Reasons Behind Genai Project Failures

𝗪𝗵𝘆 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝟵𝟬% 𝗼𝗳 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗮𝗶𝗹 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲𝘆 𝗿𝗲𝗮𝗰𝗵 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 It's not the models. It's not the data. It's the architecture. Across the industry, brilliant engineers build AI prototypes that work perfectly in Jupyter notebooks... then spend 6 months trying to productionize them. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺? Most AI projects start as experiments and never graduate to engineered systems. Here's what separates successful AI implementations from failures: 𝟭. 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻 𝗛𝗲𝗹𝗹 When API keys, model parameters, and prompt templates are scattered across 12 different files, deployment becomes a nightmare. Successful teams separate their config completely from day one. 𝟮. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗧𝗿𝗮𝗽 Teams treat prompts like throwaway code. Wrong. Your prompts ARE your product logic. Version them, test them, and organize them like the critical business logic they are. 𝟯. 𝗥𝗮𝘁𝗲 𝗟𝗶𝗺𝗶𝘁𝗶𝗻𝗴 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 That beautiful demo hitting OpenAI 100 times per second? It'll cost $500/day in production. Smart teams build rate limiting from day one, not as an afterthought. 𝟰. 𝗧𝗵𝗲 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗕𝗹𝗶𝗻𝗱𝘀𝗽𝗼𝘁 Companies regularly spend $10K/month on API calls for repetitive queries. Intelligent caching can cut AI costs by 70%. 𝗧𝗵𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻? Start with production architecture, not prototype architecture.

From Toys to Tools: Making Generative AI a True Asset in Healthcare Despite big opportunities for genAI in healthcare, there’s a huge adoption gap at the moment…hard to know exactly how big but there are hundreds of approved applications and only a handful in use in most health systems today. There are lots of very good reasons for this: safety, security, privacy among the many. Right now, many genAI applications in healthcare get great traction for a limited period and then fall into disuse…to me that’s a clear sign that these tools are not yet enabling productivity. It’s a nice to have, not a must have. So how do we move from “toys” to real efficiency-optimizing “tools"? First, why isn’t AI driving real productivity in healthcare yet? 3 primary reasons (there are more!): 1. Accuracy & Hallucination Risks – A single incorrect recommendation can have life-or-death consequences. HC is appropriately cautious here and doesn’t have the monitoring in place to guard against this. Because of these risks, AI today still needs a lot of human oversight and correction. 2. Lack of Workflow Integration – Most AI tools operate outside of clinicians’ natural workflows, forcing extra steps instead of removing them. 3. Trust & Adoption Barriers – Clinicians are understandably skeptical. If an AI tool slows them down or introduces errors, they will abandon it. How Can We Make AI a True Tool for Healthcare? 3 main moves we need to make: 1. Embed Trust & Explainability AI can’t just generate outputs—it has to show its reasoning (cite sources, flag uncertainty, allow inspection). And, it needs to check itself using other gen & non-genAI tools to double & triple check the outcomes in areas of high sensitivity. 2. Seamless Workflow Integration For AI to become truly useful, it must integrate with existing workflows, Auto-populating existing tools (like the EHR) and completing "last mile" steps like communicating with patients. 3. Reducing the Burden on our Workforce, Not Adding to It The tech is not enough…at-the-elbow change management will be needed to ensure human adoption and workflow adaptation and we will need to track the impact of these tools on the workforce and our patient communities. The Future: AI That Feels Invisible, Yet Indispensable Right now, genAI in healthcare is still early—full of potential but struggling to deliver consistent, real-world value. The best AI solutions of the future will be those that:  ✅ Enhance—not replace—clinicians’ expertise ✅ Are trusted because they are explainable and reliable ✅ Reduce administrative burden, giving providers more time for patients ✅ Integrate seamlessly into existing healthcare workflows Ultimately, if we build a successful person-tech interaction, the best AI won't be a novelty but an essential tool to enable us to see where our workflows are inefficient and allow us to change them effectively. What do you think? What’s the biggest barrier to making AI truly useful in healthcare?

