Innovation Risk Management

𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗶𝘀 𝗰𝗿𝘂𝗰𝗶𝗮𝗹 𝗳𝗼𝗿 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 & 𝗴𝗿𝗼𝘄𝘁𝗵‼️   In today’s slow-growth economy, innovation budgets are tight. The pressure to deliver successful innovations with a strong ROI has never been higher. As a result, many innovation managers feel they must constantly justify their activities, fearing the potential failure of some projects. 𝗧𝗵𝗶𝘀 𝗳𝗲𝗮𝗿 𝗼𝗳𝘁𝗲𝗻 𝗹𝗲𝗮𝗱𝘀 𝘁𝗼 𝗵𝗲𝘀𝗶𝘁𝗮𝘁𝗶𝗼𝗻: fewer experiments, fewer bold moves, and ultimately, fewer breakthroughs. According to Deloitte’s “𝘛𝘩𝘦 𝘴𝘵𝘢𝘵𝘦 𝘰𝘧 𝘪𝘯𝘯𝘰𝘷𝘢𝘵𝘪𝘰𝘯 𝘪𝘯 𝘎𝘦𝘳𝘮𝘢𝘯 𝘤𝘰𝘮𝘱𝘢𝘯𝘪𝘦𝘴” 𝟯𝟭% 𝗼𝗳 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝘀𝗲𝗲 𝗮 𝗹𝗮𝗰𝗸 𝗼𝗳 𝗿𝗶𝘀𝗸 𝗮𝗽𝗽𝗲𝘁𝗶𝘁𝗲 𝗮𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗼𝗯𝘀𝘁𝗮𝗰𝗹𝗲𝘀 𝘁𝗼 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 in their organization. This is a missed opportunity. 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝘁𝗵𝗿𝗶𝘃𝗲𝘀 𝗶𝗻 𝗮 𝗰𝘂𝗹𝘁𝘂𝗿𝗲 𝘁𝗵𝗮𝘁 𝗮𝗹𝗹𝗼𝘄𝘀 𝗳𝗼𝗿 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗿𝗶𝘀𝗸𝘀—the key word being 𝘤𝘢𝘭𝘤𝘶𝘭𝘢𝘵𝘦𝘥. Gone are the days when it was acceptable to experiment freely without a clear focus on commercial outcomes. Today, every innovation must eventually translate into measurable success. 𝗧𝗵𝗲 𝗴𝗼𝗼𝗱 𝗻𝗲𝘄𝘀? Risk can be managed. Companies can take strategic steps to balance innovation and risk: ✔️ Create transparency across your innovation portfolio. ✔️ Track progress using clear, actionable KPIs. ✔️Assess market potential for each innovation and decide on the optimal path—exit, spin-off, carve-out, or asset licensing. By embedding these practices into their innovation process, companies can confidently navigate the uncertainties of new ideas, making informed decisions at every stage. So, 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝗱𝗶𝘀𝗰𝗼𝘂𝗿𝗮𝗴𝗶𝗻𝗴 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗱𝘂𝗲 𝘁𝗼 𝘁𝗵𝗲 𝗳𝗲𝗮𝗿 𝗼𝗳 𝗳𝗮𝗶𝗹𝘂𝗿𝗲, 𝗲𝗺𝗽𝗼𝘄𝗲𝗿 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺𝘀 with the right tools and frameworks 𝘁𝗼 𝘁𝗮𝗸𝗲 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗿𝗶𝘀𝗸𝘀 𝗮𝗻𝗱 𝗮𝗰𝗵𝗶𝗲𝘃𝗲 𝗶𝗺𝗽𝗮𝗰𝘁𝗳𝘂𝗹 𝗿𝗲𝘀𝘂𝗹𝘁𝘀!   FOOD FOR THOUGHT! 💭 How does your organization handle calculated risks? Is it encouraged, or do you face resistance? Let’s discuss in the comments! ———————————— ♻️ Share this to embrace failure as a driver for innovation! 💡 Follow me, Lara Sophie Bothur, for more insights on AI, tech trends, and how to turn challenges into breakthroughs! Credits to Roberto Ferraro / Recreation of his great work!

