AI Strategy and Product Development: Learning from Mistakes and Using Tiny Bets PDF

Slide Number 139 ---------------- - Bets? Why Bets? - In other words: "Treating decisions as bets to minimize risk and maximize learning." ### Key Takeaway: - Framing decisions as bets encourages iterative, low-risk experimentation while ensuring meaningful progress. ### Key Talki...

Slide Number 139 ---------------- - Bets? Why Bets? - In other words: "Treating decisions as bets to minimize risk and maximize learning." ### Key Takeaway: - Framing decisions as bets encourages iterative, low-risk experimentation while ensuring meaningful progress. ### Key Talking Points: - **Explicit Bets:** Treat decisions as experiments with defined outcomes and risks. - **Meaningful Progress:** Ensure bets result in actionable insights or measurable impact. - **Commitment to Focus:** Provide teams with uninterrupted time to work on selected bets. - **Risk Limitation:** Cap downside risks by keeping experiments short and focused. - **Iterative Learning:** Use bet outcomes to refine and improve subsequent efforts. ### Caveats: - Avoid treating bets as guarantees of success. - Clarify the need for well-defined success criteria for each bet. - Address challenges in limiting scope while maintaining impact. - Highlight the importance of reflecting on and learning from failed bets. Slide Number 140 ---------------- - What % of New Products & Feature Ideas Fail? - In other words: "Understanding the high failure rates to inform smarter decisions." ### Key Takeaway: - High failure rates for new features emphasize the need for validation and iterative learning to avoid wasted resources. ### Key Talking Points: - **High Failure Rates:** \~60% of features see little or no lift; 20% hurt the business. - **Validation Needs:** Test and refine ideas before full-scale development. - **Learning from Failures:** Use failures to improve future processes and reduce risk. - **Prioritize Impact:** Focus on features with the highest potential value to customers. - **Cost Awareness:** Highlight the expense of building without proper validation. ### Caveats: - Avoid discouraging innovation despite high failure rates. - Clarify how to use failure metrics constructively for improvement. - Address potential resistance to adopting iterative processes. - Highlight the value of small, testable bets over large-scale launches. Slide Number 141 ---------------- - Solution Failure Rates - In other words: "Why proper validation reduces the cost of failure." ### Key Takeaway: - Building production-quality software too early is expensive; testing assumptions first saves time, money, and effort. ### Key Talking Points: - **Costly Mistakes:** Building prematurely can result in wasted resources. - **Test Early:** Use low-fidelity prototypes to validate ideas quickly and cheaply. - **Iterative Refinement:** Continuously improve based on user and business feedback. - **Impact Awareness:** Recognize that \~60% of solutions fail to deliver expected results. - **Best Practices:** Learn from examples like A/B testing to mitigate risks. ### Caveats: - Avoid skipping validation due to time pressures. - Clarify the role of iterative testing in reducing risk. - Address concerns about upfront costs for early validation. - Highlight the long-term savings of avoiding large-scale failures. Slide Number 142 ---------------- - Small AI Bets vs. Big AI Bets - In other words: "Balancing scope and risk in AI product investments." ### Key Takeaway: - Small AI bets minimize risk and allow quick iteration, while big bets require more resources but offer higher rewards. ### Key Talking Points: - **Small Bets:** Low investment, limited scope, quick to prototype, and low impact if they fail. - **Big Bets:** High investment, broader scope, longer development, and higher stakes. - **Scalability Considerations:** Small bets are easier to scale once validated. - **Risk-Reward Trade-Offs:** Weigh potential impact against the costs of failure. - **Strategic Balance:** Use a mix of small and big bets to maintain progress and innovation. ### Caveats: - Avoid putting all resources into big bets without validation. - Clarify the iterative nature of scaling successful small bets. - Address challenges in deciding when to transition from small to big bets. - Highlight the need for stakeholder alignment on investment priorities. Slide Number 143 ---------------- - AI 'Haul' of Shame: Hype Without a Cause - In other words: "Learning from overhyped AI failures to avoid the same mistakes." ### Key Takeaway: - Overhyping AI products without validating market demand leads to unmet expectations and failed launches. ### Key Talking Points: - **Market Validation:** Test customer interest before investing heavily, e.g., Anki's consumer robots failure. - **Underestimating