Innovation in Product Development

AI Product Management AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications. This is making it possible to build new kinds of things, which in turn is driving shifts in best practices in product management — the discipline of defining what to build to serve users — because what is possible to build has shifted. In this post, I’ll share some best practices I have noticed. Use concrete examples to specify AI products. Starting with a concrete idea helps teams gain speed. If a product manager (PM) proposes to build “a chatbot to answer banking inquiries that relate to user accounts,” this is a vague specification that leaves much to the imagination. For instance, should the chatbot answer questions only about account balances or also about interest rates, processes for initiating a wire transfer, and so on? But if the PM writes out a number (say, between 10 and 50) of concrete examples of conversations they’d like a chatbot to execute, the scope of their proposal becomes much clearer. Just as a machine learning algorithm needs training examples to learn from, an AI product development team needs concrete examples of what we want an AI system to do. In other words, the data is your PRD (product requirements document)! In a similar vein, if someone requests “a vision system to detect pedestrians outside our store,” it’s hard for a developer to understand the boundary conditions. Is the system expected to work at night? What is the range of permissible camera angles? Is it expected to detect pedestrians who appear in the image even though they’re 100m away? But if the PM collects a handful of pictures and annotates them with the desired output, the meaning of “detect pedestrians” becomes concrete. An engineer can assess if the specification is technically feasible and if so, build toward it. Initially, the data might be obtained via a one-off, scrappy process, such as the PM walking around taking pictures and annotating them. Eventually, the data mix will shift to real-word data collected by a system running in production. Using examples (such as inputs and desired outputs) to specify a product has been helpful for many years, but the explosion of possible AI applications is creating a need for more product managers to learn this practice. Assess technical feasibility of LLM-based applications by prompting. When a PM scopes out a potential AI application, whether the application can actually be built — that is, its technical feasibility — is a key criterion in deciding what to do next. For many ideas for LLM-based applications, it’s increasingly possible for a PM, who might not be a software engineer, to try prompting — or write just small amounts of code — to get an initial sense of feasibility. [Reached length limit. Full text: https://lnkd.in/gYY-hvHh ]

Many groundbreaking innovations stem from solving specific, sometimes minor, issues but yield profound impacts. What do you think about this one? These "incremental innovations" drive efficiency, safety, cost savings, and user experiences forward. Take a look at some examples: - Post-it Notes: Born from a failed attempt at a strong adhesive, these sticky notes revolutionized quick note-taking and reminders. - Airbags in Cars: Rather than redesigning vehicles entirely, adding airbags significantly boosted passenger safety and reduced accident fatalities. - Gore-Tex Fabric: By solving the simple problem of staying dry and comfortable, this breathable, waterproof fabric transformed outdoor clothing. - QR Codes: Improving on barcodes, QR codes store more data and offer easier scanning, revolutionizing information sharing and transactions. - Zippers: Replacing buttons and hooks, zippers streamlined fastening clothes and bags with speed and security. - Wheels on Luggage: The addition of wheels to suitcases set a new standard, making travel significantly more convenient. - Penicillin: Beyond its initial discovery, incremental enhancements in production and distribution have saved countless lives through antibiotics. - LED Lighting: The shift from incandescent bulbs to LEDs delivers substantial energy savings and longer lifespans, addressing efficiency and environmental concerns. - USB Ports: Standardizing a universal port for data transfer and charging simplified connectivity across a diverse range of devices. These examples showcase how small improvements can lead to significant advancements in various aspects of our lives. #Innovation #Progress #Efficiency #Safety via @shajapur_mandi_bhav #Technology

