be operationalized without manual delivery, claims of maturity start feeling overstated.

When AI maturity is overestimated, ROI pressure accelerates

When leaders believe their organizations are already AI-mature, expectations shift quickly from experimentation to outcomes. 90% of product leaders feel the pressure to prove ROI from AI investments has intensified.

While emphasis on ROI is healthy (no one needs AI for AI’s sake), doing so prematurely can cause organizations to over-index on short-term gains and declare success (or failure) prematurely.

When expectations outpace operational readiness, organizations often shift too quickly from experimentation to ROI. But as the research shows, fast ROI is typically driven by fundamentals—strong data foundations, clear alignment between strategy and delivery, and modernized systems—not simply by the presence of AI tools. Pushing for outcomes before those capabilities are in place can distort priorities and slow meaningful progress.

AI is a stress test for operational discipline

Organizations that rush to profess AI maturity miss out on the golden opportunity AI integration presents, an opportunity to strengthen operational discipline. AI delivers strong returns only when product teams operate with discipline: clean data, clear ownership, and alignment between strategy and delivery.

When those foundations are weak, AI tends to expose the gaps rather than close them. Incomplete data leads to unreliable outputs. Unclear priorities scatter use cases across the organization. And misaligned teams experiment in isolation, giving the illusion of AI maturity.

Companies that scale AI successfully approach it differently. They use the push toward AI as a catalyst to clean up data pipelines, clarify ownership, and simplify workflows. In other words, they focus on the "boring" part of AI rather than flashy implementations. And in the process, they improve operating discipline as much as their technology stack.

This blog features findings from our latest report, AI in product development: A reality check, a comprehensive study of how 550 product and technology leaders are actually deploying AI in their organizations. Access the full report here.