Complexity:** Delays and overpromises erode trust, as seen with Jibo. - **Execution Challenges:** IBM Watson for Oncology struggled with data and regulatory hurdles. - **Sustainability Lessons:** Avoid overbuilding without a clear path to adoption or revenue. - **Proactive Measures:** Use lightweight validation to ensure alignment with customer needs. ### Caveats: - Avoid letting hype drive product development. - Clarify the importance of realistic timelines and features. - Address challenges in managing stakeholder expectations during delays. - Highlight the long-term costs of failing to validate market fit. Slide Number 144 ---------------- - Ever Become Roadmap Roadkill Because of External Factors? - In other words: "How external forces disrupt and reshape product roadmaps." ### Key Takeaway: - External factors like regulations, market dynamics, and technological shifts can derail product roadmaps if not proactively addressed. ### Key Talking Points: - **Regulatory Shifts:** Examples like EU transparency demands can impact AI compliance strategies. - **Market Volatility:** Changes in VC funding can refocus priorities on profitability over growth. - **Technology Evolution:** Rapid innovation can make existing products obsolete. - **Environmental Concerns:** Carbon footprints and sustainability affect AI adoption and public perception. - **Adaptation Strategy:** Build flexible roadmaps to anticipate and mitigate external disruptions. ### Caveats: - Avoid rigid planning that cannot adapt to sudden changes. - Clarify the importance of monitoring industry trends continuously. - Address potential resistance to adjusting roadmaps mid-cycle. - Highlight the role of cross-functional input in proactive planning. Slide Number 145 ---------------- - In the News... - In other words: "What AI industry headlines reveal about evolving challenges." ### Key Takeaway: - High-profile headlines highlight challenges like ethical concerns, environmental impact, and regulatory pressure, shaping the future of AI. ### Key Talking Points: - **Ethical Dilemmas:** AI misuse, such as election meddling, raises accountability concerns. - **Environmental Impact:** Reports like Google's AI emissions spike drive demand for sustainable practices. - **Regulatory Focus:** Increased oversight, like chip export limits, shifts development strategies. - **Market Reactions:** Investor caution affects funding availability and market direction. - **Anticipating Trends:** Use news as a source of insights to refine strategies and prepare for future disruptions. ### Caveats: - Avoid overreacting to single headlines without context. - Clarify the difference between trends and isolated events. - Address skepticism about the direct impact of news on product decisions. - Highlight the importance of aligning responses with organizational goals. Slide Number 146 ---------------- - External Events - In other words: "Proactively classifying and responding to disruptive external events." ### Key Takeaway: - Identifying and categorizing external events ensures teams can prioritize responses and adapt strategies effectively. ### Key Talking Points: - **Classification System:** Categorize events as urgent, strategic, or observational. - **Proactive Planning:** Prepare for transparency demands, funding challenges, or competitive advancements. - **Sustainability Focus:** Respond to environmental critiques by adopting greener AI practices. - **Bias Concerns:** Build safeguards to address fairness and ethical challenges in AI. - **Rapid Innovation:** Ensure your AI solutions evolve to meet emerging market needs. ### Caveats: - Avoid spreading resources too thin across all potential risks. - Clarify the role of prioritization in managing responses effectively. - Address challenges in predicting the full impact of external trends. - Highlight the need for collaboration to develop robust mitigation plans. Slide Number 147 ---------------- - Managing Their Risk: External Forces - In other words: "Adapting to market changes with structured risk management." ### Key Takeaway: - The PESTel framework enables teams to categorize risks and make informed decisions about which to act on or monitor. ### Key Talking Points: - **PESTel Benefits:** Use Political, Economic, Social, Technological, Environmental, and Legal categories to organize risks. - **Act vs. Watch:** Identify which risks demand immediate action and which require monitoring. - **Dynamic Adaptation:** Respond to market changes proactively to minimize disruption. - **Strategic Alignment:** Ensure responses support broader business and product goals. - **Stakeholder Engagement:** Communicate risk management plans clearly across teams. ### Caveats: - Avoid over-prioritizing low-impact risks. - Clarify the iterative nature of risk assessment. - Address