---
title: "90 United We Transform questions, actions, and AI levers"
description: "Use all 30 immediate actions, 30 AI levers, and 30 critical reflection questions from the United We Transform practitioner toolkits."
last_updated: "2026-08-26"
canonical: "https://unitedwetransform.com/book/questions/"
---

# 90 United We Transform questions, actions, and AI levers

All practice material below is drawn from the practitioner toolkits in Chapters 1-10.

## Immediate actions

- Chapter 1, item 1: In your next team meeting, explicitly state the single desired outcome at the very beginning and then, at the end, verify with the group if that outcome was achieved and what the clear next steps are. (source page 52)
- Chapter 1, item 2: Identify one recurring meeting or report in your schedule that often feels unclear or low value. Propose one specific change to its agenda, format, or necessity, framing your suggestion around improving clarity and focus. (source page 52)
- Chapter 1, item 3: The next time you are assigning or receiving a task, practice the "Reset Mindset" attribute of "Uses past to redefine future" by briefly discussing any relevant learnings from similar past tasks that could inform a clearer approach this time. (source page 52)
- Chapter 2, item 1: UWT Element Quick Scan: For a current key initiative, quickly assess its alignment with just three of the nine UWT elements (e.g., Is the Destination clear? Are Human Roles well-defined? Is our Flow & Process efficient?). Identify one immediate observation. (source page 81)
- Chapter 2, item 2: "Obliteration" Brainstorm: As a team, identify one ingrained, "clarity-destroying" habit (e.g., a recurring inefficient meeting, an ambiguous communication channel) as discussed in "UWT's Mandate." Brainstorm one small step to begin redesigning or eliminating it. (source page 81)
- Chapter 2, item 3: Structural Reflection: Considering "UWT as a Catalyst for Radical Structural Renewal," identify one current organizational structure (e.g., a departmental silo, a decision-making hierarchy) that you suspect might be hindering true cross-functional, AI-augmented collaboration. (No action needed yet, just identification). (source page 81)
- Chapter 3, item 1: As a leadership team, honestly discuss your collective readiness to champion the UWT transformation, including challenging sacred cows and modeling new AI-augmented behaviors. Identify one specific action leadership will take this month to visibly support the UWT initiative. (source page 100)
- Chapter 3, item 2: Schedule a 1-hour workshop with a key team to introduce the principles of an AI-Augmented Collaborative Intelligence Mindset (Augmentation, Transparency, Accountability, Continuous Learning). Collaboratively identify one current team practice that could be improved by applying one of these principles. (source page 100)
- Chapter 3, item 3: For your team or department, draft a concise (1-3 sentences) "Implementation Mission Statement" for adopting UWT and AI. What specific, tangible benefits are you aiming for in your area through this transformation? (source page 100)
- Chapter 4, item 1: Identify One “Augmentation Opportunity”: Select one upcoming team decision. Review the “Anatomy of Decision-making” stages (Sensing Need, Illuminating Landscape, Expanding Horizon, Sharpening Choice, Supporting Commitment). Pinpoint one stage where AI assistance could most significantly improve your current process. (source page 120)
- Chapter 4, item 2: “Agentic Workflow” Brainstorm (Small Scale): Think of a recurring information-gathering task your team performs for decision support (e.g., weekly competitor news roundup). Sketch a simple “agentic workflow” where an AI agent might automate parts of the data collection or initial synthesis. (source page 120)
- Chapter 4, item 3: Critique an AI Output: Find a piece of AI-generated analysis or a recommendation (even from a public tool). As a team, practice “The Indispensable Human” role by critically evaluating its strengths, weaknesses, potential biases, and the human judgment needed to use it responsibly in a decision. (source page 120)
- Chapter 5, item 1: For your next solution design initiative, before any human brainstorming, assign one team member to conduct focused "AI Deep Research" on existing solutions, user pain points, and relevant emerging technologies for that specific problem space. (source page 134)
- Chapter 5, item 2: In your next ideation session, after initial human idea generation, introduce one AI-generated concept (created via astute prompting) as a "wild card" to deliberately stretch the team's thinking and spark new connections. (source page 134)
