What Does an AI Consultant Do? A Day-by-Day Breakdown

What does an AI consultant actually do? It is a fair question, and the honest answer is: it depends on whether you hired a builder or a slide deck merchant. At Njin, AI consulting means designing systems, building them and leaving your team able to run them. Here is what that looks like in practice.

Phase 1: Discovery and diagnosis

Before any AI is built, a good consultant spends time understanding your business. Not a surface-level intake form. Genuine discovery.

This typically involves:

• Mapping your current workflows, especially where time is wasted or revenue is leaking
• Understanding your existing tools, data and technical environment
• Identifying where AI will have the highest commercial impact
• Prioritising opportunities by effort versus return

The output of this phase is not a list of AI tools. It is a prioritised set of specific problems that AI can solve, ranked by business impact.

This is also where many bad consultants stop. A genuine discovery process takes one to two weeks. A tick-box intake session followed by a proposal deck is not discovery.

Phase 2: Strategy and roadmap design

Once the problems are clear, the consultant designs the approach. This covers:

• Which AI tools and platforms are the right fit
• How the system will integrate with your existing stack
• What the build sequence will be
• What your team will need to do and what the consultant will handle
• What success looks like and how you will measure it

A strategy that does not include a build plan is incomplete. You should leave this phase with a clear scope, a timeline and an agreed outcome. If the consultant cannot give you that, they are selling advice, not a result.

Read more about how AI strategy connects to your business goals in our piece on the AI strategy framework for founders.

Phase 3: Build and implementation

This is where most of the work happens and where the best consultants earn their fee.

A typical build phase includes:

• Setting up and configuring the AI tools and platforms
• Building custom workflows, automations and integrations
• Testing the systems against real business scenarios
• Iterating based on feedback from your team
• Documenting how everything works

At Njin, we build alongside your team rather than disappearing into a black box. You can see what is being built and why. That transparency makes the training phase much faster.

Our AI consulting services include the full build, not just the design. You get a working system, not a specification document you have to go and build yourself.

Phase 4: Training and handover

A system your team cannot operate is just a liability. Handover is not optional, it is the point.

A proper handover includes:

• Training sessions for the people who will use the system day to day
• Documentation that covers how to operate and troubleshoot
• A clear escalation path for when things go wrong
• A defined support period while your team builds confidence

The measure of a good engagement is how quickly your team can run without the consultant. If you are still dependent on them six months later, something went wrong in the handover.

What an AI consultant does not do

It is worth being clear about what falls outside scope:

• They are not a managed service provider. They build the system, they do not run it forever.
• They are not a software developer building bespoke products from scratch.
• They are not responsible for your team's adoption. That is a leadership question.

If you want someone to run a specific AI function on an ongoing basis, that is more of an agency model. See our comparison of AI consulting vs AI agency.

How to judge whether your consultant is doing good work

At four weeks in, you should be able to answer yes to these:

• Do I know exactly what is being built and why?
• Is the build tracking against the agreed scope?
• Can I see progress on something real, not just status updates?
• Does my team understand what they will be taking over?

If you are unsure how to evaluate a consultant before hiring, our guide on how to choose an AI consultant gives you the questions to ask upfront.

Curious what an engagement with Njin would look like for your business? The Fit Scorecard is the fastest way to find out. Take the Scorecard

Common questions

Does an AI consultant need to be technical?

They need to be technical enough to design and oversee a build. They do not need to be able to write production code, but they need to understand what they are asking developers or platforms to do. Consultants who cannot explain technical decisions in plain language are usually not doing the thinking themselves.

How involved does my team need to be during the engagement?

Your team needs to be involved in discovery, testing and training. The middle section (the build) can be largely consultant-led. Plan for roughly one to three hours per week of your team's time during a typical engagement.

What happens if the system does not work as expected?

That is what the testing phase is for. A good consultant builds in iteration cycles and resolves issues before handover. Any contract should include a defined support period post-launch for exactly this reason.

Can one consultant handle both strategy and build?

Yes, and that is often the better model. It removes the coordination cost between a strategy firm and an implementation partner. At Njin, we do both in a single engagement. The strategy and the build are the same project, not two separate contracts.