challenges in maintaining focus amid dynamic market conditions. - Highlight the value of PESTel as a communication tool for stakeholders. Slide Number 148 ---------------- - Activity: PESTel Planning - In other words: "Collaboratively identify and prioritize external risks for your product." ### Key Takeaway: - This activity encourages teams to use the PESTel framework to pinpoint and prioritize external forces impacting their product. ### Key Talking Points: - **Activity Structure:** Use Mural to brainstorm and categorize risks in PESTel areas. - **Collaborative Insights:** Discuss which risks require immediate action and which need monitoring. - **Strategic Focus:** Align prioritized risks with product goals and strategies. - **Document Findings:** Record insights for future reference and ongoing planning. - **Actionable Outcomes:** Translate planning into clear next steps for mitigating risks. ### Caveats: - Avoid spending too much time debating minor risks. - Clarify the importance of focusing on actionable items. - Address potential disagreements in prioritizing risks. - Highlight the opportunity to revisit and refine the plan as conditions change. Slide Number 149 ---------------- - The Toasted Bread Challenge for Homework - In other words: "Reinforce your learning with a practical take-home exercise." ### Key Takeaway: - This homework task challenges participants to apply course concepts to a real-world scenario, deepening their understanding of PESTel and risk management. ### Key Talking Points: - **Scenario Focus:** Evaluate risks and opportunities using the PESTel framework. - **Individual Contributions:** Encourage participants to reflect independently on external factors. - **Practical Application:** Apply insights to a hypothetical or actual product challenge. - **Discussion Follow-Up:** Plan for sharing and discussion during the next session. - **Skill Reinforcement:** Build confidence in applying learned concepts to real problems. ### Caveats: - Avoid assigning overly complex tasks that discourage engagement. - Clarify expectations for the homework deliverables. - Address concerns about time management for the exercise. - Highlight the opportunity to receive feedback in the next session. Slide Number 150 ---------------- - Bets? Why Bets? - In other words: "Making strategic bets to balance risk and innovation." ### Key Takeaway: - Framing decisions as bets encourages teams to take calculated risks while focusing on learning and iterative improvements. ### Key Talking Points: - **Learning Focus:** Use bets to test assumptions and gather actionable insights. - **Controlled Risks:** Limit downside by setting clear boundaries for each bet. - **Iterative Strategy:** Refine bets based on outcomes to continuously improve. - **Team Alignment:** Ensure teams are committed and focused during each bet cycle. - **Outcome Orientation:** Use bets to drive measurable progress and decision-making. ### Caveats: - Avoid treating bets as guarantees of success. - Clarify the importance of defining success criteria before starting. - Address challenges in managing stakeholder expectations for outcomes. - Highlight the need to reflect on both successes and failures for future learning. Slide Number 151 ---------------- - How Might We Shape and Measure Our Solution? - In other words: "Define success and ensure alignment through measurable outcomes." ### Key Takeaway: - Shaping solutions requires clear success metrics and alignment between user needs, business value, and measurable outcomes. ### Key Talking Points: - **Defining Metrics:** Establish measurable indicators to evaluate success. - **User-Centric Goals:** Align outcomes with user Jobs-to-be-Done and pain alleviation. - **Business Impact:** Ensure metrics also reflect organizational priorities. - **Iterative Refinement:** Adjust solutions and metrics based on findings. - **Outcome Alignment:** Tie every solution to actionable, meaningful results. ### Caveats: - Avoid vague metrics that don't reflect user or business priorities. - Clarify that success metrics should be actionable and iterative. - Address challenges in aligning diverse stakeholder expectations. - Highlight the importance of reviewing metrics regularly. Slide Number 152 ---------------- - Validating Value - In other words: "Quickly distinguish high-potential ideas from poor ones." ### Key Takeaway: - Product discovery separates valuable ideas from ineffective ones, resulting in a validated backlog ready for execution. ### Key Talking Points: - **Discovery Purpose:** Rapidly test and refine ideas to find high-potential solutions. - **Validated Backlogs:** Ensure backlog items align with user needs and business goals. - **Hypothesis Testing:** Use frameworks like Build-Measure-Learn for iterative refinement. - **Learning