AI strategy & governance

A clear direction for AI, with governance from day one.

DATA & AI

Turn enterprise AI strategy into a practical roadmap

Which AI use cases deserve investment, and what must be in place before they go live? We help business, technology and risk teams agree priorities, ownership and a governed route to delivery.

Competing pilots, unclear value and unclear accountability are reasons to pause and set direction. Agree who owns each use case, what the system may do and where people must oversee it.

Explore Data & AI.

What we deliver

Business value, with clear boundaries

Shape an advisory engagement around the decisions you need to make. Agree the depth and format of these outputs during scoping.

Prioritised use-case portfolio

Compare opportunities by value assumptions, feasibility and risk.

Decision owner and intended users
Dependencies and measures of value
Delivery sequence and approval gates

Data & AI readiness assessment

Assess what each use case needs before delivery can begin.

Available data and access constraints
Architecture and integration needs
Skills, ownership and readiness gaps

Governance operating model

Define accountability across business, technology and risk teams.

Decision rights and review forums
Human oversight and escalation
Risk ownership and change approval

Production control plan

Define production controls, implementation responsibilities and review requirements.

Cost budgets and usage review
Evaluation and tracing requirements
Release criteria and operating owners

DELIVERY APPROACH

From opportunity to accountable delivery

Bring the decision-makers together before choosing a model or platform. Each step ends with an output to review and an owner to agree.

Advisory, implementation and IP

Choose the right delivery route

Agree the operating model here; implement controls with the relevant delivery teams. Advisory, security validation and software are distinct buying decisions.

From roadmap to implementation

Move an approved use case into the service that fits the decision and data needs.

Agentic AI & GenAI Data platforms & analytics Machine learning

AI security & GRC services

Scope specialist AI testing, security controls and risk work separately from the governance operating model.

Explore AI security & GRC

Linkfields software: AI gateway & guardrails

AI Governance brings guardrails, cost and tracing through a Linkfields layer built on Portkey and Opik. It does not replace organisational accountability.

Explore AI Governance

Software is a separate choice

Products are available by licence, subscription or SaaS, with or without services. Confirm fit and scope; software is not automatically included in an advisory engagement.

Illustrative engagement pattern

An AI decision made accountable

Consider an organisation comparing internal knowledge-assistant pilots: leaders need to assess data access, user groups and oversight before deciding where to invest. This illustrative pattern is not a client case or achieved result.

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Make the investment decision

Compare the pilots against a shared decision brief. Record the value assumptions, readiness gaps and reason to prioritise, defer or stop each one.

Document ownership and controls

Agree a roadmap and risk/control record. Name business approval, data access, human-review and escalation owners; identify security validation that needs a specialist team.

Hand over with acceptance gates

Give the relevant engineering team the approved scope and control requirements. Agree what evidence will support release and how operational feedback will inform the next investment review.

Start the conversation

Bring your business priorities, current pilots and governance questions. Together, we can identify stakeholders, agree the initial scope and discuss an appropriate engagement model.

BUYER QUESTIONS

Agree the scope before you commit

Start with the decision, current initiatives and main constraint. Select a commercial model around the uncertainty, deliverables and ownership involved.