Hemant Taneja on AI adoption: 4 must-haves and common pitfalls

Stat 1: 98% of companies feel a growing urgency to implement AI. Stat 2: Only 13% are truly ready for it. Stat 3: 72% of manufacturers have already reduced costs thanks to it. The question is inevitable: Are we facing the biggest investment—and the biggest risk—of the decade? Burning the midnight oil with the MIT Technology Review a clear pattern on industry pain points emerges. A few are succeeding, and everyone else is scrambling to jump on the bandwagon. The pressure is immense: investors see high returns and want to replicate them. The CEO, under pressure, drives the urgency. The team, pushed to deliver yesterday, crafts perfect PowerPoints and steep hockey-stick projections as they rush to announce innovative projects. But in this race, two key questions are being ignored: What for? and How will we operationally assimilate it? I always say this: you can have the best AI in the world, but if there's no plan to integrate it into your actual operations, your people, and your processes, it's worthless. And from my perspective, there is no greater MUDA (waste) than a client's money invested in something that generates ZERO real value. AI isn't software you just "install." It's a paradigm shift that demands: ➡️ Clear Strategy: What REAL problem are we solving? Not what problem we could invent. ➡️ Infrastructure Readiness: Can our current foundations support this new structure? ➡️ Operational Assimilation: How will this change our team's daily workflow, and how will we measure success? Strategy and operations, You can't have one without the other. ...Good thing I use AI to polish my posts. What's your take? Let me know your thoughts in the comments. 👇 #ArtificialIntelligence #AI #OperationalStrategy #LeanManufacturing #DigitalTransformation #Muda #Efficiency #Leadership #ROI

I remember the first time I watched an AI model confidently spit out an answer that was… completely wrong. My heart sank—because that moment laid bare a truth we all know deep down: AI isn’t magic. It’s a mirror. It shows us our blind spots, our biases, our impatience. Embracing doubt revealed the hard truth: AI reflects our blind spots more than it unlocks genius—and that’s exactly why 95% of pilots fail. To join the elite 5%, leaders follow a clear playbook: 1) define your AI Delta—the gap between current enterprise value and the uplift a holistic AI strategy can unlock, or the value at risk if you mis-execute. L.E.K. research shows successful transformations drive a 19% valuation increase, while missteps can cut 9% off your worth. 2) anchor every initiative in value-creation levers, not model complexity: - Performance: Focus on high-volume, rules-based workflows where AI slashes costs or accelerates revenue. - Competition: Enhance differentiators—hyper-personalized journeys, predictive supply chains. - Opportunities: Monetize data via new products, platforms, or services. 3), pursue an integrated data + AI strategy. Avoid “scrape, hoard, and pray.” Build a queryable data network with clear ownership and governance before training your first model—preventing data debt and costly back-end rebuilds. 4), start with quick-win pilots that solve real user pain—whether saving four hours of manual work, speeding claims adjudication, or closing a sales gap—and deliver measurable ROI in 6–12 weeks. Those wins fund and de-risk the next waves while building momentum. 5), govern AI like any strategic investment: board-level oversight, KPIs defined as ROI ranges rather than point estimates, and an iterative scaling roadmap balancing ambition with guardrails. In practice, scope one pilot in a core value chain, articulate a clear value hypothesis (e.g., 15–20% cycle-time reduction, $5 M uplift), map data and tech enablers, and co-design with end users. Measure impact, capture lessons, and scale only the highest-ROI use cases. That is how you beat the 95% and join the 5%. #AIstrategy #ValueCreation #DataGovernance #QuickWins #ResponsibleAI

𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲’𝘀 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 - 𝘄𝗵𝗼’𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝗶𝘁 𝗳𝗼𝗿 𝘀𝗰𝗮𝗹𝗲? At the Spendflo AI Summit, we’re bringing together two powerhouse voices in pricing and finance to answer this: Madhavan Ramanujam, General Partner at 49 Palms Ventures & Author Todd Rakow CFO at Gladly Together, they’ll unpack how leaders can turn pricing into a growth engine in the AI era. From dual-engine growth to outcome-based pricing, this session is packed with insights that every founder, CFO, and product leader should hear. Here’s what we’ll unpack live: - What’s the #1 misconception founders have about pricing today? - Which AI pricing models signal long-term sustainability and which are red flags? - How can CFOs protect the 20% of features that drive 80% of willingness to pay? - What separates pricing leaders in the shift to outcome-based models? October 16 | Online RSVP: https://lnkd.in/gh8_Zde3 Siddharth Sridharan Rajiv Ramanan Nivas Ravichandran Disha Tyagi Madhu Mitha Thaha Osman Adithiya Shiva

