Human judgment is transforming AI services

Professional services firms are rethinking the way they deliver customer value as artificial intelligence increasingly takes over routine tasks and shifts the focus to human judgment, trusted results and new operating models.

The transition is changing the way companies structure projects, manage knowledge and measure success. Rather than simply accelerating existing processes, companies are starting to redesign workflows around AI-driven execution while maintaining the expertise and accountability that customers expect, according to Matt Cook, partner and consulting software sector leader at PwC UK, and Prasad Narasimhan Sulur, chief business officer of Certinia Inc.

“We’re seeing AI leaders win because they’re not just using better tools. They’re actually redesigning the way that value is created,” Cook said. “So it’s not just about how we can get results faster.”

Cook and Sulur spoke with Scott Hebner of theCUBE Research during an exclusive interview on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how AI is changing the economics of professional services, why governance is essential to scaling adoption, and how human expertise becomes more valuable as intelligent systems take on more responsibility. (*Disclosure below.)

AI transformation requires rethinking the way work gets done

The gap between AI adoption and measurable financial returns remains significant. “PwC’s 29th Global CEO Survey” found that only 12% of CEOs reported both revenue growth and cost reductions due to AI, while 56% reported no significant financial benefits. Cook says the results underscore the limitations of using AI as a standalone productivity tool rather than integrating it into core business operations.

“The companies that win will be the ones that can translate AI into repeatable, measurable results, not the ones that have the most tools,” he said.

This distinction is particularly important for professional services firms, where faster task completion does not necessarily translate into faster project delivery. Citing software development as an example, Sulur explained that AI-powered coding can shorten development cycles without eliminating delays in testing, integration or release processes.

To achieve meaningful improvements, the entire workflow must be redesigned rather than automating individual steps. The same principle applies to consulting engagements, where AI could help companies complete research and analysis in weeks rather than months.

Sulur also emphasized that a successful transformation depends on the collaboration of company leadership and the skills of employees. Leaders need to provide clear direction while employees develop the skills needed to use AI effectively and identify where processes can be improved.

“The combination of the top-down mandate and what I call the bottom-up competency of your organization will move the ball forward,” Sulur said.

Trust, governance and human judgment define the next AI operating model

As companies move to more consistent AI applications, reliability and accountability will become increasingly important. AI-generated recommendations can appear convincing, but contain errors that require extensive human review, potentially negating the productivity gains they were intended to provide. For professional services firms, building trust requires more than just improving model accuracy. Companies must also develop systems that provide appropriate business context, enforce permissions, and validate outputs against expected results.

Sulur identified workflow orchestration and enterprise data management as two essential components of this new architecture. Orchestration systems help AI understand project phases, required tasks and previous work, while data systems make organizational knowledge available in the appropriate context.

Unstructured information, including meeting minutes, memos, and past project documents, is a particularly valuable resource. However, organizations must determine what information is relevant to the task at hand while preventing unauthorized access to proprietary data.

Cook believes these technical capabilities must complement, not replace, professional responsibilities: “AI can accelerate evidence collection and scenario development, but it does not bring professional responsibilities,” he said. “It remains human.”

The growing importance of judgment is also changing employee expectations. Cook pointed to research from PwC that showed AI-at-risk junior positions increasingly require skills traditionally associated with senior positions, including leadership and decision-making.

Looking forward, both executives expect professional services firms will dedicate more human resources to complex client challenges as AI takes responsibility for repetitive activities. Companies that invest in trusted systems, institutional knowledge, and redesigned delivery models could gain a competitive advantage over companies that focus primarily on individual productivity improvements.

“When AI can produce more analysis than a human can read, the differentiating factor will no longer be content but better judgment,” Cook said.

The result could be a fundamentally different professional services model, where technological implementation expands capacity while human expertise determines the quality and value of business outcomes.

Here is the full video interview from SiliconANGLE and theCUBE:

(*Disclosure: Certinia sponsored this segment of theCUBE. Neither Certinia nor other sponsors have editorial control over the content of theCUBE or SiliconANGLE.)

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