AI Services Case Study — Use-Mode Project Management for a Generative AI Pilot
Updated: Aug 30
1. Organisational Problem
A large public sector organisation initiated a Generative AI pilot to explore the potential of AI tools to improve productivity, knowledge access and service delivery.
The pilot was supported at senior levels and aligned to broader digital transformation ambitions. However, unlike more traditional technology initiatives, the project began without:
Fully defined requirements
Stable or predictable delivery scope
Clear, pre-validated assumptions about outcomes
Established delivery patterns or precedents within the organisation
This created a fundamental challenge for project management:
How best to maintain delivery control when the solution, outcomes and risks are not fully knowable at the outset?
The organisation had established delivery disciplines, governance processes and reporting expectations, but the pilot did not behave like traditional project delivery.
In the Orr Consulting AI Transformation Process, this case study demonstrates the Deliver-stage role of AI Project Management in recognising, documenting and progressively reducing uncertainty while maintaining clear governance, delivery control and accountability.
2. Situation
The Generative AI pilot was positioned as an early exploration of how AI tools could support:
Internal productivity
Knowledge management
Drafting and content generation
User-facing service improvements
This was a Use-mode AI project. The organisation was applying existing Generative AI products rather than configuring and integrating a pre-built AI solution or developing a bespoke AI solution.
The principal uncertainties therefore related less to whether the underlying technology could be developed and more to where the tools would create value, how users would adopt them, how information and outputs should be governed and whether the evidence would justify wider deployment.
The project had:
Defined timelines
Allocated resources
Senior stakeholder interest
Expectations of demonstrable outcomes
However, several delivery tensions quickly emerged:
Use cases were evolving as understanding of the technology developed
Initial assumptions about value and applicability were being tested in real time
Different teams experienced different levels of benefit and adoption
Risks, particularly around data usage and governance, required ongoing clarification
Stakeholder expectations were high, but outcomes were inherently uncertain
This created a delivery environment where:
Scope was fluid
Learning was continuous
Certainty could not be assumed upfront
The project needed to maintain control and credibility while operating in this context.
The organisation therefore engaged Orr Consulting to provide structured AI Project Management support, maintaining clear governance and delivery discipline while allowing the pilot to learn and adapt.
3. Background
The organisation had established approaches to planning, governance, risk management and stakeholder reporting. These were normally applied to initiatives with more stable requirements, better-understood solutions and outcomes that could be defined more confidently in advance.
The Generative AI pilot was different. Its use cases were exploratory, benefits needed to be observed rather than assumed, and user adoption and behaviour were central to success.
A fixed and fully predictive delivery model could therefore have constrained learning, created unrealistic expectations and treated necessary adaptation as delivery failure. The project required a disciplined but progressive project management approach.
4. Action Taken
Orr Consulting applied a progressive project management approach combining clear project boundaries, governance and accountability with short, evidence-led delivery cycles.
The practices and learning from this engagement helped shape what Orr Consulting now describes as Progressive Project Management.
4.1 Project Boundaries
At the outset, the project defined:
The purpose of the pilot
The scope of tools and user groups involved
The duration and structure of the pilot
Success criteria at a high level
This ensured that, while detailed outcomes were uncertain, the project itself remained bounded and controlled.
This separated uncertainty within the pilot from uncertainty about how the pilot itself would be governed and managed.
4.2 Managing Uncertainty
Material project uncertainties were made explicit rather than hidden within plans or presented as settled facts. These included uncertainty around use-case value, adoption, information governance, output quality, expected benefits and the case for wider deployment.
In practice, these uncertainties were addressed through evidence-producing deliverables and delivery activity, while established controls managed the assumptions, dependencies, risks, materialised issues and residual exposure affecting delivery.
The objective was not to create a separate control system, but to reduce, bound or explicitly accept uncertainty while maintaining governance and accountability.
4.3 Progressive Delivery Cycles
Delivery was structured into short Discover–Design–Deliver cycles. Early cycles focused on exploration and testing; later cycles refined the most promising applications. Learning from each cycle informed the next.
Each cycle produced project outputs and evidence relating to use cases, adoption, governance, risks and expected benefits. This allowed assumptions to be tested early, direction to adapt and confidence to increase without losing control.
