Most enterprises are pursuing 10× more AI use cases than they can execute well.

McKinsey data (2024) shows the average large enterprise has identified 50–100 potential AI use cases at any given time. The median number actually deployed at scale: 5–7.

Prioritisation is the rarest and most valuable discipline in enterprise AI. An advisory firm that can identify the 20% of use cases generating 80% of achievable value is worth multiples of its fee.

50–100

AI use cases the average enterprise has identified

McKinsey, 2024

5–7

AI use cases actually deployed at scale (median)

McKinsey, 2024

80%

of value comes from the top 20% of use cases

PwC, 2024

2.8×

more likely to achieve ROI with a formal business case

Accenture, 2024

The AI use case priority matrix

Top-quartile AI organisations use a formal prioritisation framework that maps use cases across value and complexity — not technology availability or internal advocacy (BCG, 2024)

Quick WinsHigh value · High data readiness

Deploy first — build confidence and generate early ROI that funds subsequent investment

Examples: Document processing automation, customer service triage, procurement fraud detection

Strategic BetsHigh value · Higher complexity

Invest in data and infrastructure now to unlock. These are your competitive differentiation use cases

Examples: Predictive maintenance, credit risk transformation, supply chain intelligence

Fill-insLower value · Lower complexity

Deploy opportunistically when capacity allows — do not compete for priority resources

Examples: Basic reporting automation, internal FAQ bots, meeting transcription

AvoidLower value · High complexity

Do not invest. Deprioritise firmly — these consume disproportionate resources for minimal return

Examples: Highly bespoke systems for low-frequency, low-value decisions

How we identify and prioritise your AI use cases

Five stages from discovery through to a formally structured, board-ready business case for each priority use case

Stage 01

Use Case Discovery

We conduct structured discovery workshops with your P&L owners, operations leaders, and domain experts to surface the full landscape of AI opportunity. We deliberately separate discovery from prioritisation — ensuring no use case is dismissed before it has been properly assessed, and no use case advances based on internal advocacy rather than business merit

Use case longlist (typically 40–80 candidates)Business context mappingStakeholder alignment register

Stage 02

Data Readiness Pre-Screen

Before any use case advances to prioritisation, we screen it against your actual data infrastructure. MIT Sloan research confirms that data readiness — not strategic alignment — is the most common reason use cases fail during implementation. We surface data gaps early, so your prioritisation decisions are grounded in execution reality

Data readiness scores per use caseData gap registerQuick-win vs long-lead classification

Stage 03

Value-Effort Scoring

We score every use case across four dimensions used by top-quartile AI organisations (BCG, 2024): strategic alignment, business value (revenue/cost/risk), technical feasibility, and data readiness. The output is a calibrated priority matrix — not a consensus heat map — with clear quantification of expected value ranges for each candidate

4-dimension scoring matrixValue range estimates per use casePriority quadrant map

Stage 04

Portfolio Sequencing

We sequence your use case portfolio across deployment horizons: Quick Wins (high value, high data readiness — deploy first to build confidence and generate early ROI), Strategic Bets (high value, higher complexity — invest in data and infrastructure now to unlock later), and Fill-ins (lower value, lower risk — deploy opportunistically). Pareto discipline is applied: 20% of use cases generate 80% of achievable value

Sequenced deployment portfolioHorizon-by-horizon roadmapInvestment prioritisation model

Stage 05

Business Case Construction

For each priority use case, we construct a formally structured business case — the step that Deloitte research shows only 31% of enterprise AI initiatives take before development commences. Our business cases quantify expected value ranges, identify the assumptions that must hold for value to be realised, and define the measurement framework that will confirm whether they have been

Business case per priority use caseROI model and assumptions registerSuccess metrics framework

Identify the 20% that generates 80% of the value

The right question is not 'What can AI do for us?' It is 'Which five use cases, executed exceptionally, would transform our competitive position?' We will help you answer it.