Crowdsourced Innovation Ideas

Systemic Innovation Compass! Conceptual Takeaways: I. Change of Mindset There is an ongoing exploration of how to innovate in systems, i.e. acknowledging that the linear way of innovating does not work in complex situations and for transforming systems II. Systemic Approach This report encourages a systemic approach to thinking and invites the innovation community to apply for funding aimed at establishing ecosystems, rigs, or platforms dedicated to challenge-based, adaptive innovation. III. Ecosystems Systemic innovation should be adaptive, and the most adaptive way to organise innovation processes is in an innovation ecosystem. Such a system can embrace all the actors needed for handling the complexity (industry, research institutions, public agencies, civil society, and media, among others) IV. Role of Ecosystems Ecosystems can orchestrate portfolios of interventions and activities. It should include all actors and stakeholders that are needed for change, spanning from industry, research institutions to public sector, government and civil society V. Long term These platforms need to be rigged for the long-term, as they will orchestrate collaboration across sectors, and collaborations that must continue even after a project ends. The long-term perspective of an ecosystem is beneficiary when it comes to scaling new innovations VI. Scaling pilots One of the critical observations made during this project is the difficulty of scaling pilot projects, often due to the absence of essential actors within the project framework VII. Integration In a well-integrated ecosystem, relevant stakeholders can be engaged as needed, and the composition of stakeholders can evolve in tandem with the project's progress Check out this insightful project by Nina Egeli for The Nordic Council of Ministers and The Nordic Council and Nordic Innovation here: https://lnkd.in/dACvqfSV Project Partners: Demos Helsinki, Halogen, Ramboll Management Consulting and RISE Research Institutes of Sweden #innovation #systems #sustainability #foresight #future #systemsthinking #mobility #climate #logistics

I have made the mistake of seeing myself as a typical user of my product too many times. Why is it bad? How to avoid it? As the hilarious picture shows, the product creators may understand their market, but still not capture the real needs of the users. You, the Product Manager, are to be the user and business ambassador, not the actual user. Why is your perspective likely wrong? • Lack of diverse point of view: single perspective • You can't possibly know all your users' challenges • Being in tech gives you instincts no tech users miss • You will miss innovation that only users can uncover • You carry inherent biases and assumptions, as any individual • Being a product expert makes you blind to beginner challenges    ...and likely more. "Ok, smart guy, - you may say - but how do you ensure you design the right product for your users?" Way ahead of you! Here are a few actions a typical Product Manager can invest in, to ensure it's the users's perspective driving product development, not the limited PM ones: 1) KYC (Know Your Customer) client research With the work done by a dedicated company, you will deeply understand your users' needs, behaviors, and motivations. 2) Building user personas Create detailed profiles representing different user types to guide design and development decisions. Use data to identify usage patterns that can be labeled as specific types of users and polish them in a dedicated workshop. Speaking of which: 3) "Jobs to be Done" workshop With this you will identify the tasks users aim to accomplish, focusing on their goals rather than features. This is the ultimate way for PMs to identify the right problems to solve! 4) Dealing with data, not opinions Goes without saying, base decisions on analytics and user data instead of personal hunches. Especially your own. 5) Quantitative discovery (polls and surveys) Use surveys to gather measurable user insights. If you ask the right questions, you will get a representable number. You can also look for those in your reporting suite. 6) Introducing MVP quickly to understand users' reactions You can always launch a Minimum Viable Product early to collect feedback and iterate. Even embed some polls with it to gather live feedback! 7) Qualitative discovery (user interviews and observations) Engage directly with users to gain an in-depth understanding of their experiences. They will tell you whether your prototype resonates with them and they can complete assigned tasks easily. There you have it, many ways to keep your opinion away from good Product decisions. So, have you ever assumed you knew what your users wanted, only to be surprised by their actual needs? How do you get to understand your users? Sound off in the comments! #productmanagement #productmanager #userexperience P.S. To become a Product Manager who truly understands and serves your users, be sure to check out my courses on www.drbartpm.com :)

