The licenses are the cheapest part, and every firm budgets as though they are the only part. A realistic first-year AI budget for an engineering firm has four lines, and software is usually the smallest of them. Internal time is almost always the largest, it never appears in a vendor quote, and it is the line that decides whether the whole effort produces anything.

I am going to give ranges rather than fixed figures, because per-seat pricing in this market moves every couple of quarters. Verify current numbers before you commit. What does not move is the shape of the budget. The ratio between the four lines has been consistent across every firm I have worked with.

Line one: licenses

Per-seat enterprise assistants have settled into a fairly narrow band, roughly the cost of a mid-tier professional software seat per user per month. For a 50-person firm putting a general-purpose assistant in everyone's hands, you are looking at an annual number in the low tens of thousands.

Two things drive it higher. Domain-specific AEC tools price well above general assistants, sometimes per project rather than per seat, because the addressable market is smaller. And any tool touching client data needs enterprise terms rather than consumer tiers, which costs more and is not optional. The difference is whether your inputs may be used for training, which determines whether the tool can lawfully touch material covered by your client agreements.

The most common budgeting error here is buying seats for everyone at the start. Buy for the pilot team, prove the workflow, then expand. Firm-wide seats purchased before you know where the value is concentrated produce a renewal conversation about software most people opened twice.

Line two: implementation and integration

The cost of connecting a tool to where your files actually live. Highly variable, and the single largest source of budget surprise.

If your documents are already consolidated in one well-organized system, this is close to zero. You authenticate and go. If they are spread across server shares, personal drives, an old project system, and a decade of inconsistent folder conventions, this line can exceed your entire license cost, because someone has to consolidate before anything can reach the material.

That is not really an AI cost. It is deferred file management, and you would benefit from fixing it regardless. But it lands in this budget, and pretending otherwise is how first-year numbers get missed by a wide margin. It is the first four questions of the readiness assessment converted into dollars.

Line three: training

Nearly always underfunded, and the underfunding is what produces the failed pilot rather than the tool.

Budget it as internal hours rather than a course fee. Role-specific sessions on your own projects, plus a standing monthly half hour. For a 50-person firm, plan on something like ninety minutes for principals, two sessions of two hours for project managers and senior technical staff, the same for early-career engineers, plus preparation time to build the exercises on real documents.

Multiply by loaded labor rate rather than salary, because that is what it actually costs you. The number will be larger than your license line, and it should be. A firm that buys seats and skips training has bought the ability to generate unverified work quickly, which is a worse position than not adopting. The reasoning is in training engineers to trust but verify.

Line four: internal time to run it

The line nobody quotes and the one that most often determines the outcome.

Someone has to own this: evaluate tools, run the pilot, set up the baseline measurement, answer questions, adapt the QA checklist, report results. In a 50-person firm that is realistically a meaningful fraction of one capable person's time for the first two quarters, and it needs to be someone whose judgment the firm respects rather than whoever has capacity.

Price it honestly at their loaded rate. It is frequently the largest line in the budget. Firms that leave it out are not saving money; they are assigning the work to someone's evenings and then wondering why the pilot drifted.

What the shape tells you

Put the four lines side by side for a typical mid-size firm and the pattern is consistent: licenses are the smallest, training and internal time together are the majority, and implementation is the wildcard that depends entirely on the state of your files.

Which produces the most useful conclusion in this whole article. If your budget is mostly software, you have not planned an adoption. You have planned a purchase, and purchases do not change workflows. The firms that get a return spend more on their own people's time than on the tools, every time.

If licenses are the biggest line in your AI budget, you are buying software rather than adopting anything.

What to fund in year one

The sequence that wastes the least money:

  1. Start with what you already own. The AI features in your existing document suite or project platform cost nothing incremental. Watch what people actually do with them for a month. That usage pattern is the best pilot-selection data you will ever get, and it usually contradicts leadership's assumption.
  2. Fund the baseline measurement. Two weeks of someone's time, before any purchase. It is the cheapest line in the budget and the one that makes every subsequent number defensible.
  3. Buy one tool for one pilot team. Not the firm. One workflow, twelve weeks, measured.
  4. Fund training properly for that team. Disproportionate to the license cost, and correctly so.
  5. Expand only on a measured result. Then the second purchase is a straightforward conversation instead of an argument.

This sequence also fails cheaply, which matters. If the pilot does not pay off, you have spent one team's licenses and one quarter, and you have a number explaining why. Compare that to a firm-wide rollout that disappoints. Same lesson, ten times the cost, and a much harder second attempt. Most of the four reasons pilots fail are budget decisions in disguise.

The number that belongs beside the cost

None of this is a capital request in isolation. It only makes sense against what the current workflow costs you, which most firms have never quantified.

If a submittal review consumes a measurable number of hours per instance and you process a known volume per year, you have an annual cost for that workflow. That figure is nearly always larger than the AI budget aimed at improving it, and putting the two side by side converts the conversation from an expense discussion into a straightforward comparison. The measurement approach is in the four numbers that convince a partner group.

Bring both numbers to your partners. A budget request with no baseline is asking for faith; the same request beside a measured workflow cost is asking for arithmetic.

If you want a first-year number built against your actual file situation, headcount, and workflow volumes rather than a template, that is part of our readiness and roadmap engagement, it ends with a phased plan and a budget rather than a recommendation to buy something. Start a conversation.

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