IT firms grapple with deal pricing complexity in the age of AI agents

As AI agents begin working alongside humans, IT services firms are rethinking time-and-material contracts, experimenting with outcome-based and hybrid pricing models

The infusion of artificial intelligence (AI) agents across workflow processes — to improve productivity and efficiency — has brought in a new set of challenges for information technology (IT) companies. They are grappling with a new pricing model, which incorporates the value of a human and an agent who would work adjacently in projects, going ahead.

 

It is still early days of enterprise AI adoption, which has lagged expectations. And, most of the impact is seen in areas such as improving customer experience, automating processes, code generation, and upskilling.

 

However, analysts say that the traditional pricing model, which has long relied on time and material to determine how much a client pays per employee, needs to be tweaked. This comes as agents improve in performance and become more integrated in enterprise systems.

 

The first to talk about this was Mohit Joshi, chief executive officer (CEO) and managing director (MD) of Tech Mahindra, when he announced the Q3FY26 results.

 

In collaboration with research and advisory firm Forrestor, Tech Mahindra has come up with a white paper that looks at a pricing model. It incorporates human efforts and the increasing efforts of digital labour.

 

Last week, Joshi said the company is looking at using some of the parameters discussed in the paper as the base to price for some of the large programs it is winning.

 

The model distinguishes between human and digital labour, where the latter is based on token consumption.

 

Explaining the token model, Joshi said: “When we talk about token consumption, some of these tokens will be consumed by the models that we have built. Hence, we will get the benefit of those. Some of these tokens used could be built by clients or a third-party model and we will be charging a mark-up wherever appropriate on a third-party model. The idea is to bring transparency for the client on what we are charging for human labour and what is being done by digital labour.”

 

Forrester said a core challenge lies in aligning pricing models with value as AI agents become more autonomous.

 

“And, AI agents have yet to prove their value across all use cases; it’s not always evident that an LLM-infused agent is cost-effective for document automation when a simpler machine learning model could suffice. The industry is grappling with how to quantify and monetize AI agents in a market still defining their true utility,” said a white paper recently published along with Tech Mahindra.

 

“That could be one great model but unless we start winning deals and revenue starts to flow, we will have to think of some other metrics. I don’t think anybody has come up with a metric which is candidly credible and auditable,” Joshi told analysts when asked if the company was planning to disclose AI-specific revenue.

 

Essentially, the new model will be more outcome driven with improved agents set to bargain for better pricing and thus better margins. When humans and AI agents work together, pricing can no longer be anchored to effort or headcount. Time and material breaks the moment an agent can replace or augment dozens of human hours instantly.

 

“Value that we create will drive the billings, so a lot of that will be based on the traditional way and a lot of it will change as the overall AI market develops," Infosys

 

CEO Salil Parekh believes.

 

Most large deals, therefore see a combination or hybrid approach where humans and agents are priced separately, as a blended team rate of human plus agent or a combination.

 

“You will also find within the same client multiple models co-existing. Some fixed fee work for managed services, outcomes linked to savings, and for newer build creating pods that combine humans + agents. We expect this multi-model pricing environment to persist in the future,” said Jimit Arora, CEO of Everest Group.

 

This, analysts say, is done in three phases. First, firms establish a baseline economics model. Providers and clients agree on today’s cost, cycle time, error rates, and revenue or productivity impact before AI is introduced.

 

Second, pricing moves to a unit-of-outcome construct, not a unit of labour. Instead of FTEs or hours, deals are priced around metrics such as cost per transaction. And third, providers increasingly use gain-share and risk-share bands. A base fee covers platform, governance, and minimum service levels. Upside is shared when agents materially improve speed, quality, or scale.

 

“The hardest part is not pricing the technology. It is building enough trust and data transparency to agree on outcomes and attribution. Firms that can credibly measure value, not effort, will win. Those that cling to time and material will see margins and relevance erode very quickly,” said Phil Fersht, CEO of HfS Research.