How AI Affects Agency Pricing

A lot of business leaders are starting to ask: "If AI can build a website in a fraction of the time, why are marketing agencies STILL charging $50,000-$100,000 for a redesign?" That's a dang-good question. One that most agencies, frankly, would NOT like to discuss right now. But I'm here for it, so let's lean in. To start, it's quite a bit more complex than "Claude Code = Less Hours = Cheaper Website Prices" Yes, AI coding tools have massively dropped the time it takes to produce a website, but this doesn't make it "easy" for a traditional agency to start slashing prices. Why? Well, here's the reality of the economics of website builds right now, and why the traditional agency model is, as I've said before, in serious trouble: 👉 1. The "Agency Overhead" Problem: Even if AI cuts developer time by 60% or more, things like the agency's office lease, its massive SaaS stack, and bloated leadership team cost exactly the same. If traditional agencies drop their prices simply because the coding took less time, their fixed overhead wipes out their net profit. Truth be told, most legacy agencies are holding prices high because their survival demands it (and they refuse to blow up their existing structure.) 👉 2. The Rise of the "WaaS": Smart agencies know (or at least they should know) the massive $50k to $100k upfront project is an endangered species. This is creating a quiet pivot in the industry where instead of a big build, they are pushing a $5k setup fee with a $3,000/month "Website as a Service" (WaaS) retainer. It's often framed as something like "continuous AI optimization," but economically, it's also a strategy to protect their lifetime revenue - which I totally get. 👉 3. The "AI Oversight Tax": AI tools are incredible, but their coding can break or be problematic if not overseen by an expert. Part of that retained high agency fee is an invisible insurance premium. They are charging you for the risk of debugging, integrating, and securing AI hallucinations. This can't be overlooked. 👉 4. The Upcoming "Micro-Agency" Tsunami: This is what will truly disrupt the market the most. A two-person team (think: one brilliant strategist + one AI-assisted technical designer) can now execute the volume of a traditional six-person agency team. They don't have legacy overhead. They are lean and they move quick. They are also the ones who will bid $15,000 for a project an incumbent priced at $75,000 - and still walk away with a nice profit margin. What's this all mean? There's a coming pricing war in the website design/build world. Camp #1 will be legacy agencies clinging to old margins. Camp #2 will be lean, AI-first teams who don't need those margins to survive. The middle of the market is about to get hollowed out. And if you're running a full-service agency right now and you haven't rethought your pricing model, your service structure, or your overhead - the clock is ticking louder than you think. Thoughts?

Assuming your firm still follows the practice of billing for time, you can run the calculations that will chart the eventual demise of your revenue model. If you’re like most firms, Generative Artificial Intelligence currently shaves somewhere between 20 and 30 percent off the time it takes to deliver work to your clients. What do you think that figure will be next year, or five years from now? Consider what kind of revenue stream will you have when time-tracking humans are doing only 5 or 10 percent of the work. Even the most hard-core defenders of hourly billing can see this compensation model is wholly unsustainable in the world of the AI-optimized agency. There is simply no way to monetize the value of AI within the framework of hourly billing. The solution to this dilemma requires agency professionals to remove the blinders that have them trapped in the illusion that they are selling time, efforts and activities to their clients. That’s not what clients buy; they buy solutions to their business problems. So the way to capture the value you create for your clients is to stop charging for the cost of your services and start charging for the value of your solutions. Every firm of every size can make this change much easier than they think. Instead of a chart of hourly rates, develop a chart of deliverables — a “pricing guide” that indicates the price (market value) of every deliverable your agency produces, and base your pricing on the work or solution delivered instead of the hours worked. In context of an output/outcome driven compensation model, it should be of no consequence to your clients that AI-powered tools are helping you create and produce your work. Again, they’re buying the outputs, not the inputs. So as AI helps you deliver your work faster and better, both parties benefit. Your clients get better quality work faster and the agency incurs lower costs — a win/win. Even if clients insist on slightly lower pricing (because they assume AI lowers the costs of your human capital), agencies can provide lower prices and still make a healthy margin on their work. In fact, agencies should be able to earn a much higher profit, even if they agree to lower prices, because AI is such a powerful force multiplier. It’s not inevitable that agency revenues will decline, because as AI continues to enable faster work, clients are assigning higher volumes of work to their agency partners. The result can be the best of both worlds: higher revenues from a higher volume of work, and stronger margins because AI is such an efficient virtual knowledge worker.

