Innovation Risk Management

It’s easy as a PM to only focus on the upside. But you'll notice: more experienced PMs actually spend more time on the downside. The reason is simple: the more time you’ve spent in Product Management, the more times you’ve been burned. The team releases “the” feature that was supposed to change everything for the product - and everything remains the same. When you reach this stage, product management becomes less about figuring out what new feature could deliver great value, and more about de-risking the choices you have made to deliver the needed impact. -- To do this systematically, I recommend considering Marty Cagan's classical 4 Risks. 𝟭. 𝗩𝗮𝗹𝘂𝗲 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗦𝗼𝘂𝗹 𝗼𝗳 𝘁𝗵𝗲 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 Remember Juicero? They built a $400 Wi-Fi-enabled juicer, only to discover that their value proposition wasn’t compelling. Customers could just as easily squeeze the juice packs with their hands. A hard lesson in value risk. Value Risk asks whether customers care enough to open their wallets or devote their time. It’s the soul of your product. If you can’t be match how much they value their money or time, you’re toast. 𝟮. 𝗨𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗨𝘀𝗲𝗿’𝘀 𝗟𝗲𝗻𝘀 Usability Risk isn't about if customers find value; it's about whether they can even get to that value. Can they navigate your product without wanting to throw their device out the window? Google Glass failed not because of value but usability. People didn’t want to wear something perceived as geeky, or that invaded privacy. Google Glass was a usability nightmare that never got its day in the sun. 𝟯. 𝗙𝗲𝗮𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗔𝗿𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗣𝗼𝘀𝘀𝗶𝗯𝗹𝗲 Feasibility Risk takes a different angle. It's not about the market or the user; it's about you. Can you and your team actually build what you’ve dreamed up? Theranos promised the moon but couldn't deliver. It claimed its technology could run extensive tests with a single drop of blood. The reality? It was scientifically impossible with their tech. They ignored feasibility risk and paid the price. 𝟰. 𝗩𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗠𝘂𝗹𝘁𝗶-𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹 𝗖𝗵𝗲𝘀𝘀 𝗚𝗮𝗺𝗲 (Business) Viability Risk is the "grandmaster" of risks. It asks: Does this product make sense within the broader context of your business? Take Kodak for example. They actually invented the digital camera but failed to adapt their business model to this disruptive technology. They held back due to fear it would cannibalize their film business. -- This systematic approach is the best way I have found to help de-risk big launches. How do you like to de-risk?

Most projects fail. But there’s a simple technique to give yours a fighting chance. It’s not a to-do list. It’s not a fancy tool. It’s not a 12-step system. It’s a single question that flips the way you think. Here’s how it works: It’s called a “premortem.” You’ve heard of a postmortem what went wrong after a project dies. A premortem asks: What if we ran that analysis now? Before anything dies. Before the first misstep. Before failure sets in. The premortem comes from psychologist Gary Klein. Here’s how to run one: → Gather your team. → Imagine it’s 2 years in the future. → The project has completely failed. → Ask: What went wrong? No sugarcoating. No happy talk. Start listing the causes of failure. Budget misfire? Wrong team? Lack of buy-in? Scope creep? Missed deadlines? You’ll be shocked how quickly people identify risks—once they feel safe predicting failure. Why this works: It defeats irrational optimism. • It turns hindsight into foresight. • It makes risk visible. • It aligns the team before chaos hits. Because the best time to fix a problem… is before it happens. Pre-mortems don’t require special skills. Just a shift in mindset: Don’t assume success. Assume failure—and reverse-engineer your way out. Ask: What will future-you wish you had done? Then… do that now. I run a premortem for every big project I take on. Writing a book? Premortem. Launching a podcast? Premortem. Planning an event? Premortem. It never guarantees success—but it always makes success more likely. Summary: The Premortem Playbook → Imagine future failure. → List the causes. → Turn those risks into action steps. → Adjust your plan today. It’s one of the most underrated tools in your productivity toolkit. Try it before your next project. You won’t regret it.

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. 

🚗 Imagine this: You launch a new car model after years of effort. Production is smooth, the assembly line is world-class… but six months later, the headlines scream “Massive Recall.” Billions lost. Reputation damaged. All because of a design flaw that was locked in during the product development phase. Takao Sakai once said: 👉 “95% of Toyota’s profits are determined in the product development phase, not production.” And it’s true across industries: In aerospace, material choices made at the design table decide 80% of lifecycle costs. In electronics, overengineering features adds cost but not value. In manufacturing, late design changes cause delays that no production efficiency can recover. ⚡ The real challenge? Most companies pour their energy into fixing problems on the shop floor instead of preventing them during development. 💡 The smarter way: Apply Design for Manufacturability (DFM) & Concurrent Engineering. Run early simulations & prototypes to detect risks. Involve quality, supply chain, and production teams at the concept stage. Use Voice of Customer (VOC) to cut out features no one wants but everyone pays for. The truth is simple: ✅ Every mistake caught in design costs a fraction of fixing it in production. ✅ Every smart decision in development compounds into long-term profit. 🔑 What’s one thing your team does during product development that safeguards future profitability? 👇 Share your experience—it might spark ideas for someone else! #Lean #ProductDevelopment #DesignThinking #Innovation #BusinessExcellence #Quality #TQM

