Professional services companies have seen their stock prices fall 15-25% over the past month. Investors believe the code generation capabilities of the latest AI platforms — Claude, ChatGPT, Kiro — will force down revenues and reduce opportunities for new business.

I completely agree with the concern. Last year, I would have needed to hire a team or two from an outsourcing vendor to deliver IT projects that took months to complete. Today, leveraging AI code generation, the same work is done with 20% fewer people in a week.

The professional services companies that learn to leverage these tools first will sharply drop their pricing, gaining business from slower competitors — albeit at a lower revenue scale. Laggards will see their businesses fail. Add to this the ability for companies to leverage their own existing staff, armed with AI code generators, to supplant professional services firms entirely, and you have a recipe for reduced revenues and profits across the industry.

But many are missing a potential silver lining: the same AI reducing existing revenue streams is about to unlock a massive backlog of work that was previously impossible to justify.

There is tremendous pent-up demand for the cleanup, renovation, and replacement of legacy solutions across the IT landscape. Projects that organizations would very much like to execute have long lacked sufficient ROI to justify the expense. AI platforms are changing this equation by slashing the time and cost of these projects, bringing their ROI profiles firmly into the positive column.

Example 1: Legacy Mainframe Migration

In several organizations where I have worked over the past 30 years, there was a strong desire to eliminate legacy mainframes. I personally created business cases to accomplish this goal, only to see project after project postponed indefinitely due to poor ROI.

In one recent case, there was strong senior management support to eliminate a mainframe and move its functionality onto a modern technology stack. The mainframe carried an annual runtime cost of $14M, and we felt confident we could reduce that by $8M per year. We ran an RFP with several professional services firms. The best option we found was a timeline of 2.5-3 years at a cost of $18M.

Factor in the risks — project delays, cost overruns, and the regulatory and reputational risk of mis-translating a critical solution — and no one could justify it. I ended up outsourcing the mainframe and its support to a third party, saving $4.8M per year as a consolation prize.

AI platforms are particularly well-suited to translating old code and legacy technology into modern stacks. If that same project could be completed in a year or less for $2-3M, the ROI becomes a no-brainer. What was once a stalled initiative becomes an easy approval.

Example 2: Cloud Migration Strategy

Cloud migration strategies take one of two basic forms: Lift and Shift, or Clean-up and Shift. As the name implies, Lift and Shift moves applications from on-premise to the cloud in their current state, with minimal modifications. Clean-up and Shift requires that applications be renovated to adhere to certain technology stacks, protocols, and frameworks prior to being moved to the cloud.

I have always favored the first approach, arguing that application renovation is a completely separate decision, independent of where an application should be housed. Only when Clean-up and Shift is demonstrably cheaper than Shift and Clean-up — a condition I have never encountered — does the second option make more sense.

Proponents of Clean-up and Shift argue that since a new cloud environment is being created, it should be kept tidy rather than re-establishing the messy on-premise environment in the cloud. The problem with this approach is that the ROI of renovation often exceeds the savings generated by the cloud environment so that many organizations never achieve a significant migration. The debate stalls, projects are deferred, and the legacy mess remains.

In many organizations, this has been a difficult and prolonged debate to resolve. Once again, AI platforms change the equation substantially. By dramatically reducing the time and cost of application renovation, AI removes the ROI barrier that has historically made Clean-up and Shift impractical. Organizations that were previously stuck choosing between an imperfect migration and no migration at all now have a genuinely viable third option: renovate affordably, then migrate cleanly.

Example 3: Duplicate Solution Cleanup

The mantra in most organizations where I have worked seems to be: "Why buy one solution when you can buy five at eight times the cost?" Most medium and large organizations carry a large number of duplicate solutions in their landscape.

Even when organizations conduct a solution landscape assessment that clearly identifies duplications and their annual cost, there is often reluctance to eliminate them. Once again, poor ROI is a major factor — the cost of migrating users, application data, and decommissioning the old solution, frequently outweighs the savings.

AI platforms shift this ROI into positive territory, enabling more of these duplicates to be eliminated and real budget savings to be realized. Unlike the first two examples, this one carries the added complexity of a significant change management process — users of the eliminated solution must adapt to the replacement. An added wrinkle, but not an insurmountable one.

The Strategic Opportunity for Professional Services Firms

These three examples represent only a fraction of the projects that were previously unsupportable due to poor ROI and that now become not just doable, but desirable, thanks to AI. Across most large organizations, there are dozens of such initiatives sitting in the backlog — stalled not for lack of desire, but for lack of economic justification.

Professional services companies should focus their strategy on this emerging body of work. The pie may or may not be shrinking — but it is certainly being reshaped. The firms that recognize this shift early and position themselves as AI-enabled modernization specialists will find new revenue to replace what is being lost to automation and client self-sufficiency.

The market is pricing in the threat. It has not yet priced in the opportunity.

Are you seeing this dynamic in your organization? I'd love to hear from professional services leaders and enterprise IT executives in the comments.