Should you try Google’s famous “20% time” experiment to encourage innovation? We tried this at Duolingo years ago. It didn’t work. It wasn’t enough time for people to start meaningful projects, and very few people took advantage of it because the framework was pretty vague. I knew there had to be other ways to drive innovation at the company. So, here are 3 other initiatives we’ve tried, what we’ve learned from each, and what we're going to try next. 💡 Innovation Awards: Annual recognition for those who move the needle with boundary-pushing projects. The upside: These awards make our commitment to innovation clear, and offer a well-deserved incentive to those who have done remarkable work. The downside: It’s given to individuals, but we want to incentivize team work. What’s more, it’s not necessarily a framework for coming up with the next big thing. 💻 Hackathon: This is a good framework, and lots of companies do it. Everyone (not just engineers) can take two days to collaborate on and present anything that excites them, as long as it advances our mission or addresses a key business need. The upside: Some of our biggest features grew out of hackathon projects, from the Duolingo English Test (born at our first hackathon in 2013) to our avatar builder. The downside: Other than the time/resource constraint, projects rarely align with our current priorities. The ones that take off hit the elusive combo of right time + a problem that no other team could tackle. 💥 Special Projects: Knowing that ideal equation, we started a new program for fostering innovation, playfully dubbed DARPA (Duolingo Advanced Research Project Agency). The idea: anyone can pitch an idea at any time. If they get consensus on it and if it’s not in the purview of another team, a cross-functional group is formed to bring the project to fruition. The most creative work tends to happen when a problem is not in the clear purview of a particular team; this program creates a path for bringing these kinds of interdisciplinary ideas to life. Our Duo and Lily mascot suits (featured often on our social accounts) came from this, as did our Duo plushie and the merch store. (And if this photo doesn't show why we needed to innovate for new suits, I don't know what will!) The biggest challenge: figuring out how to transition ownership of a successful project after the strike team’s work is done. 👀 What’s next? We’re working on a program that proactively identifies big picture, unassigned problems that we haven’t figured out yet and then incentivizes people to create proposals for solving them. How that will work is still to be determined, but we know there is a lot of fertile ground for it to take root. How does your company create an environment of creativity that encourages true innovation? I'm interested to hear what's worked for you, so please feel free to share in the comments! #duolingo #innovation #hackathon #creativity #bigideas

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

There’s a new way of developing products. In the past, product development was iterative: define a problem, build a first version, revise until final. But with AI models and tools, the process is evolving. Because these models have general-use applications, they can be released into the market with the understanding that they’ll refine over time, learning from usage, feedback, and emerging needs. Instead of defining a singular problem and building a tool to solve it, we now identify a 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐬𝐩𝐚𝐜𝐞—a domain where the model can begin to add value and evolve. This shift requires new thinking: 𝐃𝐞𝐬𝐢𝐠𝐧𝐢𝐧𝐠 𝐟𝐨𝐫 𝐚𝐝𝐚𝐩𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲, not perfection  𝐋𝐢𝐬𝐭𝐞𝐧𝐢𝐧𝐠 𝐭𝐨 𝐮𝐬𝐚𝐠𝐞 𝐬𝐢𝐠𝐧𝐚𝐥𝐬, not just requirements  𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐮𝐬𝐞𝐫, not just for them It’s a more fluid, responsive way to innovate, and it’s unlocking new possibilities across industries. See my latest video for more:

A ceramics teacher split her class into two groups. To one group, she gave a simple instruction: “Make as many pots as you can. You’ll be graded on quantity.” To the other, she said: “Make just one pot. But make it perfect. You’ll be graded on quality.” At the end of the week, something unexpected happened. The quantity group, in their rush to produce, iterated constantly. They tried new shapes. They learned from cracked handles and warped lids. Each failure taught them something. By the end, they had made not only the most pots — but also the best ones. The quality group? They spent most of their time theorizing. Planning. Sketching. Worrying. Their pot, though done, was far from perfect. This story plays out in plenty of situations and initiatives, big or small. When building products, don’t wait for perfection. Start building. Ship. Learn. Repeat. Iteration is how excellence emerges. Doing is how you find direction. Especially in today’s AI-fueled world, where the ground is shifting fast — the teams that move, learn, and ship quickly will beat the ones that wait for perfect specs. So the next time you find yourself overthinking, remember the pottery class. Just make another pot.