AI’s Promise and Pitfalls in Agriculture - We need better and more humble Founders!! Artificial Intelligence (AI) has the potential to transform agriculture by optimizing yields, predicting crop prices, and mitigating climate risks. However, the recent collapse of Gro Intelligence, a once-celebrated agritech startup, reveals the dangers of prioritizing hype over substance. Gro’s failure, alongside other AI-driven price prediction missteps, exposes a critical flaw—founders who lack deep domain expertise in agricultural markets risk not only their ventures but also the trust of the farming community. This article "AI as the Ultimate Transformer: Founders' Shortcomings Jeopardize Its Potential in Agriculture" delves into how AI’s promise in agriculture is being undermined by misguided approaches and what can be done to ensure its responsible application. The article is based on my firsthand experience in working with multiple founders and product managers across the globe, many of whom have inflated perception about themselves, and technology. The Fall of Gro Intelligence: A Lesson in Overconfidence Founded in 2014, Gro Intelligence set out to revolutionize agricultural data analytics by using AI to forecast yields and commodity prices. With $115 million in funding, it promised insights derived from massive datasets, but cracks soon emerged. Gro overestimated its AI’s ability to navigate unpredictable market forces such as China’s strategic soybean stockpiling or India’s abrupt export bans. The company also prioritized scaling its data infrastructure over validating its models with local experts, leading to flawed predictions that failed real-world tests. Ultimately, Gro’s downfall highlights a recurring issue—founders who approach agriculture with a Silicon Valley mindset often ignore the deep complexities of global commodity markets, leading to avoidable failures. AI Price Predictions and the Danger of Superficial Models AI-powered price prediction tools have repeatedly failed due to an inadequate understanding of commodity markets. One notable example is a Chicago-based startup that attempted to predict soybean futures on the Chicago Mercantile Exchange. By ignoring factors like China’s opaque stockpiling policies and futures market mechanics, its model deviated from actual prices by 30%, resulting in massive losses for hedge funds. These cases illustrate how AI models, no matter how advanced, are ineffective when they fail to capture the intricate forces driving market prices. A Smarter Approach to AI in Agriculture For AI to succeed in agriculture, it must prioritize context over code and blend technology with human expertise. Companies that embed traders, farmers, and agronomists into their AI teams produce more accurate and practical models. Hybrid intelligence—where AI is supplemented by human oversight—has also proven effective.

Another one bites the dust… Fresh from a solar site this week, this transformer didn’t fail from age, abuse or neglect – it failed on quality. Once the lid came off, the story was obvious: Poor mechanical restraint and uneven support to the coils Localised heating and visible insulation distress Clear evidence that workmanship and design never matched the critical nature of the asset And front and centre again: aluminium windings – almost certainly selected on cost, and in many cases produced from recycled material. Aluminium can work perfectly well when it is engineered correctly, but when conductor quality, compaction and jointing are driven by price rather than performance, you create the perfect environment for: Higher electrical losses and hot spots at terminations and joints Mechanical creep under short-circuit forces and thermal cycling Accelerated insulation ageing as pockets, voids and movement develop within the winding structure On a utility-scale solar site, that is not innovation – it is a long-term liability hidden inside a steel tank. The result is exactly what you see in these images: premature failure on an asset that should have been quietly and efficiently delivering low-carbon generation for decades. This is not bad luck. It is the direct consequence of compromising on materials and build quality to shave capital cost on day one. For an owner, that quickly becomes lost generation, lost revenue and a difficult conversation with insurers and investors. Johnson & Phillips were brought in to provide: Forensic strip-down and high-quality photographic evidence Unbiased root-cause analysis for the asset owner, EPC and insurers A clear technical link between material choice, construction quality and in-service stress As the industry accelerates towards renewables, the equipment behind the panels must be better, not cheaper. If you are responsible for transformers on solar, wind or industrial sites and are not completely confident in what is sitting in those tanks, now is the time to talk about quality assurance, condition assessment and rigorous engineering oversight – not after the next one fails. Johnson & Phillips: straight-talking, evidence-based transformer expertise, from LV distribution units through to 132 kV. #Solar #Transformers #PowerQuality #Engineering #JohnsonAndPhillips #QualityMatters #Renewables

