Which AI consulting service should come first?
Usually a short readiness and use-case review, so that architecture and governance decisions are based on the processes that matter most.
AI Consulting services
AI Consulting Services and Their Deliverables
AI consulting services are easier to compare when each one is described by its result. This page lists the usual services, what a good deliverable looks like and which questions to ask before signing.
Overview of the usual services
Most offers are assembled from the following building blocks. The names differ between firms; the content is similar.
AI readiness assessment
A structured look at goals, data, systems, skills and rules, ending in a short list of strengths and gaps.
Use-case discovery and prioritisation
Workshops with departments, followed by a ranked portfolio of use cases with value, effort and risk per idea.
Data and integration review
An inventory of data sources, owners, quality and interfaces needed for the selected use cases.
Target architecture
A design for models, integration, security, logging and operation that the selected use cases can share.
Governance framework
Policies for permissions, human review, documentation and regulatory duties, assigned to named roles.
Pilot implementation
A first use case built, tested with real data and prepared for regular operation.
What a good deliverable looks like
A readiness assessment is useful when it names concrete gaps and owners, not when it only assigns a maturity score. A use-case portfolio is useful when every entry has a process owner, a measurable goal and a first estimate of data availability.
An architecture is useful when it shows how the second and third use case reuse the components of the first. A governance framework is useful when its rules can be enforced technically - for example by permissions and logs - rather than only described in a document.
A pilot is useful when it runs on real data, has a defined owner in the business and a plan for monitoring and maintenance after the consultants leave.
How AI consulting is priced
Daily rates are the most common basis, either as a time-and-material budget or as a fixed price per phase. Fixed prices shift risk to the provider but usually come with a narrow scope; daily rates are flexible but make the total harder to predict.
When comparing offers, look at the effort per result rather than the daily rate: how many days lead to a working pilot, and how much of that work will be reused by later use cases. An offer with a higher rate can be cheaper overall if it avoids rebuilding the same foundations for every project.
Questions to ask before signing
These questions make hidden assumptions visible early.
- Which results are fixed in the contract, and how is acceptance defined?
- Who on your side must be available, and for how many hours per week?
- Which tools or platforms does the provider prefer, and are there commercial ties?
- What happens to code, prompts, configurations and documentation after the project?
- How will the solution be operated, monitored and updated in the first year?
Which services to buy and which to keep in-house
Use-case collection, process descriptions and data ownership are best kept in-house; they depend on knowledge of the business. Architecture, security design and regulatory interpretation benefit from outside experience.
Many companies combine both: a short external engagement for architecture and governance, followed by internal teams that implement use case after use case on the shared foundation.
Frequently asked questions
How long does an AI consulting engagement take?
From a few weeks for an assessment to several months for strategy plus pilot. Duration grows mainly with the number of departments involved and the number of separate projects.