Do you feel anxiety when looking at your Product backlog with those 1014 tickets? What if I told you there is another way? Here are 8 ways to keep your backlog clean and actionable: 1) Differentiate between a backlog item and an idea - It's ok to have a notebook, Figma, mural, whatever, where you collect all ideas and requests. However, the backlog should only contain items you aim to work on FOR REAL within the next 1-3 quarters. 2) Set a hard limit of tickets - In my experience, only the top 20-30 tickets will actually have any chance to ever be closed as completed. There are too many new directions, opportunities, and urgent tasks coming in overriding the priority of tasks further in the backlog. Just close the items that will never happen or at least move them to your ideas space. 3) Don't make it a BUGlog - bugs are tasks like all others. They need value and effort estimation and have to be prioritized against any other product opportunity. If they don't make the cut, they don't make the cut, sorry. No point collecting bugs - they are not Pokemon! 4) Keep the tickets high quality - However, if there is something in your backlog, let it shine! Make sure to include the user story, impact hypothesis, requirements, and links to design and tracking specifications. The tickets should be able to speak for you when you are not around. 5) Try to have 3 months' worth of refined items ready to go - It might be hard (try daily refinements!) to achieve and it's worth it! With items ready for the next 3 months for the team can pick up, you will have so much time to do proper long time planning and assessment. It's worth the initial effort! 6) Introduce visual cues - It's much easier to look at the backlog if you can easily tell apart a new feature task, improvement initiatives, bugs, and research. If you add other color cues to represent item status, you will be able to tell everything at a glance. 7) Add key stakeholders to their tickets of interest - A personal update email may work. Automated status updates work too and keep relevant people in the loop with no time investment on your end! 8) Create a task document associated with a backlog item - This is basically an extended version of the ticket, where you can collect all the pre-development research and post-development results and observations. Collecting this info in one place saves you hours when it comes to writing progress updates and presentations. At the same time, your tickets remain clean and hold only the relevant information. There you go! Here are my 8 ways to keep the backlog neat and functional. Will those work for your backlog and if not, why? Or perhaps you can contribute more pieces of advice? Sound off in the comments! #productmanagment #productmanagers #backlog P.S. Having a clean backlog is one thing. Having great tasks to put there is another challenge every Product Manager faces. To be well equipped to face that challenge, check out my courses at drtbartpm. com :)

Product Discovery is the most critical area for a PM. But, it is largely misunderstood. Teams waste time and energy delivering ideas that do not work and do not drive the expected outcomes. Product Discovery 101: 1. Why do we need Product Discovery? „The first truth is that at least half of your ideas are just not going to work” - Marty Cagan, Inspired I’d argue that Product Management is, at its heart, about managing risk. And for every product, there are 5 risks that can materialize: - Value. Will it create value for the customers? - Usability. Will users figure out how to use it? - Viability. Can our business support it? - Feasibility. Can it be done (technology)? - Ethics. Should we do it? Are there any ethical considerations? What will happen if we throw random ideas into the Product Backlog? This is agile "learning by delivering."This approach results in a waste and rework. So, we would like to understand: - How can we come up with better ideas? - How can we validate those ideas before the implementation? And the answer is Product Discovery. --- 2. When Does it Happen? Continuous Discovery and Continuous Delivery. Those two streams run in parallel: - The goal of Product Discovery is to discover the product to build. - Product Delivery aims to deliver that product to the market. Product Discovery results in a validated Product Backlog. In particular, high-risk assumptions are tested before the implementation. --- 3. Who's Responsible? Some say that the Product Manager decides what to build, and Engineers and designers should focus on how to build it. Have you heard that before? It hurts my ears because Product Discovery is not a task for a single person. Make sure that a Product Designer and at least one Engineer are included. This will help you build a shared understanding and stay open to different perspectives. And if we believe that customers don't know how to solve their problems, why should a Product Manager know it? Product Managers may be tech-literate, but they are not tech experts. --- 4. What’s inside Continuous Product Discovery? There are two groups of activities: - Exploring the Problem Space to understand and define opportunities (problems, needs, desires). My favorite default approach to mapping opportunities is using the Opportunity Solution Tree, as defined by Teresa Torres. - Exploring the Solution Space to explore possible solutions, formulate testable assumptions, and run experiments to prove or disprove those assumptions. --- What's the state of Product Discovery in your company? What is one improvement you can implement starting tomorrow? Hope that helps! --- 🎁 P.S. In my free post, I described Product Discovery in depth. No email, no paywall: https://lnkd.in/dNUB__n3 And here you can download all my infographics: https://lnkd.in/d5bHGj5j