"CEOs of big US companies are more confident their AI investments will pay off, a KPMG survey found. Nearly 70% of CEOs expect AI returns within one to three years, compared with 21% of CEOs in 2024". From Business Insider. I'm circling this one like my dog circles a particularly nasty mess on the sidewalk. So, several things must have happened over the past year: 1) CEO's have suddenly understood exactly what AI can (and cannot) do for their businesses. 2) They have seen a way through the excessively high AI failure rates that McKinsey, MIT and others talk about. 3) They've done the murky math 4) They have cleaned up their data ... and by clean, I mean trusted and contextualized. 5) They have a clear plan for bringing their customers, employees and partners along for the ride. I have to go think about this a bit ... roll on cocktail hour! Happy Wednesday!

Every time I hear the question “What’s our [emerging tech] strategy?”, I confess it triggers me. Because I believe there is a better question that we could ask… Over the last decade, we’ve seen waves of emerging tech: mobile, cloud, data, AI, blockchain, AR/VR (do you remember the metaverse?!) edge, quantum, and now autonomous agents. Yet, too often, companies build strategies for the technology instead of around the business model shift it enables. Here is my contribution to what I think is a better question: 👉 How could [emerging tech] help us to redefine how we create, deliver, or capture value? The original question leads to pilots and roadmaps. This new, reframed one leads to reinvention… Boards and executives who ask the second one might be getting a different outcome, actually I have stories to share on its effectiveness. Thanks Regis Haegler for the post below, you are always insightful. HITEC Angeles Investors The Tech Series Latino Corporate Directors Association (LCDA) NACD (National Association of Corporate Directors) Insight Partners

𝐓𝐡𝐞 𝐧𝐞𝐱𝐭 𝐞𝐝𝐠𝐞 𝐢𝐧 𝐀𝐈 𝐢𝐬𝐧’𝐭 𝐬𝐢𝐳𝐞. 𝐈𝐭’𝐬 𝐝𝐢𝐬𝐜𝐞𝐫𝐧𝐦𝐞𝐧𝐭. We’ve hit the point where “bigger model, bigger spend” isn’t the smartest move. The leaders I’m watching win aren’t chasing params—they’re making cleaner choices. 𝐌𝐲 𝐫𝐮𝐥𝐞: don’t outsource discernment to the model. Build it into the team. Here’s a simple AI Decision Filter we use before a single token is spent: ✔️ Problem clarity: What decision improves if this works? (Name it.) ✔️ Outcome over output: What’s the measurable lift we expect? (Time saved, risk reduced, revenue unlocked.) ✔️ Smallest thing first: What’s the minimum viable model (or even heuristic) to prove value? ✔️ Data reality check: Do we have trustworthy, permissioned data to support this? ✔️ Human-in-the-loop: Where must judgment stay human—and why? ✔️ Safety & drift: What guardrails/monitoring will keep it useful tomorrow, not just today? ✔️ Kill criteria: What’s the pre-agreed line where we stop? Quiet is a feature here. A 30-minute slow-thinking ritual each week—naming the decision, the data, and the kill criteria—saves months of thrash and a lot of budget theater. Proof > polish. Ship a 2–4 week pilot, score it against the expected lift, then choose: scale, fix, or kill. No heroics—just clarity. If you’re leading AI work this year, your edge is less model selection and more attention selection. 𝐂𝐮𝐫𝐢𝐨𝐮𝐬: Which question in the filter would change the most decisions on your team? #AI #Leadership #Product #LLM #MLOps #Data #Strategy

