Tips for Fostering Innovation in AI Teams

I’m excited to be filming my new Udemy course on “AI for People Managers” aimed at folks who aren’t necessarily AI experts but want to help their teams use AI ethically and effectively. The great Allie K. Miller suggests that you encourage your people to experiment with AI for ~10 hours a week. This means you have to do more than offer begrudging permission. You need to demonstrate curiosity and excitement— even if you’re still learning too. Here are ten things people managers should know about AI experimentation: 1. Set clear rules upfront about what data your team can and can’t feed into AI tools, because nothing kills an AI experiment faster than a data privacy violation. 2. Frame AI as your team’s new super-powered assistant, not their replacement, so people get excited about what they can accomplish rather than worried about their jobs. 3. Start small with low-risk experiments like brainstorming or first drafts, because you want people building confidence with AI, not stress-testing it on your most important projects. 4. Make it totally okay for people to share when AI gives them weird or unhelpful results, since learning what doesn’t work is just as valuable as discovering what does. 5. Teach your team that getting good AI results is all about asking good questions, and yes, “prompt engineering” is now a legitimate workplace skill worth investing in. 6. Always have someone double-check AI outputs before they go anywhere important, because even the smartest AI can confidently give you completely wrong information. 7. Keep an eye out for AI responses that might be unfair to certain groups of people, since these tools can accidentally bake in biases that you definitely don’t want in your work. 8. Let AI inform your team’s decisions but never make the final call itself, because human judgment still needs to be the ultimate decision-maker. 9. Stay curious about new AI developments and limitations because this technology changes faster than your smartphone updates, and what’s true today might not be tomorrow. 10. Track more than just “how much time did we save” and also measure whether people are actually doing better, more creative work with AI as their sidekick. Let me know if you’re as excited about this topic as I am (and yes, I am learning alongside you too)! #ai #leadership #managers

Claire Vo and Joff Redfern are on the leading edge of 'AI for Product' globally. I spoke to hundreds of CPOs & PMs and realised they want to be more aggressive on AI and had so many unanswered questions. I couldn't think of two better product leaders to join us at Sauce's 'AI for Product' fireside in SF to answer these questions. Claire Vo is the CPO of LaunchDarkly ($3B), former CPO at Color & Optimizely, and building the PM AI CoPilot, ChatPRD. Joff Redfern is a Partner at Menlo Ventures, former CPO at Atlassian ($42B) and former VP Product at LinkedIn & Yahoo. 👇 Here are my top 6 lessons (full video in comments) 1. Upskill your product teams to be ‘GenAI fluent’ Claire: “Be aggressive with the tasks your teams offload to AI and normalise it as an executive. For example, on everything I generate with an AI tool, I put a prompt at the bottom and attribute it with an author to say ‘I’m the Chief Product Officer, I use AI tools and you should too'. Empower your team with budget and training.” 2. Embrace paradigm shifts by starting with a small team Joff: “At LinkedIn we took a small team and said ‘this is our mobile team.’ Then this gets moved to a platform team… then eventually it gets diffused throughout every team in the organisation. I see the same pattern emerging with AI. At Atlassian it started with a small team doing a spike which focused on learning and experimentation.” 3. AI enables you to achieve more with less Claire: “The cost of building is collapsing and speed of building is accelerating. Ask yourself - am I shipping as fast as I can? I’m building ChatPRD with 1.5 employees, I’m actually 0.5 of an employee as I do this at nights outside my day job as a CPO. I’m coding at nights and am support after 7pm… I truly believe there will be a 1 person, $5 billion company.” 4. Reimagine workflows from the ground up Joff: ”Many startups are trying to speed up one step of a workflow. But I think the better answer is to step back and ask - now that the marginal cost of reasoning with AI is trending to zero, what does the world look like if we were to reimagine what we’re doing today from the ground up?” 5. Use AI hack weeks to get leadership bought in Claire: “As the CPO of Color I’d run AI hack weeks every 6 weeks. I gave our exec teams pre-reading on how AI works, then they had to automate something (e.g. generate a product marketing video which our CCO had been waiting 6mo for previously). This opened their eyes to what’s possible, they understood the impact of AI.” 6. Generalists will win in the AI world Joff: "9 years ago I wrote an article called the 'PM craft triangle' which says a PM can be a General Manager, Scientist or Artist. It was difficult to be the best at all so I advised to go deep on being the best at one of these corners. But now AI allows you to be at the centre of the triangle and smooth your weaker corners with CoPilots. Generalist skill sets will do well in the AI world." Full video in comments 👇

Building an AI-First Culture: 6 Core Principles for Real Change How do you really build AI First Culture? Transforming into an AI-first organization isn’t just about adopting new tech - it's about reimagining how we think, work, and lead. Here are my six key principles which are helping us and can help any team foster an AI-first culture: 1. Mindset Reset, Not Just Tech Shift  Embrace AI as a mindset change, not just another tool. Don’t wait for perfect conditions, action and experimentation drive learning and create competitive advantage. Curiosity, fast feedback, and adaptable teams are your best strategies.    2. Human-AI Pairing at Every Level  Break out of rigid structures. Rethink work as a mix of automation (where AI takes over the repetitive), augmentation (where humans and AI collaborate), and autonomous decision-making (with humans as ultimate stewards). Design for agility and continuous improvement. Think about every single workflows which can change. Design, Research, Reporting, Content Orchestration across various touch-points, hiring, L/D and so many others.   3. Anchor on Business Impact - Orchestrating Business Value is key for success. Align AI initiatives to clear business outcomes - think Impact, not just technical achievement. Focus on tangible ROI, using metrics that measure real value like efficiency, cost savings, and better decisions.    4. Build AI Fluency Organization-Wide  AI know-how is a must-have for everyone. Recognize your team’s learning needs- engage early adopters, support the cautious, and never dismiss skeptics. Tailor your approach to each group and move everyone forward intentionally.    5. Embed AI Deeply for Compounding Benefits  Get AI into your core workflows and products early. The sooner you start, the more benefits compound over time in automation, building stronger products, and delivering customer experiences.    6. Understand AI’s Limits—And Check Its Work  Large language models are powerful but not infallible. Don’t blindly accept their output. Use them as tools, provide clear context, and always verify their work. Leverage the right tools to boost accuracy. Data governance is key here.   At its core, AI adoption thrives on purposeful action, strategic experimentation, and a relentless focus on people. The organizations that lead in this era will be those that move fast, measure impact, and keep humans at the center of every transformation.    #AIFirst #Leadership #DigitalTransformation #FutureOfWork 

