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AI Talk: How consultants are rethinking client engagements using AI 

On 9 June 2026, AI Talk host Kevin Craine was joined by Jegan Thirupathi, Director - SAP SCM Advisory and Consulting, KPMG US; Hiu Hiu S., AI Strategy & GTM | Chief AI Office, PwC; and Danielle McCormick, VP of Product Management, Global Nexis Solutions. 

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McKinsey is under pressure from clients to tie its fees to outcomes achieved — such as lower costs, higher profits or increased market share — rather than to the hours its consultants spend concocting advice designed to achieve those ends.

 

The drive to tie fees to outcomes partly reflects the way billable hours are becoming less useful as a yardstick, thanks to consultants’ own usage of AI for tasks such as data analysis and diagnosis. While billable hours, subscriptions and flat fees will always be part of the equation, the proportion of outcome-based pricing will undoubtedly expand.

 

The shift from billable hours to outcome-based pricing is nothing new, but AI is forcing it at scale. However, this shift marks a deeper change too in the consultancy-client relationship, as some of the workflows can now get done by AI-power software and clients want to see consultancies’ added value more clearly. Where consultancies can justify their contribution is the quality of judgement, which AI can’t replace.  

 

How is AI reshaping the way consultancies operate? 

AI is being used now for extracting trigger events at scale related to finance, strategy, the product or employee numbers. Then, a knowledge graph is populated with this data where it gets connected to other data points as well to see how these events impact the business operation. It’s also possible now to look at these events backwards, taking past events as predictors of future ones. AI has revolutionised information synthesis, as well as how these processes can be done at scale, providing experts with a level of specificity that couldn’t be achieved before. Unstructured data sits at the heart of this revolution, as it’s become much easier to mine unstructured data at scale. But AI helps consultants with operational and financial tasks too.  

 

However, auditability and trackability remain key criteria met through a governance layer – a hallmark of professional-grade AI. Empowered with new technologies, consultants are becoming more proactive to offer solutions before the event is triggered. The biggest challenge here is separating noise from signals. And most importantly, what consultants can bring to the table in an AI-driven world are their expert point of view, knowledge of industry benchmarks, industry best practices and a proposal for a product or service prototype based on these. They can also ensure that clients avoid AI slop and performative AI. Consultants concentrate not only on outcomes but on processes and change management as well, and the daily client meetings with the consultant are here to stay too, as the client will still want to deploy new solutions with the expert in hand, helping them pitch solutions to the board or explaining them why something has slipped through the cracks.  

 

To address the problem of shadow AI, blocking some AI platforms may solve a couple of problems, but management should also consider whether the tools allowed are fit for the workflows employees are supposed to use them for. But the importance of training staff how to make the most of available tools can’t be overstated either. The best way to ensure that employees have the right tools is to ask them what else they’d need to do a better job.   

 

The panel’s advice 

  • AI can get hold of little pockets of information that humans may overlook and thereby identify trigger events that otherwise wouldn’t be factored into decisions.  
  • No one needs more information. What we need is critical insight based on output that we can trust.  
  • What consultants can bring to the table in an AI-driven world are their expert point of view, knowledge of industry benchmarks, industry best practices and a proposal for a product or service prototype based on these.  
  • Shadow AI can’t be prevented but must be addressed to keep proprietary and customers’ data protected, as well as the risk of copyright and contract breaches.   
  • Choose AI tools that rely on credible source material and can trace the output they rely on. 
  • For the Lexis Nexis Report, click here.  
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