Innovation Risk Management

Hot Take: The way we conduct airdrops in crypto is going to change. And this is the reason why: 👇 The problem is that the airdrop expectations of web3 projects and the reality are miles apart. Here's how it goes: Airdrop Expectations • Build a community of people who believe in the project • Reward these early (human) users with airdrop tokens • Hope they will stick around and use the product    Airdrop Reality • Airdrop farmers and opportunists jump on the project • Create multiple fake accounts using complex tools • Claim & dump the airdrop tokens and move on In every single large airdrop, many, if not most, of the wallets getting airdrops belong to airdrop farmers. These airdrops farmers use complicated tools such as: • Proxies • Browser Profiles • Fake Twitter Accounts • Virtual Machines (VMs) • Disposable Email Accounts • Bots and Automation Scripts • VPNs (Virtual Private Networks) • Multi-Account Management Software And create the on-chain footprint of hundreds if not thousands of legit "human-like" wallets. Their plot is revealed only when the airdrop is complete and then there's a sudden and huge drop in activity. Often accompanied by a crash in the token price. Essentially they suck as much value as possible from VC-backed web3 projects without contributing anything. For example, in the attached Tweet, an anonymous user is bragging about how he took advantage of ZKsync airdrop: • Farmed 350 wallets • Spent ~$121k on fees/infra • Used ~$680k on liquidity pool • Received $1.2m in airdrop rewards Basically, 10x return on the capital spent. So what can projects do not to get ripped like this? Introduce new rules of the game: • Asking users to KYC • Multi season/stage airdrops • Deeper analysis of wallet behavior • Reduce the % of tokens given in airdrops • Airdropped tokens with gradual vesting periods • More complex rules for getting points for engagements Will these steps completely eliminate airdrop farmers? Probably not. But they will make their job much harder and less profitable, causing a significant decrease. P.S. What's your take on airdrops? How will they change in the future? Follow 👉 Aram Mughalyan & consider sharing ♻️ this post if you like it.

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.

From data privacy challenges and model hallucinations to adversarial threats, the landscape around Gen AI security is growing more complex every day.    The latest in Deloitte’s “Engineering in the Age of Generative AI” series (https://deloi.tt/41AMMif) outlines four key risk areas affecting cyber leaders: enterprise risks, gen AI capability risks, adversarial AI threats, and marketplace challenges like shifting regulations and infrastructure strain.    Managing these risks isn’t just about protecting today’s operations but preparing for what’s next. Leaders should focus on recalibrating cybersecurity strategies, enhancing data provenance, and adopting AI-specific defenses.   While there’s no one-size-fits-all solution, aligning cyber investments with emerging risks will help organizations safeguard their Gen AI strategies — today and well into the future. 

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

This is a pivotal time for business leaders to apply strategic foresight and systems thinking. Go beyond tariffs and stock market trends and consider the broader, longer-term impacts: 1. How might a trend toward AI deregulation in product safety affect the AI products my business relies on? 2. In what ways could shifts in immigration policy influence my workforce strategy for maintaining a competitive edge with emerging technologies? How could these policies reshape PhD talent pipelines? 3. How will evolving U.S. geopolitical relationships impact my third-party suppliers and global partnerships? 4. With the increasing influence of techno-politics, what new considerations emerge for my business strategy? Scenario planning is key in moments of change and uncertainty.

🌾#TeachMeTuesday 🌾 Why do some agricultural innovations fail to boost productivity in developing countries? A recent NBER working paper by Jacob Moscona at Massachusetts Institute of Technology and @Karthik Sastry Princeton University sheds light on the concept of "inappropriate technology" in global agriculture. Key Insights: 🌾Innovation Bias: Agricultural technologies are often developed in high-income countries, tailored to their specific environmental conditions. 🌾Ecological Mismatch: When these technologies are applied in different ecological settings, especially in developing countries, their effectiveness diminishes due to differences in pests, pathogens, and climate. 🌾Productivity Gap: This mismatch accounts for approximately 15-20% of the differences in agricultural productivity between countries. Implications: 🌾Localized R&D: There's a pressing need for research and development that considers local environmental conditions to ensure technologies are appropriate and effective. 🌾Policy Considerations: Policymakers should be concerned to spur localized R&D and tech transfer as possible, or allow for sufficient "localization" of technologies on the international market. I like the work because it lends evidence to an argument that has existed for many years if not decades. Compare also to "Global Innovation Index 2017 Innovation Feeding the World" https://lnkd.in/e6DPYSVt. As we strive for global agricultural advancement, understanding and integrating local ecological factors into technology development is crucial. 📖 For a deeper dive into the study, explore the full paper here: https://lnkd.in/eW3Naqaj

