AI Services Case Study — Defining, Tracking and Evidencing Benefits from a Generative AI Pilot
- Jan 20
- 5 min read
Updated: 3 days ago
1. Organisational Problem
A large local authority was delivering a major digital transformation programme valued at more than £100 million. As part of that wider programme, the organisation began piloting Generative AI across a number of priority departments and use cases.
There was strong interest in the potential of the technology, with expected benefits including productivity gains, faster drafting and summarisation, improved access to knowledge and better support for day-to-day service activity.
However, the organisation needed a credible way to evidence whether the pilot was delivering real value and whether that value could support future decisions on continuation and expansion.
Without a structured approach, there was a risk that:
Expected benefits would remain vague or subjective
Different pilot areas would define success in different ways
Evidence would be incomplete or retrospective
Conclusions would rely on perception rather than proof
Crucially, many of the expected benefits also depended on how effectively staff adopted and used the technology in practice. This introduced a people readiness dimension that had to be actively managed.
In the Orr Consulting AI Transformation Process, this case study demonstrates the Deliver-stage role of AI Benefits Realisation in defining, measuring and evidencing whether AI activity is creating the expected organisational value.
2. Situation
The organisation was operating in a complex public sector environment where accountability, value for money and evidence-based decision-making were essential.
A number of Generative AI pilots were being taken forward within priority service areas. These pilots were intended not only to test the practical use of the technology, but also to inform decisions on whether Generative AI should continue beyond the initial pilot phase and be expanded across a broader range of services.
The core question was whether the technology could deliver measurable value in practice and whether that value could be evidenced clearly enough to support future decisions.
The organisation therefore engaged Orr Consulting to establish a consistent AI Benefits Realisation approach across the pilot areas and provide credible evidence for future investment and scaling decisions.
3. Background
As with many organisations exploring AI, the likely benefits were a mix of quantitative and qualitative outcomes.
Some were relatively tangible, such as time savings, reduced manual effort and improved throughput. Others were less direct but equally important, such as improved quality of outputs, better staff experience, stronger consistency and enhanced access to information.
These benefits would not evidence themselves. If they were not defined before the pilot began, there would be no reliable basis for measuring progress or assessing whether it had succeeded.
Pilot completion, tool usage and positive user feedback would not, by themselves, demonstrate that organisational benefits had been realised. Benefits needed to be defined as measurable improvements in organisational performance, service activity or user outcomes.
In addition, many anticipated benefits depended directly on user behaviour, confidence and effective use of the tools. Baseline position, evidence availability and benefit maturity also varied across pilot areas, increasing the need for a consistent approach.
4. Action Taken
Orr Consulting introduced a structured AI Benefits Realisation approach, aligned to Managing Successful Programmes (MSP) principles and adapted to the specific needs of the Generative AI pilot.
The work followed Orr Consulting’s structured AI Benefits Realisation approach.
4.1 Benefit Profiles Defined
Before the pilot began, benefit profiles were developed for the relevant use cases and departments.
These profiles defined expected benefits in advance and covered both quantitative and qualitative outcomes, providing a clear view of what success would look like before delivery started.
Each profile identified the baseline position, expected improvement, measurement method, evidence source, benefit owner and anticipated realisation period.
4.2 Direct User Involvement
Users were directly involved in selecting priority use cases and in identifying, shaping and measuring expected benefit improvements.
This ensured that the pilot reflected real operational needs and increased ownership by involving those closest to the work in defining meaningful outcomes.
4.3 Measuring Benefits
The approach recognised that AI value would not be limited to simple efficiency measures.
Quantitative benefits included time savings, productivity gains and reduced administrative effort. Qualitative benefits included improved output quality, stronger user experience, better knowledge access and increased confidence in completing tasks.
This ensured a complete and realistic view of value, including the role of adoption and effective usage.
Adoption, usage and user confidence were monitored as important enabling indicators, but were not automatically treated as benefits in their own right.
4.4 Evidence and Ownership
Each benefit profile set out how benefits would be measured, what evidence would be required and who would have ownership for realisation.
Benefit ownership remained with the relevant operational areas rather than being transferred to the project team.
This established benefits realisation as an active management discipline and introduced consistency across pilot areas.
4.5 Enabling Adoption
Users were supported through training and practical vendor-led enablement.
This ensured they had the skills and confidence to apply the tools effectively in their roles. Training and support were treated as integral to benefits realisation rather than separate activities.
4.6 Benefit Tracking
Benefits were monitored throughout the pilot using a structured MSP-based approach.
Delivery was tracked against agreed benefit profiles, providing a clear audit trail linking activity to outcomes. Progress, issues and emerging evidence were reported through regular monthly project and programme governance oversight groups.
4.7 End of Pilot Review
At the conclusion of the pilot, outcomes were reviewed against the original benefit profiles.
Both quantitative and qualitative benefits were confirmed, measured and evidenced. A formal end-of-pilot review report captured benefits realisation, lessons learned and implications for future investment and scaling decisions.
Benefits supported by sufficient evidence were reported as realised. Benefits requiring further validation were retained as emerging or forecast rather than being presented as confirmed outcomes.
5. Outcomes
The benefits-realisation engagement created several notable outcomes, observations and lessons learned.
5.1 Consistent Framework
A consistent framework ensured benefits were identified, measured and assessed in the same way across pilot areas.
5.2 Measured Value
Both quantitative operational gains and qualitative improvements were confirmed, providing a rounded and credible view of value.
More than 80% of the anticipated initial benefits were realised during the pilot period.
These included time savings in drafting and administrative tasks, improved access to information, greater consistency in outputs and increased user confidence in completing day-to-day activities with tool support.
5.3 Adoption-Enabled Value
Benefits were closely linked to user adoption, confidence and effective use in practice.
User involvement, training and practical enablement helped turn access to the technology into observable operational improvements.
5.4 Emerging Benefits
Benefits not identified at the outset were also observed during the pilot.
These included wider awareness of potential Generative AI applications, improved output quality in some areas and stronger user engagement as confidence developed.
Structured tracking allowed these emerging benefits to be identified and evidenced rather than overlooked.
5.5 Investment Evidence
The evidenced benefits provided a stronger foundation for continuation, scaling and subsequent business-case decisions.
Leadership could assess future investment using real-world evidence about value, adoption and operational impact rather than relying primarily on assumptions or general enthusiasm for the technology.
5.6 Repeatable Approach
A consistent and repeatable AI Benefits Realisation approach was established for future pilots and scaled AI deployments.
This improved comparability across initiatives and provided a clearer link between AI delivery, programme governance and organisational value.
6. Final Thoughts
AI pilots can generate activity, enthusiasm and positive user feedback without demonstrating that measurable organisational value has been realised.
AI Benefits Realisation provides the discipline for defining expected improvements, establishing credible evidence and determining what value has actually been achieved.
Benefits are ultimately realised through people — through adoption, confidence, changed ways of working and effective use in real operational settings.
In this case, structured benefit profiles, direct user involvement, active tracking and formal review enabled more than 80% of the anticipated initial benefits to be realised and evidenced. The organisation also gained a stronger foundation for future investment and scaling decisions.
This AI Services Case Study is part of the Orr Consulting AI Insights Library — structured thinking for AI transformation leaders and decision makers.
If your organisation is piloting AI and needs a clearer way to define, track and evidence the value being created, we would be pleased to discuss your next AI steps.
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