6 months of free work if I failed. A deal most would walk away from—but I accepted, negotiated, and turned it into a growth opportunity. A client I’ve worked with for 2 years approached me with a bold proposal: "Hit these milestones in 6 months, or work for the next 6 months for free." At first, it sounded like an all-risk, no-reward situation. But instead of rejecting it outright, my team and I took a strategic approach. Here’s how we made it work: Out of the 3 milestones, 2 were challenging but achievable with the right execution. The third was completely unrealistic—not even 50% feasible. So we negotiated. We made it clear that goals must be realistic and measurable for success to be possible. The client agreed. But we didn’t stop there. We took control: 📌 We developed a brand-new strategy before the client even asked—to ensure we were set up for success. 📌 We added a key condition: If we delivered, he would provide 2 high-value referrals. This secured a long-term business benefit for us. 📌 We made sure the entire team was aligned, so we weren’t just taking a risk—we were making a calculated decision. The outcome? - The client was so impressed that he doubled our future fees as the project demanded double efforts too! - We’ve been working on this project for just over a month, and we’re already exceeding expectations. - This challenge is pushing us to be more creative, more strategic, and more confident. Key lessons for service providers: 1. Always evaluate before saying yes. Even high-risk deals can be turned into win-win situations with proper strategy. 2. Negotiate terms that protect your upside. Future business, referrals, or bonuses—always think about what’s next. 3. Have a solid plan before committing. We created a strategy before the client even asked—this positioned us as trusted advisors, not just service providers. 4. Clients pay for expertise, not just time. The right clients understand that great execution requires great investment. Would you take on a challenge like this? How do you handle high-stakes deals in your business? #linkedin #leadgeneration #linkedinmarketing

Project #Risk isn’t a number... It’s a #Conversation. Too often, risk is reduced to a number in a spreadsheet — a probability, a percentage, a cost impact. But projects rarely fail because of numbers; they fail because the underlying risks were never surfaced, understood, or addressed in time. Every project has moving parts — land, design, execution, finance, sales — each with its own uncertainties. Mapping risks across these dimensions is not just an exercise in control, it’s an open conversation among stakeholders. When done well, it creates shared visibility: what might go wrong, what it could cost us, and what we’ll do about it. That #dialogue is what prevents overruns, both of cost and time. Numbers may quantify risk, but conversations institutionalize #resilience. In the end, successful projects aren’t those that avoided risk, but those that acknowledged it early, shared it openly, and acted on it decisively. #RiskManagement #ProjectExcellence #LeadWithImpact

