Nobody Correctly Prices The Cost of Their AI Transformation

Two years, a few battle scars

Every conversation about AI in marketing right now collapses into two words: cost and throughput. Companies want a lower number and a faster machine, and they usually want both at once. That’s understandable given the macroeconomic pressure most companies are under right now, but the analysis usually offered to get there skips the steps that change the short-term equation substantially, without necessarily moving where the long-term destination sits.

Underneath that gap lies the assumption that a team can build a genuinely new capability while continuing to do everything it was already doing without deliberately investing in the processes and systems that would actually make it work. Protected capacity rarely comes up in these conversations, and neither does the structural work of putting the right systems in place to support it. We made that same assumption ourselves early on. We’re more deliberate and structured about this now than we were a year ago, but we’re not done getting better at it. A transformation that is both technological and human cannot run on borrowed time.

I’ve spent the better part of the last two years trying to tackle this challenge: the “agentification” of our marketing organization, or in other words, how to use generative AI to get more done at a potentially lower cost. As VP of Marketing for the Growth, Performance and Operations team at LinkedIn, I lead a team of incredibly talented subject matter experts who sit across MarTech, Media, Web, Data and Analytics, and Scaled Customer Solutions, essentially every function positioned at the center of what agentic AI promises to reshape inside a marketing organization. Although the observations that follow carry a marketing bias, I suspect most of it travels to other functions once the specific tools and job titles change.

LinkedIn pushed into this challenge early, and being part of Microsoft has meant real backing and real tooling to actually do the work rather than just talk about it. Both of those things have made a genuine difference, and I don’t take either for granted. None of what follows is a critique of how we do things internally. Sure, we fall into some of the same traps everyone else does, but this is my read on the wider industry conversation after two years in it: the pitches and PDFs from vendors and consultants, the thought-leadership circulating in C-suites, and what a lot of genuinely sharp colleagues are navigating as they work through the exact same shift.

To get started, let’s frame the problem with an analogy.

The line-shaft fallacy

Factories in the late nineteenth century ran on what historians call the “group drive” system. One central engine, water-powered or steam-powered, turned a rigid overhead network of line shafts, pulleys, and leather belts running across every floor. Machines sat wherever the shaft could reach them rather than where the work actually flowed, because the binding constraint was power distribution rather than production logic.

Electricity arrived promising to change that, and for decades it barely did. Stanford economist Paul David documented the lag in his widely cited 1990 paper, “The Dynamo and the Computer.” By the turn of the century, electric motors accounted for less than five percent of factory mechanical drive, despite the technology having existed for two decades already. The delay had nothing to do with the technology and had everything to do with the fact that, at the time, factory owners bolted a single large electric motor onto the same shafts, belts, and multi-story layouts they already had, leaving the floor plan untouched. While the fuel source changed, measured productivity barely moved for another thirty years.

Most AI transformation today is the same swap wearing a new outfit. Handing a team a chatbot, or wiring a language model into an approval chain that never changes, plays the part of the single electric motor on the old shafts: it runs without moving the number that matters because the process around it stays exactly as it was. The equivalent of tearing down the shafts is redesigning the organization itself and implementing structures where agents handle baseline execution and individual tasks and people concentrate on judgment, exceptions, and outcomes.

What gets sold vs. what actually happens

Nearly every AI transformation pitch, whether it comes from a consulting firm, a software vendor, or an internal strategy deck, makes the same promise: lower cost, higher output, achieved by moving budget from manual work to AI-enabled work. The math stays clean because the pitch stops at the long-term vision and the finished outcome, and it rarely prices what implementation actually costs: the price of unbolting the old way, the price of standing up the new one, and the added price of running both at once.

The two costs nobody prices

Split the real cost of transformation into two categories and the vendor pitch’s gap becomes obvious: the human work of finding out how the job actually gets done today, and the technical work of running AI safely once you know. Most pitches acknowledge the technical side, at least partially. The human cost is minimized at best and completely ignored in the worst cases.

Human costs

Before a workflow can be handed to an agent, it has to be codified, and in most marketing organizations, it never has been. Ask three senior marketers how they build a campaign insight, structure a positioning document, or decide what makes a message on-brand, and the answers will typically differ. Each one will be defensible, and likely none will be written down.

But that’s the actual prerequisite work: interviewing the practitioners, reconciling where their approaches genuinely differ from where the difference is just style, and standardizing a single process worth teaching to a machine. Skipping straight to writing agent instructions on top of tribal knowledge means encoding whichever practitioner happened to be in the room as though that were the organization’s best practice.

