AI consulting - enterprise
Enterprise AI consulting
Most enterprise AI programmes do not fail at the model. They fail at the handover, when the consultancy leaves and nobody owns the thing that is now in production.
Enterprise buyers rarely need to be convinced that AI can help. They have run the workshops. What they are short of is a partner who will name one constrained problem, build against it, and still be there at 2am in month four when a vendor API changes its response shape. That is the gap Digiton works in, from Lisbon, with production deployments across 8 countries.
Discovery that ends in a decision, not a deck
A useful discovery answers four questions in writing: which process is costing the most unbilled hours, what the data behind it actually looks like (not what the schema says it looks like), who signs off when the system is wrong, and what happens on the day it goes down. Anything that does not move those four forward is theatre. Two weeks is usually enough. If a proposed discovery runs a quarter, the scope is the problem.
Build to the regulator, not to the demo
An enterprise system carries obligations a prototype does not:
- Every decision the system influences needs a stored trace: inputs, retrieved context, model version, output, and who approved it.
- Personal data flows need a lawful basis and a retention rule before the first record is processed, not after legal review blocks the launch.
- Access control has to survive an internal audit. Row-level security in the database, not a filter in the application layer.
- Model choice must be swappable. If a vendor deprecates a model, that should be a config change and a regression run, not a rebuild.
The part most consultancies skip
Operating. An AI system is not a deliverable, it is a running service with drift, cost, and failure modes. It needs evaluation sets that get rerun when anything changes, alerting on refusal and error rates, a cost ceiling per workflow, and a human queue for the cases the system flags as uncertain. Digiton builds and then operates, which is why the engagement usually starts with a fixed-scope build and continues as a managed retainer rather than a staff-augmentation body count.
Where the value tends to sit
Across industries the highest-return first projects look similar: document intake and extraction with a human check, retrieval over a corpus the organisation already owns, and agents that handle the deterministic parts of a workflow while escalating the rest. Chat interfaces get the attention. Extraction and retrieval pay the bills.
Starting
Pick the process with the most unbilled hours or the longest queue, and instrument it before building anything. A short AI audit produces the four written answers above plus a build estimate, and is deliberately small enough that walking away afterwards costs almost nothing.
Frequently asked questions
What does enterprise AI consulting involve?
Three phases. A discovery that names one constrained problem and the data behind it, a build that ships to production with audit trails, access control and evaluation sets in place, and an operating phase where someone owns drift, cost, failure modes and the human review queue after go-live.
How long does a first enterprise AI project take?
A discovery should take about two weeks. A first production build on a well-scoped process typically runs six to twelve weeks depending on data access and how many approval gates the workflow crosses. Anything promising enterprise production in a fortnight is describing a prototype.
Should we build in-house or use a consultancy?
Build in-house when AI is core to the product and you can hire and retain the people. Use a partner for the first two or three systems, then take them over. The important term in any contract is that you own the prompts, the evaluation sets and the data, so a handover is possible.
Related
Ready to put AI to work?
Book a discovery audit and we will map the highest-ROI AI agents and automations for your business.
Book a discovery audit →