AI rollouts – Many companies are spending on AI but seeing weak results because adoption requires culture, workflow redesign, and leadership behavior change.
AI rollouts are being derailed long before the technology reaches the workforce.
Many C-suite leaders say AI is a priority, and Misryoum notes that the pace of investment reflects that ambition.. Yet spending alone does not translate into better productivity or clear returns.. In practice. too many organizations appear to treat AI like a software deployment led by the IT function. expecting adoption to follow automatically.. The result is familiar: low uptake, limited improvements, and “ROI” that stays theoretical.
Misryoum highlights a central issue behind these outcomes: AI initiatives are often rolled out without the behavioral and operational shift they require. Deploying AI is not just about installing tools. It demands a new way of working, new decision habits, and a workforce that is ready to change.
That is why the most frequent misstep is not choosing the wrong model or platform. but trying to use AI to speed up an existing process rather than redesigning the process itself.. Instead of asking how to do a task faster with AI. organizations are better off starting from scratch: what should humans do. what can AI do. and what activities should be avoided entirely.. Misryoum emphasizes that the workflow is where value is either unlocked or lost. especially when AI is applied to knowledge-heavy steps that benefit from synthesis and rapid insight generation.
A practical way forward is to focus on high-impact workflows rather than broad job titles or departments.. Misryoum analysis suggests that rebuilding a small set of mission-critical processes can create visible wins faster. giving teams concrete examples of what “better” looks like.. Even when the technology performs well, value often remains out of reach if the underlying work design stays stuck.
Insight: This matters because workflow redesign turns AI from a “nice-to-have” capability into a daily work mechanism. When people see outcomes quickly, adoption becomes practical rather than theoretical.
Training is important, but Misryoum notes that it rarely moves the needle on its own.. Central learning programs can be slow. and the adoption gap often persists when the organization relies on classroom-style instruction instead of sustained experimentation.. Many companies already have employees eager to learn and apply AI; the challenge is to connect them into a network and give them time. tools. and authority to lead.
Meanwhile, leadership behavior becomes the benchmark for everyone else.. If executives and managers do not use AI in their own work or reinforce expectations with accountability. skepticism tends to spread.. In this context. even internal engagement mechanisms. such as challenges or incentives for experimentation. can help normalize learning and make participation feel worthwhile.
At the core, Misryoum sees culture as the real AI strategy.. The organizations that narrow the gap between spending and impact are unlikely to be the ones with the most advanced tools alone.. They tend to build an environment of continuous learning: being honest about how roles may change. investing in development. and making it safe to experiment. including when attempts fail.
Insight: Culture reduces friction. When experimentation is rewarded and lessons are shared, AI adoption becomes an ongoing capability instead of a one-time rollout.