Moving from AI interest to governed business value
AI is no longer a future topic. It is already changing how organisations work, make decisions, manage knowledge, serve customers, support employees, develop software, analyse information and scale operations.
For many boards and leadership teams, the challenge is not lack of interest. The challenge is that AI adoption is often happening informally, inconsistently and without enough connection to business strategy, governance, data quality, security, risk, cost or operating model change. Individuals may already be experimenting with AI tools. Vendors may be making strong claims. Business functions may be looking for productivity gains. Technology teams may be under pressure to respond quickly. But without a clear approach, AI can become fragmented, duplicated, risky, expensive or disappointing.
My work helps organisations move from scattered AI activity to practical, governed deployment that delivers measurable business advantage.
Where this helps
AI deployment support is particularly useful where an organisation is:
- seeing informal or uncoordinated AI use across teams
- unsure where AI can create real business value
- under pressure from vendors, platforms or internal stakeholders to move quickly
- concerned about data protection, confidentiality, security or regulatory risk
- struggling to move beyond pilots, experiments or isolated use cases
- looking to improve productivity, knowledge management or decision support
- considering AI in customer, tenant, patient, employee or service delivery processes
- needing a practical governance model for AI adoption
- trying to understand the cost implications of AI, including consumption and token-based models
- trying to align AI with wider technology, data, cloud or business strategy
- needing board and executive management alignment on AI priorities, risk, cost and investment
The immediate opportunity is significant. But AI should not be treated as a standalone technology project. It needs to be connected to strategy, process, people, controls, data, cost and measurable value.
Governance that enables AI adoption
Good AI governance should not be designed to stop organisations moving forward. It should give boards, executives and teams the confidence to use AI in a way that is practical, safe, lawful and aligned with business value.
That means understanding the relevant governance landscape, including the EU AI Act, GDPR, the NIST AI Risk Management Framework and NIST AI 600-1: Generative AI Profile, and applying them in a way that supports adoption rather than paralyses it.
The objective is to create enough structure to help the organisation move faster with confidence: clear ownership, acceptable-use principles, risk-based controls, data and security safeguards, human oversight, vendor assessment, training, monitoring and escalation routes.
For many organisations, the risk is not only doing too much. It is also doing too little: allowing AI use to spread informally without clear accountability, exposing sensitive data, duplicating tools, creating inconsistent practices, or missing opportunities because governance is treated as a compliance exercise rather than a business enabler.
My approach is to help organisations put in place AI governance that is proportionate, practical and connected to business priorities, so that AI can be used responsibly and effectively to deliver real business advantage.
Practical AI experience, used transparently
I am an early user and adopter of AI in my own work, and I do not hide that from clients. Used well, AI can improve analysis, drafting, research, knowledge access, productivity and decision support. But it also needs to be understood and managed properly.
That means being clear about the risks: confidentiality, data protection, hallucination, bias, inappropriate reliance, weak validation, unclear ownership and poor auditability. These risks do not mean organisations should avoid AI. They mean AI needs to be used with judgement, transparency and appropriate controls.
My approach is practical: understand where AI can help, understand where it can mislead and put the right safeguards around its use. The objective is to help organisations benefit from AI while managing the risks in a proportionate and responsible way.
Understanding AI cost models
AI deployment also needs a clear understanding of cost. Traditional software costs were often built around user licences, implementation effort and support. AI introduces additional and less familiar cost drivers, including consumption-based pricing, token usage, model selection, data processing, integration costs, storage, monitoring, security controls and ongoing optimisation.
Tokenisation is an area boards and leadership teams increasingly need to understand. In simple terms, AI systems process text, prompts, documents and responses in units known as tokens. The more information that is passed into a model, processed by it or generated from it, the more cost may be incurred, depending on the platform and pricing model.
This matters because unmanaged AI adoption can create unexpected costs. Large prompts, repeated document analysis, poorly designed workflows, unnecessary use of high-cost models, duplicated tools, unmanaged integrations or broad deployment without usage controls can all increase spend without necessarily increasing value.
My approach is to help organisations understand these cost models early and manage them practically. That includes considering when to use different models, how to design efficient prompts and workflows, how to govern usage, how to monitor consumption, how to align AI costs with business value, and how to avoid treating AI as a simple extension of existing software licensing.
