Discuss your project
AI Solutions

AI strategy and readiness consulting

Leadership teams are under pressure to do something with AI, while staff are already using it without guidance. We help you cut through the noise: we look at your work, data, systems and risks, then recommend a small number of use cases worth doing and the groundwork they need.

Discuss your project How the work is delivered

A good fit if

  • Leadership wants an AI plan but does not know where to start or what is realistic.
  • Staff already use free AI tools and you need rules, approved tools and training.
  • You have bought, or are considering, Microsoft 365 Copilot and want it to pay off.
  • Ideas for AI are piling up and you need a fair way to choose between them.
  • A board, funder or regulator is asking how you manage AI risk.

Another approach may suit you better if

  • You already know the exact use case and only need it built. Our generative AI development or AI agents services may suit you better.
  • You want a slide deck about AI trends rather than decisions about your own organization.
  • Your core systems or data are not yet digitized. That groundwork usually comes first, and we can help with it.

What this service is

AI strategy and readiness consulting answers three questions for your organization: where AI will genuinely help, what has to be in place before it can, and what to do first. It is practical work grounded in your own processes, systems and data, not a presentation about trends.

Most organizations already use AI in some form. Staff try free chatbots, vendors add AI features to the software you already pay for, and Microsoft 365 Copilot is a few clicks away. Without a plan, that adds up to scattered experiments, uneven results and information leaving the organization in ways nobody approved. A short, structured assessment turns that into a few deliberate bets with clear owners and controls.

What we look at

  • The work. Which tasks are frequent, time-consuming and easy to check, and where errors would be costly. These are usually the best early candidates.
  • The data. Where the information each use case needs lives, how clean and complete it is, and who is allowed to see it. Most AI problems are data problems.
  • The systems. Microsoft 365 or Google Workspace, your ERP and CRM, file shares and line-of-business applications, and how an AI solution would connect to them.
  • Identity and permissions. Whether access rights are tidy enough that an AI assistant will not surface information to the wrong people. This matters most for Microsoft 365 Copilot.
  • Security and privacy. How each use case handles personal and confidential information, and which controls it needs.
  • People and skills. Who will own each solution, what training staff need, and how you will measure adoption.

How we choose use cases

Every candidate is scored with your team on the same criteria: business value, feasibility, data readiness, risk and cost. We favor use cases that are contained, measurable and easy for a person to review, such as reading supplier invoices into your ERP or answering staff questions from an approved policy library. Harder use cases, such as AI that takes actions across several systems, come later, once the foundations and governance are proven.

We also tell you which ideas to drop. Some problems are better solved with a clear workflow automation rule, a report from data analytics, or a fix to the underlying process.

Governance from the start

A good AI plan includes the guardrails that let you move quickly without creating new risks: an AI use policy, a register of approved tools and use cases, a simple risk review for each new use case, and rules for human review. In the US there is no comprehensive federal AI law, but existing privacy, consumer protection and sector laws, such as HIPAA, apply to information used in AI systems, and states are adding AI-specific rules, such as the Texas Responsible Artificial Intelligence Governance Act, in effect since January 1, 2026. The NIST AI Risk Management Framework gives a practical, voluntary structure for the plan. We design the roadmap with these obligations in view and work alongside your legal and privacy advisers. This is general information, not legal advice. For a deeper review, see AI governance and security and our overview of AI laws in the US.

From roadmap to results

The roadmap is yours to deliver with any partner. When you want help, we can run a proof of concept on the first use case, roll out Copilot, build custom assistants and AI agents, and support them after launch as part of your managed IT services.

What is included

The exact list is agreed in writing for each project. These are the usual deliverables and the usual boundaries.

Typical deliverables

  • Interviews and workshops with leaders and the people who do the work.
  • A review of current AI use, including tools staff use without approval.
  • A data and systems readiness review covering where key information lives, its quality and who can access it.
  • A long list of AI use cases and a shortlist scored on value, feasibility, data readiness, risk and cost.
  • A security and privacy review of the shortlisted use cases.
  • Recommendations on platforms, including Microsoft 365 Copilot, cloud AI services, private self-hosted models and custom builds.
  • An AI use policy draft for your legal and HR advisers to review, if you do not have one.
  • A roadmap with a 90-day plan, owners, rough cost ranges and success measures.
  • A leadership briefing that explains the findings in plain language.

