AI & intelligent services
Decide where AI is worth doing — before anyone builds it.
We work with leadership, IT and the people who own the process to separate the AI ideas that will pay for themselves from the ones that will not, and to leave you with a roadmap your organisation can deliver.
Opportunity assessment · Value cases · Operating model · Roadmap · Adoption
01 — Why programmes stall
Most AI programmes do not fail on the model.
They fail on the choice of problem.
Pilots multiply, budget gets spread thin and nothing reaches production, because nobody agreed up front which problems mattered, who owned them, or what working would look like.
Advisory work here is short and evidence-led. We look at your processes, data and systems as they actually are, size the opportunities against real constraints, and put them in an order that builds capability rather than debt.
You end with a decision rather than a deck: what to fund now, what to prove first, what to leave alone, and what has to be true for each of them to work.
Talk to Zealogics02 — The picture we build
Your opportunities, mapped against value and feasibility.
SEE THE WHOLE PORTFOLIO BEFORE YOU FUND ANY OF IT.
03 — What the engagement covers
Six pieces of work, sized to the decision in front of you.
What we look at
Process and workflow documentation
Systems and integration landscape
Data availability, quality and ownership
Existing pilots and what they showed
Security, risk and regulatory constraints
Skills, capacity and delivery model
What you leave with
AI opportunity assessment
Structured discovery across your functions — where work is slow, manual, judgement-heavy or knowledge-bound — turned into a candidate list with an owner and a hypothesis attached to each entry.
Value cases and prioritisation
Each candidate sized on the value it moves, the data it needs, the systems it touches and the risk it carries, then ranked so the sequence holds up in a budget meeting.
Target operating model
Who owns AI, where the platform team sits, how business units request and fund work, and what stays central — governance, security, evaluation — against what federates out.
AI roadmap
A dated sequence of proofs, pilots and production releases, with the platform and data work that has to land alongside them rather than after.
Governance and policy foundations
The policies, approval gates and risk controls you need before the first system goes live, aligned to your regulatory position and to emerging AI regulation.
Adoption and change enablement
The part roadmaps skip: role changes, training, communication and the measurement that tells you whether people actually use what was built.
04 — Decisions worth making early
The six questions leadership teams keep returning to.
These are what the first wave of pilots usually surfaces — and what advisory work exists to settle.
01
Build, buy or wait
Where a product already solves it, where the differentiator justifies building, and where the honest answer is not yet.
02
Central or federated
Which capabilities belong to a platform team and which belong to the business units closest to the work.
03
Sequencing
Which proof has to succeed before the next investment is defensible, and what platform work has to land in parallel.
04
Data readiness
What has to be true about your data before a use case is worth attempting, and what it costs to get there.
05
Risk appetite
Where human approval is mandatory, where autonomy is acceptable, and how that gets written down before build starts.
06
Measurement
The baseline you take now, so the value claimed later can be evidenced rather than asserted.
05 — How the engagement runs
Weeks, not quarters.
Advisory that runs longer than the decision it informs has already failed. We keep it short and hand over working artefacts.
Frame
Agree the decision, the scope and who has to be in the room.
Discover
Interviews, process walkthroughs and a systems and data review across the functions in scope.
Assess
Size, rank and stress-test the candidates against value, feasibility and risk.
Decide
Roadmap, operating model and governance baseline, agreed with the people who will own delivery.
Mobilise
Stand up the first proof against a defined success measure and a named owner.
06 — How it connects
Strategy that hands over to delivery.
ONE ROADMAP. THE SAME TEAM CAN BUILD IT.
07 — Sound familiar?
The conversations that start this work.
“We have a dozen AI pilots and nothing in production.”
Portfolio triage
“Everyone is asking for AI and we cannot tell which requests are real.”
Opportunity assessment
“We do not know what our AI programme should cost.”
Value cases
“The board wants an AI position and we do not have one.”
AI strategy
“We need governance in place before we go live.”
Governance baseline
“The pilot worked and nobody uses it.”
Adoption planning
08 — The rest of the practice
Six more ways we work on AI.
These services are rarely bought one at a time. Most programmes start with one and pull in the others as the work matures.
Transform
AI Transformation Services
Rebuild an end-to-end process around AI — the workflow, the systems, the roles and the measures — so the gain is structural.
Explore
Automate
Agentic AI & Automation
Agents that carry multi-step work across your systems under guardrails, evaluation and human approval.
Explore
Ground
Generative AI Solutions
Copilots, assistants and knowledge systems grounded in the material you approve — with citations, permissions and review.
Explore
Build
Enterprise AI Engineering
Custom AI applications, enterprise integration and deployment into the environment your security team already accepts.
Explore
Govern
AI Governance & Responsible AI
The framework, guardrails, evaluation and audit trail that let a risk committee approve an AI system.
Explore
Operate
AI Managed Services
We run what we built — and what others built — with monitoring, evaluation, upkeep, cost management and real support.
Explore
Have a problem worth solving?
Start with the decision you are stuck on.
Bring us the shortlist, the stalled pilot or the blank page. A short, focused assessment is usually enough to make the next call obvious.