The Boomerang Effect: How Generative AI Is Backfiring Inside the Companies Who Adopted Early They thought GenAI would reduce headcount. Not so fast boys and girls. We’re now seeing “The Boomerang Effect”, (wasn't sure what else to call it) where the companies that rushed into GenAI adoption are walking it back, often at tremendous cost. Here's what’s happening: 1. Hiring People to Manage the Machines That Replaced People GenAI was sold as a path to automation. But most enterprise models still hallucinate 20–30% of the time in real-world use (Stanford CRFM, 2024). Now companies are hiring human to: Review AI outputs Catch errors Rewrite summaries Translate prompts into usable workflows 2. Putting Brand and Customer Relationships at Risk In the rush to cut costs, companies placed GenAI between themselves and their customers. Hey why not, let's risk our reputation and customer relationships to increase our margin a few bases points! Edelman reports a 19% drop in brand trust among companies using GenAI in customer-facing roles. Now these same firms are reverse-engineering human re-insertion (is that even a thing? I made that up but I think that's what they're doing!) 3. Destroying Employee Morale Early GenAI adopters saw a 37% spike in job anxiety and a 22% drop in team cohesion (UChicago + MIT Sloan, 2024). When morale drops, so does innovation, retention, and long-term growth. Companies didn’t just move too fast. They moved in the wrong direction Whoops Now they’re paying people to manage the machines that were supposed to replace them, rebuilding customer relationships, and trying to re-engage workforces they devalued. ******************************************************************************** The trick with technology is to avoid spreading darkness at the speed of light Stephen Klein is Founder & CEO of Curiouser.AI, the only Generative AI platform and advisory focused on augmented strategic coaching, elevating individual and organizational competence, and values-based execution. He teaches AI ethics at UC Berkeley. Learn more at curiouser.ai or connect on Hubble https://lnkd.in/gphSPv_e Sources 1️⃣ Machines That Replace People Now Need People Hallucination rates: 20–30% across enterprise LLMs (Stanford CRFM, 2024) Human oversight roles up 47% YoY (LinkedIn Workforce Graph, 2025) McKinsey: Cost savings “neutralized by increased human QA” (2024) 2️⃣ Customer Trust Is Breaking Down Edelman: 19% drop in brand trust after GenAI used in customer service (2025) Accenture: 61% of consumers distrust AI-generated brand content (2024) Companies re-inserting humans between bots and customers (internal reports) 3️⃣ Morale Collapse is Real MIT Sloan/UChicago: +37% job anxiety, –22% team cohesion (2024 study) Gartner: 63% of HR leaders report “friction from GenAI workflows” (Q1 2025) HBR: Rise in “cognitive friction” and AI-induced second-guessing (Feb 2025)

🚨 The real reason 60% of AI projects fail isn’t the algorithm, it’s the data. Despite 89% of business leaders believing their data is AI-ready, a staggering 84% of IT teams still spend hours each day fixing it. That disconnect? It’s killing your AI ROI. 💸 As CTO, I’ve seen this story unfold more times than I can count. Too often, teams rush to plug in models hoping for magic ✨ only to realize they’ve built castles on sand. I've lived that misalignment and fixed it. 🚀 How to Make Your Data AI-Ready 🔍 Start with use cases, not tech: Before you clean, ask: “Ready for what?” Align data prep with business objectives. 🧹 Clean as you go: Don't let bad data bottleneck great ideas. Hygiene and deduplication are foundational. 🔄 Integrate continuously: Break down silos. Automate and standardize data flow across platforms. 🧠 Context is king: Your AI can’t "guess" business meaning. Label, annotate, and enrich with metadata. 📊 Monitor relentlessly: Implement real-time checks to detect drift, decay, and anomalies early. 🔥 AI success doesn’t start with algorithms—it starts with accountability to your data.🔥 Quality in, quality out. Garbage in, garbage hallucinated. 🤯 👉 If you’re building your AI roadmap, prioritize a data readiness audit first. It’s the smartest investment you’ll make this year. #CTO #AIReadiness #DataStrategy #DigitalTransformation #GenAI