Recently I came across a wonderful article: “Playing it safe is the riskiest career move.” That last point in that article hits home: "Growth rarely comes from staying safe." So true. In my career, I've found that the most significant leaps forward came not from one giant, reckless gamble, but from a series of intentional, calculated micro-risks. These are the moments that build the muscle of leadership. The post mentions taking on a struggling project for a turnaround. This resonates deeply. Early in my career, I was asked to lead a team that was struggling with morale, velocity, and quality. The safe move would have been to apply incremental fixes. The micro-risk was to bet on a complete cultural and operational transformation. We introduced Agile/DevOps from the ground up (agile methodology was in its early days at that time), restructured teams into empowered units, and fostered a culture of radical transparency and accountability. It was uncomfortable and challenging existing norms. The payoff? We transformed it into a high-performance unit, delivering a product recognized globally, while reducing voluntary attrition to a negligible level. Another micro-risk that has paid dividends is "Hiring people smarter than you." As a leader, your success is multiplied by the strength of your team. At another organization, while building a 150+ member Product Engineering team from scratch, I consciously hired domain experts in Data Science, Cloud Architecture, and Product Management who were far more knowledgeable in their specific fields than I was. This wasn't about ego; it was about assembling the best possible team to incubate and commercialize an award-winning platform, which went on to generate significant revenue. Their expertise elevated the entire organization. Finally, "Speaking up with a contrarian point of view" is a risk that demands courage but builds credibility. In executive meetings, challenging the prevailing strategy with data and a well-articulated alternative vision might feel risky, but it’s often the catalyst for breakthrough innovation. This approach has been key in roles from large organizations to advising startups, where asking "what if?" has helped pivot strategies toward greater impact. The compound effect of these micro-risks is a career defined not by safety, but by transformative growth and tangible impact. What’s a micro-risk you’ve taken that paid off? I’d love to hear your stories in the comments. #CareerGrowth #Leadership #MicroRisks #ProfessionalDevelopment #Transformation

Why hierarchy kills innovation (and what to do about it) We've all been in that meeting. The one where the leader says, "I want everyone to speak freely. No bad ideas. Challenge me." Then silence. Later, in the hallway, you hear what people actually think. The real concerns. The better ideas. The warnings that could have saved the project. This is the meeting after the meeting. And it's killing your transformation. Here's what's really happening. It's not that people are disengaged or resistant. In hierarchical organizations, speaking up carries real perceived risk. Will this cost me credibility? Will I be seen as difficult? Will it hurt my chances at promotion? Behavioral science calls this loss aversion. We weigh potential losses far more heavily than potential gains. And in a hierarchy, those losses feel very real. Then there's ambiguity aversion. When transformation paths are unclear, when no one can say exactly how an experiment will turn out, people freeze. Not because they're lazy. Because humans prefer known risks over unknown ones. But there's a third barrier my research identifies: commitment aversion. Even when people have ideas and see a path forward, they hesitate to commit. Why? Because commitment feels like a trap. In fast-changing transformations, locking in early can mean being wrong publicly. So people hedge. They keep options open. They wait for certainty that never comes. This isn't indecision—it's self-protection. The typical response is to preach psychological safety. But even a psych safety project alone isn't enough. People need more than reassurance. They need structure. What actually works? Three things. First, simple, repeated routines that signal "this is how we speak up here." When voice becomes normalized, like a weekly session where young people take turns reporting, the social stakes drop. It's no longer a brave act—it's just what we do. Second, a bounded, authorized space to try things. Clear parameters. No ambiguity about what's allowed. People need to know where the guardrails are before they'll move. Third, capturing what works and turning it into templates, playbooks, and checklists. This transforms isolated wins into something the whole organization can learn from. Here's the catch. None of this works if it feels manipulative. If people sense they're being managed rather than genuinely empowered, they shut down. This is why leader credibility must be established up front. Digital transformation isn't really about technology. It's about designing environments where action feels safe. Where people don't have to check their survival instincts at the door. #ESAmentor #Leadership #DigitalTransformation #Innovation #BehavioralScience #CultureChange

Beyond Technology - Addressing Emerging Threats with Security by Design In the rapidly evolving digital landscape, relying solely on technical security measures is no longer enough. Recent incidents, like a finance employee being tricked into transferring $25 million through deepfake technology, highlight the urgent need for a comprehensive approach to cybersecurity. My latest article dives deep into why Security by Design must be applied to processes and not just systems. I’ll explore the inherent insecurities in widely used technologies like email and video meetings, and how emerging AI technologies are amplifying these risks. 🔑 Key Takeaways: - Shared Responsibility: Security is not just the responsibility of IT; every manager plays a crucial role. - Avoiding False Confidence: Quick technical fixes can create a false sense of security. Real security requires addressing underlying vulnerabilities. - Practical Steps: Implementing non-technical measures such as verification protocols and regular training can significantly mitigate risks. #SecurityByDesign #CyberSecurity #DeepFakes #SocialEngineering #ProcessSecurity #Leadership #DigitalTransformation

"this toolkit shows you how to identify, monitor and mitigate the ‘hidden’ behavioural and organisational risks associated with AI roll-outs. These are the unintended consequences that can arise from how well-intentioned people, teams and organisations interact with AI solutions. Who is this toolkit for? This toolkit is designed for individuals and teams responsible for implementing AI tools and services within organisations and those involved in AI governance. It is intended to be used once you have identified a clear business need for an AI tool and want to ensure that your tool is set up for success. If an AI solution has already been implemented within your organisation, you can use this toolkit to assess risks posed and design a holistic risk management approach. You can use the Mitigating Hidden AI Risks Toolkit to: • Assess the barriers your target users and organisation may experience to using your tool safely and responsibly • Pre-empt the behavioural and organisational risks that could emerge from scaling your AI tools • Develop robust risk management approaches and mitigation strategies to support users, teams and organisations to use your tool safely and responsibly • Design effective AI safety training programmes for your users • Monitor and evaluate the effectiveness of your risk mitigations to ensure you not only minimise risk, but maximise the positive impact of your tool for your organisation" A very practical guide to behavioural considerations in managing risk by Dr Moira Nicolson and others at the UK Cabinet Office, which builds on the MIT AI Risk Repository.