Home Programs Enterprise AI Transformation
Flagship program
From AI pilots to governed AI operations
A phased program that selects the workflows where AI pays back, builds assistants and agents into your core platforms, and runs them with governance and value tracking your board can see.
Last reviewed: 29 September 2026
3–5
priority workflows in production
Month 1
value baseline and dashboard in place
Before go-live
governance controls live for every use case
Typical target ranges, agreed at baseline.
Pilots everywhere. Impact nowhere.
Most leadership teams can list a dozen AI experiments. Few can point to a workflow where AI now does meaningful work every day, with a measured effect on cost, cycle time or revenue — and with controls a regulator would accept. The result is spend without compounding value, and growing fatigue in the business. Meanwhile, the platforms you already pay for are adding AI features faster than your teams can absorb them.
- Pilots selected for novelty rather than value
- AI running beside core systems instead of inside them
- No clear owner for adoption, risk or results
Transformation happens workflow by workflow — with a method.
Why most AI transformations stall
| Where it stalls | What it costs | Our answer |
|---|---|---|
| Strategy decks without delivery | Roadmaps expire before anything ships | A roadmap and the first production workflows in one program |
| Tools first, process second | Automating broken workflows at scale | Workflow redesign before any build |
| No operating model | Nobody owns AI after the project team leaves | Named owners, runbooks and AI operations from day one |
How the program runs
1
Opportunity & Value Mapping
3–4 weeks
We interview leaders, map processes and score use cases on value, feasibility, risk and adoption effort. Every shortlisted use case gets a baseline.
Deliverable: value-ranked AI portfolio and business case.
2
Architecture & Governance Design
2–4 weeks
We define how AI connects to your ERP, CRM, productivity tools and data; classify each use case by risk; and design the controls it needs.
Deliverable: adoption architecture and control set.
3
Build & Integrate
6–12 weeks per wave
We build assistants, agents and automations inside your platforms, with human checkpoints for high-impact actions and evaluation thresholds before release.
Deliverable: production workflows.
4
Adopt & Scale
ongoing per wave
We redesign roles and procedures, train teams, and track adoption and value until the new way of working is the default.
Deliverable: adoption and value reports.
5
Run & Improve
optional
We hand over to your team or to our AI Operations & Managed Services.
Deliverable: service reports and quarterly roadmap.
Governance designed in, not bolted on
Governance is part of the build, not a review at the end. Every use case leaves the program with its controls working and its evidence documented.
- AI inventory and risk classification for every use case
- Identity, access and permissions for users and agents
- Logging and audit trail for AI-assisted decisions and actions
- Evaluation and quality thresholds before release
- Human approval for actions with financial, legal or customer impact
- Named accountable owners for each workflow
Frameworks we map to: EU AI Act · ISO/IEC 42001 · India DPDP Act · GDPR · ISO/IEC 27001 · NIST AI RMF
Move beyond pilots. Talk to a SynAdopt lead about one workflow that matters.
See it in practice
Client Zero
AI-assisted multilingual content operations
How we run AI inside our own quality-critical production pipeline, with human review, terminology control and measured quality.
Read the story
How to start and how it grows
1
Executive AI Strategy Workshop
2–3 weeks
Portfolio, risk map, business case and 120-day roadmap.
2
Wave 1
3–4 months
Two or three priority workflows in production, with governance and value tracking live.
3
Waves 2+ or managed operations
Scale to further workflows, or hand over to AI Operations & Managed Services.
Commercial options: fixed-fee phases · milestone-based · optional outcome-linked component.
Questions about the Enterprise AI Transformation Program
How long does an enterprise AI transformation program take?
The first production workflows typically go live within three to four months of starting, after a two-to-three-week workshop. A full program runs six to twelve months in waves, with each wave adding workflows and building internal capability.
Do we need a new AI platform to start?
Usually not. Most organisations already own capable AI through Microsoft, SAP or Salesforce, plus access to leading models through their cloud. We start with what you have and recommend new components only when a use case clearly needs them.
How do you choose which workflows go first?
We score candidate use cases on value, feasibility, risk and adoption effort, and weight them against your strategic priorities. The first wave favours workflows with clear baselines, willing owners and manageable risk, so early results are measurable and credible.
How do you handle AI agents that take actions in our systems?
Each agent gets its own identity and the minimum permissions it needs, every action is logged, and actions above agreed thresholds require human approval. Agents are tested against evaluation sets before release and monitored in production.
How is value measured and reported?
We agree baselines and target KPIs for each workflow in the first phase, then report monthly on adoption, operational KPIs and financial impact. The dashboard stays with you after the program ends.
Pick one workflow. We’ll show you the path to production.
A 30-minute call with a senior lead to discuss your priorities, your platforms and what a first wave could deliver.
- 30 minutes with a senior lead
- No sales script — a working conversation
- A clear view of where AI can pay back first