I like pancakes.

No, this article has nothing to do with pancakes. I woke up thinking about pancakes and decided to write about teamwork instead. That probably says something about how my brain works, but stay with me.

Pancakes are simple. Flour, eggs, milk, and heat. And definitely blueberries. You mix, you pour, you flip. Most people can make them. Fewer people make them well. Temperature matters. Timing matters. Ratios matter. You can have all the right ingredients and still end up with something flat, rubbery, or burned.

AI initiatives feel similar.

An executive announces a bold AI strategy. A task force forms. A vendor pitches. A pilot launches. A few months later, momentum fades. Not because the model failed. Not because the infrastructure collapsed. The effort stalls because no one is aligned on the actual problem worth solving.

Leaders focus on capability and skip coordination. The better question is not what AI can do, but who needs to shape how it gets used. IT cannot answer that alone. Data science cannot either. Marketing, product, operations, customer success, and finance all influence the outcome. The way those groups work together determines whether AI becomes a lever or another stalled experiment.

Silos Were Already There. AI Exposed Them.

Organizational silos existed long before machine learning became a board-level topic. Marketing and product chase different priorities. Sales and customer success measure success differently. Engineering builds what the roadmap demands, often without context.

AI magnifies the cracks.

Marketing licenses a generative content tool. Customer service rolls out a chatbot. Product experiments with predictive analytics. Each group moves quickly and independently. Soon, the company runs a collection of disconnected systems trained on different data sets, each optimized for different metrics.

Teams celebrate progress. Customers feel confused.

I have watched this happen across organizations, large and small. A customer success team launches an AI-driven health score. Meanwhile, the product team builds a churn model from another dataset. Both initiatives look impressive in isolation. Neither team coordinates with the other. Customers receive conflicting messages and redundant outreach. Trust erodes quietly.

Technology did not create the misalignment. It made it visible.

AI Is Not a Department

Too many leaders treat AI as a function to centralize. They build an AI center of excellence or hire a single expert and expect transformation to follow. Capability does not live in a department. It runs through the entire organization.

Strong AI execution starts with a shared definition of the customer problem. Before prompts get written or models get trained, the right people need to answer a simple question together: what outcome matters, and for whom?

Customer-facing teams understand friction. Product leaders understand trade-offs. Data teams understand signal and noise. Executives understand financial impact. Real alignment begins when those perspectives meet early rather than collide late.

Alignment does not slow progress. It prevents rework.

A Practical Collaboration Model

After decades of leading cross-functional programs at Google, Disney, and Fox, one pattern repeats. Teams succeed when they anchor AI in outcomes and shared accountability.

Start with the customer outcome.
Define the friction clearly. Avoid language like “we want to use AI to automate.” Replace it with “Customers struggle with X, and if we reduce it, we improve Y.” Clarity forces collaboration because no team owns the entire journey.

Tie teams to shared metrics.
Individual KPIs create defensive behavior. Shared outcomes create cooperation. When product, data, and customer-facing teams all measure success the same way, alignment stops being optional.

Distribute capability, coordinate lightly.
Centralized AI groups often create bottlenecks. Instead, build literacy across teams. Establish common data standards, evaluation criteria, and review rhythms. Create connective tissue rather than command centers.

Treat governance as an operating principle.
Governance should shape design from the start, not appear as an approval gate at the end. Clear standards define what responsible deployment looks like. Teams move faster when boundaries are visible and practical.

The Leadership Shift AI Requires

Cross-team AI work also demands a shift in leadership posture. Executives cannot delegate alignment. They must model it.

When leaders ask only about speed and savings, teams optimize locally. When leaders ask how initiatives connect to the broader customer journey, teams widen their view. The questions at the top ripple downward.

Budget decisions reinforce behavior. If every department funds AI separately, fragmentation follows. Pooled investment tied to shared outcomes sends a different signal. It communicates that the organization values integrated impact over isolated wins.

Communication cadence matters as well. Quarterly business reviews should not treat AI as a slide at the end. They should surface cross-functional dependencies, highlight shared metrics, and expose gaps in understanding. Transparency builds pressure in the right direction.

Cultural norms matter most. Encourage teams to challenge assumptions outside their lane. Invite customer-facing teams into technical planning sessions. Bring engineers into customer debriefs. When people see the downstream effect of their decisions, alignment improves naturally.

The Human Advantage

As AI systems grow more capable, human skills become more valuable. Negotiation. Empathy. Translation between engineering language and business impact. Judgment about when a technically elegant solution misses the real issue.

Cross-functional collaboration does not require everyone to learn model architecture. It requires people to listen well and challenge assumptions. The product manager who hears customer frustration every week carries insight no dataset captures fully. The engineer who understands model limitations prevents overreach. Both voices matter.

Organizations win when they elevate those conversations.

Stop Optimizing in Isolation

If you are leading an AI initiative, ask yourself a direct question: Does every team involved understand why the work matters and how it connects to others? If not, the gap is cultural, not technical.

More models will not fix that.

Companies that treat AI as a shared capability, grounded in customer outcomes and reinforced by cross-team accountability, turn experimentation into advantage. Companies that optimize in isolation create sophisticated systems that solve disconnected problems.

The difference rarely sits in the algorithm. It sits in the room.

And no, pancakes still have nothing to do with it.

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Chris Hood is an AI strategist and author of the #1 Amazon Best Seller Infailible and Customer Transformation, and has been recognized as one of the Top 30 Global Gurus for Customer Experience. His latest book, Unmapping Customer Journeys, will be published in 2026.