Rushing AI, Missing ROI: 500+ Leaders Reveal What's Slowing Product Innovation

Rushing AI, Missing ROI: What 500+ Product Leaders Say Is Slowing Innovation

A new independent study from Modus Create and Ascend2 surveyed 500+ leaders across the U.S. and Europe to understand how product teams are moving from AI experiments to integration. The headline: most teams say they've integrated AI, but the work that drives real outcomes is still spotty.

Below is a clear readout of the data, what it means for product development, and a practical plan to move faster without burning ROI.

The gap between "AI integrated" and AI that ships value

• 84% of companies report AI integration, yet only 28% use AI for prototyping and 38% for coding.
• 90% say pressure to prove ROI has intensified - speed without outcomes isn't cutting it.

"Teams have figured out how to move fast. But they need to understand how that speed connects to business value. Boards and investors aren't asking how many releases you did this quarter - they're asking what it delivered," said Sharon Lynch, Chief Executive Officer at Modus Create.

Data and cloud are the real on-ramps

• 95% are modernizing legacy systems, with a majority already operating on cloud infrastructure.
• Translation: without clean, accessible data, AI stays stuck in demos. Cloud-first data platforms are now table stakes for production AI.

Governance is the brake - and the missing operating system

• 76% say regulatory or ethical issues are slowing AI enablement.
• Many have basic monitoring, but few have model risk frameworks or formal ethics oversight.

Don't treat governance as paperwork. Treat it as enabling architecture: clear use-case approval paths, model/version registries, evaluation protocols, incident response, and human-in-the-loop thresholds. If you need a reference point, the NIST AI Risk Management Framework is a solid baseline.

Teams are being rebuilt - skills over cuts

• 91% have restructured product functions.
• 53% are reskilling developers; 43% are hiring ML engineers or data scientists; only 30% reduced traditional software-engineering roles.

The pattern is clear: keep core engineering, upskill for AI, and add data/ML depth where needed. Pair PMs with data leads early to keep problem framing and measurement tight.

Partnerships are rising as the skills gap widens

• 60% say finding the right partner is extremely important (up from 51% in 2023).
• Look for partners who bring both product thinking and MLOps discipline - not just model vendors.

What this means for product development leaders

The fastest path to ROI is boring on purpose: pick narrow use cases, land measurable wins, then scale. Treat AI as a capability in your product system - not a feature sprint.

• Clarify outcomes before tooling: define the single metric that matters (e.g., time-to-prototype, cycle time, activation rate, cost per ticket).
• Instrument baselines: measure the current state so you can prove uplift within two release cycles.
• Choose production-worthy use cases: prototyping assistance, test generation, code suggestions, retrieval for support, content QA - all with clear decision gates.
• Build a thin governance layer: policy for data use, PII handling, model evaluation, and human oversight. Keep it lightweight and auditable.
• Invest in MLOps early: model registry, prompt/version management, evaluation harness, and feedback loops.

30/60/90-day plan to move from experiments to outcomes

• Days 0-30: Pick 3 use cases with measurable upside. Define success metrics and guardrails. Audit data paths to ensure availability and compliance. Stand up a lightweight model/prompt registry. Align PM, Eng, and Legal on an approval path.
• Days 31-60: Ship two pilots into a prod-like environment. Add automated evals to CI. Roll out coding/prototyping copilots to a small cohort. Publish a simple AI usage policy and review cadence.
• Days 61-90: Scale the winning pilot; sunset the laggard. Tie OKRs to outcome metrics. Expand access, add human-in-the-loop checkpoints, and document the operating model (RACI, incident response, rollout checklist).

Team moves that pay off fast

• Pair each PM with a data lead to keep problem statements, datasets, and metrics aligned.
• Reskill developers on prompt patterns, toolchains, and evaluation best practices. A focused path like the AI Learning Path for Software Developers can speed this up.
• Codify roles for model owners, data stewards, and product reviewers to reduce approval friction.

Avoid these common failure modes

• Feature-first thinking: shipping AI features without a scorecard for value.
• Shadow data pipelines: prototypes built on datasets that can't ship.
• Governance theater: policies with no automation in CI/CD or monitoring.
• Tool sprawl: too many vendors, no shared evaluation or versioning.

Where to go next

Use the study's message as a filter: speed is easy; value is the work. Start with the use cases that tighten your product loop - prototyping, coding, testing, and support - and wire in metrics from day one.

For more practical insights on applying AI across the product lifecycle, see AI for Product Development. Product leaders building their own skills can use the AI Learning Path for Product Managers to level up on ROI-driven planning and governance.