From POC to Production: Tips for GenAI Success

Choosing the right AI Agent framework can feel overwhelming. Should you go experimental (AutoGPT, BabyAGI) or production-ready (LangChain, Rasa, LlamaIndex)? It really comes down to your primary focus: 🔄 Orchestration 💬 Chatbots 📊 Data & Retrieval The landscape is evolving FAST—agentic AI is no longer just a buzzword; it’s shaping how teams build, deploy, and scale automation. 👉 Which framework are you betting on for 2025? #AI #AgenticAI #RAG #LangChain #AutoGPT #ArtificialIntelligence #FutureOfWork

AI frameworks promise fast agents without understanding how they work. Here's the catch... 🤔 AI frameworks today sell a tempting promise: "Build agents fast, no deep knowledge required!" Great for prototypes and impressing stakeholders. But here's what happens next: Production accuracy requirements hit. Complex functionality needs emerge. Access control becomes critical. Suddenly, the framework starts working against you instead of with you. The reality? There's still a place for frameworks - especially for smaller teams or when MVP speed is everything. But for complex cases, building from scratch often wins. Just wrote a full breakdown of the options teams should consider when building agents - covering when frameworks make sense, when they don't, and everything in between. What's been your experience - frameworks helping or hurting in production?

A friend sent me this MIT report and I was shocked but not surprised. 95% of generative AI pilots fail to deliver measurable ROI. Why? In most cases it is not the technology. It is the way it is implemented. Poorly mapped processes, lack of training, and missing guardrails mean even powerful tools cannot deliver. The lesson: automation and AI success is not about chasing hype. It is about solid process design, realistic goals, and clear ownership. What steps are you taking to make your pilots succeed? https://lnkd.in/gnbsu8Mn #WillAndWaySolutions #Automation #BusinessAutomation

Ever wondered why 90% of AI product demos never make it to enterprise deployment? The "show me" era changes everything. As a product designer, I've seen countless AI demos dazzle in presentations only to stumble in real-world applications. The market is evolving. Fast. We're entering a new phase where: 🎯 Proof trumps promises 🔍 ROI beats flashy demos 💼 Enterprise adoption is the true north Here's what matters now in AI product development: •Measurable outcomes from day one •Clear integration pathways •Employee adoption metrics •Sustainable revenue models The winners? Companies focusing on digital adoption platforms. They're bridging the gap between AI capability and actual usage. Remember: A working product that delivers 20% improvement beats a perfect demo promising 200% every time. Want to build AI products that stick? Start with user pain points, not capabilities. Map the adoption journey before the feature set. Your turn: What's your biggest challenge in moving AI from demo to deployment? #ProductDesign #AI #DigitalTransformation 𝐒𝐨𝐮𝐫𝐜𝐞:https://lnkd.in/djbEeXez

“95% of generative AI pilots are failing.” https://lnkd.in/efnMjwHg That’s the headline from a recent MIT report. But here’s the catch. The definition of “failing” is doing a pilot that doesn’t directly hit the P&L within six months. That’s not failure. That’s a poor definition of success. Not every pilot is built to drive an immediate ROI. Sometimes, it's about solving a real business problem at low cost to see if something works. For example: Let’s say a company swaps out its clunky “Press 1 for…” phone tree with a simple voice AI assistant. That change might not register on a CFO dashboard. But if the company defines success as reduced wait times or less customer frustration, it’s a clear win. Faster support. Happier customers. Here’s the trick. Every pilot needs a clear definition of success. It should matter to the people using the tool. If teams can see the difference, whether it saves time, cuts complexity, or just makes their day less annoying, they’ll back it. And they’ll push for scaling it. Pilots are not always about hitting a home run on the balance sheet. Sometimes they’re about bunting your way to insight. Test something small. See if it sticks. Learn fast. Before you kick off your next AI pilot, ask: What problem are we solving? Who feels the pain today? How will we know we’re making it better? If the answer doesn’t tie to a line item on the P&L but does improve your team’s day, you might be closer to a breakthrough than you think. And if this sounds like Change Management, you're right. Implementing AI should go through the same planning and discovery process as any new tech or transformation in your org.

