How to Structure Work with AI

Emily Yang

Human-Centred AI (HCAI) Specialist

Most work suffers from too much unusable information, not too little. Learn simple structures that help AI turn noise into action.

Working with AI

Most work suffers from too much unusable information, not too little. Learn simple structures that help AI turn noise into action.

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How to Structure Work with AI

1 min 35 secs

Key learning objectives:

Use practical structures for common work tasks
Learn how to match formats to decisions and updates
Explain where AI adds speed and structure

Overview:

Many workdays are slowed not by too little information, but by information in the wrong format. Clear structures help turn updates into actions, decisions into priorities, and complex inputs into something usable. Matching the right format to the task helps AI add speed and organisation, while human judgement ensures outputs are accurate, relevant, and ready to use.

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Summary

Why is information overload such a common problem?
Modern organisations generate constant inputs through meetings, emails, chats, trackers, slide decks, and shared documents. Useful insight is often present, but scattered across different places and mixed with low-priority detail. The challenge is rarely access to information. It is the time and effort needed to interpret it, connect it, and turn it into action.

Why are summaries not enough?
Shorter is not always clearer. A summary can still combine decisions with open discussion, risks with routine updates, or assumptions with confirmed facts. This means the reader still has to do the hard work of separating what matters, what is uncertain, and what requires action. Good outputs reduce effort, not just word count.

What changes when structure is applied first?
Structure determines what becomes visible. Decisions / Actions / Open Questions makes ownership and next steps easier to spot. Options / Trade-offs / Recommendation helps compare choices and move toward a decision. Current State / Risks / Next Milestones gives leaders a fast view of progress, blockers, and priorities. The right format often matters as much as the information itself.

What remains human work?
AI can organise information quickly, but judgement still sits with people. Humans decide what is most important, what context is missing, what looks inaccurate, and what should happen next. Strong outputs come from combining AI speed with human oversight, accountability, and decision-making.

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Emily Yang

Emily Yang leads Human-Centred AI and Innovation at a global financial institution and serves on the organisation’s AI Safety and Governance committees. Her work focuses on advancing responsible and trustworthy AI systems that balance innovation with accountability. She is among the first practitioners in the industry to apply Human-Centred AI at scale. With over a decade of experience in human-computer interaction and user experience, Emily has held roles across tech startups, corporate venture builders, and major technology companies. Her journey into AI began with studies in biochemistry and neuroscience, followed by a research master’s in HCI and natural language technologies, during which she published work on perceived empathy and emotional intelligence in virtual agents.

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