Marketing Procurement

Outcome-based compensation in the age of AI

“I don’t want to be paid the same as any other agency; I want to be paid what we deserve, because of what we deliver, for our clients.”

With AI radically transforming the planning, concepting and delivery of Marketing Services, the traditional labour-based compensation model is under increasing pressure.

WFA’s research among major global advertisers earlier this year concluded that 74% of respondents were seeking to better align agency compensation to business performance with their major agencies.

Conversely, the ANA’s 2025 triennial study on agency compensation revealed that 84% of respondents were utilising Labour-based models with their agencies.

This was the highest number ever in over a half-century of research, with the remaining 16% split evenly between Commission and Other models; the latter of these principally consisting of value and outcome-based models.

With this major discrepancy between expectations and reality, companies on both sides of the relationship now have the opportunity – and imperative – to redefine how performance and success get fairly monetised.

Consequently, this roadmap includes Guiding Principles, Practical Models, a Phased Plan, as well as a Sample Template, to help relationships transition to outcome, solutions, and value-based models.

As the unheralded godfather of successful value-based compensation models, Carl Johnson — Founder & CEO of Anomaly — has been saying for over 20 years, “I don’t want to be paid the same as any other agency; I want to be paid what we deserve, because of what we deliver, for our clients.”

Additionally, this white paper below includes an enhanced Appendix detailing AI Tools by their various relevant uses.

I. Guiding Principles for Outcome-based Compensation

1. Anchor Pricing to Impact

Position agency value around business outcomes: revenue, cost savings, conversion rates, or speed to market – not merely staff effort.

2. Bundle Capabilities Across Functions

To credibly adopt outcome-based models, agencies must expand their clients’ scopes – effectively combining Media, Creative, and Production; this will allow for deeper alignment and better control of results.

3. Treat AI as Embedded Infrastructure

Clients are unlikely to pay a separate “AI fee” indefinitely. Instead, AI should be woven into an agency’s offering and pricing structure, with clearly defined and agreed-to efficiency and quality gains.

4. Get Ahead of the RFP Process

The established pitch process and Procurement’s expectation still lean toward FTE/pricing comparisons. New models must be introduced before RFP issuance, with intermediaries positively advocating on their behalf.

II. Practical Models for Agencies to Monetise AI

1. Model: FTE + Tech Surcharge (Hybrid)

Description:  1–3% technology fee on top of standard FTE-based fees to cover AI tools and infrastructure

Ideal for:  Transitional clients hesitant to immediate change

2. Model:  Efficiency-Priced Outputs

Description:  Charge/pay for deliverables, not hours – enhanced by AI productivity for non-retainer work

Ideal for:  Creative, Production, Market Research-heavy project work

3. Model:  Outcome-Based Compensation

Description:  Link compensation to measurable business outcome (e.g. revenue growth, lead conversions, cost savings)

Ideal for:  Integrated campaigns with multi-disciplinary scopes

4. Model:  IP Licensing and Subscription Fees

Description:  Charge/pay to utilise agency’s AI tools, proprietary prompts, or internally-developed LLMs

Ideal for:  Agency relationships with significant AI investments

5. Model:  Performance Bonuses & Sponsorships

Description:  Monetise over-delivery via shared success metrics or “sponsor-like” goal alignment

Ideal for:  KPI-based media, CRM, or growth / launch campaigns

III. Roadmap: Transitioning to Outcome-Based

Phase 1: Preparation (0 – 6 Months)

• Audit AI tools, efficiency objectives, and cost/staff savings goals
• Identify top relationships where realisable outcomes are most evident
• Discuss and evaluate new pricing structures across all relevant stakeholders
• Begin implementing hybrid pricing proposals that cover “tech fees” and outputs

Phase 2: Piloting (6 – 12 Months)

• Launch outcome-based pilot projects with willing parties
• Track time/cost savings, increased output, and performance data
• Create and document alignment with shared KPIs and client success metrics
• Enable IP licensing or subscription monetisation for agency AI assets, as appropriate

Phase 3: Institutionalisation (Year 2)

• Establish and standardise “Scope of Benefits” templates for outcome-based SOWs
• Formalise new compensation parameters with client Legal / Procurement input
• Launch AI-driven ROI dashboards to quantify outcome and value delivery
• Begin embedding new models in all RFP development and submissions

IV. Outcome-Based Compensation Template

Clearly-Defined Client Objectives & KPIs

• Goal 1: [e.g. X% web conversions or sales growth]
• Goal 2: [e.g. X% identifiable production cost reduction – hard savings]

