Why You Need Pragmatism in GenAI Projects

🚨 MIT Study: 95% of GenAI pilots are failing. MIT just confirmed what’s been building under the surface: most GenAI projects inside companies are stalling. Only 5% are driving revenue. The reason? It’s not the models. It’s not the tech. It’s leadership. Too many executives push GenAI to “keep up.” They delegate it to innovation labs, pilot teams, or external vendors without understanding what it takes to deliver real value. Let’s be clear: GenAI can transform your business. But only if leaders stop treating it like a feature and start leading like operators. Here's my recommendation: 𝟭. 𝗚𝗲𝘁 𝗰𝗹𝗼𝘀𝗲𝗿 𝘁𝗼 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵. You don’t need to code, but you do need to understand the basics. Learn enough to ask the right questions and build the strategy 𝟮. 𝗧𝗶𝗲 𝗚𝗲𝗻𝗔𝗜 𝘁𝗼 𝗣&𝗟. If your AI pilot isn’t aligned to a core metric like cost reduction, revenue growth, time-to-value... then it’s a science project. Kill it or redirect it. 𝟯. 𝗦𝘁𝗮𝗿𝘁 𝘀𝗺𝗮𝗹𝗹, 𝗯𝘂𝘁 𝗯𝘂𝗶𝗹𝗱 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱. A chatbot demo is not a deployment. Pick one real workflow, build it fully, measure impact, then scale. 𝟰. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝗵𝘂𝗺𝗮𝗻𝘀. Most failed projects ignore how people actually work. Don’t just build for the workflow but also build for user adoption. Change management is half the game. Not every problem needs AI. But the ones that do, need tooling, observability, governance, and iteration cycles; just like any platform. We’re past the “try it and see” phase. Business leaders need to lead AI like they lead any critical transformation: with accountability, literacy, and focus. Link to news: https://lnkd.in/gJ-Yk5sv ♻️ Repost to share these insights! ➕ Follow Armand Ruiz for more

How to Be Pragmatic in the Age of Agentic AI Agentic AI is taking center stage in our industry. Every day I see new headlines, vendor pitches, and analyst hot takes proclaiming this as the dawn of a new era. The hype is understandable—agentic AI is powerful and disruptive. Yet amidst this sea of optimism, I believe the job of technology leaders is to remain focused on what actually works. After decades in enterprise tech, here’s what I’ve learned: Hype cycles come and go. What matters is your ability to separate genuine value from marketing enthusiasm. Here’s how to be pragmatic in the age of agentic AI: - Start with Your Pain Points: Agentic AI should solve real business problems, not just be a trophy implementation. Don’t start with the technology—start with the issue that needs fixing. - Pilot, Measure, Adjust: Instead of launching into full-scale adoption, run small pilots aimed at high-impact areas. Measure outcomes against defined KPIs. Double down on successes, and don’t be afraid to pull the plug on projects that stall. - Beware the Cheerleaders: There are a lot of consultants and providers promising the moon right now. Ask for evidence, case studies, and honest post-mortems—not just success stories with cherry-picked metrics. - Invest in Skills + Change Management: Agentic AI is as much a people issue as it is a technology issue. Make sure your teams are ready, your processes are mature, and you have the right guardrails in place. - Stay Curious, Stay Skeptical: Be open-minded about the potential, but scrutinize every claim. Pragmatism means questioning the status quo, even when everyone else seems on board. - Bottom line: The best way forward is to focus on outcomes, not hype. In the age of agentic AI, the winners will be those who keep their heads, learn from real-world deployments, and never stop asking: “Is this delivering measurable value?” Let’s work together to ensure agentic AI delivers on its promise—by keeping our eyes on what works and what doesn’t. That means not attacking me just because I'm looking at the actual capabilities of the technology. 🙏 #AgenticAI #Pragmatism #EnterpriseIT #DigitalTransformation