Swarming is a fundamental architecture of collective intelligence, and thus to the potential of agentic systems. A Google DeepMind team have built "model swarms". Starting with a set of LLM "experts" (agents), they apply a utility function for them to optimize. A few aspects of the paper I found the most interesting: 🤝The models adapt in a collaborative search process, modifying both their weights and the direction of change in weights, exploring possibility spaces for best results. 💡Across single task, multi-task domains, rewards models, and human-assessed domain expertise achieved superior performance, reaching up to 21% better performance compared to 12 baselines for single LLM models. 🔄The models' collective emergent behavior enabled them to solve problems that no individual agent could solve initially, with a 53% increase in correctness measures. 🌍 Reflecting other recent research results in agentic systems, performance was better when initial diverse models were used instead of homogeneous pools, with a 35% improvement in outcomes. 💎The researchers found that through the evolutionary approach, "the experts that ended as the best didn’t necessarily start as the best". By participating in the model swarm they can apply initial strengths in different ways to achieve peak performance. 🧬They use Particle Swarm Optimization (PSO) as simpler, more flexible, and better suited to LLM systems than Genetic Algorithms or Ant Colony Optimization. There is no question that much of the potential of agentic architectures will be achieved by evolutionary systems such as this. A space to watch closely. Lots more great insights in the paper, link in comments.

🎯 Product Innovation Secret: Your Users Are Already Building Your Next Big Feature Dream11 SVP of Product Vaibhav Kokal revealed how their most successful feature came from an unexpected place: their users were already building it on Telegram. Their popular "Guru" feature wasn't conceived in a boardroom or through complex market research. The inspiration? Their own users... on Telegram! 🤯 Here's why this is brilliant: 1.Dream11's users were creating informal prediction communities on Telegram 2.Instead of fighting this behavior, they turned it into their "Guru" feature 3.Result: Massive engagement boost and organic user acquisition 🎯 Key Takeaways: • Your best product ideas might be hiding in plain sight • Innovation often means observing and adapting, not inventing • Users will find ways to fulfill their needs - your job is to make it easier 🔍 Real-World Application: → Check your app's Reddit/Discord/Telegram communities → List the top 3 unofficial workarounds users have created → Evaluate which one could become your next native feature 💡 This reminds me of how Instagram stories came from observing how people were using Snapchat, or how Twitter's hashtags emerged from user behavior. 👉 Watch the full breakdown on my Behind The Featuren YouTube Channel: Link in comments #ProductInnovation #UserBehavior #ProductStrategy #FeatureDiscovery #ProductGrowth #GameDesign #GrowthStrategy

𝗔𝗜 𝗛𝗮𝗰𝗸𝗮𝘁𝗵𝗼𝗻 𝘄𝗶𝘁𝗵 𝗠𝗲𝘁𝗮 𝗶𝗻 𝗠𝘂𝗻𝗶𝗰𝗵 – 𝘄𝗵𝗲𝗿𝗲 𝗔𝗜 𝘀𝗼𝗹𝘃𝗲𝘀 𝗱𝗮𝗶𝗹𝘆 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀! 🤍 [𝗔𝗱/𝗔𝗻𝘇𝗲𝗶𝗴𝗲] Two days, three teams and various backgrounds. But one shared mission: rethinking how cities support their citizens. The city of Unterschleißheim (close to Munich) realized: The digital usage gap for e-government services in Germany is at 33 percent. Almost 45 percent of Germans refuse to use them for fear of making mistakes or because they are too complicated to use. Behind those numbers? Frustration, long queues, probably a missing understanding and overloaded staff. The task for the participants: Use Meta‘s open-source model Llama to design AI solutions that make bureaucracy accessible, understandable, and even a little more human. What fascinated me most? – How quickly a problem can turn into a working prototype in less than 48 hours – Watching AI instantly translate bureaucratic jargon into plain language – Seeing ideas grow when tech, design, and social impact come together And what I find most beautiful is how a shared tech mission dissolves differences – in that moment, everyone is equal, everyone is one!! 🤍🥹 One pitch that stood out: ReDI School, a digital school for migrants, presented a Digital Support System to guide users through German forms in their native language – via Chrome extension or WhatsApp. Simple, smart, and scalable. And the winning team: Pretzels!! They built an integrated AI chat feature that makes processes smoother and more intuitive – congrats! Today, AI chat agents are no longer optional … They're the interface of the future, seamlessly connecting humans and systems. In his keynote, Niklas von Weihe from Circus showed how a 3.5-ton AI robot can prepare 500 dishes for various use cases - from hospitals, schools to supermarkets. It tackles food waste and staff shortages, while personalizing meals with AI. I like. Walking out of the hackathon, I wasn’t just impressed by the tech, but by the human potential AI unlocks when applied thoughtfully. Thank you XIBIX Solutions for organizing the hackathon and bringing those great ideas to life - see you hopefully 🔜 for another tech event! 𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗶𝗻𝗴 𝗽𝗲𝗼𝗽𝗹𝗲 – 𝗶𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗲𝗺𝗽𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝘁𝗵𝗲𝗺 𝗶𝗻 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀 𝗮𝗻𝗱 𝘀𝗶𝘁𝘂𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗹𝗶𝗳𝗲! ✨ What problem would you want us to solve in the next AI hackathon?