- Chapter 5, item 3: Select one aspect of a current solution blueprint that requires meticulous detailing or checking for consistency (e.g., user interface guidelines, technical specifications). Task a team member (acting in a Cognitor capacity) to explore if an AI tool could assist in this refinement task. (source page 134)
- Chapter 6, item 1: Mindset Self-Check: Review the "Cognitor mindset" priorities (e.g., Orchestration over Origination, Comfort with Ambiguity). Identify one attribute you personally want to strengthen and one small action you can take this week to practice it (e.g., consciously deferring to an AI-generated summary before offering your own analysis). (source page 154)
- Chapter 6, item 2: Process Choreography Sketch: Take a recent team decision or a small part of a solution design process. Briefly sketch how a Cognitor might have structured it differently using a simple Scan-Focus-Act cycle, identifying where AI could have played a role. (source page 154)
- Chapter 6, item 3: Tech Palette Exploration: Choose one category from "The Cognitor's Tech Palette" (e.g., generative AI, agentic automation). Dedicate 30 minutes to research one new tool in that category and consider how it could specifically help in orchestrating a human-AI collaborative task. (source page 154)
- Chapter 7, item 1: Select the one experiment from this chapter that most closely mirrors a current challenge or strategic priority your team is facing. In your next team meeting, briefly present the "Situation" and "Complication" from that scenario and facilitate a 15-minute discussion on its parallels to your own situation. (source page 171)
- Chapter 7, item 2: Identify a current project in your organization that is struggling due to a lack of clear data-driven insights or an inefficient process. Drawing inspiration from the chapter's examples, brainstorm with a colleague how a "Cognitor-led" approach might unlock progress or offer a new, valuable perspective. (source page 171)
- Chapter 7, item 3: Choose one of the "Resolutions" in the experiments (e.g., accelerating policy development, enhancing cybersecurity, aligning leadership). List three specific, high impact questions a Cognitor would need to ask of both humans and AI to begin tackling a similar problem in your organizational context. (source page 171)
- Chapter 8, item 1: For an upcoming project implementation, select one aspect of "Architecting Execution" (e.g., task sequencing, risk identification). Dedicate a brief team session to brainstorm how AI could (even if you don't have the tools yet) provide a more data-informed starting point for that aspect. (source page 183)
- Chapter 8, item 2: During your next project check-in or progress review, consciously adopt a "Human-AI Partnership" lens. If you were using AI for real-time monitoring, what predictive insight would be most valuable right now? What human judgment would be needed to act on it? (source page 183)
- Chapter 8, item 3: For a recently completed project, reflect on the "Verifying True Impact" stage. How were success metrics tracked and strategic intent validated? Identify one point in that verification process where AI analytics could have provided deeper or more objective insights. (source page 183)
- Chapter 9, item 1: Identify One "High-Impact Decision Zone": Collaboratively identify one recurring, critical decision-making area in your organization that currently suffers from bottlenecks or a lack of data-driven insight, making it a prime candidate for initial augmentation efforts. (source page 199)
- Chapter 9, item 2: Sketch a Human-AI Workflow: For the decision zone identified above, sketch a high-level revised workflow that explicitly incorporates at least one AI augmentation step (e.g., AI for initial data gathering, AI for option generation). (source page 199)
- Chapter 9, item 3: "Augmented Culture" Micro-Action: Select one principle from "Cultivating the Augmented Culture" (e.g., encouraging safe experimentation with a new AI tool, fostering data literacy by sharing an AI-generated insight). Commit to one small team action this week that reinforces it. (source page 199)
- Chapter 10, item 1: Personal UWT Leadership Commitment: Reflect on the five key actions for leaders outlined in "The Leadership Mandate." Select one specific action you will personally champion with renewed focus and visibility within your sphere of influence over the next 90 days to advance your organization's UWT journey. (source page 207)
- Chapter 10, item 2: "State of the UWT Union" Communication: Draft a brief, authentic message (for your team, department, or a key stakeholder group) summarizing the core value of the UWT approach for your organization's future. Reiterate the importance of clarity and human-AI collaboration, and highlight one recent or upcoming Moment that exemplifies this. (source page 207)
