How to make AI work for Customer Success: Steerco's approach

🌪️ 95% of Generative AI pilots are failing. That’s not a typo — it’s the wake-up call from MIT’s latest report https://lnkd.in/dUPfgSrD The culprit? Not the technology itself, but a cocktail of unclear strategies, inflated expectations, and lack of real integration. CFOs and business leaders are discovering that AI isn’t just a plug-and-play tool — it’s a cultural and operational shift. So what’s the way forward? 🔹 Start with laser-focused, measurable outcomes 🔹 Engage the entire organization, not just IT 🔹 Measure impact and adapt in real time At Foreworth, we believe the winners won’t be those who rush into AI pilots, but those who turn hype into measurable ROI. ✨ The question is: will you be part of the 95%… or the 5% that actually delivers?

95% Fail. We’re Building for the 5%. MIT just reported that 95% of GenAI pilots fail. Why? Wrong focus. Wrong workflows. Wrong incentives. At Kaunt AI , we obsess over the opposite: - We don’t build vanity AI. - We don’t drop “chatbots” on top of broken processes. - We hardwire AI into the workflows that move P&L: invoice handling, AP compliance, and financial control. GenAI isn’t failing. Companies are failing at execution. Kaunt is determined to stay in the 5% — because real automation doesn’t talk, it does the work. Read the article here: https://lnkd.in/epBRCKNn

A recent MIT report found that 95% of generative AI pilots at large companies are failing. That’s a staggering number, but it’s also a massive opportunity. Why? Because small to medium-sized businesses are the right size to get this right. SMBs are agile, nimble, and closer to their people and processes. They don’t need a dozen committees to test a new tool. They can move fast, iterate quickly, and adopt AI in ways that are practical, role-specific, and immediately impactful. At ONS Consulting Group, we built PACE Arc to help organizations of all sizes adopt AI with purpose and precision. But it’s the SMBs who often have the clearest path to success, because they can actually do what others are still debating. Let’s stop chasing hype and start building value. https://lnkd.in/gzgbHrvt #PACEArc #AIAdoption #SMBLeadership #DigitalTransformation #AgileBusiness #AIForTeams #CopilotStrategy #AIExecution #ONSConsultingGroup #AIWithPurpose #AIOpportunity #SetThePACE

🚨 95% of generative AI pilots deliver… zero measurable business impact. MIT’s “GenAI Divide: State of AI in Business 2025” report spells it out: only about 5% of enterprise AI initiatives are truly transforming the bottom line. The problem isn’t the AI—it’s the integration. Over the past couple years I’ve had the privilege of collaborating with Support and Services leaders to shape an AI for CX strategy. Our key lessons? • Embed AI into workflows—don’t just add it as an afterthought. • Back-office automation is where the real ROI lies—not always the customer-facing “wow” factor. • Partner smart: vendor-led solutions often outperform internal builds. • Measure P&L impact over adoption metrics—real success shows up where numbers move. Investors are reacting. AI-linked stocks like Nvidia and Palantir dropped significantly—Nvidia around 3%, and Palantir nearly 10%—as markets question whether AI hype is outpacing actual value. Rather than just reporting gloom, I’m crowdsourcing clarity. The opportunity? Be among the 5%. ⸻ Questions I’m pondering—and hoping you’ll weigh in on: 🔹 How are you measuring success in your AI pilots—beyond adoption? 🔹 What approaches helped you cross the “learning gap” and land real ROI? Hoping for conversations where accountability meets ambition— to lead AI adoption from hype to hard results. https://lnkd.in/gwekTtFX

Unless you've been living under a rock for the past fortnight, you'll have heard about the MIT report that's been doing the rounds ⬇️. The report asserts that just 5% of businesses are seeing measurable ROI from AI. The other 95%? Zip. Nada. Crickets. It's not that the technology isn't good, it's great. The problem is that so many organisations see AI as a shiny widget they can bolt onto broken operations, rather than a strategic and holistic plan. The report echoes this sentiment, it cites "brittle workflows, lack of contextual learning, and misalignment with day-to-day operations" as the main culprits for AI failure. I've said it before and I'll say it again... Get your house in order first, before you start to automate. Orchestrate your processes, get a handle on who is doing what, how long it takes, where service delivery works, and where it needs refinement. Once you have the visibility and data that orchestration brings, then you can automate. Otherwise, you'll just end up automating what your noisiest colleague requests. If you need help turning chaotic operations into smooth running services, my door is always open. https://lnkd.in/e_XMdCMV #AIROI #MITreport #ArtificialIntelligence #GenAI #AIBusiness

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

MIT’s latest report shows 95% of enterprise AI pilots fail. Is there a bubble or executive misalignment ? 1️⃣ Boards chasing AI hype with unrealistic expectations. 2️⃣ Investing in flashy pilots (sales/marketing) instead of back-office efficiency. 3️⃣ Poor workflow integration, not poor models. 4️⃣ Initially ,true success comes from focus on one high-impact use case & doing it right . 5️⃣ Real ROI = efficiency + measurable business outcomes, not demos. It’s a strategic failure to mine the gold in the rush . The 5% winning are those aligning AI with clear business goals and execution discipline. #AI #MIT #ML #Pilot #Strategy #Execution

Here’s the uncomfortable truth about AI right now: a lot of what’s marketed as “science” is still science fiction. Recent findings out of MIT highlight a stark divide; most generative AI pilots aren’t delivering returns, and only about 5% make it into production with measurable impact. That’s not doom and gloom; it’s a signal to get serious about evidence over hype. What separates science fact from fiction in AI programs? ➡️ Clear, boring problems > shiny demos Choose workflows with stable inputs/outputs, owned data, and concrete KPIs (handle time, first-call resolution, dollars collected). Demos don’t count, deployed outcomes do. ➡️ Architecture that can survive Tuesday Production means observability, fallback paths, human-in-the-loop, cost controls, and data governance—before the pilot, not after the press release. ➡️ Change management is the product Winning teams train users, tune with real transcripts, and rewrite workflows around the AI—not just bolt a model onto legacy processes. If you’re evaluating AI this quarter, ask: • Which business metric moves, by how much, and by when? • What’s the fail-safe when the model is wrong or slow? • What does it take—people, process, and budget—to run this every day, not just on stage? In a market where only a small fraction reaches production, the advantage goes to leaders who insist on instrumentation, integration, and iteration. Less sci-fi, more shipping. #AI #GenerativeAI #MLOps #DigitalTransformation #ChangeManagement #ContactCenter #EnterpriseSoftware #DataGovernance Ref: https://lnkd.in/gDwFaH3P

95% of generative AI pilots fail, MIT says We often blaim tech. We often blaim ‘AI Hype’ But maybe, the problem is us… Dare I say most of the causes could be ‘fixed’ by proper, good product practice? And dare I suggest now to jump in feet first without having done proper needs discovery? 👉 What real needs are we solving? 👉 What outcomes will matter to users? 👉 How will we measure success? This isn’t an “AI problem.” It’s a product problem Needs discovery always comes before pilots. Always. Curious if your pilots are failing, or succeeding, and why? #genAI #product #productdiscovery https://lnkd.in/dVgGY4Vb