First:** Reduce time and expense by validating ideas before building. - **Focus on Value:** Prioritize ideas with measurable user and business impact. ### Caveats: - Avoid over-validating minor ideas at the expense of big opportunities. - Clarify the role of discovery in aligning product and organizational goals. - Address resistance to adopting iterative discovery processes. - Highlight that discovery is an ongoing, not one-time, effort. Slide Number 153 ---------------- - Get the Right People in the Room - In other words: "Collaborate effectively by involving key contributors early." ### Key Takeaway: - Involving cross-functional stakeholders ensures accurate scope assessment, aligned expectations, and better decision-making. ### Key Talking Points: - **Technical Input:** Consult engineers and DevOps to clarify scope and technical impact. - **Stakeholder Alignment:** Use simple metaphors to explain complexities. - **Budget Clarity:** Collaborate with finance to define realistic budgets. - **Validated Assumptions:** Base discussions on verified data and insights. - **Historical Context:** Link past efforts to predict future success. ### Caveats: - Avoid excluding critical stakeholders from early discussions. - Clarify the need for transparency when discussing timelines and costs. - Address potential communication gaps between technical and non-technical teams. - Highlight the role of collaboration in reducing downstream risks. Slide Number 154 ---------------- - Risks & Assumptions - In other words: "Reduce risks by validating assumptions early." ### Key Takeaway: - Mitigating risks starts with testing assumptions through lightweight discovery strategies before making heavy commitments. ### Key Talking Points: - **Minimizing Risk:** Validate early to avoid costly errors later. - **Discovery Strategies:** Use low-cost methods like landing pages and interviews. - **Learning from Failures:** Examples like Webvan highlight the cost of untested assumptions. - **Success Stories:** Buffer's demand validation and Amazon's A/B testing showcase effective strategies. - **Iterative Refinement:** Continuously test and learn to refine your approach. ### Caveats: - Avoid over-investing in unvalidated ideas. - Clarify the importance of small, targeted experiments in reducing risks. - Address resistance to using lightweight validation techniques. - Highlight the value of learning from both successes and failures. Slide Number 155 ---------------- - Discovery Strategy: The Solution Hypothesis - In other words: "Test solutions through clear hypotheses, experiments, and metrics." ### Key Takeaway: - A structured solution hypothesis ensures alignment between problem-solving and measurable outcomes, validated through experimentation. ### Key Talking Points: - **If/Then Hypotheses:** Propose solutions and predict their impact on personas. - **Validation Experiments:** Use TADs (Tiny Acts of Discovery) to test ideas efficiently. - **Success Metrics:** Set clear, measurable criteria to assess hypotheses. - **Deadline-Driven Testing:** Define timelines to evaluate outcomes quickly. - **Iterative Process:** Use findings to refine and improve solutions. ### Caveats: - Avoid setting vague or unmeasurable success criteria. - Clarify the role of metrics in guiding decision-making. - Address potential challenges in designing effective validation experiments. - Highlight the need for time-boxed testing to maintain momentum. Slide Number 156 ---------------- - AI & Tiny Acts of Discovery (TADs) - In other words: "Leverage small-scale tests to validate AI desirability and viability." ### Key Takeaway: - TADs enable teams to validate AI solutions by conducting low-cost, focused experiments across desirability and viability. ### Key Talking Points: - **Viability TADs:** Use methods like synthetic data, market sizing, and Monte Carlo simulations to assess feasibility. - **Desirability TADs:** Test user interest with guerrilla interviews, social listening, and AI-generated journeys. - **Data-Driven Validation:** Leverage AI to analyze patterns and refine ideas. - **Low-Cost Testing:** Conduct experiments quickly and affordably to minimize risk. - **Iterative Insights:** Use results to guide further discovery efforts. ### Caveats: - Avoid assuming desirability without testing real-world user interest. - Clarify the limitations of synthetic data in predicting market behavior. - Address potential bias in data mining and analysis. - Highlight the iterative nature of discovery with AI. Slide Number 157 ---------------- - Success Metrics - In other words: "Are we measuring how well we help our customers get their JTBD completed and alleviate their pains?" ### Key Takeaway: - Success metrics measure user engagement and feedback to evaluate whether the product delivers meaningful value. ### Key