- Chapter 10, item 3: Identify a "Continuous Learning" Initiative for UWT: Based on the holistic UWT framework (all nine elements), identify one specific area where your team or organization needs to deepen its collective learning or capability regarding AI integration or collaborative practices. Propose one concrete initiative (e.g., a targeted workshop, a cross-functional knowledge-sharing session, a pilot with a new AI tool) to address this. (source page 207)

## AI levers

- Chapter 1, item 1: AI for Information Synthesis: Experiment with an AI summarization tool on a lengthy document, email thread, or meeting transcript relevant to a current team challenge to quickly distill key insights and identify areas of ambiguity. (source page 52)
- Chapter 1, item 2: AI for "Clarity Check" on Communications: Before sending an important internal announcement or project update, run the draft through an AI writing assistant with a prompt like "Critique this for clarity and conciseness for a busy executive audience." (source page 52)
- Chapter 1, item 3: AI for Researching "Reset Mindset" in Action: Use an AI search engine or research assistant to find 2-3 case studies or articles about organizations that successfully navigated major change by "reinventing" themselves or "reframing setbacks," and share key lessons with your team. (source page 52, 53)
- Chapter 2, item 1: AI for Understanding UWT Elements: For one of the nine UWT elements that feels least clear to your team (e.g., Strategic Bets, Stakeholders & Value), use an AI research assistant to find 1-2 concise articles or case studies explaining its importance in organizational success. (source page 81)
- Chapter 2, item 2: AI for Comparing Frameworks: If your team uses Design Thinking or Agile, use an AI writing assistant with a prompt like: "Explain to a business leader how UWT can provide strategic alignment for our existing [Design Thinking/Agile] efforts, emphasizing AI's role." (source page 81, 82)
- Chapter 2, item 3: AI for Visualizing the Collaborative Intelligence Canvas (Conceptual): Using an AI-powered diagramming tool or even by prompting a generative image AI, try to sketch a very basic visual representation of how the nine UWT elements might interconnect for a specific project or for your team, as a precursor to using the formal Collaborative Intelligence Canvas. (source page 82)
- Chapter 3, item 1: Select a well-defined internal challenge. Task a small team to use at least one AI research or data analysis tool to gather insights about this problem, aiming to produce a clearer problem definition than current methods allow. (source page 100)
- Chapter 3, item 2: For your UWT implementation, use an AI tool or advanced search techniques to help identify key internal influencers, potential resistors, and departments most likely to benefit from early UWT adoption. Use this to inform your engagement strategy. (source page 100, 101)
- Chapter 3, item 3: For an upcoming strategic meeting or workshop, use an AI writing assistant to help you outline the agenda using the STACK framework. Ask the AI to suggest questions or data points relevant to each STACK element for that specific topic. (source page 101)
- Chapter 4, item 1: AI for “Illuminating the Landscape”: For your next strategic discussion, assign a team member to use AI research agents or NLP tools to gather and synthesize a broad range of information (market data, customer feedback, internal reports) to provide a comprehensive, data-rich situational overview before human deliberation begins. (source page 120, 121)
- Chapter 4, item 2: AI for “Expanding the Horizon”: When facing a complex problem requiring a novel solution or decision, use generative AI tools with carefully structured prompts to generate a diverse set of initial alternative options or scenarios that go beyond the team’s immediate thinking. (source page 121)
- Chapter 4, item 3: AI for “Sharpening the Choice”: When comparing a shortlist of decision alternatives, explore using AI tools to help structure the analysis-for example, by creating a weighted scoring model based on agreed criteria, or by summarizing the pros and cons of each option based on available data. (source page 121)
- Chapter 5, item 1: AI for Amplifying Empathy: Utilize NLP-based AI tools to analyze large volumes of unstructured user feedback (e.g., survey responses, online reviews, support chat logs) to quickly identify common pain points, unmet needs, and sentiment trends, providing a rich, data-driven foundation for design. (source page 134)
- Chapter 5, item 2: AI for Diverse Ideation & Concept Generation: Employ generative AI tools with well-crafted prompts (that include defined user needs, constraints, and desired attributes) to rapidly produce a wide spectrum of initial solution concepts, visual mockups, or user flow diagrams to broaden the creative exploration space. (source page 134, 135)
