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Scoping and translating business needs into ML solutions

Instruction and application
In Progress

Have you ever sat in a meeting where someone says, ‘We should use machine learning to solve this’ –but no one can quite explain what ‘this’ actually is?

Maybe the business challenge is vague, the data needs are unclear or the success metrics are undefined. Without a clear path from business problem to ML solution, even the most advanced models can miss the mark.

Many ML projects fail – not because of flawed models but because of poor scoping. The problem isn’t clearly defined. The data isn’t available or usable. The success criteria are vague. And the technical team is left guessing what the business actually wants.

Unit context​

This unit focuses on the key early phase of any ML project – translating business needs into actionable ML plans. You’ll learn how to engage stakeholders, define success metrics and structure ML problems in a way that sets your team up to build solutions that actually deliver value.

From scoping MVPs to crafting business cases, you’ll develop the skills to lead ML projects from idea to impact – starting with a clear, well-defined goal.

Why does this unit matter?​

In today's data-driven world, businesses have more information than ever before – but data without direction is useless. Companies want to leverage ML for automation, predictions and optimisation, but without a structured approach to scoping and planning, ML projects can fail due to unclear objectives, misaligned expectations or poor feasibility analysis.

By mastering how to translate business needs into ML solutions, you will be able to:

  • Identify the right ML use cases that drive real business impact.
  • Communicate with stakeholders to ensure alignment between technical feasibility and business expectations.
  • Avoid common pitfalls like misinterpreted requirements, impractical AI solutions or projects that don’t deliver value. Understanding how to scope ML projects effectively will set you apart in any data-driven role, whether you're a data scientist, business analyst or product manager.

Learning objectives​

By the end of this unit, you will be able to:

  • Evaluate and prioritise ML opportunities based on their alignment with business needs and potential ROI.
  • Translate complex business requirements into well-defined ML problem statements, demonstrating the ability to scope ML engineering solutions.
  • Analyse technical constraints such as data availability, latency and scalability to inform the scoping of ML solutions that meet business requirements.
  • Develop compelling business cases for ML projects, demonstrating alignment with organisational strategy and quantifiable success metrics.

Before you continue, make sure you've completed the following units:​

  • AI ML Fellowship Module 2 Unit 1: Strategic alignment and organisational readiness for ML.

Action item: Pause and think​

Before diving into the unit, take a moment to reflect on how this topic connects to your role and responsibilities. Think about the way ML projects begin in your organisation – or how they should.

This reflection will help you stay grounded in your own context as you move through the unit. Keep your answers in mind – you’ll revisit them when applying these skills later.