If you’re leading AI initiatives, here is a strategic cheat sheet to move from "𝗰𝗼𝗼𝗹 𝗱𝗲𝗺𝗼" to 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘃𝗮𝗹𝘂𝗲. Think Risk, ROI, and Scalability. This strategy moves you from "𝘄𝗲 𝗵𝗮𝘃𝗲 𝗮 𝗺𝗼𝗱𝗲𝗹" to "𝘄𝗲 𝗵𝗮𝘃𝗲 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝘀𝘀𝗲𝘁." 𝟭. 𝗧𝗵𝗲 "𝗪𝗵𝘆" 𝗚𝗮𝘁𝗲 (𝗣𝗿𝗲-𝗣𝗼𝗖) • Don’t build just because you can. Define the Business Problem first • Success: Is the potential value > 10x the estimated cost? • Decision: If the problem can be solved with Regex or SQL, kill the AI project now. 𝟮. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗼𝗳 𝗼𝗳 𝗖𝗼𝗻𝗰𝗲𝗽𝘁 (𝗣𝗼𝗖) • Goal: Prove feasibility, not scalability. • Timebox: 4–6 weeks max. • Team: 1-2 AI Engineers + 1 Domain Expert (Data Scientist alone is not enough). • Metric: Technical feasibility (e.g., "Can the model actually predict X with >80% accuracy on historical data?") 𝟯. 𝗧𝗵𝗲 "𝗠𝗩𝗣" 𝗧𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻 (𝗧𝗵𝗲 𝗩𝗮𝗹𝗹𝗲𝘆 𝗼𝗳 𝗗𝗲𝗮𝘁𝗵) • Shift from "Notebook" to "System." • Infrastructure: Move off local GPUs to a dev cloud environment. Containerize. • Data Pipeline: Replace manual CSV dumps with automated data ingestion. • Decision: Does the model work on new, unseen data? If accuracy drops >10%, halt and investigate "Data Drift." 𝟰. 𝗥𝗶𝘀𝗸 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 (𝗧𝗵𝗲 "𝗟𝗮𝘄𝘆𝗲𝗿" 𝗣𝗵𝗮𝘀𝗲) • Compliance is not an afterthought. • Guardrails: Implement checks to prevent hallucination or toxic output (e.g., NeMo Guardrails, Guidance). • Risk Decision: What is the cost of a wrong answer? If high (e.g., medical advice), keep a "Human-in-the-Loop." 𝟱. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 • Scalability & Latency: Users won’t wait 10 seconds for a token. • Serving: Use optimized inference engines (vLLM, TGI, Triton) • Cost Control: Implement token limits and caching. "Pay-as-you-go" can bankrupt you overnight if an API loop goes rogue. 𝟲. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 • Automated Eval: Use "LLM-as-a-Judge" to score outputs against a golden dataset. • Feedback Loops: Build a mechanism for users to Thumbs Up/Down outcomes. Gold for fine-tuning later. 𝟳. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 (𝗟𝗟𝗠𝗢𝗽𝘀) • Day 2 is harder than Day 1. • Observability: Trace chains and monitor latency/cost per request (LangSmith, Arize). • Retraining: Models rot. Define when to retrain (e.g., "When accuracy drops below 85%" or "Monthly"). 𝗧𝗲𝗮𝗺 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 • PoC Phase: AI Engineer + Subject Matter Expert. • MVP Phase: + Data Engineer + Backend Engineer. • Production Phase: + MLOps Engineer + Product Manager + Legal/Compliance. 𝗛𝗼𝘄 𝘁𝗼 𝗺𝗮𝗻𝗮𝗴𝗲 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 (𝗺𝘆 𝗮𝗱𝘃𝗶𝗰𝗲): → Treat AI as a Product, not a Research Project. → Fail fast: A failed PoC cost $10k; a failed Production rollout costs $1M+. → Cost Modeling: Estimate inference costs at peak scale before you write a line of production code. What decision gates do you use in your AI roadmap? Follow Priyanka for more cloud and AI tips and tools #ai #aiforbusiness #aileadership

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

Most teams do a post-mortem after a project ends. Almost nobody does a pre-mortem before one starts. I get it. When a team is energized and excited about a new project, asking them to imagine everything that could go wrong feels like bad energy. Like you're jinxing it before it even begins. So here's how I actually do it - Before a major project kicks off (especially one with hard deadlines, complex dependencies, and a lot of moving parts), I go back through past retrospectives. The ones where things went sideways. And the ones where things went well but almost didn't. I look for the patterns. The assumptions that turned out to be wrong. The dependencies that slipped. The scope that expanded. The risks nobody named out loud until it was too late. Then I imagine we're already at the end of this new project, and it failed. What went wrong? Usually, I do this alone. Getting a team to properly commit to a retrospective is already hard enough. Getting them to imagine failure while they're excited about starting is almost impossible. So I don't always ask them to. I bring the insights in quietly, fold them into the plan, and let the team focus on building. On one recent project with a strict deadline and multiple workstreams, this process surfaced that one particular path was significantly riskier than the others. We made a decision early to scope it as a nice-to-have rather than a committed deliverable. That path didn't make the deadline. The project did. Almost a decade of launching products at Google has taught me that the best risk management doesn't happen in a document. It happens in the quiet moment before the project kicks off, when someone is willing to ask: What are we not seeing yet?

The Trade-Off Between Innovation and Security: A Lesson from AI and Phishing Scams Singapore’s Budget 2025 introduces new financial initiatives, and within hours, phishing scams on Telegram (like the one on the image) are exploiting it. These scams are becoming more automated, sophisticated, and convincing—a stark reminder of how AI can be used for both progress and harm. The rise of open-source AI models like DeepSeek and Llama poses a similar dilemma. Unlike OpenAI’s ChatGPT, which is closed-source, these models allow anyone to fine-tune and modify them. This openness accelerates innovation and collaboration—but also enables misuse. Just as scammers adapt AI to impersonate governments and businesses, bad actors can train AI for large-scale disinformation, deepfakes, and phishing attacks. So where do we draw the line between open innovation and security? - Open-source AI fosters faster research and collaboration, but also makes it easier for criminals to exploit. - Stricter regulation improves safety and accountability, but risks slowing down technological progress. Governments, businesses, and researchers must act before AI-driven cyber threats outpace regulation. We need: ! Stronger AI governance to balance innovation with responsibility. ! Smarter cybersecurity measures that anticipate AI-driven scams. ! Better public education to help people recognize and resist AI-powered fraud. The same technology that drives progress can also be weaponized. How do we ensure AI remains a force for good? #AI #Cybersecurity #AIGovernance #AISingapore #SGBudget2025 #DigitalTrust