Your AI Passed the Demo. Now It Has to Pass Thanksgiving Dinner. Last quarter, three CEOs asked me the same question in different words: "Why does our AI feel brilliant but fragile?" Translation: "Why can I explain our revenue model to my board, but not how our algorithms make decisions?" Here's the uncomfortable truth: Most organizations treat governance like a prenup - necessary, awkward, something you deal with before the fun stuff. But the companies printing money right now? Their governance is foreplay. It's not slowing down innovation. It's the reason innovation can run at full speed without exploding. At Airbnb and Ericsson, I watched the same pattern play out: 🔹Compliance issues didn't drop because we added more rules, they dropped because we taught data scientists, ML engineers and lawyers to speak the same language. 🔹Data privacy went from cost center to profit center. Companies that make AI explainable make it profitable. The ones that don't? They're building technical debt they'll pay back in congressional hearings. Here's the mental model shift: ❌ Old question: "How do we control AI?" ✅ New question: "How do we make AI accountable in real time?" When your exec team can see - on one dashboard - where risk lives, what ROI looks like, and what's shipping next, decision velocity explodes. That's not governance theater. That's compound trust at scale. The 2030 litmus test: 🔹The winners won't be the ones with the most compute. 🔹They'll be the ones where trust compounds faster than complexity. 🔹Where AI systems get smarter, safer, and more profitable with each iteration. 🔹 Where you can explain your algorithm to a regulator, a board member, and your mom - and all three nod. If you're building systems where intelligence and integrity aren't competing priorities but the same architecture, let's talk. Because the next era belongs to leaders who can operationalize trust as precisely as they deploy transformers. What's the biggest trust gap in your AI systems? 🔹 Data integrity 🔹 Explainability 🔹 Executive clarity #AIGovernance #ResponsibleAI #TrustAtScale #AILeadership #TechStrategy #WomenInTech

Feeling stuck on what to actually do with AI? You’re not alone. Over the past few months, I’ve spoken with leaders from all walks—solopreneurs, nonprofits, mid-market firms, and enterprise organizations. The same concerns keep surfacing: “Where do we even start?” “What if we pick the wrong tools?” “Is this automation, AI, or something else?” “How do we know it will actually help us—not just create more busy work?” "How do we ensure privacy and compliance is adhered to?" "Our data is a mess... how do we clean it up so that AI is helpful?" Here’s the truth: most organizations want to be “doing AI,” but very few are actually implementing it well. Some are experimenting (Copilot, meeting summaries, automation workflows, etc). Others are overwhelmed by the pace of change. Almost all are asking: How do we make this real for our business? -The good news: you don’t need a moonshot to start. Many of the tools you already pay for have AI baked in—you just aren’t using them to their full potential. -The challenge: waiting too long means you’ll fall behind. The companies that are investing this year in data readiness, workflows, and AI planning will be lightyears ahead by 2026. 2026 might as well already be here. But the groundwork needs to be laid now...policies, governance, training, use cases, and a real strategy that matches your business goals. Next year will belong to the organizations that treat AI as more than a curiosity. The question isn’t “Should we?” anymore. It’s: “What’s our AI roadmap, and how do we start executing it—today?” If you don’t have that roadmap yet, make it your top planning priority for 2026. If you want help with this, shoot me a DM and let's get after it.

Four years ago in Expansión I wrote about an invisible enemy in organizations: decision noise—unwanted variability in judgments that should be identical. The message aged… almost too well. What’s changed since 2021? The stakes. AI isn’t a pilot anymore; it now shapes product, risk, pricing, and ops. Decision volume exploded and so did new noise: model drift, inconsistent prompts, fragmented data, last-mile overrides, rushed governance. What hasn’t changed: the cure. ·      Treat decisions like products. Design them, instrument them, and own the outcomes. ·      Measure bias and noise. Know the difference and track both. ·      Automate the repeatable. Keep humans for the truly unique calls. ·      Track the “noise tax.” In pricing, underwriting, performance, forecasts: measure dispersion, close gaps, audit with AI copilots. ·      Build an experiment culture. Test, learn, and improve. Every week. In our work with global leaders, the winners industrialize decision-making: clear workflows, trusted data, governed AI, and people trained to challenge judgment—starting with their own. I’m resharing that 2021 piece because the thesis is no longer a nice-to-have; it’s a fire alarm. The conclusion hasn’t changed. Only the cost of ignoring it: less noise = more P&L, more speed, less rework. You can find the article in english in the comments section below.

Your weekly reminder: know that data problem you’ve been ignoring? Yeah… it’s not going anywhere. You can’t deliver on your AI ambitions without sorting your data first. And every new use case you dream up? It begins and ends with data! You don’t have to fix everything, everywhere, all at once but you do have to start somewhere BECAUSE there’s no organisational transformation without data transformation. Gartner says 75% of data leaders who don’t make themselves indispensable to AI success will lose their seat at the table. So yeah… might be worth sorting that out. Show your data some love instead of treating it like a problem AND it’ll love you tenfold back ❤️