AI Agents Don’t Buy Seats—Why Your Pricing Should Follow Suit In the past 12 months, a clear pattern has emerged: as AI systems replace manual effort with automated intelligence, pricing structures tied to “seats” no longer reflect the value customers receive. Pricing models have surfaced as a hot topic with every portfolio company at Mosaic Ventures and is top-of-mind for nearly every founder building applied-AI products. When one person and an AI agent can outperform an entire legacy team, charging per user starts to feel arbitrary; what matters is how much business impact the product delivers. Founders are experimenting with three broad approaches: 1. Usage-metered plans that bill against tokens, API calls, or minutes of inference time. These create a direct bridge between consumption and margin and nudge teams to track cost from day one. 2. Outcome-based pricing that charges per lead booked, ticket resolved, or document drafted—tying revenue to measurable results. It’s the software analogue of value-based care. 3. Hybrid “starter bundle plus runway” tiers: a predictable monthly fee with a healthy allowance of AI credits, then pay-as-you-go beyond that. This balances budget certainty for customers with upside capture for the vendor. Across our portfolio, a few design principles keep showing up: 1. Anchor on a metric the customer already tracks. If your product shortens sales cycles, price per opportunity accelerated—not per login. 2. Bundle enough volume to eliminate credit anxiety. No one wants to ration prompts. 3. Expose real-time usage. Transparent dashboards prevent bill shock and build trust. 4. Instrument cost early. Metering and billing belong in the product backlog, not the finance queue. 5. Plan for non-linear jumps. When a model upgrade multiplies compute, re-grade tiers before your gross margin does it for you. AI’s promise is to shift human effort from repetitive execution to higher-order creativity. If our pricing still counts bodies instead of business results, we undermine that promise. The companies that map price to outcomes—while keeping the buying experience refreshingly simple—will capture the most upside. I’d love to hear how others are managing the move from seats to usage and outcomes. What’s working, what still feels messy, and where do you see the biggest opportunities to innovate on pricing? #appliedAI #pricing #startups

AI isn't just changing agencies - it's exposing a key flaw in agency-client relationships. The question now isn't whether AI will disrupt - it's how agencies will respond: The creative agency business model has historically relied on billable hours. Remuneration is for time spent rather than results delivered. This creates a fundamental misalignment: • Time-based models can reward inefficiency and penalise overdelivery • Clients want results delivered efficiently, without compromising quality Now, AI is forcing a reckoning. It will fundamentally challenge how agencies work - what they do, how they do it, and why. But in its simplest form, adding AI to agency workflows means that tasks that once took hours can be completed in minutes. So when time-based billing breaks down, you need to ask: what's my agency's value proposition? Enter the next evolution: A shift to outcome-based pricing - charging for results, not hours. This model creates three major advantages: • Perfect alignment: Agency success becomes directly tied to client success • Innovation incentives: Teams are motivated to leverage AI for efficiency • Value transparency: Clients understand exactly what they're paying for However, there are challenges: • Attribution can be difficult with multi-agency teams • Success metrics aren't always clear, or even measurable • Over-optimising for performance can come at the cost of brand equity Regardless, this evolution is already happening: Some marketing agencies bill on leads generated rather than campaign hours. Development teams charge for project outcomes rather than coding hours. Creators charge based on reach and engagement, not production time. For agency leaders ready to embrace this shift, here are four actionable steps: 1. Audit your value delivery: Identify where you truly create client value beyond time spent 2. Test hybrid pricing: Combine traditional retainers with performance incentives as a transition step 3. Define clear metrics: Establish measurable outcomes that directly connect to client business goals 4. Position AI as a value multiplier: Show how automation enhances strategy rather than threatens billable work Embracing AI-driven efficiency is table stakes for agencies. The next generation of agencies will monetise thinking and creativity, not time. Because this isn't just adaptation - it's evolution toward a more aligned, future-proof relationship between agencies and clients. So: If you moved to outcome-based pricing tomorrow, what single metric would best prove your agency's unique value to clients?