Talking Points: - **Customer Metrics:** Focus on engagement (e.g., sign-ups, prototype use). - **Feedback Speed:** Prioritize quick feedback loops for actionable insights. - **Adoption Indicators:** Track willingness and enthusiasm for early use. - **Usability Testing:** Evaluate usability and friction points in real-time. - **Proof of Concept:** Use metrics to validate concept viability early. ### Caveats: - Avoid using metrics that don't provide actionable insights. - Clarify the need for user-focused metrics over vanity metrics. - Address resistance to tracking adoption during early stages. - Highlight the importance of aligning metrics with long-term goals. Slide Number 158 ---------------- - Activity: Solution Hypothesis - In other words: "Test hypotheses through focused experiments to ensure alignment." ### Key Takeaway: - Crafting and testing solution hypotheses align development efforts with measurable user and business outcomes. ### Key Talking Points: - **Hypothesis Creation:** Use "If/Then" statements to link insights to solutions. - **Testing Focus:** Plan experiments like prototypes or interviews to validate. - **Metric Alignment:** Define specific metrics to assess experiment outcomes. - **Collaborative Approach:** Work with cross-functional teams for input. - **Time Efficiency:** Allocate 20 minutes to maintain focus and momentum. ### Caveats: - Avoid crafting overly complex hypotheses that are hard to test. - Clarify the role of metrics in determining experiment success. - Address challenges in reaching team consensus on hypothesis statements. - Highlight the iterative nature of refining both hypotheses and tests. Slide Number 159 ---------------- - How Might We Test & Measure Success? - In other words: "Ensure hypotheses and experiments align with clear success metrics." ### Key Takeaway: - Testing solutions with defined success metrics ensures alignment with user outcomes, business goals, and feasibility constraints. ### Key Talking Points: - **Hypothesis Validation:** Use experiments to confirm or refute solution assumptions. - **Success Metrics:** Align metrics with user outcomes and business value. - **Iterative Testing:** Use small, low-risk experiments for faster learning. - **Cross-Functional Input:** Involve diverse teams to align success criteria. - **Actionable Feedback:** Use results to refine the solution and approach. ### Caveats: - Avoid testing without a clear hypothesis or success criteria. - Clarify how metrics tie back to customer JTBD and business goals. - Address resistance to testing at early stages. - Highlight that success may include learning from failure. Slide Number 160 ---------------- - Building What We Can't Unlearn - In other words: "Avoid building irreversible features until assumptions are validated." ### Key Takeaway: - Avoid committing to large-scale builds until assumptions have been validated to minimize risk and ensure feasibility. ### Key Talking Points: - **Risk Awareness:** Highlight the cost of building without validation. - **Focus on Learning:** Test assumptions to confirm feasibility and desirability. - **Iterative Approach:** Use prototypes or MVPs to gather actionable insights. - **Avoid Overbuilding:** Resist the urge to finalize features before testing outcomes. - **Real-World Lessons:** Learn from high-profile failures where irreversible builds led to costly mistakes. ### Caveats: - Avoid using MVPs as shortcuts to bypass testing. - Clarify the importance of balancing speed with thorough validation. - Address potential pressure to skip early-stage testing. - Highlight the importance of stakeholder alignment on iteration. Slide Number 161 ---------------- - Desirability + Viability Tests - In other words: "Balance customer needs with business goals when testing." ### Key Takeaway: - Combining desirability and viability tests ensures solutions meet both customer expectations and business sustainability requirements. ### Key Talking Points: - **Customer Fit:** Use surveys, interviews, and prototypes to validate desirability. - **Business Value:** Assess financial impact and feasibility through pilots or simulations. - **Risk-Reward Trade-Offs:** Balance customer needs with operational realities. - **Iterative Insights:** Refine solutions through multiple test cycles. - **Cross-Team Collaboration:** Ensure customer and business teams align on outcomes. ### Caveats: - Avoid over-prioritizing one metric (desirability or viability) over the other. - Clarify that tests should inform rather than finalize decisions. - Address challenges in balancing customer preferences with cost constraints. - Highlight the iterative process of refining both types of tests. Slide Number 162 ---------------- - Storyboarding the Solution - In other words: "Visualize and test user scenarios before