- Chapter 5, item 3: AI for Rapid Prototyping & Feasibility Simulation: Leverage AI-powered low-code platforms to quickly build interactive digital prototypes for early user testing, and/or use AI simulation tools to model the potential performance, resource requirements, or system interactions of a proposed solution before significant investment. (source page 135)
- Chapter 6, item 1: AI for Strategic Question Framing Practice: Use an AI writing assistant or chatbot as a sparring partner. Feed it a complex problem statement and ask it to help you generate 5-10 diverse, high-impact strategic questions a Cognitor might pose to a team or another AI. (source page 154)
- Chapter 6, item 2: AI for Simulating Human-AI Interaction Design: For a hypothetical project, use AI (even a simple flowchart tool guided by AI suggestions) to map out a potential human-AI workflow, defining touchpoints, data handoffs, and where human critical evaluation of AI output is essential. (source page 154, 155)
- Chapter 6, item 3: AI for Personalized Cognitor Development (Ref: "The Deliberate Path"): Encourage aspiring Cognitors to use AI-powered learning platforms to identify personalized courses or resources focusing on their specific skill gaps (e.g., data literacy, prompt engineering, AI ethics) as outlined in their "Discovery & Self-Assessment." (source page 155)
- Chapter 7, item 1: AI for Deep Knowledge Synthesis: As seen in the Policy Development and Strategic Decision-Making scenarios, identify one area where a lack of accessible, synthesized knowledge is a major bottleneck for your team. Experiment with using AI to research and summarize a complex topic to provide a shared, data-informed foundation for a discussion. (source page 171, 172)
- Chapter 7, item 2: AI as an Expert Augmentation Co-Pilot: Reflect on the Service Scaling and Cybersecurity examples where AI augmented experts. Identify one highly skilled role in your team that is currently bogged down by routine data analysis or monitoring. Explore AI tools that could act as a "co-pilot" to handle these tasks, freeing up the expert for higher-value judgment and action. (source page 172)
- Chapter 7, item 3: AI for Stakeholder & Ecosystem Mapping: Inspired by the "Connecting a Movement" and "Curriculum Co-Creation" scenarios, use AI research tools to analyze and map the key players, influencers, and data sources within your own professional ecosystem or for a specific stakeholder group, revealing new opportunities for collaboration or engagement. (source page 172)
- Chapter 8, item 1: AI for Dynamic Road mapping & Risk Assessment: Utilize AI planning tools (or AI-assisted brainstorming) to deconstruct a complex implementation into tasks, identify dependencies, and model potential risks. Use AI to explore multiple implementation pathway options, evaluating trade-offs in speed, cost, and resources. (source page 183)
- Chapter 8, item 2: AI for Real-Time Implementation Intelligence: Implement or simulate AI-powered dashboards that track key progress indicators and provide predictive alerts for potential delays or budget overruns. Use AI to rapidly synthesize diverse feedback streams during iterative rollouts for quick adaptation. (source page 183, 184)
- Chapter 8, item 3: AI for Impact Measurement & Learning Synthesis: Employ AI analytical tools to objectively assess whether key success metrics were achieved post-implementation. Use AI to help analyze performance data to identify patterns, attribute outcomes, and synthesize lessons learned to inform future projects and continuously improve your UWT Data & Knowledge base. (source page 184)
- Chapter 9, item 1: AI for Decision Workflow Mapping & Optimization: Use AI-powered process mining or workflow analysis tools to map your existing critical decision workflows. Leverage AI to identify inefficiencies and model how redesigned human-AI collaborative workflows could improve velocity and quality. (source page 199)
- Chapter 9, item 2: AI for Building the "Data Substrate": Implement or pilot AI tools for data integration, quality assurance, and intelligent knowledge management to ensure the data fueling your decision and design processes is robust, accessible, and AI-ready. (source page 199, 200)
- Chapter 9, item 3: AI for Monitoring Ethical Governance & Impact Metrics: Explore AI tools that can assist in monitoring AI systems for potential bias or drift (Ethical Governance). Simultaneously, use AI dashboards to track both traditional and new "Impact Metrics" (like Decision Velocity or Stakeholder Confidence) related to your augmented operations. (source page 200)
- Chapter 10, item 1: AI for Monitoring UWT Maturity & Transformation Progress: Explore or conceptualize using AI-powered dashboards or assessment tools that can help track key metrics related to your organization's adoption and maturity across the nine UWT elements and the overall health of your "Augmented Operating Engine." (source page 208)