I came across a recent Science study that analyzed 77 patents from AI-first drug discovery companies. Compared to traditional, lab-based developers, these companies were significantly more likely to patent small-molecule compounds without conducting any in vivo studies. The study also found a consistent pattern: less biological data, fewer ADMET experiments, limited formulation detail, and earlier filings overall. Only 23% of these AI-driven patents included any animal testing. Yet many disclosed hundreds of molecules with minimal experimental validation. This raises a red flag. It looks less like a push toward de-risked innovation and more like broad IP positioning based on computational output. From a business standpoint, I understand the pressure to file early. Patents shape competitive advantage, attract capital, and signal pipeline momentum. But if filings outpace meaningful validation, the result is an innovation bottleneck. Untested molecules sit protected but undeveloped, blocking others from advancing or investing in them. AI has the potential to reshape drug discovery, but not by scaling noise. Its real commercial value lies in helping us prioritize high-quality candidates that are both novel and actionable. Filing before experimental validation shifts risk downstream, creates friction in licensing, and undermines investor confidence in the actual readiness of assets. If we want AI to accelerate not just ideation but actual development, we need to realign incentives. Strong IP should be linked to strong evidence. Otherwise, we risk building a patent landscape that looks impressive on paper but slows real progress. 📄 https://lnkd.in/eZdHvx-U #ai #drugdiscovery #biotech

𝗧𝗵𝗲 𝗧𝗵𝗿𝗲𝗲-𝗙𝗮𝗰𝘁𝗼𝗿 𝗦𝗵𝗼𝗿𝘁𝗵𝗮𝗻𝗱: 𝗔 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁 After exploring all five factors in our patent "sweet spot" series, I want to share the three-factor shorthand I use with clients when time is limited but the stakes are high. When these three conditions align, patent protection often becomes essential—and going without it becomes genuinely risky. 𝗧𝗵𝗲 𝗧𝗵𝗿𝗲𝗲 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗙𝗮𝗰𝘁𝗼𝗿𝘀: 1. 𝗕𝗿𝗼𝗮𝗱 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻: Your invention solves a valuable problem in a surprising and broadly applicable way. It's not just a narrow technical tweak, but a fundamental breakthrough that opens new possibilities or dramatically improves existing solutions. 2. 𝗘𝗮𝘀𝘆 𝘁𝗼 𝗖𝗼𝗽𝘆: Once your product hits the market, competitors can reverse-engineer or replicate your innovation without the years of development effort you invested. Your breakthrough becomes their shortcut. 3. 𝗛𝗶𝗴𝗵 𝗜𝗻𝗳𝗿𝗶𝗻𝗴𝗲𝗺𝗲𝗻𝘁 𝗗𝗲𝘁𝗲𝗰𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: When competitors use your innovation, you'll know. Whether through product analysis, market observation, or technical evaluation, infringement won't remain hidden. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗖𝗼𝗺𝗯𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗜𝘀 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 When all three factors converge, you face a perfect storm of opportunity and vulnerability: • 𝗠𝗮𝘀𝘀𝗶𝘃𝗲 𝗺𝗮𝗿𝗸𝗲𝘁 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 from your broad, valuable innovation • 𝗜𝗻𝗲𝘃𝗶𝘁𝗮𝗯𝗹𝗲 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻 because copying is straightforward • 𝗘𝗻𝗳𝗼𝗿𝗰𝗲𝗮𝗯𝗹𝗲 𝗽𝗿𝗼𝘁𝗲𝗰𝘁𝗶𝗼𝗻 because infringement is detectable 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗽𝗮𝘁𝗲𝗻𝘁 𝗽𝗿𝗼𝘁𝗲𝗰𝘁𝗶𝗼𝗻, 𝘆𝗼𝘂'𝗿𝗲 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹𝗹𝘆 𝗳𝘂𝗻𝗱𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗼𝗿𝘀' 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗮𝗻𝗱 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁. 𝗧𝗵𝗲 𝗧𝗿𝗮𝗱𝗲 𝗦𝗲𝗰𝗿𝗲𝘁 𝗔𝗹𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝘃𝗲 Some clients consider keeping such innovations as trade secrets, guarding them carefully both internally and when licensing. While this can work, it still leaves you exposed to two significant risks: • 𝗥𝗲𝘃𝗲𝗿𝘀𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Once you commercialize, competitors can legally analyze and copy what they discover • 𝗜𝗻𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: Other innovators may arrive at the same solution through their own R&D efforts Unlike patents, trade secrets provide no protection against these scenarios. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 When you encounter this three-factor combination, patent protection typically offers the strongest risk-adjusted return on investment. The alternative—hoping competitors won't notice, reverse-engineer, or independently develop your breakthrough—is rarely a viable long-term strategy. This shorthand helps cut through complex IP strategy discussions to focus on the core question: Are you sitting on a valuable, copyable, detectable innovation? If yes, patents likely deserve serious consideration. #patents #ip #innovation