In the past few months, while I’ve been experimenting with it by myself on the side, I've worked with a variety of companies to assess their readiness for implementing #GenerativeAI. The pattern is striking: people are drawn to the allure of Gen AI for its elegant, rapid answers, but then often stumble upon age-old hurdles during implementation. The importance of robust #datamanagement is evident. Foundational capabilities are not merely helpful but essential, and neglecting them can endanger a company's reputation and business sustainability when training Gen AI models. Data still matters. ⚠️ Gen AI systems are generally advanced and complex, requiring large, diverse, and high-quality datasets to function optimally. One of the foremost challenges is therefore to maintain data quality. The old adage “garbage in, garbage out” holds true in the context of #GenAI. Just like any other AI use case or business process, the quality of the data fed into the system directly impacts the quality of the output. 💾 Another significant challenge is managing the sheer volume of data needed, especially for those who wish to train their own Gen AI models. While off-the-shelf models may require less data, custom training demands vast amounts of data and substantial processing power. This has a direct impact on the infrastructure and energy required. For instance, generating a single image can consume as much energy as fully charging a mobile phone. 🔐 Privacy and security concerns are paramount as many Gen AI applications rely on sensitive #data about individuals or companies. Consider the use case of personalizing communications, which cannot be effectively executed without having, indeed, personal details about the intended recipient. In Gen AI, the link between input data and outcomes is less explicit compared to other predictive models, particularly those with clearly defined dependent variables. This lack of transparency can make it challenging to understand how and why specific outputs are generated, complicating efforts to ensure #privacy and #security. This can also cause ethical problems when the training data contains biases. 🌐 Most Gen AI applications have a specific demand for data integration, as they require synthesis of information from a variety of sources. For instance, a Gen AI system designed for market analysis might need to integrate data from social media, financial reports, news articles and consumer behavior studies. The ability to integrate these disparate data sets not only demands the right technological solutions but also raises complexities around data compatibility, consistency, and processing efficiency. In the next few weeks, we’ll unpack these challenges in more detail, but for those that can’t wait, here’s the full article ➡️ https://lnkd.in/er-bAqrd

Here are my Top AI Mistakes over the course of my career - and guess what thebtakeawaybis - deploying AI doesn’t guarantee transformation. Sometimes it just guarantees disappointment—faster (if these common pitfalls aren’t avoided). Over the 200+ deployments I’ve done most don’t fail because of bad models. They fail because of invisible landmines—pitfalls that only show up after launch. Here they are 👇 🔹 Strategic Insights Get Lost in Translation Pitfall: AI surfaces insights—but no one trusts them, interprets them, or acts on them. Why: Workforce mistrust OR lack of translators who can bridge business and technical understanding. 🔹 Productivity Gets Slower, Not Faster Pitfall: AI adds steps, friction, and tool-switching to workflows. Why: You automated a task without redesigning the process. 🔹 Forecasting Goes From Bad → Biased Pitfall: AI models project confidently on flawed data. Why: Lack of historical labeling, bad quality, and no human feedback loop. 🔹 The Innovation Feels Generic, Not Differentiated Pitfall: You used the same foundation model as your competitor—without any fine-tuning. Why: Prompting ≠ Strategy. Models ≠ Moats. IP-driven data creates differentiation - this is why data security is so important, so you can use the important data. 🔹 Decision-Making Slows Down Pitfall: Endless validation loops between AI output and human oversight. Why: No authorization protocols. Everyone waits for consensus. 🔹 Customer Experience Gets Worse Pitfall: AI automates responses but kills nuance and empathy. Why: Too much optimization, not enough orchestration. 👇 Drop your biggest post-deployment pitfall below ( and it’s okay to admit them - promise) #AITransformation #AIDeployment #HumanCenteredAI #DigitalExecution #FutureOfWork #AILeadership #EnterpriseAI

