From idea to production.
The six phases of an AI deployment.
If you have been told your organisation needs to "do something with AI" and you are trying to figure out what that involves, this page is a map. Six phases, from the first business-case conversation through to running, evaluated, expanding production AI. Plain English. Not a sales pitch - a map.
Six phases, in order.
Phase
01
Discovery & business case
Identify candidate processes · estimate ROI · prioritise
→ Answers: "Where could AI actually help us?"
Phase
02
Feasibility & architecture choice
Data readiness · build vs. buy vs. API · on-prem vs. cloud · security & compliance constraints (GDPR, EU AI Act)
→ Answers: "How will we build this, and on what?"
Phase
03
Pilot
One narrow use case · often on cloud or API first · measure against baseline
→ Answers: "Does this actually work for us?"
Phase
04
Infrastructure & integration
Procure hardware · provision environment · connect data sources · pipelines, access controls, monitoring
→ Answers: "How does this run reliably inside our company?"
Phase
05
Rollout & adoption
User training · process redesign · change management
→ Answers: "Are people actually using it, and getting value?"
Phase
06
Operate, evaluate, expand
Model evaluation · drift monitoring · fine-tuning · scale to next use case · governance reviews
→ Answers: "How do we keep it good and grow the programme?"
Four concerns present in every phase.
Some responsibilities don't sit in any single phase - they run alongside the whole journey. If your organisation does not assign owners for these from the start, they become invisible until something goes wrong.
Data governance
Who owns which data, what quality it has, what classification each set carries, who is allowed to use it for AI.
Security
Identity, access, network boundaries, key management, audit trails - applied to the AI infrastructure as it is to the rest of IT.
Compliance
GDPR, the EU AI Act, sector-specific regulations. Compliance is a phase-zero conversation, not a phase-six fix.
AI governance
Who authorises models, evaluates outputs, monitors drift, retires systems. Often the slowest layer to mature - start it early.
Where LM TEK plugs in.
LM TEK is directly involved in two phases. Phase 4 - infrastructure & integration - where we supply the hardware platform that the system integrator builds on. And Phase 6 - operate, evaluate, expand - where we support the hardware lifecycle alongside the AI consultancy who runs the workloads.
We are not the right call for Phase 1 or Phase 3. Discovery, business case, and pilot work belong with strategy advisors and AI consultancies. We will happily recommend a partner who handles those phases well - that is what the routing CTA at the bottom of this page is for.
Phases 2 and 5 are partner work too. Phase 2 - the architecture choice between on-prem, cloud, and hybrid - is where we engage early if the answer is moving toward on-prem; otherwise we wait. Phase 5 - rollout and adoption - is business-side, not technical.
Where are you in the journey?
Tell us what phase you are at and we will recommend the right partner for the next step. If you are at Phase 4 already and looking at hardware, we will route you to the platform conversation directly.