Inventors' Biggest Fear: “What if someone copies my idea with a small tweak and I lose everything?”🧐 You’re not alone. Many inventors hesitate to publish or launch their innovation fearing competitors might steal it with minor changes. Especially when your idea is a slight advancement, a new twist, a smarter design, a more efficient process and it feels vulnerable. So how do you protect your IP and sleep 🛌 better at night? Here’s a simple roadmap:👩🏻💼 ✅File a Provisional Patent Early- Secure your priority date. Even if your invention isn’t fully ready, this locks your idea legally before others can grab it. You get 12 months to finalize and file a complete patent. ✅ Use Trade Secrets Wisely- If your innovation includes a formula, recipe, or process that can be hidden, keep it confidential. Sign NDAs with employees and partners. Not everything needs to be patented to be protected. ✅Combine IP Rights- Use a mix of protections: ▪️Patent for technical novelty ▫️Design patent for product appearance ▪️Trademark for your brand name/logo ▫️Copyright for your manuals, designs, or code ✅ Broaden Your Patent Claims- Write your patent smartly. Cover not just the core feature but also possible variations competitors might attempt. A strong patent fence keeps copycats out. ✅ Publish Smartly (Defensive Publication) If you're not patenting something, publish it publicly. It becomes prior art, as a result, blocking others from getting a patent on a similar idea. 👩🏻💼You can consider this as a Real Example: A startup redesigned a coffee cup lid to prevent spills. Just a small tweak. They filed a provisional patent, kept the manufacturing technique a trade secret, and launched confidently. Today, their lid is in cafes across 3 countries, protected by strategy, not just fear. 👩🏻💼Don’t let fear kill your innovation. Protect it smartly. File early. Keep secrets. Use layered protection. Think like a creator and a strategist. #IPR #InnovationProtection #PatentStrategy

Have you ever spent endless hours on a project just to end up realising that a more straightforward method would have been more effective? This common mistake, referred to as over-engineering, can cause needless complexity and inefficiency when developing new products. Understanding Over-engineering > Over-engineering happens when a solution gets more difficult than it needs to be, usually by adding features or functionalities that do not directly meet the needs of customers. > This can lead to higher costs, longer development cycles, and less user-friendly products. Real-World Example: The Juicero The Juicero, a high-tech juicing machine, was released in 2016. It cost $700 and was designed to squeeze proprietary juice packets with considerable force. Later on, though, it was found that the costly machine was not essential because the same juice bags could be squeezed by hand. The company was eventually shut down as a result of the public outcry following this disclosure. My Own Story: The Overly Complex Website I was in a team early in my career that was assigned with creating a company website. We included the newest interactive elements and design trends in an effort to wow. Feedback received after the launch, however, indicated that visitors found the website overwhelming and challenging to use. In our pursuit of innovation, we had failed to realise the website's main purpose, which is to provide easily comprehensible information. I learnt the importance of simplicity and user-centred design from this experience. Useful Tips to Prevent Over-Engineering 1. Pay attention to the essential needs: Focus on key features that meet user needs and clearly explain the issue you're trying to solve. Don't include features that aren't directly useful. 2. Adopt Incremental Development: Begin with an MVP that satisfies the fundamental specifications. By using this method, you may get user input and decide on new features with knowledge. 3. Put Simplicity First: Use the KISS philosophy, which stands for "Keep It Simple, Stupid." Simpler designs are frequently easier to use and more efficient. 4. Verify Assumptions: Talk to users to learn about their wants and needs. This guarantees that the things you create will actually be useful to them. 5. Promote Open Communication: Create an environment where team members are at ease sharing thoughts and possible difficulties. Over-engineering tendencies can be recognised and avoided with the support of this collaborative environment. Have any of your initiatives involved over-engineering? How did you respond to it? Post your thoughts and experiences in the comments section below!

