Embedded scoping
Forward deployed engineers sit with the teams doing the work and write the scope from what they see, not from a brief.
Discipline07 · Enterprise Solutions
In shortUpdated 30 Oct 2026 · Labs 24 FDE Squad
Enterprise AI implementation at Labs 24, in short
Enterprise AI implementation is taking AI from a single pilot to dependable use across an organisation: the right owners, approvals, systems, training and standards. Labs 24 does it with forward deployed engineers who embed inside the client's teams, so scoping, security review, procurement and rollout happen alongside the build instead of after it.
- Model
- Forward deployed engineers inside your teams from day one
- Stakeholders
- Sponsor, business owner, IT, security, data owners and procurement mapped and engaged
- Approvals
- Security questionnaires, data processing terms and vendor onboarding supported
- Rollout
- Pilot to first team, second team, business unit and company standard
- Handover
- Internal team trained, runbooks and standards documented, embedded pods via Teams24
- Cadence
- Weekly demos, fortnightly steering, one named owner on each side
01 / What it means
Most AI pilots die in the org chart, not the code.
The pilot worked. Then it stalled in a security review, nobody owned it after the demo, and the next team started again from zero. Enterprise solution engineering is the work that stops that: inside your organisation, with your people.
One named owner on each side, from the first meetingAI delivered from outside
- Scope written by people who never saw the work
- Security review starts after the build
- No owner once the vendor leaves
- Every new team starts from zero
AI delivered forward deployed
- Engineers inside the team from day one
- Security and procurement run alongside the build
- A named owner on your side before go-live
- Shared components the next team reuses
- Your people trained to run and extend it
02 / Capabilities
Enterprise AI implementation capabilities.
Eight parts of enterprise solution engineering we own end to end, from the first stakeholder meeting to the company-wide standard.
Stakeholder mapping
Sponsor, business owner, IT, security, data owners and procurement identified early, with a clear ask for each.
Security and procurement support
Security questionnaires, data processing terms, architecture reviews and vendor onboarding answered with real technical detail.
Solution design
Requirements, architecture and acceptance criteria agreed with business and IT, written down and signed off.
Programme management
Weekly demos, fortnightly steering, a shared risk log and one decision-maker per side, so blockers surface early.
Rollout and change
Pilot to first team to the next, with champions, short training and feedback loops that keep people using it.
AI governance setup
An intake process for new use cases, shared standards for models and data, and a small group that approves what ships.
Handover and capability
Your engineers trained to run and extend the system, with embedded pods through Teams24 when you need more hands.
03 / Rollout path
From one pilot to a company standard.
Five stages, each with its own people, approvals and deliverables. Pick a stage to see what it takes.
Who is involved
One team lead, a data owner and the Labs 24 squad.
Gates to pass
Access to one workflow's data; a security pre-check.
What you get
One slice running on real work, measured against how it was done before.
Done when
One slice beats the old way on real work
Who is involved
Team manager, IT, the data owner and a champion from the team.
Gates to pass
Full security review signed off; support process agreed.
What you get
The whole team using it, a runbook, and adoption tracked weekly.
Done when
The whole team uses it without help
Who is involved
A second business owner and whoever owns the shared platform.
Gates to pass
Approved patterns reused; sign-off from the new data owner.
What you get
Gateway, connectors and evals reused, so the second launch takes a fraction of the first.
Done when
The second team launches mostly on reused parts
Who is involved
Business unit head as sponsor, finance, procurement and IT architecture.
Gates to pass
Budget approved, vendor onboarding complete, platform standards agreed.
What you get
A shared AI platform in your accounts, an intake process for new use cases, and an internal team trained.
Done when
New use cases arrive through the intake process
Who is involved
Executive sponsor, an AI council, security, legal and data protection.
Gates to pass
Governance policy, model and data standards, audit readiness.
What you get
Company AI standards, a reusable platform, and internal forward deployed capability, with Teams24 pods if needed.
Done when
AI standards are part of how the company builds
04 / Who does what
A RACI for an enterprise AI project.
Who is responsible, accountable, consulted and informed at each step. Agreed at the start, so nobody waits on a decision nobody owns.
| Activity | Exec sponsor | Business owner | IT and security | Data owner | Labs 24 squad |
|---|---|---|---|---|---|
| Pick the first slice | A | R | C | C | R |
| System and data access | I | C | A | R | C |
| Build and test | I | A | C | C | R |
| Security sign-off | I | C | A | C | R |
| Go-live decision | I | A | C | C | R |
| Run and support | I | C | A | I | R |
| Scale to the next team | A | C | C | C | R |
R = responsible, does the work. A = accountable, makes the call. C = consulted. I = informed. Labs 24 stays responsible for running the system until handover is signed off.
