AI strategy, readiness and verified ROI
Find out where AI will pay off, what it will cost to run and what has to be true first
AI pilots that never reach production
Many organisations run AI pilots that impress in a demonstration and stall afterwards. The data is not ready, the cost of running the model was never estimated, or nobody owns the result.
Boards now ask for a return on AI spending they can verify. That needs an honest view of readiness and a short list of use cases worth funding.
What you get
Where you stand
An honest assessment of your data, systems, risks and skills.
What to do first
A ranked list of AI use cases, each scored on value and feasibility.
What it costs to run
A token-economics model showing what each use case will cost to run.
A roadmap that proves its return
A roadmap with the return each step must prove before the next is funded.
What we deliver
AI and digital maturity assessment
An audit of your data, systems, risk and skills, showing what is ready for AI now and what must be fixed first.
Use-case prioritisation
Candidate use cases gathered from your teams and scored on business value, feasibility, data readiness and risk.
Token economics
The running cost of each use case, from model choice, request volume, caching and routing, set against its expected benefit.
Verified ROI roadmap
A phased roadmap where each step has a measure of return agreed in advance and checked before the next step is funded.
How it works
Our recommendations come from principal engineers who build sovereign AI, multi-agent systems and data platforms, so feasibility is judged by people who deliver it.
Listen to the business
We interview executives and teams to collect the problems where AI might help, and the measures they care about.
Audit the foundations
We review data sources, platforms, integrations, governance and skills against what each use case needs.
Score and model
We score use cases on value and feasibility, and model their running cost and expected return.
Agree the roadmap
We present the priorities, the roadmap and the first pilot to your executives, with the reasons for each choice.
Technologies and standards
The assessment covers data, systems, risk and skills, and every use case carries a token-economics model. That is the cost of running it, not only of building it.
- Methods and practices
- AI and digital maturity assessment
- Use-case prioritisation
- Token economics
- Verified ROI roadmapping
How to start
This work is usually the digital transformation and AI ROI blueprint: a system and data maturity audit, prioritised use cases, a token-economics model and a board-ready roadmap.
What happens next
Agreeing a time
Our team emails you to agree a time for the session.
Pre-consulting session
A free 30-minute online pre-consulting session: we go through your goals, systems and constraints.
Readiness review
In a free 48-hour readiness review, a Principal Architect checks your project against the 6-point Definition of Ready and recommends the track that fits.
Choosing the track
Scope-ready projects receive a binding proposal within 3–5 business days. Concept-stage projects start in the Blueprint Studio. A live system that is failing, undocumented or needs integration starts with a diagnostic of 1–2 weeks. The Blueprint Studio and the diagnostic each have a fixed fee, quoted in the proposal.
The quickest way in is the Digital transformation and AI ROI blueprint package, one of our rapid engagement packages.
Questions and answers
Who is the assessment for?
For executives who must decide where to invest in AI: chief executives, chief digital, data and AI officers, and boards. It suits organisations starting out and those whose pilots have stalled.
What will we have at the end?
A maturity assessment, a ranked list of use cases, a token-economics model, a roadmap with agreed measures of return and a proposal for the first pilot. You own all of it once each milestone is paid.
What are token economics?
Language models are paid for by the volume of text they read and write, measured in tokens. Token economics estimates that running cost per use case, and how caching and smaller models can reduce it.
How do you verify the return on AI?
Each use case gets a measure and a baseline before work starts, such as hours saved, errors avoided or revenue gained. The pilot is judged against that measure before the next phase is funded.
Do you need access to our data?
We need to understand your data, not copy it. We review sources, schemas, quality and access rules with your teams, and look at samples only where you approve, under your security rules.
Does the assessment cover regulation and responsible AI?
Yes. Each use case is checked against the rules that apply to it, such as the EU AI Act and data protection laws. Our regulatory and AI governance advisers join where the risk calls for it.
Are we committed to XSIS for the work that follows?
No. The roadmap and specifications use vendor-neutral standards, so any capable team can deliver them. If you choose us, our AI and data engineers build what the roadmap recommends.
Start with a conversation, not a contract.
Tell us what you need to build, connect or secure. An architect goes through it with you, then recommends where to start.