The false parallel

It’s tempting to look at a function that has already made this transition, such as customer support, and assume marketing can follow the identical playbook. The job itself sits at a different point on the spectrum of how codifiable work actually is, wider than the two-function comparison usually offered, and that difference has nothing to do with how willing marketers are to standardize their work.

None of this is an excuse to skip the codification work, but Marketing’s subjectivity is real, and the practical move is to assess your own organization’s actual context, function by function, rather than assume a playbook built for a highly codified team transfers over unchanged.

This is why marketing transformation timelines routinely run longer than the tools would suggest, and why the software is so rarely the actual bottleneck. A codified, agreed process to hand the software is what was missing in the first place.

Where the impact will show up first

Most marketing organizations already outsource the more task-based, repeatable slice of their work, templated content, routine media execution, standard campaign builds, to agencies, vendors, and contractors, while keeping the more subjective, judgment-heavy work in-house. This means that the first visible cost impact of AI is likely to show up in external vendor spend, well before it shows up in the internal team’s structure, simply because that’s where the already-codified work lives. Watching internal headcount for early savings or productivity gains while the actual shift happens one layer removed, inside agency retainers, is a good way to conclude AI isn’t working when it already is.

Watching internal headcount for early savings or productivity gains while the actual shift happens one layer removed, inside agency retainers, is a good way to conclude AI isn’t working when it already is

The shift to a different, comparably expensive work

I want to call out that comparably expensive work since it doesn’t show up on an org chart the way a copywriter role does. It looks like an editorial reviewer reading a daily sample of AI-generated content against brand and legal standards, at a volume no single person could review in full. It looks like someone whose job is tuning the prompts and guardrails based on what that reviewer keeps catching, so the same mistake doesn’t recur next week. It looks like an escalation path that gets used the day an agent says something about a competitor, a customer, or a product feature that isn’t true, and someone senior enough has to decide how to respond publicly, fast. None of these existed as job descriptions two years ago.

Technical costs

The technical side is easier to price, but the vendor pitches hardly include the full extent.

The five phases of an “agentification” journey

Every transformation pitch treats its functional categories as though they can transform in parallel, with independent budget lines. In practice, AI transformation moves through five sequential phases, and skipping ahead adds to your technical debt rather than reducing it. Each phase carries its own human cost and its own technical cost, and conflating the two is part of what makes the timeline feel unpredictable.

Costs concentrate in Phases 1 through 3, before most functions show any measurable return. Savings start to show in Phase 4 and only fully land in Phase 5, often well beyond the single fiscal year most budget conversations are scoped to.

During Phases 2 and 3, while legacy and new systems run in parallel, program costs are more likely to increase before they decrease like we have seen in the chart above. Budgeting for a steadily decreasing line is how transformation initiatives lose organizational confidence, even when the underlying work is on track.

The rushing tax

This is what tends to happen when an organization tries to build a genuinely new capability with accelerated timelines and without protecting any capacity for it: the same team, the same roadmap, the same headcount, now also responsible for interviewing practitioners, retraining itself, and standing up new governance, on top of everything it was already doing. Something has to give, and if it’s not the work, it’s the team’s sanity.

Five specific risks compound from there:

Every one of these is cheaper to prevent in Phase 1 than to remediate in Phase 5, but often the problem is that Phase 5 is exactly where they become most clearly visible.

A checklist worth answering before you fund the reallocation

Before signing off on any AI transformation roadmap, I would advise leaders to get answers to some of the following questions:

Each question here maps to a phase or cost above. If you can’t answer most of them at the outset, pad the timeline for foundation work, not just for the AI implementation itself.

The first step is simpler (and cheaper) than you think

Every agent eventually runs on some version of a plain document, a markdown file or MD file, which is a set of instructions, a knowledge base entry, and the summary of your organizational intrinsic knowledge and soft process. That file is the actual deliverable of the first step of the human cost described above. The pilots, the governance, the orchestration, all of it sits on top of that document. Which means the real first move isn’t a rollout plan organized by function or by role. It’s getting your subject matter experts to sit down to write that document, and codify that knowledge. Getting there starts with the same handful of questions no matter which corner of marketing is asking them.

None of this requires a vendor, a pilot budget, or a signed contract. It requires someone willing to sit with the people doing the work and write down what actually happens. Every phase after this one runs on borrowed time until that document exists.