Regular checkpoints reviewed the evidence produced, the uncertainty remaining and whether to continue, refine, pivot or stop particular activity
4.4 Managing Evolving Scope
Scope was actively managed as an evolving construct:
New use cases were identified and assessed during the pilot
Some initial ideas were deprioritised or dropped
Focus was adjusted toward areas showing the most value
This avoided forcing the project into a fixed scope that did not reflect emerging understanding.
Scope change was therefore treated as evidence-led refinement rather than uncontrolled expansion.
4.5 Strengthening Governance
Given the nature of AI, particular attention was paid to governance:
Data usage and handling were monitored and controlled
Acceptable use of AI tools was defined and communicated
Risks were reviewed regularly as understanding developed
Stakeholder oversight was maintained through structured reporting
This ensured that control was maintained even as delivery evolved.
Governance decisions were revisited as evidence developed, recognising that the project’s risk profile could change alongside its use cases, users and delivery approach.
4.6 Tracking Benefits
Benefits were not assumed — they were actively tracked:
Expected benefits were defined at the outset
Real-world usage and outcomes were monitored during the pilot
Both anticipated and unanticipated benefits were captured
Results were reported through formal governance channels
This created a clear link between delivery activity and measurable value and provided the basis for defining, measuring and tracking benefits through a structured AI Benefits Realisation approach throughout the pilot.
4.7 Supporting Adoption
The project recognised that outcomes depended heavily on users:
Users were actively engaged in testing and feedback
Training and support were provided
Use cases were shaped based on real user experience
Confidence in using AI tools was built over time
This helped translate technical capability into practical value.
Adoption evidence also helped distinguish between limitations in the technology, weaknesses in particular use cases and issues that could be addressed through training, guidance or support.
5. Outcomes
The project management engagement created several notable outcomes, observations and lessons learned.
5.1 Controlled Pilot Delivery
The pilot was completed within its defined parameters despite evolving use cases, assumptions and delivery learning. Clear project boundaries, progressive delivery cycles and structured governance enabled the project to adapt without losing control or credibility.
5.2 Benefits Evidenced
More than 80% of the anticipated pilot benefits were realised during the pilot period, with additional previously unrecognised benefits also identified.
This demonstrated that benefits could be observed and tested during delivery rather than assumed in advance or assessed only after completion.
5.3 Understanding Value
The pilot improved organisational understanding of where Generative AI created the most value, where outcomes varied between user groups and which use cases warranted further development.
This enabled future decisions to be based on observed operational evidence rather than general enthusiasm for the technology.
5.4 Stakeholder Confidence
Regular risk review, controlled data use, acceptable-use guidance and structured reporting increased confidence among executive, risk and delivery stakeholders.
The project demonstrated that exploratory AI activity could proceed within credible governance arrangements.
5.5 Investment Evidence
The pilot created a more robust evidence base for subsequent business-case and scaling decisions. Leadership was able to consider future investment with clearer information about adoption, benefits, governance requirements, practical use cases and delivery capability.
This created a controlled path from exploratory pilot activity towards more structured and scalable AI adoption.
5.6 Reducing Uncertainty
The engagement demonstrated that effective AI Project Management does not depend on removing uncertainty before delivery begins.
It depends on making material uncertainty explicit, producing the evidence required and using delivery cycles and governance checkpoints to reduce, bound or explicitly accept it.
The practices and learning from this Use-mode engagement contributed to the development of Orr Consulting’s Progressive Project Management approach.
6. Final Thoughts
AI Project Management is not about abandoning control. It is about applying control in a delivery environment where requirements, adoption and value may emerge progressively.
In this Use-mode pilot, the principal uncertainties concerned where existing tools could create value, how users would adopt them, how information and outputs should be governed and whether the evidence justified wider investment.
Clear boundaries, evidence-led Discover–Design–Deliver cycles, structured governance and active benefits tracking allowed the pilot to adapt without losing control. More than 80% of anticipated benefits were realised, additional benefits were identified and leadership gained stronger evidence for future investment decisions.
Learning from this engagement helped shape Orr Consulting’s Progressive Project Management approach. The case demonstrated that disciplined project leadership can accommodate uncertainty without disguising it through unrealistic upfront assumptions.
This AI Services Case Study is part of the Orr Consulting AI Transformation Library — structured thinking for AI transformation leaders and decision makers.
If your organisation would benefit from stronger delivery control across an uncertain or evolving AI project, we would be pleased to discuss your next AI steps.
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