Build & Operate AI · Layer 04
AI implementation, built to pass risk, audit and the regulator first time
Most AI pilots stall between the demo and production. Not because the model fails, but because risk, security and compliance arrive too late and send the project back to the start.
We are an AI implementation partner for regulated firms. We help fintechs, technology companies and asset managers choose the right use cases, design the architecture with controls built in, roll out tools such as Copilot and Claude safely, and get to production with sign-off already in hand.
01 · What good looks like
What is good AI implementation?
Good AI implementation is getting an AI use case from idea to production, delivering measurable value, with the controls your risk function, auditors and regulator will ask for already in place.
In a regulated firm, speed comes from building governance in, not from skipping it. Good AI implementation has five parts:
- Use cases chosen on value and risk together, so weak ideas stop early and strong ones get funded
- An architecture with controls designed in: model choice, data boundaries, guardrails and logging from the first sprint
- Enterprise tools such as Copilot, Claude or ChatGPT Enterprise rolled out with the right configuration, access and data boundaries
- A clear gate between pilot and production: tests passed, oversight defined, monitoring live, owners signed off
- An operating model that keeps it running: owners, intake, change control and adoption measured against value
You can tell it is working when three things are true:
- Value is measured. Every use case has a business owner and a number it is meant to move.
- Controls are built in, not bolted on. Risk and security review the design, not the finished product.
- Approval comes first time. Go-live is a checkpoint, not a negotiation.
Every use case then ends in one of four decisions:
02 · Why do it
Why most AI pilots never reach production
The technology is rarely the problem. Projects stall on the questions nobody asked at the start. Four patterns repeat:
- A solution looking for a problem. The pilot impresses, but nobody owns the business case.
- Governance at the end. Risk and security see the design for the first time at go-live, and send it back.
- Tools switched on, not rolled out. Copilot goes live with default settings and finds every file a user can open.
- No owner after launch. Nobody monitors it, improves it or decides when to switch it off.
Sources: Gartner, July 2024; MIT NANDA, The GenAI Divide: State of AI in Business 2025.
What regulators expect when you deploy AI
Firms deploying AI in the EU
Deployers of high-risk AI must follow instructions for use, assign human oversight, monitor and keep logs (Article 26), from 2 December 2027. Chatbots must tell users they are AI from 2 August 2026.
Deployment duties are yours, even when the model is someone else’s.
EU financial entities, since January 2025
AI model and platform providers are ICT third-party providers: contracts, exit plans and a register of information apply.
Your AI vendor contracts need DORA terms before go-live.
UK firms
No AI-specific rules. Consumer Duty, SM&CR and outsourcing rules apply to how AI is deployed and who is accountable for it.
A named senior manager must own each material deployment.
US banks and advisers
Interagency guidance on third-party relationships applies to AI vendors, and the SEC’s examination priorities include how advisers use AI.
Vendor due diligence and accurate AI claims are checked.
Singapore financial institutions
The Guidelines on AI Risk Management, finalised October 2026, expect lifecycle controls and accountability for third-party AI, phased in from October 2027.
Controls are expected from design, not added after launch.
AI management standard and US risk framework
Both expect AI to be planned, built and operated within a managed lifecycle with defined roles.
Implementation done this way is already halfway to certification.
Summaries for orientation, as of October 2026. Check how each applies to your firm.
03 · How to decide
Seven decisions before you build
AI implementation is mostly a set of decisions. Make them in the first weeks and the project moves fast. Leave them to go-live and it stops.
Decision 1 · Use caseWhich problem is worth solving?
Score each idea on value, feasibility and risk. Fund the few with a clear owner and a number to move.
Decision 2 · SourcingBuild, buy or partner?
Buy where an enterprise tool already does the job, build only where your data or process is the advantage, and partner to close skills gaps.
Decision 3 · ArchitectureWhich model and platform?
Choose on task fit, cost, data residency and exit options, not on the leaderboard.
Decision 4 · Data boundariesWhat data can it see?
Set what the AI may read and where data may go, before connecting it to SharePoint, email or customer records.
Decision 5 · AutonomyHow much should it decide on its own?
Start with AI that suggests and a person who decides. Increase autonomy only when the evidence supports it.
Decision 6 · ControlsWhich controls go in the design?
Guardrails, access, logging and human oversight, sized to the risk tier and agreed with risk and security up front.
Decision 7 · Production gateWhat does go-live require?
Agree the tests, sign-offs and monitoring needed for production before the pilot starts.
What already exists
Tools you may already have
None of these is wrong. Each solves part of the problem. The gap is joining value, engineering and risk into one plan that gets to production.
Enterprise AI tools
Gives youCopilot, Claude or ChatGPT Enterprise: fast value for everyday work.
MissesSafe defaults for your data. Out of the box, they can reach everything a user can.
Cloud AI platforms
Gives youAWS Bedrock, Azure AI Foundry or Google Vertex AI: models, tooling and security building blocks.
MissesWhich use cases to build, and the controls your regulator expects on top.
Large consultancies
Gives youScale, programme management and broad transformation.
MissesHands-on AI risk depth, and often independence from the platforms they resell.
