Case Study: Defining, Tracking and Evidencing Benefits from a Generative AI Pilot
- orrconsultingltd
- Jan 20
- 6 min read
1. Organisational Problem — Evidencing the Value of Generative AI Through Structured Benefits Realisation
A large local authority in the public sector was delivering a major digital transformation programme valued at more than £100M. 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 type of engagement sits within the Deliver stage, where structured benefits realisation ensures AI activity translates into evidenced 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.
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.
A structured approach was therefore required to define expected benefits in advance, establish ownership and measurement methods and track progress throughout delivery using an MSP-aligned model.
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
A structured AI Benefits Realisation approach was introduced, aligned to MSP principles and adapted to the specific needs of the Generative AI pilot.
4.1 Benefit Profiles Defined Before Pilot Commencement
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.
4.2 Users Directly Involved in Use Case Selection and Benefit Definition
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 Quantitative and Qualitative Measures Established
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.
4.4 Ownership, Measurement and Evidence Requirements Agreed
Each benefit profile set out how benefits would be measured, what evidence would be required and who would have ownership for realisation.
This established benefits realisation as an active management discipline and introduced consistency across pilot areas.
4.5 Users Supported Through Training and Vendor Enablement
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 Tracking Applied Throughout Pilot Delivery
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 Pilot Review Informed by Evidenced Benefits
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.
5. Outcomes
The structured approach enabled the organisation to evaluate the Generative AI pilot with greater rigour, consistency and confidence.
5.1 Benefits Were Clearly Defined and Measured
A clear framework ensured benefits were consistently identified, measured and assessed across pilot areas.
5.2 Quantitative and Qualitative Value Were Evidenced
The pilot confirmed both operational gains and qualitative improvements, providing a rounded and credible view of value.
Realised benefits were closely linked to user adoption, confidence and effective use in practice.
5.3 Most Anticipated Benefits Were Realised, with Additional Value Also Identified
More than 80% of anticipated initial benefits were realised during the pilot period.
These included benefits such as 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.
In addition, some benefits not identified at the outset were also observed during the pilot. These included wider awareness of where Generative AI could support other service activities, improved quality of outputs in some areas and stronger engagement from users once confidence in the tools increased.
This reinforced the value of structured tracking throughout delivery, as it allowed both expected and emerging benefits to be identified and evidenced.
5.4 Leadership Had a Stronger Basis for Decision-Making
Structured evidence enabled better-informed decisions on continuation and expansion.
The approach improved comparability across pilots and reduced subjectivity in assessing value.
5.5 A Stronger Foundation Was Created for Future Business Cases
Evidenced benefits from the pilot provided a strong foundation for subsequent business case development.
This enabled future investment decisions to be informed by real-world outcomes rather than assumptions, supporting a more confident and structured approach to scaling Generative AI.
5.6 A Repeatable Benefits Realisation Approach Was Established
A consistent and repeatable approach to benefits realisation was established for future AI initiatives.
6. Recommended Next Steps
Following the pilot, the organisation was positioned to:
use evidenced benefits to support future AI business case development
refine benefit profiles as use cases mature
apply the same structured approach to scaled AI deployments
strengthen integration with programme and portfolio governance
continue building confidence through evidence-led adoption
7. Final Thoughts
AI pilots often generate interest and positive early feedback. Without a structured approach to benefits realisation, however, it can be difficult to determine whether that promise has translated into demonstrable value.
This case study shows that successful AI adoption depends on the discipline used to define, track and evidence value in practice. It also reinforces a broader lesson: benefits are realised through people — through adoption, confidence and effective use in real operational settings.
In this case, structured benefits management, combined with user involvement, training and formal review, created the conditions for value to be realised and evidenced. The result was not simply a positive pilot, but a stronger foundation for future AI investment and scaling decisions.
This Insight 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 piloting AI but is uncertain how to define, track and evidence the value being created, a structured AI Benefits Realisation approach can provide clarity and confidence.
If this case study reflects challenges your organisation is currently facing, Orr Consulting would be pleased to discuss your next AI steps.
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