MIRAS
Case Study — AI Strategy & Transformation

AI-Powered Investment Prioritisation and Budget Allocation

An AI decision framework that ranks where to invest, checks whether conditions support it, and replaces gut-feel arguments with evidence

A business was allocating budget across hundreds of product categories, but the rationale changed depending on who ran the process. We built an AI-powered decision framework that ranks every option by evidence and checks whether conditions can support the investment — turning weeks of inconsistent manual review into hours of consistent, defensible recommendations.

The problem with manual budget allocation

Anyone who makes investment allocation decisions without a consistent AI framework will recognise this.

Hundreds of categories across multiple markets. Every quarter, different people ran the process differently using different criteria. One person's top priority was another's afterthought, and nobody could explain why. This is the core inefficiency that AI-powered decision-making is built to solve.

"Invest £200k in Category X."

Reviewer A, Q2 planning

"Pause Category X entirely."

Reviewer B, same meeting

Same data. Same quarter. Different conclusion.

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Our AI decision framework approach

We studied how the team was making decisions, where the logic broke down, and built an AI framework to replace the inconsistency.

They had plenty of data. What was missing was a consistent, AI-driven way to turn it into a recommendation. We built a framework that asks three questions in order, and stops if the conditions are not right — removing human bias from the prioritisation process entirely.

01
Can it absorb more investment?
If the area cannot support more investment right now, the AI framework stops. It checks whether the foundations are healthy before any capital allocation decision is made.
02
What is the evidence-based return?
What is the performance on existing spend? Is there enough data to be confident? The AI model uses standardised windows with fallbacks when recent data is thin — removing guesswork from ROI assessment.
03
How big is the real, addressable opportunity?
The framework looks past the headline number to what you can actually capture. It uses AI-driven analysis to separate broad-based potential from concentrated revenue that looks bigger than it is.
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AI-generated prioritisation output

The budget meeting used to be a political argument. Now it starts with an AI-ranked recommendation.

Prioritised Recommendations
Q2 2026
# Area Action Score Reason
1 Category A Invest 91 Strong return, healthy conditions
2 Category B Scale 84 Proven performance, growing demand
3 Category C Hold 72 Concentrated, riskier than it appears
4 Category D Test 65 Emerging, limited data
5 Category E Pause 38 Conditions cannot support investment
1 item flagged Framework-generated
The AI framework does not replace judgment — it replaces the blank page. The team still overrides when they know something the data does not. AI-assisted decision-making with human expertise at the final step.
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Impact of AI-powered prioritisation

What the team got back from replacing manual review with an AI decision framework.

Before

  • Weeks of inconsistent manual review every quarter
  • Criteria changed depending on who ran the process
  • Budget went to whoever argued loudest
  • High headline numbers mistaken for real opportunity

After

  • AI framework completes review in hours, not weeks
  • Consistent, AI-enforced criteria every quarter
  • Every recommendation backed by evidence, not opinion
  • Concentration risk flagged automatically by the AI model
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AI strategy & transformation

Every investment decision should be defensible — and AI makes that achievable at scale.

This team went from weeks of manual review and gut-feel arguments to an AI-generated ranked list where every recommendation traces back to the data that produced it. Six weeks to build, and they have been running the AI framework on their own since. The same approach — applying AI strategy to remove inconsistency from high-stakes decisions — works wherever judgment is currently doing the work that data should.

#AIStrategy #AIDecisionMaking #GenerativeAIForBusiness #MarketingAIAutomation
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