AI strategy that ranks your use cases before you fund a build

You receive a written roadmap, not just a workshop. It contains a ranked list of AI use cases with the reasoning behind each score, a summary of data and system gaps, build-versus-buy recommendations, governance rules covering staff use, suppliers and human review, and a phased plan with owners, dependencies and success measures. Where a decision depends on technical feasibility, it also includes the results of a small prototype run on your own data.

We score every candidate on the same criteria and start with those that combine clear value with low delivery risk. The criteria are business value, availability and quality of data, integration effort, the impact if the system gets something wrong, and cost to run at your volumes. Scores are agreed with your team and assumptions are written down. The first choice is usually a contained process where results are easy to measure.

An AI readiness assessment checks whether your organisation can deliver and run a specific AI use case successfully. We review four areas: data, meaning whether the right information exists and can be reached; systems, meaning whether integrations and hosting are in place; people, meaning who will own, review and maintain it; and governance, meaning how decisions, risks and incidents will be handled. The result is a list of gaps, ranked by which use cases they block.

A data audit shows whether an AI system can be accurate before you spend money building it. We check where the relevant data lives, how complete and consistent it is, who owns it, how it can be accessed and whether it contains personal or sensitive information. Gaps such as missing fields, scanned documents or systems without APIs are cheaper to fix early, and some use cases are better postponed until the data improves.

Buy when a mature product covers the process well and your data can safely go to that vendor; build when the process is distinctive, needs deep integration or the data must stay under your control. Many organisations end up with a mix: vendor tools for common tasks and custom work where it creates an advantage. We compare options on fit, data handling, lock-in, total cost over several years and how easily you could switch later.

It depends on how many teams, use cases and data sources are in scope, and the timeline is fixed in the written proposal before work starts. A focused engagement on one business unit with a handful of candidate use cases is the shortest form. Time is added mainly by interview availability, approvals to access sample data, the number of systems to audit and any feasibility prototypes. Organisation-wide work is usually split into phases, each ending with decisions leadership can act on.

It depends on scope, and the price is fixed in the written proposal once the discovery call has made that scope clear. It rises with the number of teams to interview, the systems and data sources to audit, whether feasibility prototypes are needed and how much governance documentation you require. A fixed price keeps the advice focused rather than stretched to fill time, and if you later build with us, the discovery work carries straight into delivery.

By recording a baseline before anything is built and comparing the same measures afterwards. For each shortlisted use case the roadmap names those measures, such as handling time per request, rework rate, backlog size or response time, and says how they will be collected. It also estimates the cost to build and run, including model usage and the staff time spent reviewing output. We do not promise percentages in advance; prototype and pilot results show whether the case holds.

We build risk and governance into the roadmap rather than adding them at the end. For each use case we identify what could go wrong, such as inaccurate output, exposure of personal data or unfair decisions, and set controls like human review, logging, access limits and monitoring. We flag where rules such as UK GDPR, the EU AI Act or Saudi Arabia's Personal Data Protection Law may apply, and your legal advisers confirm your obligations, since we are not a law firm.

Yes, if staff already use public AI tools, a short written policy is one of the quickest ways to reduce risk. It should state which tools are approved, what information must never be entered, such as customer records or unreleased financials, when output needs checking by a person, and who approves new tools. We draft policies that match how your teams actually work, and pair them with an approved alternative so people are not pushed back to consumer apps.

Yes, the roadmap is written so that any capable team, internal or external, can deliver it. It describes use cases, data requirements, architecture options and success measures without assuming a particular supplier, and you own the document outright. You may choose us for the first phase because we already know the context, but that is a separate decision with its own proposal. If another vendor's product is the right fit, the roadmap will say so.

Usually not, because many first use cases rely on existing language models, retrieval and automation rather than custom-trained models. What you do need is a business owner for each use case, someone who understands the underlying data, and a clear route for reviewing and improving results. The roadmap shows which roles to develop internally, which a partner can cover, and when a use case such as custom forecasting justifies dedicated machine learning skills.

Yes, we provide independent technical reviews of AI proposals from vendors or internal teams. We check whether the stated accuracy is realistic for your data, how the system will integrate and be hosted, what happens to your data, what it will cost to run at your volumes and how hard it would be to switch away later. You receive written findings and questions to put to the vendor, so the decision rests on evidence rather than a demo.