Big 4 or Specialist: Who Should Implement Your Enterprise AI?

If you are choosing who implements enterprise AI in your business, you are almost certainly looking at two kinds of firm: a global consultancy, or a specialist.

Most comparisons of the two are about size and day rate. Those are the least useful differences. The ones that decide whether the programme works are more specific.

Who actually does the building

This is the question worth asking first, and the answer is rarely in the proposal.

In a large consulting engagement, the people who win the work are frequently not the people who do it. The team that arrives is often more junior than the team that pitched, and it will rotate. That is not a criticism of anyone involved; it is how a large firm staffs a pipeline.

A specialist team is small enough that the people in the room are the people who build. It is worth asking directly: will the people presenting today be on the delivery team, and for how long?

How long before something reaches a real user

The second question is when a person in your business first touches something that works.

A large programme typically opens with a discovery phase — current state, target state, roadmap, governance framework. There is real value in that work. The risk is that by the time it concludes, the technology has moved, the sponsor has changed roles, and the momentum has gone.

The alternative is to connect one system, build one agent for one team, and let them use it while the strategy is still being written. That is not a rejection of planning. It is a recognition that in this field, a working thing teaches you more than a document about the thing.

What your team can do afterwards

This is the one that separates the two models most sharply.

The traditional model is built to be renewed. The knowledge lives with the firm, so the next agent, the next integration and the next change all mean another engagement. Nobody is being underhanded — it is simply what the commercial model rewards.

The alternative is to treat enablement as the deliverable. If your own people can build the next agent without us, the engagement worked. That is a harder thing to sell and a better thing to buy.

The test is simple: six months after go-live, can your team build the next one on their own? Ask any firm you are considering to describe how their engagement produces that outcome.

Depth in the platform

Enterprise AI platforms are deep products with genuine implementation craft. Permissions modelling, connector behaviour, how the index handles your particular mess of legacy systems — these are learned by doing it repeatedly.

A generalist firm will have people who have done it once or twice, among a great many other things. A specialist has done little else. For the specific job of getting a platform properly deployed, that concentration matters more than headcount.

When a large consultancy is the right answer

There are engagements where it plainly is. If the work spans a global operating-model change across a dozen countries, needs a regulator-facing name on the report, or comes with a mandate to restructure the organisation around it, that is what large firms are built for and a small team is not a sensible substitute.

Be honest about which of those you are actually doing. Many programmes described that way are, underneath, an attempt to get a few important jobs done well.

The questions to ask either way

Whoever you are talking to, these four surface most of what matters:

• Who is on the delivery team, and are they here today?
• When does a real user first touch something working? If the answer is measured in quarters, ask why.
• What can our team do without you at the end? Listen for whether enablement is a deliverable or a hope.
• What have you deployed on this platform, and what went wrong? Anyone who has done it several times has a good answer to the second half.

None of those questions is hostile. They are the ones we would ask, and the ones we are happy to be asked.