With GenAI, I was flying blind on pricing. I had no real framework, no playbook, no idea how to tie value to usage - until Michael Mansard dropped his GenAI monetisation series last year. It reframed and unlocked everything for me. If GenAI disrupted SaaS pricing, Agentic AI obliterates it. You’re no longer selling access to tools; you’re selling “digital workers” - Agents that act, collaborate, and deliver outcomes. How do you price that? I’ve been waiting for Michael’s new series on monetising Agentic AI - and it's here: The COMPASS Framework, Part 1. I’ll set out a taster below, but want you to go and read the full article (link in the comments) Michael introduces a 3x3 matrix to guide pricing metric typology, based on two critical axes: 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐀𝐠𝐞𝐧𝐭’𝐬 𝐰𝐨𝐫𝐤: Is it automating tasks, orchestrating processes, or achieving strategic goals? 𝐋𝐞𝐯𝐞𝐥 𝐨𝐟 𝐯𝐚𝐥𝐮𝐞 𝐚𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧: Can its output be clearly linked to measurable results, or is it more diffuse? From this, four pricing models emerge - building directly on archetypes introduced in Michael’s earlier 2024 GenAI monetisation research - each tailored to a different kind of Agentic behaviour: 𝐏𝐞𝐫 𝐀𝐠𝐞𝐧𝐭: Think retainers - predictable cost for always-on utility, even if value is hard to isolate. 𝐏𝐞𝐫 𝐀𝐜𝐭𝐢𝐯𝐢𝐭𝐲: Like time & materials - charging for discrete actions, API calls, or compute blocks. 𝐏𝐞𝐫 𝐎𝐮𝐭𝐩𝐮𝐭: Tangible deliverables - resolved tickets, code commits, saved checkpoints. 𝐏𝐞𝐫 𝐎𝐮𝐭𝐜𝐨𝐦𝐞: Performance-based - pricing tied to a business KPI the agent helps deliver. Like any good framework, it’s not just descriptive - it’s prescriptive. It helps you make decisions, not just describe them. Michael doesn’t just map where we are - he forecasts where we’re going: “We're heading toward a value attribution battleground, where a single customer outcome might be achieved not by one Agent, but by a team of Agents from different vendors.” Think about it: When multiple autonomous Agents from different companies collaborate to deliver a single result - who gets credit? Who gets paid? Who owns the outcome? Each Agent may contribute differently: one gathers data, another analyses it, a third executes the action. But from the customer’s point of view, it’s one seamless result. It challenges basic assumptions of value attribution in pricing models: How do you split revenue when the outcome is co-produced? Who logs the “win”? Who invoices? Who maintains the customer relationship? It breaks traditional pricing logic - per seat, per API call, even per outcome - and demands interoperable telemetry and trust between vendors. It opens up new models: shared revenue, micro-commissions, Agent marketplaces, even arbitration layers. We’re entering a new era of multi-Agent, multi-vendor ecosystems - where telemetry becomes strategy. Michael has done it again - and provided the map we needed to navigate this. Go read it.

CEO of a 30-500 person agency? Here’s the "AI Transformation" danger no one is talking about. Most agencies won’t fail because they ignored AI. They’ll fail because they improved delivery faster than they redesigned their commercial models. - Efficiency shows up immediately. - Pricing logic and commercial models lag. - Costs don’t fall fast enough. - Margin leaks quietly. By the time you react, procurement-thinking has anchored a lower baseline. In my latest sector report "Marketing Agency Reset 2026" I call this "The Agency Chasm". This is the dangerous middle ground between: - “We get the reset” and - “We’ve structurally rebuilt the business.” Here’s the common pattern: 1️⃣ AI improves delivery efficiency and effectiveness. 2️⃣ Clients expect more for the same fee. 3️⃣ Margin compresses. 4️⃣ Leadership gets dragged into firefighting. 5️⃣ Reinvention stalls. Not because the strategy was wrong. Because the sequencing was. The agencies that cross the chasm don’t “embrace AI” faster. They change the economics first. 1️⃣ Commercial architecture before visible efficiency. Outputs. Outcomes. Productised scopes. Hybrid pricing. Margin and cash-flow protection designed in. 2️⃣ Operating model redesigned to match. Roles shift. Headcount resets deliberately. Utilisation stops driving behaviour. AI becomes default workflow. 3️⃣ Controlled rollout. One segment/client at a time. Re-pricing planned. Cash-flow stress-tested before migration. Growth and margin are protected during transition. Everyone else does it backwards: - Improve delivery first with AI - Clients sense efficiency - Pricing/scope pressure follows - Costs lag - Economics weaken That’s not transformation. That’s margin erosion disguised as progress. If this feels horribly familiar, then you should read the full 49-page report to pressure-test your 2026 Agency business plan. Comment below and I’ll send it.

