Case Study — Developing an AI Business Case for Scaling Generative AI Licences
- Jan 23
- 6 min read
Updated: May 16
1. Organisational Problem
Following a successful pilot of generative AI tools within selected teams, a UK public sector organisation began exploring the purchase of licences to enable wider use across the workforce.
Initial feedback from the pilot was positive. Staff reported productivity improvements in drafting, summarisation and research activities, and there was growing enthusiasm among senior leaders to scale adoption more broadly.
An initial business case for generative AI licences was developed and approved in principle, based on relatively low unit costs and perceived productivity benefits.
However, as the proposal progressed, the organisation’s Audit and Risk Committee raised a number of concerns regarding the robustness of the business case and the absence of supporting governance and strategic context.
In particular, the Committee noted that the proposal had been developed without:
A defined AI Strategy or Roadmap
An understanding of organisational AI Capability and Maturity
Structured AI Use Case Discovery to identify priority applications
Appropriate governance and assurance arrangements
While the pilot had demonstrated potential, there was no overarching structure to guide how generative AI should be adopted, governed or scaled across the organisation.
In the Orr Consulting AI Transformation Process, this type of engagement sits within the Design stage, ensuring that AI investments are supported by a structured, evidence-based business case aligned to organisational capability, governance and strategy.
2. Situation
The organisation operated within a complex public sector environment, with a strong focus on accountability, value for money and risk management.
Following the pilot, there was increasing momentum to scale access to generative AI tools across a wider group of users.
The organisation had already completed a structured pilot process in which benefits were defined, tracked and evidenced. This provided a more credible evidence base for the business case and helped shift the discussion from initial enthusiasm to informed consideration of wider rollout.
While the cost per licence appeared relatively low, the potential scale of deployment meant that total investment would be material. In addition, the organisation would need to demonstrate that any investment represented appropriate use of public funds.
The organisation also faced a common public sector constraint: while one-off capital funding could potentially support initial deployment, committing to ongoing recurring licence costs required a higher level of scrutiny and long-term justification.
The Audit and Risk Committee concluded that the proposal did not yet meet the standard expected for a material organisational investment.
In particular, the Committee highlighted:
No defined AI Strategy or Roadmap
No AI Capability and Maturity baseline
No formal governance framework or acceptable use policy
Limited assessment of data protection, information security and Shadow AI risks
The Committee therefore concluded that the proposal treated AI as a procurement decision without sufficient consideration of the broader organisational implications of adoption.
The leadership question therefore became:
"How do we move from a successful pilot to controlled, value-driven and governable adoption of generative AI?"
3. Background
Like many public sector organisations, the authority was experiencing growing exposure to generative AI through both formal pilots and informal experimentation.
The initial business case focused primarily on licence procurement and assumed that benefits would emerge through increased access to AI tools. However, the Audit and Risk Committee identified that the underlying issue was not the procurement of licences, but the absence of a structured approach to AI adoption.
A more comprehensive business case was therefore required to test value, risk, governance, capability and organisational readiness before wider investment could be considered.
4. Action Taken
Orr Consulting was engaged to facilitate and advise on the development of a structured AI Business Case, ensuring that the organisation retained ownership of both the outputs and the decisions required for delivery.
The engagement reframed the proposal from a straightforward licence purchase into a broader question of how generative AI should be introduced, governed and scaled across the organisation.
Using Orr Consulting’s Developing Robust AI Business Cases methodology, the proposal was reassessed across the following areas.
4.1 Deliverables and Timeline
The business case defined a targeted initial rollout of generative AI licences to priority user groups, supported by governance controls, training and clear milestones for deployment, review and benefits monitoring.
4.2 Cost and Investment Profile
A fuller view of cost was developed, covering licence expenditure, training and enablement, governance implementation and ongoing support.
This showed that, although unit costs appeared low, the total cost of ownership at organisational scale was more significant. Particular attention was given to the recurring nature of licence costs, which required clear justification in the context of public sector funding constraints and long-term affordability.
4.3 Benefits Realisation
Expected benefits were defined in relation to prioritised use cases, including productivity improvement, greater consistency of outputs, faster turnaround of communications and better reuse of organisational knowledge.