“95% of internal generative AI pilots have no measurable impact on profit and loss.” - MIT/Forbes That stat may sting a little... But it’s not shocking. Most companies are swinging AI at vague problems, hoping something sticks... Hoping it solves everything... something. If you haven't been in one of these strategy meetings, I can tell you, it's maddening. BUT! There's good news! The report also found that success rates are twice as high when companies buy from specialized vendors instead of trying to duct-tape their own models into workflows. That’s the whole point of Steerco! We don’t do “AI for everything.” We solve one very specific, very painful problem for Customer Success: preparing presentations, success plans, and account reviews without burning hundreds of hours. AI works when it’s pointed at something clear and specific. That’s why our customers see impact fast, because the problem is clear, and the solution is purpose-built. https://lnkd.in/gvdfJTiQ

Excellent insights Jasim Puthucheary, completely agree with your take. The real opportunity with GenAI isn’t about chasing the next big thing, but about thoughtfully improving processes where it matters most. As you pointed out, the most successful projects focus on solving a specific pain point, collaborating with end users, and measuring real business impact—whether that’s increasing revenue or reducing costs. Too often, companies get caught up in the hype instead of redesigning workflows for genuine efficiency and value. Your post is a great reminder that sustainable AI success comes from practical, targeted solutions—not just adding AI for the sake of it. Thanks for sharing these lessons—this is exactly the mindset we need for meaningful transformation

Why Most Generative AI Pilots Fail (And What I Keep Telling Our Customers) This week, MIT put out a report saying 95% of generative AI pilots are failing. Not surprising, but when you peel the onion, something more interesting shows up. The data shows: 🔹 Companies that work with specialized vendors succeed about 67% of the time 🔹 Companies that try to build everything in-house succeed about 33% of the time That gap is massive! And it matches exactly what I keep telling pharma teams when we sit down together: Don’t build for where AI is today. Build for how quickly it’s going to change. Here’s the trap I see over and over: an internal team spends months aligning on the “right” architecture, the “right” guardrails, the “right” stack. But by the time they’re ready, the underlying tech has already shifted. A model that was state-of-the-art in March looks outdated by May. This isn’t the pre-GenAI era. Back then, you could train an XGBoost model, deploy it, and then just check for drift every quarter. Generative AI doesn’t work that way. It’s alive, evolving, and moving faster than any IT procurement cycle. That’s why I believe the only smart strategy is: 🌟 Stay flexible—avoid lock-in to today’s models or workflows. 🌟 Work with vendors who live in this space daily—shipping updates weekly, learning from multiple deployments, and absorbing the changes for you. 🌟 Think agility, not permanence—GenAI isn’t infrastructure you set and forget; it’s a shifting layer you need to ride. So when I see that 95% failure rate, I don’t read it as “AI doesn’t work.” I read it as proof that old ways of building tech don’t work anymore. This is a completely new way of building.

So, you’ve probably heard about generative AI, right? It’s that cool tech that’s changing the game for businesses. Big names are already using it to revolutionize how they operate, and it's pretty exciting to see. Think about it: companies like Google and OpenAI are using AI to create content, automate tasks, and even personalize customer experiences. It’s not just for tech giants though; businesses of all sizes are jumping on board to streamline their processes. For instance, AI can help with marketing strategies, customer service via chatbots, and even product development. Imagine cutting down on your workload and speeding up response times while also saving money. Sounds like a dream come true, right? But here’s the deal: many businesses struggle with operational headaches, like slow responses and high costs. That’s where we come in. At Data Science London, we specialize in automation workflows and AI-powered chatbots. We help businesses achieve faster results, improve accuracy, and relieve some of that heavy workload. If you want to see what generative AI can do for you, check out [Make.com](https://lnkd.in/ecwFCrHp). Let’s turn those dreams into reality! Vikky - datasciencelondon.uk Source: https://lnkd.in/dEnXKf9f #GenerativeAI #Automation #BusinessSolutions #Innovate #TechTrends

Thank you TechRadar for sharing my thoughts on the benefits of AI that go beyond time-savings and into decision-making. I've experienced this in my own work with AI tools, and we're also seeing it at AlphaSense where our customers use our GenAI capabilities not only to work faster but also to make more informed, strategic decisions. In today’s world, speed matters. But GenAI is more than a tool for saving time. It’s also reshaping how organizations make decisions and innovate. Read the full article here: https://lnkd.in/e3qcQD8t