A Team of LLMs Outperform a Single Giant LLM 🔥 Ever wondered if we could get more out of our AI without breaking the bank? That's exactly what the folks at Together AI have been working on. They've come up with something called Mixture-of-Agents (MoA), and it's pretty cool. What's the big deal about MoA? Imagine a bunch of AI models sitting around a table, bouncing ideas off each other. That's kind of what MoA does. It's like a team of language models working together, learning from each other's strengths. Why does this matter? Well, training massive language models can cost a fortune. And sometimes, these big models are only good at specific tasks. MoA offers a smarter way to use what we've already got. Think of MoA like a group of experts brainstorming to solve a complex problem. ↪ Each expert brings their own knowledge and perspective. ↪ As they discuss and share ideas, everyone learns from each other. ↪ With each round of discussion, their collective solution gets better and more refined. ↪ It's not about passing along a single message, but building on each other's insights. The secret sauce: Teamwork makes the dream work Here's what makes MoA stand out: • Collaboration is key: LLMs tend to up their game when they see other models' responses. MoA taps into this, creating a kind of AI brainstorming session. • Layer by layer improvement: As the process moves through different layers, each set of agents builds on what came before. It's like a game of leap-frog, but everyone's moving forward together. • Open-source power: In some tests, MoA using only open-source models managed to outperform GPT-4. That's a big deal for anyone watching their budget. What's next? This approach could change how we think about scaling AI. Instead of always going bigger, we might start focusing on smarter collaboration between models. It's like the old saying goes: Two heads are better than one. Or in this case, maybe a dozen AI heads are better than one giant one. What do you think? Could this team-based approach be the future of AI?

Modern corporations are creating innovation ecosystems where internal teams work directly with portfolio companies, sharing resources, expertise, and market access. This integration goes far beyond traditional corporate-startup partnerships: ➡️ Shared Technology Platforms: Portfolio companies gain access to proprietary corporate platforms and APIs, while corporations benefit from rapid external innovation cycles. ➡️ Cross-Pollination of Talent: Employees move between corporate R&D teams and portfolio companies, creating knowledge transfer and cultural bridges. ➡️ Collaborative Product Development: Joint development projects between corporate teams and startups are becoming more common, leading to products that neither could create independently.

Transforming How We Think About Collaboration: The 'Collaborative Innovation' Approach 🪄 🎯 𝗕𝗲𝗴𝗶𝗻 𝘄𝗶𝘁𝗵 𝗔𝘂𝗱𝗮𝗰𝗶𝗼𝘂𝘀 𝗚𝗼𝗮𝗹𝘀 Instead of seeking lowest-common-denominator agreement, start with a powerful vision that attracts committed changemakers. 👥 𝗜𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻𝗮𝗹 𝗦𝘆𝘀𝘁𝗲𝗺 𝗥𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Rather than "open door" meetings, carefully select participants to ensure the whole system is in the room — from grassroots to grasstops. 🔄 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗖𝗼-𝗰𝗿𝗲𝗮𝘁𝗶𝗼𝗻 Move away from "develop-then-present" to working together in real-time, leveraging collective intelligence. ⚡️ 𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗲 𝗧𝗲𝗻𝘀𝗶𝗼𝗻 Stop pushing for false harmony and start using differences as catalysts for innovation. ✨ 𝗘𝗮𝗿𝗹𝘆 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗶𝗻𝗴 & 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Build the strategy through action rather than endless planning sessions. What's powerful about this approach is how it transforms resistance and diversity into sources of innovation. It's not about getting everyone to agree — it's about weaving different perspectives into transformative interventions. Insights from Russ Gaskin, CoCreative and Ashoka's Leading Multi-stakeholder Collaborations course💡 🤔 How do you navigate the tension between inclusion and focused action in your collaborative work? #SystemicChange #Collaboration #Innovation #Leadership #CollectiveImpact

