Day 1 Individual capability and human AI partnership

Start with your own work. Explore how an ongoing relationship with AI can support thinking, learning, and action, and how to retain responsibility for the result.
 

• Frame a useful task, provide relevant context, and work iteratively with an AI assistant.

• Recognise uncertainty, challenge assumptions, and judge when an answer needs stronger evidence or expert review.

• Set boundaries around memory, confidential information, delegated work, and human decision-making.
   

Your practical output: A human-AI working agreement setting out purpose, context, responsibilities, boundaries, and checks.

Day 2 Team collaboration

Explore how AI changes the way a team thinks and works together. Examine how facilitation shapes the behaviours AI may amplify, including whose voice is heard and how the team reaches a decision.
 

• Design collaborative activities that combine human expertise with AI contributions.

• Use facilitation to surface assumptions, disagreement, and missing perspectives.

• Explore changing roles and flexible teaming while keeping capacity, ownership, and shared understanding visible.
   

Your practical output: A team collaboration design with working agreements, contribution and review roles, and a facilitated activity.

Day 3 Workflows and automation

Follow the work beyond one person or team. Identify where AI can improve the whole flow and where faster production may shift effort or risk downstream.
 

• Map handoffs, queues, checking, rework, and the decisions that determine value.

• Design a workflow combining human work, AI assistance, and bounded automation, with clear acceptance criteria and recovery paths.

• Identify the context and evidence each next step needs, and measure value alongside tool costs, checking effort, and quality.

Your practical output: A redesigned workflow with automation opportunities, evidence requirements, human review points, and measures of improvement.

Governance and organisational intelligence
Day 4 Enabling constraints and the Proven Trust Envelope

Design governance that helps people know what they can do, what evidence they need, and when to involve someone else. Explore enabling constraints: boundaries that make responsible action possible and clarify accountability.

Work with the Proven Trust Envelope, an approach for defining the conditions within which an AI-supported action can be trusted for a particular purpose. These conditions depend on evidence, context, authority, and controls, and need to be reviewed as those things change.
 

• Define the intended use, permitted actions, decision rights, evidence requirements, and limits of a bounded use case.

• Distinguish access to information from authority to act, and identify when an action may proceed, needs approval, or must stop.

• Design confidentiality boundaries between individual, team, departmental, and organisational workspaces.

• Explore proportionate pathways for experimentation and operational use, and how governance evidence can support risk and regulatory discussions.

Your practical output: A draft trust envelope and decision record, including permissions, approval triggers, confidentiality boundaries, and review conditions.

Day 5 Organisational intelligence

Bring the whole journey together. Explore how an organisation can use evidence from its work and people's experience to understand what is happening, choose a useful response, and learn whether that response helps.

Examples from Hyphoria illustrate how documents, meeting transcripts, and delivery evidence can be examined through organisational lenses to reveal possible patterns of alignment, friction, and emerging risk. You will consider how people test those interpretations before acting.
 

• Trace an insight back to its sources and assess the strength, independence, relevance, and limits of the evidence.

• Explore patterns across teams without assuming that information may cross every confidentiality boundary.

• Connect insight to a decision, an owner, an intervention, and evidence of what happens next.

• Develop an initial plan for improving organisational learning and adaptive capacity.
   

Your practical output: An organisational intelligence and learning plan connecting a question, permitted evidence, interpretation, action, and review.

Together, the five outputs form one practical portfolio showing how your case develops from individual AI use into a coherent way of working and learning.