Independent, vendor-neutral advice
A key part of this service is independence.
There is a great deal of AI advice in the market, but much of it is tied to a platform, product, reseller relationship or implementation agenda. Microsoft, Salesforce, AWS, Google and specialist AI platforms can all provide powerful capabilities, but the right answer depends on the organisation’s strategy, data, systems, risk appetite, operating model, people, cost profile and budget.
My role is to help leadership teams understand what is genuinely useful, what is premature, what needs stronger governance, what will cost money to scale and what should be prioritised. The recommendation is based on what is right for the organisation, not on what a vendor is trying to sell.
Building AI around real organisational knowledge
If the issue crosses more than one area, for example strategy, governance, AI, technology delivery or executive capability, the first step is usually a short conversation to clarify the problem, understand the business context and decide what support would be most useful.
That is why I favour practical, focused pods working with people inside the organisation to identify where AI can make a real difference. These pods should combine business knowledge, technology capability, data understanding, change experience and governance discipline. Their role is to find practical opportunities, test them quickly, understand the risks and turn the best ideas into governed adoption.
The aim is not to create AI activity for its own sake. It is to build depth of understanding, identify opportunities that matter and deliver improvements that people can see and use: better decisions, faster access to knowledge, reduced manual effort, improved service, stronger controls or increased organisational capacity.
This also helps avoid a common problem: AI programmes that are designed too far away from the work. Successful AI deployment needs executive sponsorship, cost discipline and governance, but it also needs close engagement with the teams who understand the processes, exceptions, data quality issues and service pressures. That is where many of the real opportunities, and real risks, become visible.
What my experience brings
I bring a combination of CIO, CFO, Chartered Accountant, computer engineering and transformation experience to AI deployment. That matters because AI decisions are rarely just technical. They raise questions about productivity, cost, risk, governance, operating model, investment discipline, supplier dependency, controls and organisational readiness.
My experience includes business strategy, ERP and platform delivery, Microsoft Dynamics, Salesforce, cloud adoption across Azure and AWS, analytics capability development, knowledge management, operational restructuring, governance improvement, supplier management and major programme oversight across pharma, AEC, healthcare, financial services, distribution, public/semi-state environments, start-ups and fast-growth companies.
That background helps identify where AI can make a practical difference, but also where the foundations are not yet strong enough. Weak data, unclear ownership, poor process definition, fragmented systems, immature governance, unrealistic expectations, unmanaged cost models or underdeveloped business cases can all limit AI value.
How I help
Support can include:
- identifying practical AI use cases linked to business priorities
- assessing current AI use, risks, costs and opportunities
- developing an AI deployment roadmap
- defining AI governance, decision rights and accountability
- aligning AI governance with EU AI Act, GDPR, NIST AI RMF and NIST AI 600-1 Generative AI Profile principles
- reviewing data readiness, security and confidentiality risks
- identifying and managing risks such as hallucination, bias, inappropriate reliance and weak validation
- helping prioritise use cases by value, feasibility, risk and cost
- supporting board and executive management discussions on AI
- developing business cases for AI-enabled change
- helping organisations understand consumption, tokenisation and emerging AI cost models
- shaping adoption, training and communication plans
- establishing practical pods to work with business teams on real opportunities
- aligning AI with existing Microsoft, Salesforce, ERP, cloud, data and analytics environments
- reviewing supplier proposals, pricing models and vendor claims
- moving from pilots or experimentation to governed implementation
The focus is practical deployment, not AI theatre. The aim is to identify where AI can improve work, reduce friction, support better decisions, strengthen service delivery or create capacity, and then put the governance, cost control and delivery model around it.
Why this matters
Business Advantage through Technology depends on making the right technology choices and turning them into operational value.
AI raises both the opportunity and the risk. Used well, it can improve productivity, decision support, knowledge access, service quality, software delivery and organisational capacity. Used poorly, it can create unmanaged risk, fragmented effort, duplicated tools, poor data use, weak accountability, unexpected cost and misplaced confidence.
The organisations that gain most from AI will not necessarily be those that adopt the most tools fastest. They will be those that connect AI to clear business priorities, build the right controls, understand the economics, work closely with the people who understand the organisation, support adoption properly and measure whether AI is actually improving performance.