Why is SAP consultancy so expensive?

On most large SAP programmes, implementation consultancy is the single largest cost line — routinely a bigger number than the software itself. A large share of that spend is not deep technical work: it is structured, repeatable knowledge work, billed at specialist rates because the in-house team had no efficient way to do it themselves. AI is starting to change that part of the bill — without touching the work that is genuinely specialist.

Where does the money actually go?

Ask a finance director what frightens them about an SAP upgrade and the answer is rarely the technology. It is the invoice.

Strip a programme back and the external spend tends to fall into two piles.

The first pile is specialist build and configuration — the work that genuinely requires deep, current SAP expertise. That is money well spent.

The second pile is everything that surrounds the build: readiness assessments, requirements gathering, data profiling and cleansing preparation, test-scenario design, cutover planning, stakeholder communications, training material. This work is essential, but it is not specialist in the same way. It is structured, repeatable, knowledge work — and historically it has been billed at specialist rates simply because the in-house team had no efficient way to do it themselves. Even where a dedicated process platform is in place, the discovery and mapping workshops that feed it are usually consultant-run — which is its own line on the bill.

Where does AI change the maths?

The economic argument for AI is no longer speculative. McKinsey's analysis puts the potential value of generative AI across the economy in the trillions of dollars, concentrated precisely in structured knowledge work; their ongoing research shows organisations moving from experiment to adoption, and spending forecasts from Gartner point the same way — investment is rising, not levelling off.

For an SAP programme, the practical effect is narrow but real: the second pile of spend — the surrounding work, not the specialist build — is exactly the kind of structured knowledge work AI is good at supporting. An in-house team with the right method can now produce a credible readiness assessment, a first-pass data-quality review, or a set of business test scenarios in hours rather than billing weeks of external time for the same output.

This does not replace the specialists. It changes what you need them for — and shrinks the second pile.

Does this only work if the AI use is any good?

There is a real caveat, and it is the whole game. Ad-hoc AI use — typing a vague question and hoping — produces vague, inconsistent, sometimes confidently wrong output. That is worse than useless on a programme where decisions carry real money.

The difference is method. A structured, context-rich approach — one that sets the role, the inputs, the constraints and the required format — produces output you can actually take into a steering meeting. That skill is learnable, but learning it across a team, mid-programme, is its own cost.

That is the gap our toolkits close. They package the method, so your team gets specialist-quality structure on the surrounding work without paying specialist rates to acquire the skill first.

The bill will never go to zero — nor should it. But the share of it that is really just structured knowledge work, billed externally for want of a better option, is no longer a fixed cost of doing business.