Is the agency model actually changing? How? Two Digiday pieces give interesting signals: one on S4 Capital Group pushing a subscription model for the AI age, and one on WPP betting on outcomes. Links in first comment. For 100+ years, agencies have been priced like labor: FTEs. Cost plus. Hours as the unit of value. That model created many of today’s distortions: misaligned incentives, bloated scopes, and endless value conversations. Now two pricing models are emerging: 1️⃣ Flat fee, SaaS-like subscription. A predictable monthly or annual fee for a defined capability. Not “10 people on your account.” More like always-on creative production + optimization, or always-on performance ops, delivered through standardized workflows, platforms, and automation. This forces agencies to productize. It also fits the new AI economics. 2️⃣ Outcome-based Compensation tied to measurable business impact: incremental revenue, profit, retention, CAC/LTV, conversion rate. Not vanity metrics. Real commercial outcomes. Incentives finally align, but only if measurement, definitions, and governance are tight. These models look a lot like tech pricing models. Pricing is moving from inputs (people and hours) to outputs (outcomes) and platforms (repeatable systems). Subscriptions may be the waypoint. Outcomes may be the destination. My bigger bet: Brands will run fewer “agency pitches” as the starting point. They will start with a tech stack and operating model design exercise: 🔹What outcomes are we trying to drive? 🔹What workflows do we need to optimize? 🔹What data and measurement layers are required? 🔹What is best solved by software vs partners vs in-house? Agencies will still matter, but they become one module in the architecture, not the foundation. Curious: which agency pricing and operating models do you think will win in the next 3 to 5 years? #advertising #media #tech

Read this before you sign that “agentic AI” contract. What the pitch sounds like “𝘏𝘢𝘯𝘥𝘴-𝘧𝘳𝘦𝘦 𝘢𝘶𝘵𝘰𝘯𝘰𝘮𝘺.” “𝘈𝘨𝘦𝘯𝘵𝘴 𝘵𝘩𝘢𝘵 𝘣𝘰𝘰𝘬 𝘤𝘢𝘭𝘭𝘴, 𝘤𝘩𝘢𝘴𝘦 𝘭𝘦𝘢𝘥𝘴, 𝘳𝘶𝘯 𝘺𝘰𝘶𝘳 𝘣𝘢𝘤𝘬 𝘰𝘧𝘧𝘪𝘤𝘦.” “𝘍𝘪𝘹𝘦𝘥 𝘱𝘳𝘪𝘤𝘦 𝘱𝘦𝘳 𝘵𝘢𝘴𝘬. 𝘊𝘩𝘦𝘢𝘱𝘦𝘳 𝘵𝘩𝘢𝘯 𝘢 𝘩𝘶𝘮𝘢𝘯.” Great story. Here’s the footnote no one reads: (what really drives the bill) → Infinite loops – One task → five retries → ten tool calls. → Vector hunts – Every “quick lookup” hits a paid DB. → Latency back-offs – Slow APIs trigger serverless cold starts. → Hidden GPU time – Background fine-tunes you never ordered. → Downstream blast radius – Agents lighting up SaaS, SMS, and email APIs you pay for separately. Those costs don’t show up in the glossy deck - only on your statement. Due-diligence checklist: → Cost per successful outcome, not per request. → Token and API caps baked into the SLA. → Throttles for retries, depth, and external calls. → Exit terms if the model mix or pricing changes mid-contract. If your vendor can’t break down unit economics before you sign, imagine the surprise after you deploy. Autonomy is priceless - until the invoice proves otherwise. #FinOps #Mavvrik #AgenticAI