Some of the best AI breakthroughs we’ve seen came from small, focused teams working hands-on, with structured inputs and the right prompting. Here’s how we help clients unlock AI value in days, not months: 1. Start with a small, cross-functional team (4–8 people) 1–2 subject matter experts (e.g., supply chain, claims, marketing ops) 1–2 technical leads (e.g., SWE, data scientist, architect) 1 facilitator to guide, capture, and translate ideas Optional: an AI strategist or business sponsor 2. Context before prompting - Capture SME and tech lead deep dives (recorded and transcribed) - Pull in recent internal reports, KPIs, dashboards, and documentation - Enrich with external context using Deep Research tools: Use OpenAI’s Deep Research (ChatGPT Pro) to scan for relevant AI use cases, competitor moves, innovation trends, and regulatory updates. Summarize into structured bullets that can prime your AI. This is context engineering: assembling high-signal input before prompting. 3. Prompt strategically, not just creatively Prompts that work well in this format: - “Based on this context [paste or refer to doc], generate 100 AI use cases tailored to [company/industry/problem].” - “Score each idea by ROI, implementation time, required team size, and impact breadth.” - “Cluster the ideas into strategic themes (e.g., cost savings, customer experience, risk reduction).” - “Give a 5-step execution plan for the top 5. What’s missing from these plans?” - “Now 10x the ambition: what would a moonshot version of each idea look like?” Bonus tip: Prompt like a strategist (not just a user) Start with a scrappy idea, then ask AI to structure it: - “Rewrite the following as a detailed, high-quality prompt with role, inputs, structure, and output format... I want ideas to improve our supplier onboarding process with AI. Prioritize fast wins.” AI returns something like: “You are an enterprise AI strategist. Based on our internal context [insert], generate 50 AI-driven improvements for supplier onboarding. Prioritize for speed to deploy, measurable ROI, and ease of integration. Present as a ranked table with 3-line summaries, scoring by [criteria].” Now tune that prompt; add industry nuances, internal systems, customer data, or constraints. 4. Real examples we’ve seen work: - Logistics: AI predicts port congestion and auto-adjusts shipping routes - Retail: Forecasting model helps merchandisers optimize promo mix by store cluster 5. Use tools built for context-aware prompting - Use Custom GPTs or Claude’s file-upload capability - Store transcripts and research in Notion, Airtable, or similar - Build lightweight RAG pipelines (if technical support is available) - Small teams. Deep context. Structured prompting. Fast outcomes. This layered technique has been tested by some of the best in the field, including a few sharp voices worth following, including Allie K. Miller!

I am constantly thinking about how to foster innovation in my product organization. Building teams that are experts at execution is the easy part—when there’s a clear problem, product orgs are great at coming up with smart solutions. But it’s impossible to optimize your way into innovation. You can’t only rely on incremental improvement to keep growing. You need to come up with new problem spaces, rather than just finding better solutions to the same old problems. So, how do we come up with those new spaces? Here are a few things I’m trying at Duolingo: 1. Innovation needs a high-energy environment, and a slow process will kill a great idea. So I always ask myself: Can we remove some of the organizational barriers here? Do managers from seven different teams really need to say yes on every project? Seeking consensus across the company—rather than just keeping everyone informed—can be a major deterrent to innovation. 2. Similarly, beware of defaulting to “following up.” If product meetings are on a weekly cadence, every time you do this, you are allocating seven days to a task that might only need two. We try to avoid this and promote a sense of urgency, which is essential for innovative ideas to turn into successes. 3. Figure out the right incentive. Most product orgs reward team members whose ideas have measurable business impact, which works in most contexts. But once you’ve found product-market fit, it is often easiest to generate impact through smaller wins. So, naturally, if your org tends to only reward impact, you have effectively incentivized constant optimization of existing features instead of innovation. In the short term things will look great, but over time your product becomes stale. I try to show my teams that we value and reward bigger ideas. If someone sticks their neck out on a new concept, we should highlight that—even if it didn’t pan out. Big swings should be celebrated, even if we didn’t win, because there are valuable learnings there. 4. Look for innovative thinkers with a history of zero-to-one feature work. There are lots of amazing product managers out there, but not many focus on new problem domains. If a PM has created something new from scratch and done it well, that’s a good sign. An even better sign: if they show excitement about and gravitate toward that kind of work. If that sounds like you—if you’re a product manager who wants to think big picture and try out big ideas in a fast-paced environment with a stellar mission—we want you on our team. We’re hiring a Director of Product Management: https://lnkd.in/dQnWqmDZ #productthoughts #innovation #productmanagement #zerotoone