"This white paper offers a comprehensive overview of how to responsibly govern AI systems, with particular emphasis on compliance with the EU Artificial Intelligence Act (AI Act), the world’s first comprehensive legal framework for AI. It also outlines the evolving risk landscape that organizations must navigate as they scale their use of AI. These risks include: ▪ Ethical, social, and environmental risks – such as algorithmic bias, lack of transparency, insufficient human oversight, and the growing environmental footprint of generative AI systems. ▪ Operational risks – including unpredictable model behavior, hallucinations, data quality issues, and ineffective integration into business processes. ▪ Reputational risks – resulting from stakeholder distrust due to errors, discrimination, or mismanaged AI deployment. ▪ Security and privacy risks – encompassing cyber threats, data breaches, and unintended information disclosure. To mitigate these risks and ensure AI is used responsibly, in this white paper we propose a set of governance recommendations, including: ▪ Ensuring transparency through clear communication about AI systems’ purpose, capabilities, and limitations. ▪ Promoting AI literacy via targeted training and well-defined responsibilities across functions. ▪ Strengthening security and resilience by implementing monitoring processes, incident response protocols, and robust technical safeguards. ▪ Maintaining meaningful human oversight, particularly for high-impact decisions. ▪ Appointing an AI Champion to lead responsible deployment, oversee risk assessments, and foster a safe environment for experimentation. Lastly, this white paper acknowledges the key implementation challenges facing organizations: overcoming internal resistance, balancing innovation with regulatory compliance, managing technical complexity (such as explainability and auditability), and navigating a rapidly evolving and often fragmented regulatory landscape" Agata Szeliga, Anna Tujakowska, and Sylwia Macura-Targosz Sołtysiński Kawecki & Szlęzak

“Fail fast” is fatal in agriculture. The recent piece in AgTechNavigator highlights what many in our field already know: the Silicon Valley mantra doesn’t translate to the farm. For growers, a failed trial isn’t a pivot…it’s a season lost, margins cut, and trust eroded. (https://lnkd.in/egFD5pEZ) Jason Weller of JBS is right to call out the “last mile” as agtech’s chasm of death. Too often, we see brilliant platforms and biologicals stuck in demo mode because they never reach the farmer in a way that fits real agronomic, economic, and social conditions. From my perspective, three truths stand out: 1. Trust is the technology. Without agronomists, cooperatives, and farmer networks backing innovation, no sensor or microbe will gain traction. 2. Adoption is the bottleneck. Farmers don’t need promises they need proof of profitability, reliability, and integration into existing practices. 3. Partnerships are infrastructure. Public–private alliances, like Brazil’s traceability accelerator, are what convert point solutions into systemic change. This is where Corporate Venture Capital must evolve. Investing is not enough! We need to de-risk adoption, co-develop solutions with farmers, and measure success in hectares, yields, and resilience, not just valuations. AgriFoodTech innovation will only move the needle when adoption barriers are treated as seriously as invention. Because in ag, there is no MVP. There is only trust…or failure. #AgriFoodTech #CVC #InnovationStrategy #Sustainability #StartupScaling

In a world where most leaders focus on individual performance, collective psychological context determines what's truly possible. According to Deloitte's 2024 study, organizations with psychologically safe environments see 41% higher innovation and 38% better talent retention. Here are three ways you can leverage psychological safety for extraordinary team results: 👉 Create "failure celebration" rituals. Publicly acknowledging mistakes transforms the risk psychology of your entire team. Design structured processes that recognize learning from setbacks as a core organizational strength. 👉 Implement "idea equality" protocols. Separate concept evaluation from originator status to unleash true perspective diversity. Create discussion frameworks where every voice has equal weight, regardless of hierarchical position. 👉 Practice "curiosity responses”. Replace judgment with genuine inquiry when challenges arise. Build neural safety by responding with questions that explore understanding before concluding. Neuroscience confirms this approach works: psychologically safe environments trigger oxytocin release, enhancing trust, creativity, and collaborative problem-solving at a neurological level. Your team's exceptional performance isn't built on individual brilliance—it emerges from an environment where collective intelligence naturally flourishes. Coaching can help; let's chat. Follow Joshua Miller #workplace #performance #coachingtips