Mpesa just got an insurance license, and it's about to shake up the insurance industry like never before! Imagine a world where you can buy insurance as easily as you buy airtime. Here’s what’s coming: "Press 1 for Life Insurance" – Soon, insurance will be just a few clicks away on Mpesa. Car insurance, life insurance... Insurtech is here to make insurance purchasing as casual as sending Ksh 50 to your favorite cousin. Underwriting with AI – Thanks to algorithms, Mpesa might soon know more about your risk profile than you do. Instead of high-premium mystery quotes, expect insurance plans that understand your needs – from the cautious commuter to the "I swear I'll be safe" adrenaline junkie. The Rise of Flexible Plans – Get ready for insurance that can match your life’s rhythm. Mpesa could start offering policies that adjust based on whether you’re bungee jumping on weekends or just trying to navigate Nairobi traffic on a Monday. Embedded Insurance Everywhere – In the not-so-distant future, you might get life coverage when you buy a smartphone or health insurance with your gym membership. Your “Welcome to Mpesa” starter pack might just come with insurance, too. So buckle up; insurance in Kenya is about to become as easy as buying groceries. Just don’t be surprised if you start seeing push notifications from Mpesa reminding you to update your “adventure insurance” before your next weekend getaway! With M-Pesa obtaining an insurance license, banks and insurance companies in Kenya face a shift in the competitive landscape. Here are the implications: Increased Competition: M-Pesa can undercut traditional insurers with lower premiums and faster onboarding, particularly targeting low- to middle-income individuals and small businesses who are typically underserved by traditional insurers. Enhanced Customer Reach: M-Pesa's reach gives it a significant advantage, especially in remote and underserved areas where insurance penetration is low. Banks and traditional insurers may need to expand their digital outreach to stay competitive. New Product Innovations: With M-Pesa's tech capabilities, it could drive more innovative, user-friendly, and flexible insurance products—such as microinsurance, which may be bundled with other mobile-based financial services. This pushes traditional providers to innovate as well. Increased Financial Inclusion: For the financial sector, especially banks, M-Pesa’s entry could be beneficial in terms of financial inclusion, as more individuals who may not have previously accessed financial products like insurance are now introduced to it. This could create cross-selling opportunities if banks can partner effectively with M-Pesa. Pressure on Cost and Efficiency: M-Pesa’s digital platform allows for efficient, low-cost operations, which may put pressure on traditional providers to reduce their overheads and improve efficiency. Traditional banks and insurers to stay competitive?🤔

Why do some companies struggle to go from 1 highly successful product to multiple highly successful products? The need for great operations is a common disease in companies that are scaling, especially companies that are going from 1-2 successful products to multiple products that are sold/adopted separately from the core product. Once a company reaches a certain scale, its senior management implicitly begins to view great operations as the most reliable marker of a given team’s (and its leader’s) competence. And they accordingly create incentives for operational excellence, uniformly across all teams. These incentives do tend to produce better results for the teams working on the core product. But these same incentives tend to produce worse results for the teams working on newer products. It is only a really shrewd senior leader who says to an early stage team at a QBR or product review: “it is fine that your team isn’t firing on all cylinders on operations. that is to be expected at this stage. the main & only priority right now is to gain customer insight & creatively build the right things that create differentiation for us in this market.” When senior leaders don’t say this, and when they instead fixate on the operations optics of early stage teams, it makes it nearly impossible for the company to replicate its initial success for its newer products.