Your data science team
Gives youDeep knowledge of your data and systems.
MissesTime, and the risk and regulatory lens that gets a model approved.
AI strategy decks
Gives youA direction and a list of ideas.
MissesAnything in production. A strategy does not ship.
Vendor proofs of concept
Gives youA quick, free demonstration.
MissesYour data, your controls, and the path from demo to production.
Named platforms are examples. T3 is model- and platform-agnostic and does not resell AI platforms.
Putting the decisions together
What this usually means for your firm
Where to start depends on where AI creates value for you and who you answer to.
Fintech
Onboarding, fraud, servicing and operations.
- Start with
- Use case scoring across operations and customer journeys, then a first production use case with controls designed in.
- Watch for
- Customer-facing AI that falls under Consumer Duty, and vendor contracts that miss DORA terms.
- Regulatory lens
- FCA Consumer Duty and SM&CR, DORA for EU entities, and the EU AI Act for credit decisions.
Technology company
AI in your product and your internal operations.
- Start with
- Architecture and control design for AI product features, plus a safe enterprise rollout for staff.
- Watch for
- Shipping features faster than security can review them, and model provider lock-in.
- Regulatory lens
- EU AI Act provider and deployer duties, ISO/IEC 42001, and customer due diligence.
Asset manager
Research, client reporting and operations.
- Start with
- A governed Copilot or Claude rollout for investment and client teams, then research and reporting use cases.
- Watch for
- Assistants that can read everything on shared drives, and AI-written client content without review.
- Regulatory lens
- SEC examination priorities, the FCA, MAS guidelines, and DORA for EU firms.
04 · How we help
How we help: discover, design, pilot, scale
We combine AI engineering with risk and regulatory depth, so the people who design your AI are the same people who know what your regulator will ask. That is what gets projects approved first time.
We are model- and platform-agnostic, we do not resell platforms, and we hand ownership to your teams.
Use case discovery and scoring
T3 with your business and technology leads
Funds the use cases that will reach production
Architecture and control design
T3 with your engineers and security team
Controls in the design, not added after
Pilot and rollout
Your teams, led and supported by T3
Real users, real data, measured value
Operating model and handover
T3, handing over to your owners
It keeps running after we leave
Depth follows risk. A customer-facing credit assistant gets full control design and a formal go-live gate. An internal drafting tool gets a lighter path to production.
What you get
Five deliverables
Each one is usable on its own. Together they take AI from idea to production, with the evidence your risk function and regulator will ask for.
- 01
AI Use Case Prioritisation
Your ideas scored on value, feasibility and risk, with a ranked roadmap and a recommendation to build, buy, partner or stop for each.
NIST AI RMF MapISO/IEC 42001 cl. 6.1 - 02
AI Architecture & Control Design
Model and platform choice, data boundaries, guardrails, logging and human oversight, designed with your engineers and agreed with risk.
OWASP LLM Top 10ISO/IEC 42001 Annex A - 03
Enterprise AI Rollout
Copilot, Claude or ChatGPT Enterprise configured with the right access, data boundaries, usage policy and training for staff.
EU AI Act Art. 26 and 50DORA third-party risk - 04
Pilot-to-Production Gate
The tests, sign-offs and monitoring a use case needs before go-live, agreed up front and evidenced at the gate.
NIST AI RMF MeasurePRA SS1/23 - 05
AI Operating Model
Owners, intake, change control and adoption metrics, so AI keeps delivering value after launch.
ISO/IEC 42001 cl. 5FCA SM&CR
Timing
From idea to production in about twelve weeks
Use case discovery and scoring, build, buy, partner or stop decisions, and a ranked roadmap.
Architecture and control design agreed with risk, then a pilot with real users and real data.
Go-live gate, production launch and handover to your owners with an operating model.
After that, each new use case follows the same path, faster, because the controls and gates already exist.
05 · Questions
What leaders ask us
Do you implement AI, or only govern it?
Both. We design the architecture and controls and work with your engineers, or your chosen platform, to pilot and launch. Governance is built into how we implement, not done as a separate exercise.
Should we build or buy?
Buy where an enterprise tool already does the job well, and build where your own data or process is the advantage. Research suggests bought and partnered AI succeeds more often than internal builds, so build should be a deliberate choice.
We have already rolled out Copilot. Is it too late?
No. We review the configuration, what it can access and how it is being used, then fix data boundaries and add the controls that were skipped.
Which models and platforms do you work with?
We are model- and platform-agnostic: Claude, OpenAI, Gemini and open-weight models, on AWS, Azure or Google Cloud. We do not resell any of them.
How is this different from a large consultancy?
The people who design your AI also know what your risk function and regulator will ask. That is what gets projects approved first time rather than sent back at go-live.
How does implementation connect to the rest of AI governance?
Your inventory and risk framework decide which use cases need which controls. Implementation builds them in, and security, testing and compliance then confirm they work.
Next layers
Where AI implementation connects
Get started
Ready to get AI into production?
A 45-minute call with a T3 implementation specialist. We will look at your use cases, where they are stuck, and outline a realistic path to production.