Most enterprise AI projects fail to deliver business value. New MIT research found 95% of GenAI initiatives show zero measurable ROI despite $30-40 billion in investment. The pattern is consistent: - Impressive demos get built quickly - Pilots launch with excitement - But accuracy collapses in real workflows The issue isn't infrastructure or compute. It's that current systems must be grounded in knowledge from both public data and private company data, with enhanced reasoning capabilities and robust fact-checking for complex workflows. Organizations that succeed: - Partner with vendors rather than build internally (2x success rate) - Demand workflow integration over flashy features - Measure business outcomes, not model benchmarks At You.com we see this repeatedly. Companies come to us after failed experiments with generic tools because they need accuracy they can trust, research-grade citations, and systems that can really integrate. Here's what actually works: → End-to-end solutions that integrate deeply into workflows → Composable building blocks that developers can combine We do both. We're powering nearly a billion queries monthly through our APIs, delivering more accurate answers and reliable results for LLMs and agents. For enterprises that need complete solutions, we build custom AI systems that learn from their data and processes. Interesting that the research also shows real ROI often comes from back-office automation (replacing BPO contracts, cutting agency spend, automating document processing), not the flashy front-office use cases getting all the attention. Building a demo is straightforward, but success goes to systems that can learn and evolve.

95% of GenAI initiatives are failing. That’s not a technology problem. That’s a leadership problem. MIT’s new State of AI in Business 2025 report shows that despite $30-40B invested, only 5% of pilots deliver measurable business value. The rest? Shiny demos, stalled pilots, and “innovation theater.” Why? Because leaders are distracted by the wrong things. 👎 Visible initiatives (e.g. user-facing chatbots, sales applications) over valuable ones (e.g. back office automation) 👎  Pilots that don’t integrate with real workflows 👎  Adoption without business transformation AI allows you to automate without thinking deeply — and that’s both its promise and its peril. Because you won't like what you've automated if you don't force yourself to do the work. ❌ Optimize the wrong goal? ❌ Train on a flawed dataset? At scale, you don’t just make a mistake — you automate a catastrophe. That's why the lessons learned from individual AI use (fun and flexible) don't translate well to projects at scale. The future of #leadership in the age of #AI isn’t about mastering the tools. It’s about mastering ourselves: ✅ Asking better questions ✅ Clarifying what outcomes we really want ✅ Bringing humility and responsibility to the table Because when you extend yourself with technology, you’d better make sure you’re worth extending. If this resonates with you, reshare 🔄 this post so it reaches those who need to hear it the most. #AIinAction

A new study from MIT confirms what a lot of us working in tech have been sensing... "Despite $30–40 billion in enterprise investment into GenAI, this report uncovers a surprising result in that 95% of organizations are getting zero return." 95% of orgs are getting ZERO return on their GenAI investments!!! Because what most software vendors are saying about AI is hype. Enterprises are being told that with a little bit of prompt engineering and some AI agents you can automate big swaths of your business. That's not how this works. That's not how any of this works. There is huge untapped value in AI. But it won't come from AI alone. It will come from the hard and important work of integrating AI into your business, understanding when yor need deterministic processes and when you want LLM creativity. You can't just force agents (see what I did there 😀) into your business. You have to be wiling to drive transformation. And transformation takes work. But here's where there is a real potential to unlock the creative power of AI. AI can make transformation - real transformation, not just incremental band-aids, more accessible than ever. AI can help transform their legacy systems to free enterprises from the boat anchor of tech debt. AI can help us rethink and reimagine the workflows that support their customers and run their operations. AI can change the ways in which we engage our customers, moving us from spam-based marketing to truly personalized engagement. MIT notes that "what's really holding it back is that most AI tools don't learn and don’t integrate well into workflows." We think we can change that.

While it's very easy (and not surprising) to see the AI naysayers jumping all over the new MIT Study highlighting the challenges of GenAI deployments in the enterprise, the reality--as is often the case--is much more nuanced than the headline first suggests. Having published my own study on the subject about a year ago, I can confirm that the ROI proof points from GenAI can be difficult to come by. In these early days of AI deployments, however, I've argued that pure ROI isn't always the goal--improvements in processes and efficiencies often are. How you measure those and translate them into measurable financial outcome is a murky and extremely inexact science, and that's where part of the problem lies. At the same time, one of the most surprising findings from the TECHnalysis Research study "The Intelligent Path Forward: GenAI in the Enterprise" was that most companies weren't spending any time or resources to train their employees how to integrate these new tools into their existing workflows. This creates a usability gap that prevents most of these GenAI tools from being widely accepted--a point confirmed by the new MIT study. That, in turn, often leads to limited success or even outright failure when they are deployed. The bottom line is organizations who want to succeed with GenAI need to thoroughly think through not only the tools but the process of how they are integrated into the day-to-day workflows of regular employees. Critical to this is training and education. It's not the technology itself that is the problem, it's how it's being used. https://lnkd.in/g_z3TZgm