Prioritise ML opportunities by strategic value

Instruction and application

Complete

You have explored how to align ML initiatives with organisational goals and assess readiness. This section focuses on what to do when you identify multiple possible use cases and cannot pursue all of them at once. The goal is to prioritise opportunities based on impact, feasibility and strategic fit.

Discover ML opportunities across business functions

ML can add value across almost every business function, but effective discovery starts with real business problems:

• Inefficiencies or repetitive manual processes.
• High-value decisions that could improve with better predictions.
• Large underused stores of structured or unstructured data.

Three practical techniques help surface opportunities:

1. Process mining

Analyse digital footprints in system logs (CRM, ERP, HR tools) to understand how work actually happens.

Why use it?

• Identifies bottlenecks, delays and process deviations.
• Reveals inefficiencies often invisible to teams.
• Helps find candidates for automation and optimisation.

Example: A bank used process mining on loan approvals and found that manual document verification caused major delays, creating a strong case for ML-based classification.

2. Pain-point analysis

Interview or survey teams to discover frustration points, delays and recurring complexity.

Why use it?

• Adds a human-centred lens to use-case discovery.
• Surfaces operational issues not obvious from data alone.
• Helps prioritise what matters most to day-to-day users.

Example: An HR function identified long delays matching internal candidates to roles, leading to an ML recommendation solution for real-time matching.

3. Competitive benchmarking

Research how peers and leaders in your industry are applying ML and what outcomes they are achieving.

Why use it?

• Identifies capability gaps in your own strategy.
• Strengthens the case for investment.
• Provides validated patterns for high-impact use cases.

Example: A logistics firm benchmarked competitors using ML route optimisation and piloted a similar approach to improve fleet efficiency.

Tip

Combine data-driven methods (process mining), human insight (pain-point analysis) and external evidence (benchmarking) to build a strong prioritisation pipeline.

Prioritise initiatives using strategic frameworks

Not all ML opportunities are equal. Some are easy to deliver but low impact; others are transformational but complex. A feasibility-impact matrix helps evaluate each use case by asking:

• How feasible is this initiative with current data, tools and skills?
• How much impact could it have on business goals and KPIs?

	High impact	Low impact
High feasibility	Quick wins (low-hanging fruit)	Optional small-scale experiments
Low feasibility	Strategic bets (longer-term initiatives)	Deprioritise or revisit later

Estimating high-level ROI and KPI alignment

Impact assessment should connect to measurable outcomes:

• Will this use case reduce costs or increase revenue?
• Does it directly support one or more organisational KPIs?
• Can you estimate time savings, efficiency improvements or CX gains?

You do not need a full ROI model initially, but you do need a credible value estimate aligned to leadership priorities.

Assessing strategic alignment

Even promising use cases may be poor choices if they:

• Conflict with digital transformation direction.
• Clash with current business processes or initiatives.
• Cannot scale across teams, departments or markets.

Prioritise use cases that support long-term strategy, fit the technology road map and reinforce customer value.

Scenario example: Prioritising ML in finance

You are comparing two ML ideas in finance:

• Use case A: Automating invoice matching - manual, error-prone and time-consuming work with clean available data and low implementation risk.
• Use case B: Predicting fraud patterns across regions - potentially high impact but requiring substantial integration, infrastructure and time investment.

Feasibility-impact result:

• Use case A is aquick win with strong near-term ROI and KPI alignment.
• Use case B is astrategic bet with high long-term value and higher delivery complexity.

Key points

Prioritisation always involves trade-offs. Evaluate cost vs scalability, accuracy vs interpretability, and speed vs robustness before committing.

Trade-off quiz

You need visible value this quarter with limited resources. Which option should you prioritise first?

• A. High-feasibility invoice automation with immediate process gains
• B. Global fraud model with high complexity and long lead time

Correct Answer: A

Feedback: Prioritising quick wins builds momentum, demonstrates ROI and creates capacity for larger strategic bets.