Organizational Culture: The Key to AI Strategy
Every company depends on two essential units to keep its engine running: operations, the teams that manage customer interactions, sales, compliance, and day-to-day workflows; and internal technology, the teams and platforms that build and maintain the systems those operations rely on. Both are critical. Both are under pressure. And yet, despite being deeply interdependent, they often find themselves at odds.
Operations teams are tasked with speed and accuracy. They deal with client demands, regulatory deadlines, and shifting priorities. Their focus is immediate impact: resolving issues quickly, clearing backlogs, closing deals and innovating the product or service.
Internal technology teams, often CRM or platform engineers, are tasked with scalability and sustainability. They balance production support, new project builds, and long-term architecture. Their focus is keeping systems reliable while also planning and building for future growth.
The friction arises in the space between these worlds. Operations need quick solutions; technology worries about creating brittle systems. Operations submit requests without fully defined requirements; technology responds with solutions that don’t solve the real workflow problem. Leadership pulls each group in different directions, forcing them to innovate and compete for capacity. And while none of these gaps are the fault of a single team, every team contributes, through the way they are structured, the priorities they juggle, and how they collaborate (or don’t). The result? Unsafe workarounds, compounding inefficiencies, and problems ignored until they become too big to avoid.
For years, companies have tolerated these inefficiencies and even built workarounds for them. But AI changes the stakes. Artificial intelligence introduces a new speed of change and a new power of production, magnifying both strengths and weaknesses. When foundations are solid, AI can supercharge productivity. When processes are broken, AI just accelerates the dysfunction.
This is one reason why many AI projects stall. As the famous MIT study on the State of AI in Business 2025 found, most GenAI pilots fail to progress due to weak change management, lack of executive leadership support, underwhelming results, and resistance to adopting new technology (The GenAI Divide State of AI in Business 2025). Leaders often issue directives to “implement AI” without weaving it into strategy, culture, and structure. They underestimate the need to change not just tools, but the matrix of their organization.
The optimistic reality is that these challenges are solvable. With deliberate alignment of people, processes, and technology, companies can reduce the friction between operations and internal tech, creating the cohesion required for AI adoption to succeed. Implementing an AI strategy is ultimately an exercise in organizational change and adoption, which is driven by culture. The organizations that thrive in this new era will be the ones that rethink their internal ways of working, not just to survive AI-driven change, but to use it as a multiplier for innovation and resilience.
I created CoEvo Consulting to help organizations work through these challenges and pave the runway for AI to elevate them. After more than a decade leading internal technology teams, redesigning CRMs and automating workflows, and more recently studying AI strategy at MIT, I now help organizations bridge these gaps. Through CoEvo, I partner with leaders to:
Audit internal technology and workflows to uncover inefficiencies
Design automation strategies that scale across teams
Build AI adoption roadmaps with guardrails for governance and culture
Because in this era of intelligent technology, success doesn’t come from chasing the newest tool. It comes from strengthening the foundation that makes those tools work.