Not included unless agreed separately

  • Building the solutions themselves, which is quoted separately once the roadmap is agreed.
  • AI platform, model and cloud subscriptions, which you buy from the vendor.
  • Legal advice on privacy, employment or sector regulation of AI.
  • Organizational change programs beyond the training and communication in the roadmap.

What we will need from you

Most delays in this kind of work come from access and decisions, not from the technical build. Knowing these early keeps the project predictable.

  • A sponsor in leadership who can make decisions and remove obstacles.
  • Time from the people who do the work in each area we review.
  • Read access to relevant systems, sample documents and data, with personal information protected as agreed.
  • Your current policies on privacy, security, records and acceptable use.
Delivery

How the work is delivered

Each stage ends with something you can review before the next one starts.

  1. Discover

    Interviews, workshops and a survey of how people work today, where time goes and where AI is already being used.

    Output: Current-state summary and a long list of use cases.

  2. Assess readiness

    Review data sources, systems, integrations, identity and permissions, security controls and policies against what the use cases need.

    Output: Readiness findings and gaps.

  3. Prioritize

    Score the use cases with your team on value, feasibility, data readiness, risk and cost, and agree a shortlist.

    Output: Scored shortlist and recommended first projects.

  4. Plan

    Turn the shortlist into a roadmap with owners, costs, success measures and the governance each step needs.

    Output: AI roadmap, 90-day plan and policy draft.

  5. Brief

    Present the findings and recommendations to leadership and agree the next decisions.

    Output: Leadership briefing and agreed next steps.

Testing and handover

  • Every recommended use case has a named business owner and a measurable success criterion.
  • Risks are rated, with the controls needed before each use case goes live.
  • Cost ranges separate one-time build costs from ongoing license and usage costs.
  • The roadmap distinguishes quick wins from foundations such as data clean-up and permissions.
  • Findings are written for leadership, with technical detail in appendices.

What affects the cost

We do not publish package prices. Each estimate is based on an agreed scope, in US dollars, with taxes shown separately. These are the things that move the number most:

  • The number of teams, locations and processes in scope.
  • The number and complexity of systems and data sources to review.
  • Whether a policy, risk assessment or Copilot readiness review is included.
  • The depth of security and privacy review your sector requires.
  • Workshops in person or remote.

Questions buyers usually ask

How long does an AI readiness assessment take?

For a single organization with a handful of core systems, a few weeks from kickoff to leadership briefing is typical. Larger or regulated organizations take longer. We confirm the timeline in the written scope.

Will you just recommend Microsoft products?

No. We recommend what fits your data, security requirements, existing licenses and budget. That may be Microsoft 365 Copilot, another cloud AI service, a custom solution or, sometimes, no AI at all.

What happens after the assessment?

You own the roadmap and can deliver it with any partner. Most clients ask us to run a proof of concept on the first use case, so they can see results on their own data before committing to a larger build.

Do we need an AI policy before we start?

If staff already use AI tools, a short policy is one of the quickest ways to reduce risk. It covers approved tools, what information may be entered, human review and disclosure. We draft it with you for your legal and HR advisers to review.

Can you help us respond to a board or funder asking about AI risk?

Yes. The assessment gives you an inventory of AI use, rated risks and the controls in place or planned, which is the information boards and funders usually ask for.

Do you work with public agencies and prime contractors?

Yes. For state and local agencies, health programs and their contractors, the assessment also covers the agency's AI policies and procurement requirements, such as Maryland's Responsible AI Policy for state units and their vendors. See our health and public sector work.

Service

AI governance, risk and security

Policies, risk assessments, shadow AI discovery and security testing, so your organization can use AI with confidence and show how risks are managed.

Guide

AI readiness checklist

A practical worksheet to check strategy, data, permissions, security, governance and skills before you invest in AI, and to score readiness honestly.

Ready for an AI plan you can act on?

Tell us about your organization and where AI keeps coming up. We will reply to arrange a conversation and outline what a readiness assessment would cover for you.

Book an AI readiness conversation