One of the biggest reasons behind the failure of GenAI exploration/implementation is not defining the achievable milestone but only focusing on the North Star. When teams start exploring this technology, business leaders propose a highly visionary goal. It typically consists of somewhat automated decision-making. All the financial and economic estimations are being done to support that big dream. Such goals start falling apart from the first iteration, and the business case made behind that use case starts crumbling. It’s common to see that teams who explore this technology with a mindset to generate incremental value on existing solutions (manual vs rule-based vs AI/ML) help set the right expectations. It bolsters the chances of success and helps evaluate ROI, especially the potential risk factor to implement this technology at scale. There are many cases available right now where automation failed badly for GenAI. Selling a car for $1 or providing incorrect information to get a flight ticket refund are just a few that got media attention. These are just the big stories; countless other lessons fly under the radar. Before diving deep into GenAI, ask the big "Why?" Are you looking to automate decision-making, boost productivity, or simply make existing processes more efficient? Clearly define the risk appetite and costs attached to it. Avoid falling into the trap of "let’s focus on automation. In the worst case productivity boost will be achieved". Your worst case can soon be transformed into the best case. Interestingly, the business case initially built may not even support it. Set clear, attainable goals and prepare to be amazed by what AI can truly achieve. #ExperienceFromTheField #WrittenByHuman

Why are many organizations failing to put GenAI into production? In the recent "Data Powered Enterprise" report by Jerome Buvat and his team at CR showed that the vast majority of people are struggling with this. One of the reasons is that we are far beyond "#MLOps" when talking about GenAI applications, we need to not just think about how Data Scientists support applications but how our traditional low cost application support teams support fundamentally unstable applications. Your L1 support folks cannot just "recreate" an issue that a user had in a brand new session, it WILL produce different results, which means they will regularly close tickets with "unable to reproduce". This means your traditional approach to operations WILL NOT WORK for your #GenAI applications, failure to reproduce issues that absolutely happened will be a massive challenge. This is something that Weiwei Feng, Pinaki Bhagat and Bikash Dash are working on right now: How do you turn AI metrics and contexts into approaches that enable traditional L1/L2 support models to cost effectively support those solutions? #LLM #ITOperations #ApplicationSupport Ron Tolido, Myriam Chave, Anne Laure Thibaud (Thieullent), Mark Oost, Luc Ducrocq, Niraj Parihar, Padmashree Shagrithaya, Aruna Pattam, Mark Roberts, Subrahmanyam KVJ, Tvishee Kumar, Sumit Cherian

The ROI conundrum Data quality is perhaps the most significant barrier to successful AI implementation. As organizations venture into more complex AI applications, particularly generative AI, the demand for tailored, high-quality data sets has exposed serious deficiencies in existing enterprise data infrastructure. Most enterprises knew their data wasn’t perfect, but they didn’t realize just how bad it was until AI projects began failing. For years, they’ve avoided addressing these fundamental data issues, accumulating technical debt that now threatens to derail their AI ambitions. Leadership hesitation compounds these challenges. Many enterprises are abandoning generative AI initiatives because the data problems are too expensive to fix. CIOs, increasingly concerned about their careers, are reluctant to take on these projects without a clear path to success. This creates a cyclical problem where lack of investment leads to continued failure, further reinforcing leadership’s unwillingness. Return on investment has been dramatically slower than anticipated, creating a significant gap between AI’s potential and practical implementation. Organizations are being forced to carefully assess the foundational elements necessary for AI success, including robust data governance and strategic planning. Unfortunately, too many enterprises consider these things too expensive or risky.

Your $3m GenAI project with a consulting firm is probably going to fail... We hear from customers all the time things like - "Accenture quoted us $500k to do a scoping analysis for the next 6 months on if we can use AI for this" "BCG told us for $10m we can fully automate this process 100% with AI but will take 3 years to build" You have probably seen that the consulting firms are doing billions in AI revenue each quarter. And yet... the success stories are far and few between. Most enterprise buyers are burnt out from over promises. Large scale engagements that end without a working product or adoption. The framing is all wrong which is why these don't work. Consulting firms are too big to start small which is exactly what you need to do with Generative AI. Start as small as you can, a single use case, even at times a single user and then iterate. Speed is everything. The faster you can iterate while getting real user feedback the more likely your project is to succeed. Business plans don't work in generative AI because users don't behave like you expect them to. This requires a certain level of risk tolerance and willingness of teams to change the way things are done. This is why you see far more case studies for successful GenAI rollouts for startups than you do the big consulting firms. Startups can solve problems end-to-end in a pilot faster than your big consulting firm can put together the scoping document. This is why I think the share of revenue will continue moving towards new challengers in the AI space. Agree or disagree?