- Chapter 10, item 2: AI for Disseminating the UWT Narrative & Best Practices: Leverage AI-driven knowledge management systems and internal communication platforms to ensure that key UWT principles, success stories (impactful Moments), lessons learned, and best practices from your ongoing journey are effectively captured, curated, and made easily accessible across the organization. This supports continuous learning and alignment with your overarching Mission. (source page 208)
- Chapter 10, item 3: AI for Strategic Foresight & UWT Adaptation: As your organization and the AI landscape evolve, use AI modeling tools and trend analysis to explore potential future disruptions or strategic opportunities. Use these insights to proactively adapt your UWT system application, ensuring your organization remains agile and its Movement stays relevant. (source page 208)

## Critical reflection questions

- Chapter 1, item 1: Considering the nine UWT elements introduced, which two currently represent the biggest sources of ambiguity or "haze" within our team/organization? What’s one initial step we could take to address one of them? (source page 53)
- Chapter 1, item 2: How might our team's ingrained communication habits or decision-making processes be inadvertently reinforcing a "Fixed Mindset" rather than the adaptive "Reset Mindset" needed for UWT success? (source page 53)
- Chapter 1, item 3: If we were to fully embrace AI as a partner in "solving for clarity," what is the single most significant organizational or cultural roadblock we would need to overcome first? (source page 53)
- Chapter 2, item 1: How does viewing UWT as an "AI-augmented organizational operating system" (rather than just a methodology like Design Thinking or Agile) change our perspective on the depth and breadth of transformation required in our organization? (source page 82)
- Chapter 2, item 2: Considering UWT's potential to drive "Radical Structural Renewal," what is the biggest cultural fear or resistance we might face if we seriously proposed redesigning established team structures or reporting lines to better enable human-AI collaboration? (source page 82)
- Chapter 2, item 3: Which of the nine UWT elements, if significantly improved through focused effort and AI augmentation, would have the most immediate positive cascading effect on the other elements and overall organizational clarity? (source page 82)
- Chapter 3, item 1: What are the most significant internal obstacles or resistances our leadership might face in driving the UWT revolution, and how can these be proactively addressed to ensure sustained commitment? (source page 101)
- Chapter 3, item 2: As we cultivate the AI-Augmented Collaborative Intelligence Mindset, how will we ensure that transparency and accountability are maintained, especially when AI tools provide insights that challenge existing beliefs or influence critical decisions? (source page 101)
- Chapter 3, item 3: What current knowledge silos within our organization would be most critical to break down to ensure that learnings from our UWT implementation and AI experimentation are effectively disseminated and leveraged enterprise-wide? (source page 101)
- Chapter 4, item 1: Considering the “Grounded Benefits of AI Augmentation” (Amplified Capacity, Enhanced Velocity/Rigor, Accelerated Cycles, Scale/Personalization, Democratized Expertise), which of these benefits would deliver the most immediate and significant value to our team’s current decision-making challenges? (source page 121)
- Chapter 4, item 2: As we begin to “Demystify the Digital Teammate” and explore AI agents, what is the biggest mindset shift or skill gap our team needs to address to effectively collaborate with and trust these “agentic workflows” in decision support? (source page 121)
- Chapter 4, item 3: Reflecting on “The Indispensable Human” role in leading augmented teams, how can we ensure that as AI takes on more analytical tasks, we are actively developing our team’s critical thinking, ethical judgment, and strategic synthesis capabilities to effectively partner with AI? (source page 121)
- Chapter 5, item 1: Considering the different stages of solution design (empathy, requirements, ideation, prototyping, refinement), where does our team currently experience the most significant bottlenecks or limitations that AI augmentation could most effectively address? (source page 135)
- Chapter 5, item 2: As we integrate AI more deeply as an "ideation partner," what specific prompting strategies and human-led "sensemaking" processes do we need to develop to ensure we are not just generating many ideas, but are effectively curating, combining, and elevating them into truly innovative and viable solutions? (source page 135)
