Allostasis/Services/Track 02 — Transformation/T.04
T.04 · TRANSFORMATION

AI Implementation &
Enablement
.

Identify, prove and deploy AI use cases on Azure OpenAI, Azure AI Foundry and Copilot Studio — scoped to outcomes the business can actually measure.

ServiceT.04 — AI Implementation & Enablement
TrackTransformation
Typical scope8–16 weeks, scoped per use case
EngagementArchitecture-led, with AI engineering delivery
PrerequisiteFoundations + Data Platform readiness

Why this matters

AI proofs-of-concept don't fail technically. They fail commercially.

Most mid-market AI initiatives stall not because the model didn't work, but because no one defined what success would look like, what data the workload would touch, or how it would integrate with the systems people actually use.

We run AI implementation as engineering, not theatre: use cases scored against commercial value, scoped to data the foundation can support, built on Azure-native services, with a measured production hand-over.

What it includes

Six work-streams, scoped to your environment.

01

Use case discovery

Workshop-led identification and scoring of candidate AI use cases against value, feasibility and data readiness.

02

AI architecture

Reference architecture across Azure OpenAI, AI Foundry, AI Search and Copilot Studio — sized to the workload.

03

Responsible AI guardrails

Content filtering, prompt-injection defence, evaluation harnesses, and human-in-the-loop design.

04

Proof of value

Single highest-value use case built end-to-end, with measured uplift against a defined baseline.

05

Production deployment

Hardened deployment, monitoring, cost controls and integration into the systems users already work in.

06

Operating model

Use-case intake, evaluation, deployment and governance handed to internal IT and business owners.

Engagement sequence

How an AI engagement runs.

STEP 01 · WEEKS 1–2

Discovery & scoring

Candidate use cases scored on value, feasibility and data readiness. Top one or two selected.

→ Prioritised use-case list
STEP 02 · WEEKS 2–4

Architecture & baseline

Reference architecture, evaluation baseline and responsible-AI guardrails defined.

→ Approved AI architecture
STEP 03 · WEEKS 4–10

Proof of value

Selected use case built end-to-end and measured against baseline with real users.

→ Validated commercial uplift
STEP 04 · WEEKS 10–16

Productionise & handover

Hardening, integration, monitoring and operating model handed over.

→ Production AI capability

Outcomes

What you have at the end.

VALUE

Commercial uplift, measured.

A use case with a baseline-against-now comparison your finance team can defend.

RESPONSIBLE

AI you can deploy without a board incident.

Content filtering, evaluation and human-in-the-loop design built in — not retrofitted.

REPEATABLE

An operating model for the next ten use cases.

Intake, evaluation, deployment and governance owned by your team — not by us.

Before you start here

Foundations check

AI implementation depends on identity, data and architecture readiness more than any other service. If those have not been validated, start with a Readiness Conversation — we will sequence the path honestly.

Other transformation services

What sits alongside.

The next step

Run AI as engineering — not theatre.

Forty-five minutes with a senior architect. We'll ask about your candidate use cases, your data reality and your risk posture — and tell you honestly which to start with.