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Async review

Instruction and application
In Progress

Recap core topics:​

  • Unit 1: Effective communication in machine learning
  • **Unit 2:**Stakeholder engagement and management

Unit 1: Effective communication in machine learning​

In Unit 1, you explored:

  • Adapting communication styles: How to adjust tone, language and framing when communicating with technical vs non-technical stakeholders to ensure clarity and relevance.
  • Core communication principles: Transparency, consistency and empathy as the foundations for trust and collaboration in ML project teams.
  • Clarity and framing: Techniques for simplifying complex technical concepts without losing accuracy, helping bridge gaps between data teams and business leaders.
  • Structured communication: Using frameworks such as the ‘who, what, when, how’ approach to plan and deliver targeted, purposeful updates throughout the ML life cycle.
  • Common challenges: Managing misalignment, inconsistent updates and competing priorities through clear, audience-specific communication.

Unit 2: Stakeholder engagement and management​

In Unit 2, you explored:

  • Stakeholder identification and analysis: How to map project stakeholders by their level of influence and interest, ensuring that attention is focused where it drives the most impact.
  • The stakeholder matrix and communication planning: How to use the influence-interest matrix and a structured communication plan to deliver the right message, at the right time, through the right channel for each stakeholder group.
  • Engagement strategies: Practical approaches to balance competing priorities, build trust and maintain alignment across executive sponsors, technical teams and end users.
  • Handover documentation: How to create approval-oriented reports that clearly frame the problem, solution, business benefits and next steps — making stakeholder approval easier and faster.
  • Securing and confirming approval: The importance of formal documentation, confirmation emails and post-approval communication to ensure that decisions are clear, traceable and acted upon.

Communicating with impact​

  • Adapt your tone, detail and framing to match technical and non-technical stakeholders.
  • With technical audiences, focus on accuracy, data transparency and process clarity.
  • With non-technical audiences, emphasise outcomes, relevance and business value.
  • Avoid jargon when it creates confusion — aim for clarity, not complexity.

Mapping stakeholder influence and interest​

Use a stakeholder matrix to visualise influence and interest levels:

  • High influence, high interest: Manage closely.
  • High influence, low interest: Keep satisfied.
  • Low influence, high interest: Keep informed.
  • Low influence, low interest: Monitor periodically.

Tip​

Prioritise your engagement strategy based on influence and interest, not justseniority or visibility.

Planning communication and managing priorities​

A strong communication plan outlines:

  • **Who:**Key stakeholders and message owners.
  • **What:**Critical messages or updates.
  • **When:**Timing and frequency of communication.
  • **How:**Preferred channels and formats.

Balancing stakeholder priorities​

  • Use structured, empathetic communication to manage conflicting priorities.
  • Maintain transparency and balance between technical and business perspectives.

Action item: Poll — how do you engage your stakeholders?​

Start with a quick stakeholder-focused poll. This will help you gauge how you think about identifying, prioritising and engaging different stakeholders in ML projects.

There are no right or wrong answers — just choose the option that best reflects your approach or experience.