There are three types of organizations I’ve work with: 1️⃣ Those that consist of homogeneous groups. These teams share similar backgrounds, perspectives, and approaches, which can result in groupthink and limited innovation. 2️⃣ Those that consist of diverse teams. They have diversity on paper but struggle to unlock its full potential due to the absence of true inclusivity. 3️⃣ The rarest: those that consist of diverse and inclusive teams. These organizations know how to leverage their diversity through intentional inclusion, creating an environment where everyone contributes to a shared success. Here’s how I help organizations move from the first two types to the third: 🧠 Mindset In many diverse groups, diversity is seen as a challenge rather than an advantage. I help teams embrace a Diversity-Sum Mindset™, where varied perspectives are combined to fuel creativity, drive innovation, and amplify results. 🧡 Psychological Safety Diverse groups often lack the conditions needed for all voices to be heard, leading to disengagement or missed opportunities. I work to build Psychological Safety, where trust flourishes, and team members feel empowered to contribute their boldest ideas without fear. 🎯 Decision-Making Decision-making in diverse groups can be dominated by a few voices, stifling collective insight. Through Inclusive Decision-Making, I help teams integrate different viewpoints into cohesive strategies that lead to more balanced, innovative outcomes. 🕸 Collective Intelligence When teams can’t synergize their diverse ideas, they miss out on true innovation. I guide teams toward harnessing Collective & Collaborative Intelligence, unlocking their full potential to solve complex problems and adapt to new challenges. I designed this blueprint because I saw it from practice: the teams that thrive are those that embed diverse perspectives into their everyday actions, supported by a strong mindset, trust, and inclusive decisions. 🤔 P.S.: What kind of team have you been part of—homogeneous, diverse, or truly diverse & inclusive?

AI Swarm Intelligence: Lessons from Nature to Optimize Business Decisions Ever notice how birds flock in perfect sync or ants find food with uncanny efficiency? That same principle many simple units acting together drives AI swarm intelligence. Instead of a single, resource-heavy model, small AI agents locally interact, share findings, and converge on the best solution. Understanding Swarm Intelligence What is Swarm Intelligence? Swarm intelligence is a collective behavior exhibited by decentralized, self-organized systems. Think of it as many “small brains” working together to form a super-intelligent system without any centralized control. This principle is observed in nature, Ant Colonies & Bird Flocks. In AI Terms: Swarm intelligence leverages multiple simple & small AI agents that interact locally with one another, leading to a global problem-solving strategy. Instead of relying on one monolithic, resource-heavy model, these agents collectively explore and optimize solutions. Swarm Intelligence in Action Practical Example Logistics: Agents independently assess routes, share data, and collectively decide the most efficient path,adapting instantly to traffic or demand shifts. This decentralized approach can quickly adapt to traffic changes, accidents, or sudden demand spikes, much like a flock of birds adjusting its course on the fly. Business Optimization with Swarm Intelligence Supply Chain Management: Scenario: A global retailer manages inventory across multiple warehouses. Swarm Approach: Small AI agents monitor local inventory levels, predict demand fluctuations, and communicate with each other to optimize stock distribution. Result: A highly adaptive, efficient supply chain that minimizes stockouts and reduces excess inventory. Adaptive and Resilient: Unlike traditional AI models, a swarm-based approach is inherently flexible. If one agent fails or encounters an unexpected obstacle, others seamlessly fill the gap. It’s like having a team of friends where if one friend forgets the directions, the rest can still get you to the party on time. Scalability: Swarm intelligence scales naturally. Whether you have 10 or 10,000 agents, the system’s performance improves as more data points contribute to the collective decision. Example: In urban planning, a swarm of sensors and agents can collaboratively monitor traffic, pollution, and energy consumption, leading to smarter, more responsive cities. Cost Efficiency: Instead of investing in one supercomputer model, businesses can deploy numerous smaller, cost-effective agents that work together, often yielding faster and more robust results. As we look to the future, It’s not just about creating smarter algorithms, it’s about reimagining how multiple, simple agents can collectively tackle complex challenges, much like nature has perfected over millions of years. What do you think? How could swarm intelligence transform your industry or business model?