Diagnosing the AI Readiness Gap in Professional Services

Professional services firms are rushing to adopt AI—but many are still struggling to turn pilots into profit. In an Enterprise Times podcasts Steve Brooks sat down with Raju Malhotra, Chief Product and Technology Officer at Certinia, to unpack what’s really behind the AI readiness gap and how services organisations can finally bridge it.

Malhotra challenges the often-quoted statistic that “95% of GenAI projects deliver zero return.” The real story, he argues, is that early AI initiatives were primarily designed for experimentation and learning, not hard ROI. What’s changed in just the last few months is intent: organisations now expect production-grade ROI, not proofs of concept.

According to Malhotra, that ROI comes in two forms, he said, “If you peel the onion on ROI, it really boils down to two broad categories. One category is about efficiency, and the second one is about effectiveness.”

• Efficiency – productivity gains, time savings, and the ability to deliver more within the same capacity
• Effectiveness – doing things that simply weren’t possible before, unlocking new services, revenue streams, and business models

Seeking the holy grail

This shift is forcing firms to rethink how work is done. The “holy grail” of outcome-based or value-based delivery has long been hindered by fuzzy attribution—who really created the value: the client or the consultant? AI changes the equation. Hybrid teams of humans and AI agents make it easier to track impact and credibly move towards outcome-based models.

But Malhotra is clear: AI success isn’t just about picking the right model. Many organisations over-index on tools and underinvest in data foundations and operational standardisation. High-quality, unified customer, project, and services data—structured and unstructured—is now the critical launchpad. So are well-documented, standardised processes that both humans and AI agents can reliably follow.

Certinia’s work with early adopter customers reveals three consistent themes behind successful AI initiatives:

1. A focus on ROI in production, not experiments for their own sake
2. Acceptance that delivery is becoming human + agent by design
3. A deeper commitment to data integrity and connected workflows across the sell–deliver–manage lifecycle

For professional services leaders, the message is clear: AI is no longer a side experiment. It’s a strategic lever for EBITDA, utilisation, and growth—provided your data, processes, and culture are ready for a hybrid future.

To hear what else Raju Malhotra had to say, listen to the podcast.

Where can I get it?

You can listen to the podcast by clicking on the player below. Alternatively, click on any of the podcast services below and go to the Enterprise Times podcast page.

 

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