Professional Service
AI Project
Activation
From strategy to production-ready use cases: measurable value, governance and execution.
GenAI
MLOps
Use Cases
PoC → MVP
AI Governance
We take AI out of “lab pilots” and embed it into business processes: prioritised use cases, data that is ready, governed models, measurable results.
Many organisations want to “do AI”, but they get stuck on three recurring obstacles: (1) use cases that are not prioritised or not measurable, (2) data that is not ready or not reliable, (3) a lack of governance and of the capability to operationalise solutions (MLOps/LLMOps). We support management in activating AI initiatives with an end‑to‑end approach: from selecting use cases and building the business case, to preparing data, developing PoC/MVPs, and taking solutions into production with risk, security and adoption under control. The outcome is an AI initiative pipeline that is aligned with the business and capable of generating tangible value.
Many organisations want to “do AI”, but they get stuck on three recurring obstacles: (1) use cases that are not prioritised or not measurable, (2) data that is not ready or not reliable, (3) a lack of governance and of the capability to operationalise solutions (MLOps/LLMOps). We support management in activating AI initiatives with an end‑to‑end approach: from selecting use cases and building the business case, to preparing data, developing PoC/MVPs, and taking solutions into production with risk, security and adoption under control. The outcome is an AI initiative pipeline that is aligned with the business and capable of generating tangible value.
01
01
Use Case Discovery & Prioritisation (value-driven)
02
Business Case, ROI & Funding Model
03
Data Readiness & Data Product for AI
04
Choosing the approach: traditional ML vs GenAI
05
Solution Architecture & Integration into business processes
06
MLOps / LLMOps (operationalisation and control)
07
AI Governance, Risk, Security & Compliance (pragmatic)
08
Change Management & Adoption (AI that is actually used)
Delivery Methodology
A three-phase journey,
from value to go-live
PHASE 01
Diagnosis & Mapping (Opportunity & Readiness)
Objective
Select the right use cases and understand what is needed to activate them quickly.
Output
AI Use Case Map + AI Readiness Snapshot + prioritised backlog.
PHASE 02
Design & Activation (PoC → MVP)
Objective
Demonstrate value with an MVP that works and can be industrialised, avoiding “throw-away” PoCs.
Output
MVP ready for pre‑production + updated business case + scaling plan.
PHASE 03
Consolidation & Autonomy (Go‑Live & Scale)
Objective
Put the solution into production, stabilise it, and make AI activation repeatable across multiple use cases.
Output
Go‑Live Pack + KPI/adoption dashboard + AI operating model (run & governance).
02
AI Use Case Portfolio & Prioritisation Deck
AI Business Case & KPI Framework
Data Readiness Report & Data Remediation Backlog
MVP/PoC Deliverable (demo + metrics)
Solution Architecture & Integration Blueprint
MLOps/LLMOps Setup Pack
AI Governance Pack (policies, roles, controls)
Go‑Live & Adoption Kit
Scaling Roadmap (3–12 months)
“Ready-to-use” modules (to accelerate)
Internal AI Copilot
Knowledge search, policies, procedures, Q&A over documents and tickets: an AI assistant that truly knows your organisation.
Document automation
Data extraction, classification, validations and workflow: less manual work, greater accuracy and process speed.
Forecasting & optimisation
Demand, inventory, production capacity, maintenance: sharper predictions for faster operational decisions.
Quality & anomaly detection
Process deviations, fraud, operational anomalies: automatic detection to reduce risks and hidden costs.
Typical timelines (indicative)
AI Opportunity Sprint
2–4 WEEKS
Use case portfolio + business case + AI readiness + prioritised backlog.
MVP
6–10 WEEKS
Dataset + model + minimal integration + metrics + pre‑production.
Go‑Live & Scale
8–16+ WEEKS
Industrialisation, governance, rollout and extension to new use cases.