- Chapter 5, item 3: What is the most critical mindset shift required for our designers and engineers to move from viewing AI as a simple automation tool to embracing it as a genuine creative collaborator in the design ensemble, under the guidance of a Cognitor? (source page 135)
- Chapter 6, item 1: Considering the five value areas of the Cognitor (e.g., Strategic Question Framing, Narrative Weaving), which one currently represents the biggest capability gap within our team or organization when it comes to effectively leveraging AI? (source page 155)
- Chapter 6, item 2: How can our organization best support "The Deliberate Path" to cultivating Cognitors, moving beyond ad-hoc AI training to a more structured approach that includes practical application, mentorship, and community building? (source page 155)
- Chapter 6, item 3: What are the primary cultural or structural barriers within our organization that might hinder individuals from fully embracing the "Mindset Shift: From Knowing to Orchestrating Knowing" essential for the Cognitor role, and how can leadership address them? (source page 155)
- Chapter 7, item 1: Beyond the specific sectors presented, what are the underlying patterns of UWT application (e.g., AI for accelerating knowledge work, AI for making systems transparent, AI for scaling human expertise) that are most relevant to the core challenges our organization faces today? (source page 172)
- Chapter 7, item 2: Considering the various resolutions in this chapter, what is the most significant "Reset Mindset" shift required for our team or leadership to move from simply using AI tools to truly partnering with AI in the co-design of solutions and processes? (source page 172)
- Chapter 7, item 3: What is the single biggest barrier (e.g., data accessibility, cultural resistance, lack of Cognitor-like skills) in our organization that would prevent us from successfully implementing a UWT experiment similar to those described, and what is one concrete step we could take to begin addressing it? (source page 172)
- Chapter 8, item 1: How can we best leverage AI's ability to "democratize information" regarding implementation pathways and trade-offs to foster greater team buy-in and more informed collective decisions during the "Architecting Execution" phase? (source page 184)
- Chapter 8, item 2: In "Navigating Implementation," what are the most crucial human oversight and ethical checkpoints we need to embed when relying on AI for real-time monitoring, adaptive execution, or quality assurance to ensure responsible and effective use? (source page 184)
- Chapter 8, item 3: When "Verifying True Impact," how do we ensure a balance between AI-driven quantitative analysis of outcomes and the essential human-led validation of strategic alignment and overall qualitative value realization, ensuring our Impact Metrics tell the full story? (source page 184)
- Chapter 9, item 1: As we aim to build an "Augmented Operating Engine," which of our current organizational structures or deeply ingrained cultural norms represents the most significant barrier to creating seamless human-AI decision and solution design workflows? (source page 200)
- Chapter 9, item 2: How can we ensure that our "Ethical Governance" framework for AI not only establishes clear guardrails but also fosters a proactive culture of ethical inquiry and responsibility among all team members interacting with AI? (source page 200)
- Chapter 9, item 3: Considering "Iterative Deployment," what is our organizational capacity for rapid experimentation, learning from both successes and failures with AI pilots, and then effectively scaling what works across different teams or functions? (source page 200)
- Chapter 10, item 1: Considering the entirety of the UWT system and the journey outlined in this manuscript, what is the single most significant cultural or structural impediment our leadership team must now courageously and collaboratively address to fully unleash the sustained power of AI-augmented collaboration and the Clarity Imperative across our organization? (source page 208)
- Chapter 10, item 2: How can we as leaders ensure that the "continuous journey" aspect of UWT becomes deeply embedded in our organizational rhythm, actively preventing complacency and fostering a resilient, enterprise-wide commitment to ongoing learning, adaptation, and ethical AI integration? (source page 208, 209)
- Chapter 10, item 3: Looking towards the next 1-3 years, as AI capabilities continue to advance at an accelerated pace, what is our leadership's proactive plan for developing the next wave of Cognitors (or those with Cognitor-like skills) and ensuring our entire workforce remains AI-literate, ethically grounded, and empowered within the evolving UWT system? (source page 209)

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