HFS Roundtable: The AI pricing reset: Agents, tokens, and outcomes

Agentic AI is beginning to break one of the fundamental assumptions underpinning the business and IT services industry: that people, time and effort are reasonable proxies for value.

As AI agents take on work previously performed by provider teams, forward-deployed engineers command premiums for scarce expertise, and enterprise knowledge is increasingly codified into reusable AI assets, traditional FTE, time-and-materials and even fixed-price constructs are becoming harder to defend. Yet simply replacing people-based pricing with tokens or consumption creates a different problem: enterprises risk paying for activity rather than business value.

So, what should enterprises actually buy and pay for in the AI era?

On October 6 in New York, HFS Research and EXL will bring together a select group of senior enterprise technology, AI, operations, finance, and sourcing leaders for a candid discussion about the next generation of commercial models for business and IT services.

The discussion will be informed by new HFS research into how enterprise buyers are rethinking the economics of AI-enabled services, including the balance between fixed fees, consumption, transactions, scarce expert talent, and measurable business outcomes.

From selling capacity to selling value:

We will explore:

What replaces the FTE? As humans and AI agents increasingly work together, should enterprises pay per person, process, transaction, agent task, token, outcome, or some combination of these?
Are tokens really a commercial model? Consumption pricing may make AI economics visible, but does paying for tokens or agent activity simply create a new version of paying for effort rather than results?
What is scarce expertise worth? Forward-deployed engineers, model specialists, and domain experts can be critical to making AI work inside complex enterprises. When is premium named-resource pricing justified, and what should buyers demand in return?
Who owns the value created from enterprise context? As providers codify institutional knowledge, decision rules, and expert judgment into models, golden datasets, evaluation frameworks, and reusable AI assets, who owns the resulting intellectual property and how should that value be reflected in the commercial model?
Can outcomes finally become the unit of value? Gain-share and outcome pricing have been discussed for decades. Does AI make outcomes easier to measure and attribute, or does the increasingly complex technology stack make attribution even harder?
What does the hybrid commercial model look like? Could the emerging answer combine a foundation or subscription fee, consumption-based economics and an outcome-linked upside/downside rather than relying on one pricing mechanism?

What you will leave with:

A clearer view of how leading enterprises are approaching the shift from labor-based to AI-native commercial models.
Practical perspectives on where fixed, consumption, transaction, and outcome-based pricing each make sense across the AI services stack.
A framework for thinking about the economics of human expertise, AI agents, enterprise context, and measurable outcomes as distinct sources of value.
Peer insight into what procurement, finance, technology, and business leaders will need to change if commercial models are to keep pace with AI-enabled delivery.

Who should attend:

CIOs, CTOs, CAIOs, and senior enterprise leaders across technology, operations, finance, procurement, and sourcing who are responsible for AI-enabled services, commercial strategy, and provider relationships. Seats are limited to preserve candid, off-the-record discussion.

Venue: The Apella

450 E 29th St, NY

HFS participants

This event is produced in collaboration with EXL

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