Jennifer Perret

Read about the mission and the purpose for the startup of the Institute for Infrastructure and Interoperable Data in Learning in our first white paper. "The institute will assume stewardship and maintenance of essential TLA-related open source technologies and conformance systems. It will also reconstitute elements of the research capacity previously led by ADL." #Interoperability #DataStandards #LearningTechnology https://lnkd.in/exNYjsYj

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Stop Saying “95% of AI Projects Fail” (and Why the viral stat is misleading—and what it really tells us about AI adoption) “Despite $30–40 billion in enterprise investment into GenAI, this report uncovers a surprising result: 95% of organizations are getting zero return.” — MIT/NANDA GenAI Divide, 2025 That line went viral. It’s dramatic, quotable, and fits neatly into a simple narrative: AI is failing. But as always, the truth is more complicated—and far more interesting. After hearing that statistic repeated in every conference and client meeting, I decided to apply a bit of scientific inquiry and read the report myself. What I found wasn’t a story of technological collapse, but one of expectations set impossibly high. report here https://lnkd.in/gyfP4Gwk The authors define “success” as an AI project that reaches production and delivers measurable P&L impact. Hmmm... that’s not a low bar—it’s an Olympic bar! For a technology still in version 1.0 of enterprise adoption, that’s like judging early e-commerce or mobile apps purely by whether they transformed global profit margins in their first year. If we had done that, we might have concluded that 95 % of the cloud failed too. The reality: AI isn’t failing—it’s maturing. And the report itself supports that view. Buried deeper in the findings is a clue to what’s really working: “The most effective AI-buying organizations no longer wait for perfect use cases or central approval. Instead, they drive adoption through distributed experimentation, vendor partnerships, and clear accountability.” That’s the real insight—success favors experimentation, not perfection. In my workshops and client engagements, I’m constantly urging leaders and teams to keep using every tool they can get their hands on—learning daily what works and what can improve. Organizations that learn quickly, partner strategically, and iterate relentlessly are already seeing measurable productivity gains, even if they don’t yet hit the P&L line. And those small, invisible wins? They compound. Every analyst who automates a task, every employee who drafts faster or thinks smarter—each adds a tiny efficiency gain. Over time, those micro-gains add up to massive enterprise value, even if the lines aren’t yet visible on the balance sheet. (See the 1% better rule.) The takeaway: the “95% failure” headline doesn’t prove AI is overhyped. It proves the bar for “success” was drawn too narrowly. We’re still early in the game. The teams that keep learning, iterating, and redefining what success looks like—those are the ones that will win when the score really counts.

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🔍 Rethinking Root Cause: Embracing Complexity Through Interactions and Adaptations Many are beginning to argue that in the realm of complex systems, traditional Root Cause Analysis (RCA) often falls short. Really enjoyed Lorin Hochstein's recent article, "Not Causal Chains, but Interactions and Adaptations," in which he delves into this issue, challenging the linear causality model that RCA relies upon. While acknowledging the UK Health and Safety Executive's guide for its recognition of multiple causes and the dismissal of "human error" as a sole explanation, Lorin argues that the very foundation of RCA, a sequential chain from root to immediate causes, doesn't align with the realities of complex systems. In my experience, complex systems are characterised by intricate interdependencies and unpredictable interactions. Failures in such systems rarely stem from a single cause; instead, they emerge from a complex interplay of factors that interact in unforeseen ways. Lorin suggests that focusing solely on identifying and eliminating root causes may lead us to overlook the broader web of interactions that contribute to system failures. This perspective invites us to shift from a linear, cause-and-effect mindset to one that embraces the complexity and adaptability inherent in modern systems. By understanding how components interact and adapt, we can develop more resilient systems capable of withstanding unforeseen challenges. However, this approach is not easy. Moving away from RCA requires new methodologies and frameworks that can effectively capture the nuances of complex systems. More importantly, at least to my mind, it also demands a cultural shift in how organisations perceive and address failures. I think it was Albert Einstein who once said, “We cannot solve our problems with the same thinking we used when we created them.” To build resilience in the face of complexity, we must evolve our methods of inquiry. Moving beyond the hunt for single root causes and toward a deeper understanding of interactions and adaptations allows us to respond more thoughtfully to failure and uncertainty. In doing so, I believe we not only improve our systems but also our thinking. Read Lorin's article here: https://lnkd.in/epjTduy2 #ComplexSystems #SystemsThinking #RootCauseAnalysis #Resilience #OrganisationalLearning

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Key dimensions to evaluate AI outputs include accuracy, relevance, coherence, completeness, helpfulness, safety, tone and style, contextual awareness, and creativity. https://lnkd.in/gR7cWct9

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Putting #AI into production is only one part of the journey. True industrialization means integrating process design, governance, platforms, and operating model decisions in a deliberate way. Without that integration, scaling often remains fragile. Moving from pilots to enterprise adoption requires structure and intent. In the full conversation, Babak Hodjat and Sanjiv Bambal discuss how organizations can move from isolated pilots to scalable adoption. https://cogniz.at/3NfzKmR #EnterpriseAI #AIIndustrialization #AIGovernance Cognizant

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AI promises efficiency, but as this article points out, it can also reinforce silos when implemented in a department-first way. I’ve seen this happen across industries—each function adopts its own AI tools, celebrates local wins, but the organization misses the bigger opportunity. The fix is more than technical—it’s organizational: ✅ Start with enterprise-wide outcomes, not isolated department goals ✅ Use a hub-and-spoke model (central governance + embedded domain expertise) ✅ Incentivize shared KPIs that reward collaboration instead of siloed metrics ✅ Build on common data foundations so teams aren’t training models on fragmented inputs ✅ Invest in change management and leadership sponsorship to sustain adoption ✅ Review cross-function progress regularly to prevent silos from creeping back in When AI is deployed with this kind of systems thinking, it unifies rather than fragments. And the biggest beneficiary is the customer, because every function is aligned to deliver a seamless experience. #AI #DigitalTransformation #EnterpriseTech

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Most teams don’t struggle with AI agents because the technology is immature. They struggle because prompting is not treated with design discipline. An agent prompt is not a single instruction. It’s a system of assumptions, constraints, and decision rules. When those aren’t explicit, reliability breaks down fast. That’s why two teams can use the same models and get completely different outcomes. This guide breaks down how to design agent prompts the way operators design systems. Intentionally, repeatably, and with scale in mind. Full breakdown here: https://lnkd.in/gzhuQgRW #Agents #Prompting #AI

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For years, The Cuckoo’s Egg by Clifford Stoll has been required reading in my Introduction to Cybersecurity course, and although the technology described in The Cuckoo's Egg is undeniably dated, its human lessons remain timeless. The modems are slow, the systems primitive, and the investigative tools far less sophisticated than what our students encounter today, yet the core qualities on display relentless curiosity, vigilance, persistence, and moral courage are exactly what define effective cybersecurity professionals. Stoll’s refusal to ignore a minor accounting discrepancy, his determination to pursue the truth despite skepticism from colleagues, and his unwavering commitment to evidence over opinion model the mindset we strive to cultivate in our students. Cybersecurity is not simply about mastering tools or frameworks; it is about developing the intellectual discipline to question anomalies, the resilience to endure doubt, and the integrity to follow facts wherever they lead. Cliff Stoll stands as a living example of what success looks like in this field not because of the era’s technology, but because of the enduring character traits that made his investigation possible. #captechu #cybersecurity

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