05 / How it ships
Embed, design, pilot, plan the next team.
The first four steps set up everything the rollout depends on. A demo every Friday and regular steering calls.
On your floor, in your channels, at your stand-upsStep 01 · Embed
Owners and gates
We join your teams, map the stakeholders and list every approval the project will need.
- Stakeholder map
- RACI agreed
- Approval list
Step 02 · Design
Design and paperwork
Solution design signed off; security questionnaire and data terms answered in parallel.
- Design sign-off
- Security pack submitted
- Friday demo
Step 03 · Pilot
One team, side by side
The first slice runs beside the team, with a champion collecting feedback daily.
- Pilot running
- Champion in place
- Feedback log
Step 04 · Launch
Live, then the plan
Go-live for the first team, owner named, and the rollout plan for the next teams agreed.
- Go-live sign-off
- Named owner
- Rollout plan
06 / How we work
How the engagement runs.
The working rhythm and the documents that keep a multi-team programme moving.
Cadence
Daily in your channelsFriday demosFortnightly steeringMonthly sponsor review
Documents
Stakeholder mapRACISolution designSecurity packRisk log
Handover
RunbooksTraining sessionsAI standardsUse-case intake
Need a long-term embedded team after handover? Pods come through Teams24, part of the Labs 24 group.
07 / In production
Enterprise AI implementations in production.
Under NDA, so clients stay unnamed. Each one shipped inside the client's own processes, approvals and infrastructure.
Transit authority rewards layer
Delivered through a public authority's approvals, compliance reviews and IT standards, with the authority owning the system.
Multi-stakeholder
Digital banking offer engine
Shipped as a licensed module through a bank's vendor onboarding, security review and release process.
Vendor onboarding
Rubber manufacturing ops tool
Rolled out to the shop floor with shift champions and training, so it became part of how the plant runs.
Adoption on the floor
08 / The squad around it
The squad, brought into your organisation.
Enterprise solution engineers bring in the right specialists at the right stage, and keep them pointed at the same goal.
01
Agent Engineering
The agents most rollouts start with.
02LLM Engineering
Model choices that hold up at company scale.
05Production & Cloud
A shared platform in your accounts.
06Integration
Connectors each new team can reuse.
10Architecture
Standards and build-or-buy calls.
11Security & Compliance
Security packs and audit evidence.
09 / FAQ
Enterprise AI implementation FAQ.
What is enterprise AI implementation?
Enterprise AI implementation is taking AI from a single pilot to dependable use across an organisation: the right owners, security and procurement approvals, system access, training, rollout and shared standards. Labs 24 does it with forward deployed engineers embedded in the client's teams.
What is a forward deployed engineer?
A forward deployed engineer is a software engineer who works inside the customer's organisation instead of from a distance: watching the work, scoping it, building the system on real data and staying until it runs. Demand for the role has grown sharply as companies move AI from pilots into production. Labs 24 is built around forward deployed squads.
How is a forward deployed engineer different from a consultant or solutions engineer?
A consultant usually advises and hands over a plan. A solutions engineer typically supports a sale of an existing product. A forward deployed engineer builds and ships the working system inside the client's environment and owns the outcome until handover.
Why do AI pilots fail to scale across an enterprise?
Most stall outside the code: no named owner after the demo, security review started too late, procurement and data terms not ready, and each new team starting from zero. Labs 24 runs these tracks alongside the build and turns the first slice into reusable components.
How do you get an AI system through security review and procurement?
By starting on day one: mapping every approval needed, answering security questionnaires and data processing terms with real technical detail, and giving IT and security a design they can review before anything is built.
Who should own an AI system inside the company?
A named business owner should be accountable for outcomes, with IT accountable for running it and a data owner for the data it uses. Labs 24 agrees a RACI at the start and stays responsible for running the system until handover is signed off.
Do we need an AI governance framework before starting?
Not a full framework. A short intake process, a few model and data standards and a small approval group are enough to start; they grow as more teams adopt AI. Labs 24 sets these up as part of scaling past the first team.
How does an enterprise AI rollout progress?
In stages: a proven pilot, the first team using it fully, a second team reusing the same components, a business unit on a shared platform, and finally company-wide standards. Approvals and team readiness set the pace more than the engineering does.
Can you provide a long-term embedded team?
Yes. After handover, embedded engineering pods are available through Teams24, part of the Labs 24 group, so the internal roadmap keeps moving.