Over the past year, I’ve been tracking a trend that’s now impossible to ignore. AI is not replacing comms jobs one-to-one. But it is replacing pieces of those jobs. And companies are reorganizing teams because of it. That’s the theme of my latest short video and the focus of the newest episode of 𝗧𝗵𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗼𝗿, which I’ve officially resumed. Here’s the data: 📉 𝟳𝟯 𝗽𝗲𝗿𝗰𝗲𝗻𝘁 of marketing leaders using AI have already reduced external content spend. 📉 𝟴𝟯 𝗽𝗲𝗿𝗰𝗲𝗻𝘁 say they expect to reduce agency spending further as AI tools improve. 📉 𝟭𝟭 𝗽𝗲𝗿𝗰𝗲𝗻𝘁 say they would eliminate agencies for basic content work once AI becomes “good enough.” And brands are saying the quiet part out loud. 🚫 𝗠𝗼𝗻𝗱𝗲𝗹𝗲𝘇 plans to cut agency fees by up to 50 percent. 🚫 𝗨𝗻𝗶𝗹𝗲𝘃𝗲𝗿’𝘀 𝗔𝗜 𝗦𝘁𝘂𝗱𝗶𝗼 is producing assets 30 percent faster. 🚫 𝗪𝗣𝗣 says brands using AI are reducing what they pay their agencies. And it’s showing up inside my own business. In 2024, about 80 percent of my clients were in-house comms teams. This year, 60 to 70 percent are agencies looking to adapt. 𝗧𝗵𝗮𝘁 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝗰𝗼𝗶𝗻𝗰𝗶𝗱𝗲𝗻𝗰𝗲. Companies have spent the past two years pouring money into AI infrastructure and workflow redesign. One Nvidia chip costs as much as a car, and companies are buying them by the thousands. That money has to come from somewhere. So organizations are shifting dollars from lower-margin areas they believe have less strategic impact. And many have concluded that PR and comms fall into that category. 𝗔𝗴𝗲𝗻𝗰𝗶𝗲𝘀 𝗳𝗲𝗲𝗹 𝘁𝗵𝗶𝘀 𝗳𝗶𝗿𝘀𝘁. 🚫 Scopes are shrinking. 🚫 2026 decisions are being delayed. 🚫 Brands are internalizing work they used to outsource. This is not about AI replacing communicators. It is about the economics of AI reshaping the financial foundation of agency and in-house relationships. I’m looking forward to continuing this conversation on the podcast and here on LinkedIn, because this moment matters for every communicator, at every level. Would love your thoughts or feedback. How are you seeing this play out inside your organization? 👇🏾

Stop selling products and services. Start selling intelligence and outcomes. I've spent the last 90 days analyzing how the most successful agencies and consultants are positioning themselves in 2025... ... and what I discovered will make you question everything you think you know about running a service business. While 87% of agencies are still competing on deliverables, the top 3% have made a fundamental shift that's allowing them to charge 4x more while working with better clients. Here's what they figured out: → Clients don't buy your product - they buy the intelligence behind it → They don't want your service - they want the outcome it delivers → They don't care about your process - they care about your insights The Intelligence Economy is here. AI can now replicate most deliverables. What it can't replicate? Your industry intelligence, strategic thinking, and ability to connect insights to business outcomes. 3 shifts the winning agencies made: 1. From "We create content" to "We decode what works in your industry" Instead of selling content creation, they sell competitive intelligence. They analyze what's working for competitors, identify market gaps, and position clients strategically. 2. From "We manage campaigns" to "We predict market movements" They don't just execute - they use data patterns to forecast industry trends and position clients ahead of shifts before competitors see them coming. 3. From "We deliver projects" to "We guarantee transformations" They stopped selling outputs and started selling measured business outcomes. Their pricing reflects the value they create, not the hours they work. The brutal truth? Your Photoshop skills aren't your competitive advantage anymore. Your ability to synthesize market intelligence and predict what will work IS. If you're struggling with: ❌ Price competition ❌ Clients treating you like a vendor ❌ Difficulty scaling without hiring more people ❌ Constant need to prove your value You're still thinking like a service provider. The agencies charging premium rates think like intelligence providers. They don't compete on creativity. They compete on insight. They don't sell what they make. They sell what they know. They don't deliver projects. They deliver futures. The shift is simple but profound: Stop asking "What can we create for you?" Start asking "What intelligence do you need to dominate your market?" Bottom line: AI will commoditize execution. It will never commoditize strategic intelligence. The question isn't whether this shift will happen. It's whether you'll lead it or get left behind.