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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Not automatically. AI may reduce the effort behind a deliverable, but an agency’s fee also reflects expertise, quality assurance, risk, and the outcome the client is buying. Measure how AI changes your costs, delivery, quality, and results; then adjust the scope or price only when the evidence and client agreement justify it.
What the available evidence says about agency pricing
There is no settled agency-wide rule that AI-assisted work should cost less—or that agencies should always hold or raise their fees. A 2026 Productive survey found agencies split among keeping or increasing fees, lowering prices, seeing little effect, and still working out a model. The survey covered 174 agencies and consultancies using Productive, surveyed March 17–31, 2026. Its respondents leaned toward digitally mature, process-oriented agencies and were majority Europe-based, so the results are useful context rather than a census of all agencies. Productive’s 2026 report also found that, among agencies reporting multiple positive AI situations, 52% were keeping or increasing prices with improved margins, up from 47%; among agencies still implementing AI, 61% were still figuring out a pricing model, up from 47%. These are survey associations, not proof that holding prices causes higher margins.
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Other studies reinforce the need to avoid a one-size-fits-all answer. In Promethean Research’s 2026 State of Digital Services survey, value-based pricing usage fell from 31% of agencies in 2024 to 18% in 2025. Promethean describes this as a signal from a single survey wave, not a final verdict. The survey was conducted in February 2026, published in March, and based on 119 completed responses primarily in North America; 74% of respondents were in the United States. Promethean’s benchmark report gives the finding useful context, but it does not establish that value-based pricing caused slower growth.
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Forrester’s 2026 study of US marketing agencies found that nine in ten used generative AI, half used agentic AI for marketing execution, and 61% classified AI as a cost of doing business. Its findings apply to US marketing agencies, not every kind of agency. Forrester’s release argues that efficiency gains should support investment in talent, creativity, and differentiation. Read the study release.
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Choose a fee model that matches what you can observe
A useful way to choose is to ask two questions: Can you observe and attribute the client outcome? Can you observe and predict your inputs and costs? Stanford Digital Economy Lab’s 2026 strategy map uses those dimensions to reason about pricing structures and points toward hybrid and asset-based models as AI changes the economics. It is a strategic framework, not an experiment proving one model works best for agencies. See Stanford’s strategy map.
| Model | Works best when | What to define | AI-related trade-off |
|---|---|---|---|
| Time-based | Scope or inputs are uncertain, and time records help manage the work. | Rates, what counts as billable time, reporting, and approval for scope changes. | It is straightforward to connect fees to effort, but efficiency gains can be harder for the agency to retain as margin. |
| Fixed project | Deliverables are bounded and requirements are stable. | Acceptance criteria, revision limits, exclusions, and change-control terms. | If AI reduces the cost of producing the agreed deliverables, the agency’s margin may improve without changing the client’s agreed scope. |
| Retainer or subscription | The client needs recurring work, continuing access, or ongoing outcomes. | Included capacity, response times, service boundaries, and how extra work is handled. | Faster delivery should not silently turn a defined service into unlimited scope. |
| Outcome-based | The result can be measured and credibly attributed to the agency’s work. | A baseline, attribution rules, client cooperation, external factors, and how risk is shared. | It can align the fee with results, but is difficult to manage when the agency cannot observe or control what drives the outcome. |
| Hybrid | Some inputs are predictable, but outcome attribution is incomplete. | Which work is fixed-fee, recurring, or performance-linked, and what triggers a change. | A setup or fixed-scope fee can sit alongside a recurring component or measurable performance element. |
When value-based pricing is realistic
Value-based pricing is not simply charging more because a client’s work matters. The agency needs a credible basis for connecting its expertise to value the client recognizes, and a fee structure the agency can deliver profitably. Promethean’s 2026 guidance lists several prerequisites:
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- Committed leadership.
- Utilization above approximately 60%.
- A clear understanding of the agency’s cost floor.
- Specialized expertise or intellectual property that supports the value claim.
- Incentives and internal language that are not centered on timesheets.
These are Promethean’s recommendations, not a universal accounting or legal standard. If your agency cannot explain its cost floor, show differentiated expertise, or agree on a measurable engagement outcome, a fixed-scope or hybrid model may be more workable than asserting a value premium.
A practical process for deciding whether to change a fee
- Define what the client buys. Document the deliverables, judgment, strategy, integration, quality control, risk reduction, support, and agreed results. Separate these from internal production steps such as drafting or analysis.
- Compare the old and new cost picture. Include labor, AI subscriptions or usage, review and correction, implementation, security, and ongoing maintenance. Measure the total instead of assuming AI has reduced it.
- Track quality and outcomes alongside speed. Record acceptance, revisions, reliability, client satisfaction, delivery time, and relevant business measures such as conversion or revenue. Set a baseline and agree on attribution rules with the client where those measures will affect a fee.
- Decide whether AI changes the scope, price, or capacity. If the same outcome and scope can be delivered at lower cost without added risk, retaining margin or strategically lowering the price may both be reasonable. If AI enables a better, faster, broader, or newly possible outcome, explain that expanded value and price the agreed scope accordingly. This is a decision framework, not a causal result established by the surveys.
- Agree on acceptable AI use before work begins. Confirm what the client permits, what use must be disclosed, and what data may be used. Thomson Reuters Institute reported that 40% of firm respondents in its 2026 professional-services research had received conflicting client instructions to use and not use AI on matters. That finding covers professional services, not specifically all agency types. The report also found that only 18% of professional-services survey respondents knew their organization tracked AI ROI in some manner. Read the Thomson Reuters Institute report.
- Review the model after you have evidence. Revisit costs, quality, and results once the work has been delivered. Do not promise a guaranteed return or link fees to results you cannot observe or attribute.
How to explain the fee to clients
Anchor the conversation in the agreed scope and outcome, not in the number of internal production hours AI changes. Explain what the agency remains responsible for: judgment, integration, review, reliability, and delivery against the agreed criteria. If a lower cost leads you to offer a lower fee, describe the scope or commercial reason for the adjustment; do not imply that AI use alone determines the price. If AI expands what the engagement can accomplish, explain the additional value and agree on the new scope before work proceeds.
Be explicit about permitted tools, disclosure, and data handling. That matters because client expectations may conflict, as the Thomson Reuters Institute’s professional-services findings illustrate. Keep the terms specific to the engagement rather than assuming that a general client preference resolves every project’s requirements.
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