Finding The Right Balance Between Risk And Innovation

We have one rule in my org for evaluating tech projects called "The One Miracle Rule" 🔮 When assessing any complex initiative, map out every critical element: technical challenges, resource needs, timeline constraints, team dependencies, and organizational changes. Here's the rule: You get ONE "miracle" - ONE major unknown you'll need to solve along the way. That's your innovation space. But if you need multiple miracles (like inventing novel ML features/signals AND building unprecedented infrastructure AND collaborating with two or more separate orgs), it's time to pivot: "Neat idea, maybe when it's a one-miracle project..." Why this works: Innovation thrives on pushing boundaries, but execution demands pragmatism. One miracle? That's ambitious yet achievable. Multiple miracles? That's where I've seen too many projects spiral into missed deadlines and burned-out teams. The most successful projects I've led weren't necessarily the most ambitious - they were the ones that found that sweet spot between innovation and realistic execution paths. Interesting twist: In today's LLM era, many previous "miracles" in NLP have become "just" difficult engineering challenges. But productionizing these capabilities at scale? That might still count as your one miracle, depending on your requirements. I'm aiming for those LLM productionization to become "business as usual" in my team.

When uncertainty looms, innovation teams are at risk of being on CFO’s chopping block. Most recently, I joined a half-day roundtable with an outstanding group of corporate innovators, convened by Peter Temes at the ILO Institute during which we tackled this pressing reality and paradox: Companies invest in innovation during good times... but they NEED it most during uncertain ones. This plays out in two ways: 🚫 The First Camp: Slashes innovation budgets at the first sign of trouble. "We’ll restart when things stabilize," they promise. By the time stability returns, competitors have already leapt ahead. 🤦♂️ The Second Camp: Keeps innovation teams intact—but strangles their impact. ROI on experiments must be immediate. Quarterly returns on long-term bets. Zero tolerance for the failures that actually drive learning. I’ve seen both—sometimes inside the same company. The result? Innovation teams lose morale. The best talent disengages—or walks. Stakeholders pull support. A "one-and-done" mindset kills promising ideas before they can grow. 💡 Look at financial services. They came late to the internet, mobility, and social media. Now they’re risking the same mistake with AI, ceding direct customer relationships to fintechs and risking relegation to utility status. Why does this cycle persist? Because the short-term savings of cutting innovation are immediately visible. The long-term catastrophe is invisible... until it's too late. 🔥 Here’s how to keep innovation alive when budgets tighten: 1️⃣ Dramatically lower the cost of individual experiments 2️⃣ Prioritize customer-backed innovation for real-time feedback 3️⃣ Create distributed innovation networks across the org 4️⃣ Speed up cycles by challenging slow status quo processes 5️⃣ Position innovation as risk management, NOT risk-taking ⏳ Don’t let uncertainty kill your company’s future. The best organizations don’t innovate despite uncertainty. They innovate because of it. 🚀 Innovation isn’t a luxury—it’s a lifeline. Julie Fishman, Alex Trotta, Miles Garrett, Andy Grove, Anthony Di Bitonto, Kate Pomeroy (née Stubbs) #innovation #leadership #learning

Risk Aversion and Status Quo For nearly a year, I’ve been using Tesla’s Full Self-Driving (FSD) supervised mode, and as someone who works on large-scale software systems, I can’t help but admire the balance Tesla strikes between innovation and safety. FSD is far from perfection. One can easily spot problems here and there but one can also see its rapid progress in each iteration. What impressed me is not what it can do now but what FSD can be in 3 years with this innovation speed. Institutions reaching stability often drift into risk-averse habits. New ideas, even compelling ones, face a deluge of objections: “Is it worth it?” “What if it fails?” These concerns, while legitimate, often lead to endless deliberation. Phrases like “let’s think it through” become euphemisms for “let’s not do it.” Risk aversion becomes the default, and the easiest path forward is to repeat what has worked before. Opening 10 more Starbucks locations or rolling out the same software stack in one more region feels safe, familiar, and justifiable. Over time, even ambitious new hires adopt this cautious mindset, prioritizing “sameness” over bold changes. Tesla’s approach, by contrast, thrives on informed risk-taking. Their work on FSD exemplifies a culture that embraces experimentation and iterative improvement without compromising safety. Instead of shelving bold ideas due to potential downsides, Tesla deploys features incrementally, gathers real-world data, and refines relentlessly. This cycle allows for rapid evolution while addressing the risks inherent in such an ambitious project. Contrast this with traditional automakers like GM, which pioneered electric vehicles with the EV1 in the 1990s but abandoned the effort to protect gas-powered revenues. Their risk aversion effectively ceded the EV market to newer players. Now, these same companies are playing catch-up as Tesla reaps the rewards of bold decisions made years ago. The difference lies in how these organizations perceive risk. Tesla understands that avoiding risks entirely is itself the riskiest strategy in the long term. Its leadership fosters a culture where calculated risks are encouraged, not stifled. Every iteration of FSD represents a willingness to tackle complex challenges that others might deem “too risky” to pursue at scale. This mindset is a lesson for all of us: the pursuit of innovation requires a conscious decision to prioritize growth over comfort. It’s easier—and often safer in the short term—to defend the status quo. But in the long run, this safety can lead to stagnation. Risk aversion might protect today’s success, but it’s bold, well-informed decisions that create tomorrow’s breakthroughs. Tesla’s journey with FSD is a reminder that transformative innovation doesn’t come from avoiding risks—it comes from managing them with vision and courage. For any leader or organization, the challenge isn’t just to ask, “What if we fail?” but also, “What if we don’t even try?”

Enterprise AI: Strategy & Executive Alignment Post topic: How to Balance Innovation with Risk in Enterprise AI Every enterprise wants to innovate with AI. But few are prepared for the governance, safety, and trust challenges that come with it.   The tension is real: Move too fast: you risk compliance, hallucinations, or reputational damage Move too slow: you fall behind in productivity, automation, and employee expectations   ** Here’s how leading organizations are finding the balance:   🔹Create AI sandboxes for safe experimentation Let teams build and test in controlled environments with pre-approved models, tools, and data.   🔹Shift governance left Bring legal, risk, and compliance teams into the design phase, not just the launch gate. If AI is embedded early, approval isn’t a blocker—it’s a partner.   🔹Start with low-risk, high-value use cases Summarization, retrieval, classification, internal copilots—these give you room to learn without high stakes.   🔹Define escalation paths for high-risk use If AI is making decisions that affect people, money, or compliance—add human review, audit logs, and explanation layers.   🔹Build a culture of shared accountability Innovation and risk shouldn't sit in silos. Create cross-functional AI councils or tiger teams that bring everyone to the table.   Responsible AI isn’t about saying “no.” It’s about creating the conditions for scalable, trusted innovation.   💬 What’s helped you balance speed and safety in your AI efforts?   Next up in the #EnterpriseAI series: Architecture & Infrastructure - Choosing the Right LLM Stack for Your Enterprise (OpenAI, Azure OpenAI, Anthropic, open source)   #EnterpriseAI #AIInnovation #ResponsibleAI #AIGovernance #AIAdoption #DigitalTransformation #GenAI #AICompliance #AIEthics #EnterpriseIT   Antonio Grasso Antonio Figueiredo Faisal Khan Dr. Ludwig Reinhard Rakesh Darge Fauzia I. Abro Adithyaa Vaasen Arnav Kulkarni Aditya Ramnathkar Richard Sturman Phil Fawcett Thorsten L. Taysser Gherfal Sagar Chandra Reddy Faisal Fareed Andy Jiang Khaliq Malik