Executive-Team AI Misalignment Is Widespread
Close to a third of organizations report that executives and day-to-day teams are misaligned on AI strategy, according to Adobe's 2026 AI and Digital Trends report conducted with Oxford Economics. Another 47% say alignment exists but is partial at best. The top driver of that gap is executive misunderstanding of AI, ahead of resistance to change.
The stakes are concrete. When leadership and build teams picture different outcomes, budgets get consumed, deadlines slip, and customers meet products that don't match what was promised. For those overseeing AI for Executives & Strategy, the gap between vision and execution is now a measurable cost center.
Agentic Infrastructure Lags the Ambition
Adobe's report also found that 42% of organizations plan to design distinct AI agent personalities for different audiences. That figure runs into a second number from the same study: 72% of organizations have tools to connect systems for generative AI use cases, but only 37% have that setup for AI Agents & Automation.
Agentic AI is the layer that actually drives a distinct agent personality. When a personality moves from a chat window to a voice interface, mobile app, or call center script, it can collapse. Even before a tone meeting is scheduled, there is a disconnect between what a brand wants and what its systems can provide.
Alignment Workshops Cut Rebuild Costs
Doug Hughmanick, founder and head of creative at ANML, a design and development agency, starts every project with a workshop for leadership and the build team. "We get leadership and the build team in the same room early and work through a few concrete questions together, what the experience should do, who owns which call, what 'done' means," he said. "Settling that upfront is what prevents the expensive rework later."
Misalignment rarely announces itself early. It appears mid-project, after the budget is spent and the deadline has passed. Fixing it then costs more than the hard conversation beforehand.
AI Personality Is an Engineering Decision
How an AI assistant sounds seems like a branding task. Hughmanick treats it as a product and engineering decision too. How an assistant sounds when it fails, how fast it responds, and what it refuses to do carry as much engineering weight as brand voice.
ANML encodes those choices as design tokens and machine-readable components that flow directly from Figma into the build. "The brand travels into the product as live rules instead of a static guideline someone reinterprets later," Hughmanick said. "That's how the personality survives contact with what's technically possible."
Launch Timing Outpaces Team Agreement
Brands designing distinct AI agent personalities are moving faster than most teams have resolved what those personalities should do. A brand announces an AI assistant with a certain tone, capabilities, and limits before the team building it has agreed on any of that internally. Customers then meet a product that acts differently than promised.
"The gap shows up after launch as an experience that doesn't match the pitch, and walking that back costs far more than the hard conversation would have beforehand," Hughmanick said. ANML settles what the experience actually does before the client announces anything.
Why this matters for executives and strategy leaders
The Adobe data points to a specific failure pattern: strategy outruns infrastructure, and leadership outruns team alignment. The fix is not more vision documents. It's structured early alignment between executives and build teams, with concrete answers to what the experience should do, who owns which call, and what "done" means. Cross-discipline teams that include strategists, designers, and engineers from the first workshop catch feasibility problems as design constraints instead of rebuilds. For executives, the cheapest moment to resolve AI misalignment is before the project starts. The most expensive is after launch.
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