The AI Transformation is a Business Model Transformation

It can be tempting to frame the adoption of generative AI as a tech initiative. Something for innovation labs, IT, or the analytics team to pilot while the rest of the business watches from the sidelines. But the professional services firms leading the charge right now know better.

Adopting AI isn't just about introducing new tools. It's about rethinking how value is created, delivered, and monetized. In other words, it’s a business model transformation.

Why cross-functional teams are replacing standalone labs

Several firms we've spoken with have reorganized their internal AI efforts away from siloed experimentation and toward integrated, cross-functional teams. One leader described forming a working group broadly with executives, consultants, engineers, and data scientists—not to develop a single AI tool, but to reimagine how the business works, as we explored in The Productization Playbook.

This kind of structural change reflects a growing understanding: AI isn’t a department. It’s a new layer in how work gets done. And more importantly, it affects every type of work. When consultants, technologists, and delivery leaders learn and iterate together, they accelerate both adoption and alignment. They also position themselves to move beyond internal productivity gains and toward real market impact.

Internal adoption builds external credibility—and unlocks revenue

There's a direct line between how convincingly a firm uses AI internally and how credibly it can advise clients on the same. One services executive put it bluntly: "If we don’t transform ourselves with AI, we look like ambulance chasers."

Firms that have prioritized internal enablement—rolling out enterprise AI tools, creating communities of practice, even hosting peer-led office hours—are now seeing new types of client demand. They’re being asked not just to deliver services more efficiently, but to help clients reimagine their workflows, roles, and even leadership development with AI in mind.

This inside-out approach unlocks revenue in surprising places. Embedding AI in executive coaching or strategy facilitation sessions, for instance, has helped some firms upskill client leaders in real-time while also demonstrating new ways of working.

From cost savings to client-facing innovation

Most firms begin with internal efficiency. That’s natural. AI is great at summarizing, drafting, coding, and synthesizing—the kind of work that often clogs the arteries of billable teams. But the most exciting shift we’re seeing is the move from internal use cases to external, client-facing solutions.

In some firms, this means spinning up new SaaS offerings or agent-driven platforms. In others, it’s about productizing repeatable workstreams and offering clients "co-pilots" to navigate complex decisions. Regardless of the format, the mindset shift is clear: the same capabilities that reduce internal delivery friction can also be packaged into scalable value for clients.  We cover how to package and commercialize products in our recent post, "Time & Materials is Dead. What Comes Next for AI-Enabled Services?"

This is not unlike the broader productization journey we help clients undertake. GenAI just accelerates it.

Navigating the real blockers: contracts, culture, and client expectations

Of course, the transformation is not without its challenges. Culture is one of the biggest. Professional services firms have long prized polish, perfection, and deep expertise. But successful AI adoption often requires something else: a willingness to experiment, iterate, and share half-baked ideas.  What is critically important is to understand how to validate the AI's output and check references to be sure that you can balance rapid iteration and imperfect solutions with making sure you do not put forward falsehoods that destroy credibility (e.g. law firms citing cases that don't exist, or experts citing research papers that don't exist are unacceptable errors).

Then there are the contractual and regulatory headwinds. Firms working with public sector clients, or those in highly regulated industries, often face barriers to embedding AI in delivery. Long procurement cycles and restrictive clauses can slow progress, even when the need is urgent.

Finally, many clients are still figuring out their own AI strategies. Some are eager and AI-native; others are wary or constrained. This creates a new responsibility for services firms: not just to sell smarter solutions, but to help clients get ready for them.

What's next?

If there’s a throughline across all our recent conversations, it’s this: the firms making the most of AI are the ones treating it as a catalyst, not a shortcut. They’re building new capabilities, not just buying tools. They’re rethinking pricing, delivery, and performance measures. And most importantly, they’re moving from seeing AI as a productivity lever to understanding it as a growth enabler.

This transformation won’t be quick. It won’t be linear. But for those willing to step up, lead cross-functional change, and reimagine how value is created, the payoff could be enormous.

Curious where your firm sits on the AI-to-productization maturity curve? Let's talk.