The business case also considered what benefits were expected, where they would arise, when they would be realised, who would be accountable and how they would be measured. In the public sector context, ongoing recurring licence costs would need to be supported by evidenced efficiency savings and demonstrable operational benefits over time. This provided a basis for later tracking through the Orr Consulting AI Benefits Realisation methodology.
4.4 Risk Identification
A structured risk assessment considered data exposure, inappropriate reliance on AI-generated outputs, inconsistent usage, unclear accountability and the continued emergence of Shadow AI.
To mitigate these risks, the business case incorporated the introduction of the Orr Consulting AI Governance and Assurance Framework, including an Acceptable Use Policy, defined oversight and accountability, structured risk processes and targeted staff education.
4.5 Alignment with Strategy
The business case was explicitly aligned to organisational priorities relating to service improvement, efficiency and value for money, and to the emerging direction of the organisation’s AI strategy.
This avoided the risk of treating licence procurement as an isolated technology decision and instead positioned it as part of a more structured approach to AI adoption.
4.6 Delivery Capability
The organisation’s AI capability and maturity were considered to assess its readiness to adopt generative AI at scale.
This highlighted gaps in governance, organisational understanding and delivery capability, confirming that the investment decision needed to reflect not only the potential value of the technology, but the organisation’s ability to implement and sustain it in a controlled way.
4.7 Options Appraisal
The business case evaluated three broad options:
Do nothing and continue informal use of external tools
Limited targeted rollout to specific user groups
Broader organisational rollout with governance controls
This enabled leadership to weigh value, risk and pace of adoption before proceeding.
5. Outcomes
The reassessed business case provided a more robust and structured foundation for decision-making.
The key outcomes were as follows.
5.1 Refined Investment Decision
Rather than proceeding with a broad rollout, the organisation agreed a targeted initial deployment of generative AI licences focused on priority user groups aligned to high-value use cases.
5.2 Governance Established
The business case confirmed that adoption of generative AI would be accompanied by the implementation of a formal AI Governance and Assurance Framework.
This ensured that access to AI tools would be supported by clear policies, oversight and risk management.
5.3 Strategic Alignment
The use of structured AI Use Case Discovery ensured that the investment was aligned to specific organisational objectives and operational priorities.
5.4 Value for Money
The organisation was able to demonstrate a clearer link between investment, expected benefits and organisational outcomes, supporting public sector accountability requirements.
5.5 Reduction of Shadow AI Risk
By providing approved tools alongside clear governance and guidance, the organisation reduced reliance on uncontrolled external AI tools.
Overall, the business case shifted from a procurement-led decision to a structured, governance-led approach to AI adoption.
6. Recommended Next Steps
Following approval of the revised business case, the organisation agreed the following next steps:
Implement the AI Governance and Assurance Framework alongside licence deployment
Deliver targeted training and awareness sessions to support responsible use of generative AI
Monitor usage and benefits associated with prioritised use cases
Refine and expand the use case portfolio over time
Use insights from initial deployment to inform broader AI Strategy and Roadmap development
These activities would support a controlled and scalable approach to AI adoption aligned with organisational capability and maturity.
7. Final Thoughts
Generative AI licences are often perceived as a low-cost and low-risk entry point into AI adoption.
However, this case study demonstrates that the decision to introduce generative AI at organisational scale is not a simple procurement exercise.
Without structured consideration of strategy, capability, governance and risk, such decisions can lead to uncontrolled adoption and limited realisation of value.
In this case, the structured business case acted as a critical governance gateway between the Design and Delivery stages of the Orr Consulting AI Transformation Process.
Importantly, the business case gateway functioned as intended. Rather than allowing a procurement-led decision to proceed unchecked, it required the organisation to test value, governance, capability and risk more fully before committing to wider adoption.
It enabled the organisation to move from pilot learning and early momentum into a controlled implementation pathway supported by strategy, governance, capability and defined value-for-money considerations.
The most important outcome was not the approval of licences, but the recognition that successful AI adoption requires structured transformation rather than incremental procurement.
This Case Study is part of the Orr Consulting AI Insights Library — structured thinking for AI transformation leaders and decision makers.
8. Call to Action
If your organisation is considering investment in generative AI tools but is uncertain how to evaluate value, manage risk or scale adoption effectively, a structured AI Business Case can provide a practical starting point.
If this case study reflects questions your organisation is currently considering, Orr Consulting would be pleased to discuss your next AI steps.
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