committing resources." ### Key Takeaway: - Storyboarding helps visualize user interactions and workflows, ensuring the solution aligns with customer expectations and JTBD. ### Key Talking Points: - **User Scenarios:** Map out workflows to identify potential pain points. - **Solution Testing:** Use storyboards to validate assumptions before building. - **Team Alignment:** Collaborate with stakeholders to refine user flows. - **JTBD Context:** Align every storyboard step with the customer's job to be done. - **Early Feedback:** Test storyboards with users for actionable insights. ### Caveats: - Avoid skipping storyboarding in the rush to build. - Clarify how storyboards reduce risk by visualizing workflows. - Address resistance to investing time in pre-build validation. - Highlight the importance of connecting storyboards to measurable outcomes. Slide Number 163 ---------------- - Building for Iteration - In other words: "Design for scalability and adaptability from the start." ### Key Takeaway: - Build solutions with iteration in mind, enabling teams to adapt quickly to feedback and changing requirements. ### Key Talking Points: - **Scalable Design:** Ensure solutions can grow with user demands. - **Modular Development:** Build components that can be adjusted independently. - **Feedback Loops:** Integrate tools for real-time feedback and learning. - **Flexibility First:** Avoid overcommitting to rigid workflows or architectures. - **Success Stories:** Learn from iterative successes like Slack's pivot from gaming to enterprise. ### Caveats: - Avoid over-designing for scalability at the expense of current needs. - Clarify how to balance flexibility with clear direction. - Address challenges in managing expectations during iteration. - Highlight the long-term benefits of iterative adaptability. Slide Number 164 ---------------- - Why Validate First? - In other words: "Validation reduces waste and ensures product-market alignment." ### Key Takeaway: - Validating assumptions before building saves resources, reduces risks, and ensures the product aligns with user and business needs. ### Key Talking Points: - **Risk Reduction:** Test high-risk assumptions to avoid costly mistakes. - **Resource Efficiency:** Focus efforts on ideas with validated potential. - **Customer Insights:** Align features with user feedback and expectations. - **Iterative Refinement:** Use validation to improve ideas before full-scale development. - **Case Studies:** Highlight examples where early validation saved resources and enhanced outcomes. ### Caveats: - Avoid assuming validation is only for early-stage products. - Clarify that validation should be continuous throughout the product lifecycle. - Address resistance to delaying builds for additional testing. - Highlight the cost savings of avoiding unvalidated launches. Slide Number 165 ---------------- - Activity: Validation Plan - In other words: "Create a roadmap for testing assumptions and measuring outcomes." ### Key Takeaway: - A validation plan ensures teams focus on the riskiest assumptions, testing them systematically to reduce risks and maximize impact. ### Key Talking Points: - **Plan Structure:** Outline what to validate, how, and by when. - **Hypotheses First:** Prioritize assumptions based on risk and importance. - **Testing Tools:** Use lightweight experiments to validate quickly and affordably. - **Collaborative Effort:** Work with cross-functional teams to align on the plan. - **Outcome-Oriented:** Ensure every test ties back to measurable business and user goals. ### Caveats: - Avoid creating overly complex validation plans that slow progress. - Clarify the role of metrics in defining test success. - Address challenges in securing resources for testing. - Highlight the iterative nature of refining validation plans over time. Slide Number 166 ---------------- - Revisit the Toasted Bread Challenge - In other words: "Apply course concepts to refine your earlier work." ### Key Takeaway: - Revisiting the Toasted Bread Challenge allows participants to deepen their understanding by applying new frameworks and insights. ### Key Talking Points: - **Re-evaluation:** Revisit earlier work with a focus on refining solutions. - **Apply New Tools:** Incorporate concepts like success metrics or validation strategies. - **Collaborative Feedback:** Use peer input to uncover blind spots and improve ideas. - **Iterative Thinking:** Reinforce the value of ongoing learning and refinement. - **Outcome Focus:** Tie improvements directly to measurable customer and business outcomes. ### Caveats: - Avoid overcomplicating the exercise with unnecessary layers. - Clarify that the goal is refinement, not perfection. - Address concerns about time constraints for revisions. - Highlight the importance of aligning changes with course learnings.