How to Adopt AI in Development

This seems to be on everyone’s mind: how to operationalize your product team around AI. Peter Yang and I recently chatted about this topic and here’s what I shared about how we are doing this at Duolingo. For improving our product: -Using AI to solve problems that weren’t solvable before. One of the problems we had been trying to solve for years was conversation practice. With our Max feature, Video Call, learners can now practice conversations with our character Lily. The conversations are also personalized to each learner’s proficiency level. -Prototyping with AI to speed up the product process. For example, for our Duolingo Chess, PMs vibe-coded with LLMs to quickly build a prototype. This decreased rounds of iteration, allowing our Engineers to start building the final product much sooner. -Integrating AI into our tooling to scale. This allowed us to go from 100 language courses in 12 years to nearly 150 new ones in the last 12 months. For increasing AI adoption: -Building with AI Slack channels. Created an AI Slack channel for people to show and tell and share prototypes and tips. -“AI Show and Tell” at All-Hands meetings. Added a five‑minute live demo slot in every all hands meeting for people to share updates on AI work. -FriAIdays. Protected a two‑hour block every Friday for hands-on experimentation and demos. -Function-specific AI working groups. Assembled a cross-functional group (Eng, PM, Design, etc.) to test new tools and share best practices with the rest of the org. -Company-wide AI hackathon. Scheduled a 3-day hackathon focused on using generative AI. Here are some of our favorite AI tools and how we are using them: -ChatGPT as a general assistant -Cursor or Replit for vibe coding or prototyping  -Granola or Fathom for taking meeting notes -Glean for internal company search #productmanagement #duolingo

Throwing AI tools at your team without a plan is like giving them a Ferrari without driving lessons. AI only drives impact if your workforce knows how to use it effectively. After: 1-defining objectives 2-assessing readiness 3-piloting use cases with a tiger team Step 4 is about empowering the broader team to leverage AI confidently. Boston Consulting Group (BCG) research and Gilbert’s Behavior Engineering Model show that high-impact AI adoption is 80% about people, 20% about tech. Here’s how to make that happen: 1️⃣ Environmental Supports: Build the Framework for Success -Clear Guidance: Define AI’s role in specific tasks. If a tool like Momentum.io automates data entry, outline how it frees up time for strategic activities. -Accessible Tools: Ensure AI tools are easy to use and well-integrated. For tools like ChatGPT create a prompt library so employees don’t have to start from scratch. -Recognition: Acknowledge team members who make measurable improvements with AI, like reducing response times or boosting engagement. Recognition fuels adoption. 2️⃣ Empower with Tiger Team Champions -Use Tiger/Pilot Team Champions: Leverage your pilot team members as champions who share workflows and real-world results. Their successes give others confidence and practical insights. -Role-Specific Training: Focus on high-impact skills for each role. Sales might use prompts for lead scoring, while support teams focus on customer inquiries. Keep it relevant and simple. -Match Tools to Skill Levels: For non-technical roles, choose tools with low-code interfaces or embedded automation. Keep adoption smooth by aligning with current abilities. 3️⃣ Continuous Feedback and Real-Time Learning -Pilot Insights: Apply findings from the pilot phase to refine processes and address any gaps. Updates based on tiger team feedback benefit the entire workforce. -Knowledge Hub: Create an evolving resource library with top prompts, troubleshooting guides, and FAQs. Let it grow as employees share tips and adjustments. -Peer Learning: Champions from the tiger team can host peer-led sessions to show AI’s real impact, making it more approachable. 4️⃣ Just in Time Enablement -On-Demand Help Channels: Offer immediate support options, like a Slack channel or help desk, to address issues as they arise. -Use AI to enable AI: Create customGPT that are task or job specific to lighten workload or learning brain load. Leverage NotebookLLM. -Troubleshooting Guide: Provide a quick-reference guide for common AI issues, empowering employees to solve small challenges independently. AI’s true power lies in your team’s ability to use it well. Step 4 is about support, practical training, and peer learning led by tiger team champions. By building confidence and competence, you’re creating an AI-enabled workforce ready to drive real impact. Step 5 coming next ;) Ps my next podcast guest, we talk about what happens when AI does a lot of what humans used to do… Stay tuned.

