Developing Robust AI Business Cases: From Strategy to Defensible Investment
- orrconsultingltd
- 2 days ago
- 4 min read
1. Insight
Many organisations now have a growing pipeline of potential AI initiatives.
Ideas emerge from AI strategy discussions, use case discovery workshops, early pilots, and vendor proposals. In many cases, these ideas are well intentioned and aligned to genuine organisational needs.
Yet a familiar challenge quickly emerges:
“We like the idea — but are we confident enough to invest?”
This is the point at which AI ambition must transition into formal decision-making and that transition is rarely straightforward.
AI business cases are expected to bridge the gap between intent and commitment — but too often they are rushed, inconsistent, or treated as an administrative hurdle rather than a strategic control point.
2. Why This Matters
Within the AI Transformation Process, business case development plays a critical role.
It sits at the end of the Design stage, following:
AI Capability & Maturity Assessment
AI Use Case Discovery
AI Strategy and Roadmap development
It is the final decision gate before the organisation commits resources and formally enters the Delivery stage.
This positioning matters.
Once delivery begins:
funding is committed
delivery teams mobilise
expectations are set
and risk exposure increases materially
A weak or poorly framed AI business case at this point does not just affect one initiative — it undermines confidence in the wider AI agenda.
3. What Sits Behind the Business Case Question
When leaders and boards ask for an AI business case, they are rarely asking for a financial model alone.
They are trying to gain confidence across several dimensions at once.
3.1 Confidence That the Initiative Supports Strategy
At this stage, boards want to see a clear line of sight between:
agreed AI strategy and priorities
the proposed programme or project
and the organisational outcomes it is intended to support
AI business cases that are disconnected from strategy — even if technically interesting — struggle to justify investment.
3.2 Clarity on Benefits and Ownership
AI benefits are often described in broad terms such as productivity, efficiency, or improved decision-making.
Decision-makers want to understand:
what specific benefits are expected
where they will arise
who is accountable for realising them
and how they will be measured over time
Without this clarity, AI business cases can feel aspirational rather than investable.
3.2 Understanding Cost, Complexity, and Risk
AI initiatives frequently involve unfamiliar cost drivers and dependencies, including:
data preparation and integration
change and adoption effort
governance and assurance controls
organisational capability constraints
Leaders want confidence that:
assumptions are explicit
risks are surfaced honestly
and delivery complexity has not been understated
T
his is especially important where AI maturity is still evolving.
3.3 Delivery Credibility and Readiness
Even where the case for value is strong, boards want reassurance that the organisation is ready to deliver.
They look for evidence that:
scope and sequencing are realistic
dependencies have been considered
lessons from previous initiatives have been applied
and the proposed approach is proportionate to scale
A business case that ignores delivery reality rarely survives scrutiny.
4. Benefits of Structured AI Business Case Development
Applying a structured, Managing Successful Programmes (MSP)-aligned approach to AI business case development provides significant benefits.
4.1 Clear Transition from Strategy to Delivery
Well-constructed AI business cases translate strategic intent into:
defined scope
clear outcomes
and explicit delivery propositions
This ensures that only initiatives aligned to agreed priorities progress into delivery.
4.2 Proportionate, Scalable Decision-Making
AI business cases should be flexed to suit size and scale.
A small, low-risk AI project does not require the same level of formality as a large, cross-organisational AI programme or capital investment.
Using MSP principles allows:
proportionality without loss of rigour
consistency without bureaucracy
and clarity regardless of scale
4.3 Stronger Investment and Prioritisation Decisions
When AI business cases are developed consistently:
initiatives can be compared on a like-for-like basis
prioritisation becomes transparent and defensible
and funding decisions feel deliberate rather than opportunistic
This is particularly important where multiple AI initiatives are competing for limited investment.
4.4 Better Foundation for Delivery and Benefits Realisation
An MSP-aligned business case does not end at approval.
It establishes:
benefit ownership
baseline measures
and success criteria
These become critical inputs into programme governance, assurance, and benefits realisation once delivery begins.
5. Risks If AI Business Cases Are Not Addressed Properly
When AI business case development is rushed or treated informally, several risks commonly arise:
initiatives progress without clear strategic alignment
benefits are overstated or poorly defined
costs and dependencies are underestimated
delivery challenges emerge late
confidence in AI investment weakens following setbacks
Over time, this can lead to:
pilot fatigue
fragmented delivery
and reluctance to commit further funding — even where opportunities are genuine
6. Final Thoughts
AI business cases are not about slowing progress.
They are about ensuring that progress is intentional, investable, and defensible.
Within the AI Transformation Process, business case development is the moment where strategy is tested against reality — before resources are committed and expectations are set.
Applying established MSP principles, tailored to organisational context and scale, provides leaders with the confidence to move from ambition to action.
That discipline is not a constraint on innovation.
It is what allows innovation to proceed responsibly and at pace.
7. Call to Action
Developing effective AI business cases is about more than securing approval.
It is about giving leaders confidence that:
the right initiatives are being pursued
for the right reasons
at the right time
and at the right scale
Orr Consulting supports organisations in developing robust, MSP-aligned AI business cases for both AI programmes and individual AI projects, proportionate to their size and complexity.
For significant capital investments, Orr Consulting can also support business case development using the Five Case Model, in line with HM Treasury Green Book guidance.
For organisations ready to move from AI strategy into committed delivery, structured business case development is the critical final step in the Design stage of AI transformation.
Subscribe to Orr Consulting to receive occasional emails with practical AI Insights and updates.
Comments