Deliverables

• Creative: [AI-augmented deliverables]
• Media: [Applicable channels and/or targets]
• Analytics: [Attribution and optimisation parameters]

Compensation Structure

• Base Fee: $X per month
• Outcome Fee: $Y tied to achievement of agreed client objectives and/or KPIs
• Tech Fee / IP License: $Z monthly (named access to AI platform/tools)
• Success Fees: Specific amounts tied to surpassing clearly defined client benchmarks

Stewardship

• Monthly (preferably) or quarterly progress review with real-time dashboards
• Payments, made timely, tied to actual and pre-defined business metrics

V. Overcoming Challenges

1. Challenge:  Client CFOs resist variable payments

Recommended Solution:  Pre-negotiate KPI caps and create fixed bonus pools (properly accrued by Finance) tied to business results

2. Challenge:  Intermediaries default to FTE in pitches

Recommended Solution:  Educate intermediaries and Procurement pre-RFP; include alternate models with FTE for comparability

3. Challenge:  Typical inertia / lack of templates

Recommended Solution:  Build cross Client / Agency pricing innovation teams and reward new model deployment and adoption

Conclusion: Evolve or Become Less Competitive

Most Marketing Services relationships still operate under the traditional FTE or Time & Materials pricing models. However, AI is systematically eliminating the very staff hours on which this model depends.

According to recent Forrester findings, over 75% of agencies now use AI, yet only 6% are successfully monetising it. This disconnect is putting the entire commercial structure of agencies at existential risk.

Agencies can’t monetise AI while clinging to outdated pricing models. It’s time to shift from labour and input-based (and even output-based) models to ones focused on outcomes, solutions, and value creation.

As value-based pricing guru Ron Baker, author of Implementing Value Pricing and Time’s Up!, has noted: “Pricing innovation happens on the supply side. If firms don’t reinvent their models, someone else – competitor, customer, or consultant – will gladly do it for them.”

For those agencies not monetising AI investments effectively, the very tools driving productivity gains will also eat away at their margins.

The time to act is now – before change is forced upon agencies by client Procurement & Finance CFOs, industry intermediaries, more nimble competitors, or, reminiscent of the digital/social revolution, AI-native players.

AI Tools Appendix – 2025 Enhanced

By Nick Jones, Managing Partner & Principal, OWND

Creative and Copywriting

• ChatGPT-4o mini (free) – huge for concepting, first drafts, thought-starters, client emails, and pitch decks
• Perplexity AI (free tier) – best for fast, reliable research when you need quick context or competitor POVs
• ai (free tier) – excels at long-form documents such as strategy papers and RFP responses
• ai or Jasper ($39 per month) – more polished AI writing specifically tuned for marketing outputs

Visuals and Design

• Canva (free) – not just templates; now features text-to-image, copy assist; perfect for client mock-ups.
• Figma FigJam (free) – whiteboarding, workshops, concept mapping; great for collaborative brainstorms.
• Stable Diffusion (free via Hugging Face/Automatic1111) – for quick visual experiments and mood boards
• MidJourney ($10per month) – higher-quality, on-brand concept art and campaign visualisation.

Video

• Runway Gen-2 ($12 – $28 per month) – text-to-video for pitch sizzles or quick comps
• Veo 3 (Pro $70 per month) – Google’s text-to-video solution

Operations and Workflow

• Notion AI (free tier) – centralise project briefs, to-dos, meeting notes, and will also summarise/organise
• Zapier (free tier) – automate repetitive admin (e.g. move client form submissions into Slack or Asana)
• Trello or ClickUp (free tiers) – task/project management with some AI assist

Social and Content

• CapCut (free) – short-form video editing for social; includes AI templates, captions, resizing.
• Opus Clip (free limited) – turn long client interviews/webinars into short clips with AI auto-editing
• ChatGPT + Canva for Carousels – perfect for LinkedIn thought leadership

Productivity

• Notion Plus ($8 per user / month) – makes AI more deeply integrated into your workflow
• Slack AI ($12 per month add-on) – auto-summarises threads, drafts replies, finds info across channels

Research and Strategy

• Perplexity Pro ($20 per month) – live web + citation-rich research (insights, competitor audits, trend scans)
• Kantar Marketplace Lite (pay per project, sometimes <$50) – for low-cost testing of ideas with actual data

Social / Marketing Ops

• Buffer or Metricool ($15 – $30 per month) – AI-assisted posting + analytics for multiple client accounts
• Descript ($12 per month) – edit podcasts/videos by editing text; great for client content.