With every new spiral of AI evolution, the gap between ambition and reality is getting wider, costing organizations more than they might think at first glance.
I’ve been in those meetings. While the promise of quick AI ROI excites executive leadership, the teams tasked with execution know that behind it lie complex trade-offs, gradual implementation and systems that, unfortunately, do not change overnight.
In Solvd CIO & CTO research on AI in 2025, one finding stood out: 71% of CIOs and CTOs say their executive leaders’ expectations for AI ROI are unrealistic. That’s not just a data point; it signals a trust gap. And if we don’t address this, both the strategy and the people behind it will pay the price.
Pressure only adds fuel to the fire. Nearly half of executives (46%) told us their most significant responsibility is to deliver revenue-generating AI strategies. It’s a huge mandate, but too often it turns into promised results before teams have a chance to weigh in. Timelines get crushed, bold goals get rolled out and basics like evaluation frameworks slip through the cracks.
Why the gap keeps widening
No time to align. Due to calendars being overloaded with urgent tasks, it can be difficult, and sometimes even impossible, to find time for a serious discussion of the scope of work, trade-offs and feasibility.
Perfection expectations. Leaders want flawless, deterministic results from inherently probabilistic AI. Small failures feel like big failures and weak evaluation frameworks mean teams can’t prove what’s working.
No room to experiment. Without cultural and technical sandboxes, teams can’t test, fail fast or learn safely. This is killing iteration speed.
Skill gaps everywhere. Let’s be real: AI is moving so fast no one’s caught up. Leaders, teams, entire orgs are learning in flight. This makes alignment on expectations even harder.
Overpromising. With AI hype outpacing what anyone can deliver, shaky assumptions turn into big promises and mistrust when reality hits.
Roadmaps that can’t keep up. The current pace of innovation progress makes a “year plan” outdated within three months. This makes adaptability a cornerstone.
Overlapping ownership. Complex interdependencies blur responsibility. Decisions move from one department to another, and momentum slows down.
Blind spots on defensibility. Some organizations still over-invest in custom builds instead of analyzing how long a brand-new model will be relevant.
The path from trust deficit to confidence
Closing the trust gap isn’t about lowering ambition only, it’s about setting the table, so ambition and execution work together. Here’s what I’ve seen as a good strategy to move the needle:
- Step 1. Make time to align. Don’t let the calendar dictate the vision. Carve out real space for leaders and teams to sync on what’s possible.
- Step 2. Anchor in evidence. Build strong evaluation frameworks to measure and monitor progress. As a result, it will stop being a matter of belief.
- Step 3. Keep upskilling. Keeping the fingers on the pulse of the innovation is just as critical as model choice. Teams and leaders alike need continuous learning.
- Step 4. Let roadmaps breathe. The best ones adapt as AI accelerates. Treat them like living docs, not contracts.
- Step 5. Simplify ownership. Clear accountability speeds decisions and reduces bottlenecks.
- Step 6. Create safe spaces to experiment. Give teams room to test, fail and learn without fear of blowback.
At the end of the day, defensibility isn’t about chasing every shiny breakthrough. It’s about building systems that compound in value as models improve and building trust, so leaders and teams feel confident navigating the unknown together. The companies that succeed won’t be the ones chasing every new release. They will be the ones building alignment, trust and adaptable systems, turning AI into a true competitive advantage.
