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Research Report
The AI Transformation 100: Concrete Ideas for Fixing How We Work
Organizations are investing heavily in AI but still struggling to generate meaningful returns, often because executives misunderstand how the technology delivers value.
Nearly one-third of companies report misalignment between leadership and frontline teams on AI strategy, and executive misunderstanding is the top driver of that gap, according to Adobe’s 2026 AI and Digital Trends report.
It’s a disconnect showing up in uneven adoption and stalled outcomes. While executives tend to focus on financial metrics like cost savings and revenue growth, practitioners report deeper integration into day-to-day workflows — and more friction in making AI systems reliable and usable at scale.
What results is a pattern of early gains that fail to translate into sustained ROI, even as organizations accelerate spending and deployment.
Table of Contents
- Misalignment Starts With Different Views of AI
- ROI Expectations Don’t Match Operational Reality
- Pilots Scale Fast — But Break Under Pressure
- Executive Misconceptions Continue to Undermine Strategy
- Fix the Workflows, Not Just the Technology
Misalignment Starts With Different Views of AI
At the core of the issue is a fundamental difference in how executives and practitioners experience AI. The report found leadership tends to view AI as a strategic lever, while operational teams deal with its behavior in production environments.
Adobe’s data reflects that divide. While around 33% of respondents said executives and practitioners are misaligned on AI strategy, a further 47% said alignment is only partial.
Practitioners are also more likely to report meaningful adoption across workflows, suggesting leadership may underestimate both progress and complexity.
- Executives focus on strategy and outcomes; practitioners focus on execution and reliability
- Day-to-day friction — edge cases, integration gaps, inconsistency — defines real adoption
- Misalignment leads to partial deployments and continued human oversight
- Practitioners often have a more accurate view of how AI performs in production
Related Article: AI in the C-Suite: More Use, More Optimism, More Uncertainty
ROI Expectations Don’t Match Operational Reality
The mismatch becomes most visible in how organizations define ROI. “Executives focus on outcomes such as cost savings and productivity gains,” said Frank Dickson, group vice president for security and trust at IDC. “Practitioners focus on effort and reliability.”
Many still rely on customer experience metrics like satisfaction and retention as proxies for success, creating ambiguity around what AI is delivering.
Vamsi Duvvuri, technology, media and entertainment and telecommunications AI Leader at EY Americas, pointed to structural challenges in how AI value is distributed. “Most AI costs don’t map neatly to a single use case — that makes costs look opaque and ROI either overstated or misunderstood."
In short:
- Financial ROI expectations often overlook operational complexity
- Efficiency gains are frequently offset by rework and oversight
- Many organizations lack formal frameworks to measure AI ROI
- Costs span infrastructure, data and operations, making value difficult to isolate
Pilots Scale Fast — But Break Under Pressure
AI continues to deliver strong early results in pilot environments, but those gains often stall when organizations attempt to scale.
The report suggested there is measurable improvement in areas such as content production (76%) and productivity (69%), however most organizations have not embedded AI across workflows.
What breaks down isn’t the technology, but the absence of a deliberately designed operating model to sustain AI at scale. In practice, that gap shows up as fragmented execution. Teams launch multiple pilots, but without clear ownership or integration, those efforts remain isolated. “Practitioners experience this as spreading effort too thin across many workflows rather than providing depth in a single end-to-end use case,” Duvvuri explained.
Among the central challenges facing organizations include:
- Early pilot success does not translate directly to enterprise scale
- Fragmented pilots create isolated gains rather than systemic value
- Lack of ownership and integration limits long-term impact
- Scaling requires a defined operating model, not just broader deployment
Executive Misconceptions Continue to Undermine Strategy
Misalignment is reinforced by persistent misconceptions about how AI works. Adobe’s report found executive misunderstanding of AI ranks as the top driver of misalignment, ahead of resistance to change or communication gaps.
“Common misconceptions include assuming AI can fully automate complex tasks,” said Dickson. “Another frequent issue is assuming early pilot success will translate directly to scaled deployment.”
Duvvuri noted a related issue: executives often confuse technical capability with organizational readiness.
Plus, he added, many leaders treat the build-versus-buy debate as a simple, binary vendor choice rather than a complex architecture decision. “They forget that most AI capabilities actually span multiple layers of the tech stack, requiring a more nuanced approach."
Related Article: The 6 Leadership Skills Your AI Investment Depends On
Fix the Workflows, Not Just the Technology
Closing the gap requires rethinking how AI is applied inside the organization, with workflow redesign as solid starting point for improving ROI. “If a process is fragmented or inefficient, AI will scale those issues rather than solve them," said Dickson.
Reframing workflows, simplifying steps and clarifying decision points often unlock more value than adding AI on top of existing practices. “That means focusing less on expanding pilots and more on integrating AI into end-to-end workflows,” added Dickson.
Duvvuri recommended prioritizing fewer, more disciplined initiatives tied to real business processes. “This approach tests the future operating model and ensures that the pilot exists with adoption, controls and economics already proven before broader scaling."
Organizations must also rethink measurement: traditional ROI models are often too rigid for AI, where value emerges across multiple layers. Metrics that reflect operational reality — such as error rates, rework and adoption — provide a clearer picture of performance than high-level productivity estimates.
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Practitioners are already integrating AI into workflows and identifying high-value use cases, while executives remain focused on outcomes that are harder to measure in early stages.