𝗜'𝗺 𝗵𝗲𝗮𝗿𝗶𝗻𝗴 𝘀𝘁𝗼𝗿𝗶𝗲𝘀 𝗮𝗯𝗼𝘂𝘁 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗳𝗮𝗶𝗹𝘂𝗿𝗲𝘀. Employees are NOT using it - they don't see the value or don't know how to. And I know exactly why... All fancy AI licenses are worthless because you are: 🚫 Throwing licenses at employees 🚫 Forcing top-down adoption 🚫 Assuming people will "figure it out" 🚫 Focusing only on technology The truth? Having AI isn't enough; effective adoption is key. Here's what successful companies do differently (5Es): ✅ Educate: Show AI capabilities w/ use cases & benefits ✅ Empower: Provide proper training and support ✅ Enable: Create space for experimentation ✅ Engage: Address concerns openly ✅ Execute: Implement clear adoption strategies Here's a 3-step framework that transformed our AI/RPA Automation adoption rates 👇 Start with WHY - Connect AI/Automation to business objectives - Show Organizational & personal benefits - Address replacement fears head-on Enable through HOW - Structured training programs - Hands-on workshops - Real-world use cases Support with WHAT - Clear implementation roadmap - Regular feedback sessions - Celebration of small wins Remember: Having AI isn't enough. Success lies in your people adopting it. What do you think? ---- 🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX #AI #innovation #technology #automation

Most leaders fail at getting their teams to adopt AI tools. Here's what actually works: This framework comes from Claire Vo (Creator of ChatPRD and 3X CPO) who shared it with the Surpa community in a recent event we did. The playbook is simpler than you'd think: 1/ Open up experimentation budgets Don't lock down tools behind approvals. Let people try things. ↳ Want to test Cursor? Go for it ↳ Interested in Deep Research? Try it ↳ New AI tool catching your eye? Experiment The only rule? Share what you learn. 2/ Create a public "Building with AI" channel Make it the central hub for AI experiments ↳ Share your wins AND failures ↳ Post prompts that worked ↳ Ask questions freely ↳ Document unexpected use cases 3/ Document winning recipes Create a central playbook that includes: ↳ Successful use cases ↳ Exact prompts that worked ↳ Common pitfalls to avoid ↳ ROI calculations 4/ Lead by example Be the most active experimenter yourself Share your own failures openly Show what "good" looks like Pro tip: Add your AI workflow to docs ↳ Include prompts you used ↳ Share how you got to the output ↳ Help others learn by example The goal isn't perfect adoption. It's creating a culture where AI experimentation becomes the norm. What strategies have worked in your organization? 

Most companies aren’t failing at AI adoption because of the tech. They’re failing because employees are afraid to use it. Tools are rolling out fast. But usage? Still stuck in pilot mode. 52% of employees using AI are afraid to admit it. And when managers don’t model usage themselves, team adoption stalls. One thing is clear: AI adoption doesn’t just happen. You have to design for it. Here are 10 strategies that actually work: 1. Track adoption and set goals. Measure usage patterns and benchmark performance across teams. Make AI part of your performance conversations, like Shopify does. 2. Engage managers. If they use AI, their teams are 2 to 5x more likely to follow. Enable them, train them, and let them lead by example. 3. Normalize usage. More than half of AI users hide it. Reframe the narrative. AI isn’t cheating, it’s table stakes. 4. Clarify policies. Without clear guidelines, people freeze. Spell out what’s allowed and what’s not. 5. Promote early wins. A great prompt that saves hours? Share it. Celebrate it. Build momentum. 6. Share best practices. Run prompt-a-thons. Create internal libraries. Make experimentation part of the culture. 7. Deploy AI agents strategically. Use ONA to spot high-friction workflows. Insert agents where they’ll have the biggest impact. 8. Balance experimentation with safe tooling. Watch what tools employees are adopting organically. Then invest in enterprise-grade tools your teams already want. 9. Customize by role and domain. Sales, HR, engineering, each needs a tailored strategy. Design workflows that reflect the reality of each team. 10. Benchmark yourself. How does your AI usage compare to peers? Track maturity, share progress, and stay competitive. From our work at Worklytics, these are the tactics that move organizations from pilot mode to performance. You can find the full AI Adoption report in the comments below. Which of these 10 is your org already doing and what’s next on your roadmap? #FutureOfWork #PeopleAnalytics #AI #Leadership #WorkplaceInnovation