95% of AI pilots fail due to enterprise systems, not AI. How to succeed with back-office automation.

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

95% of enterprise GenAI pilots are failing *to deliver financial impact*. Not because of regulation. Not because the models are weak. MIT’s new research is clear: the problem is us. Enterprises don’t know how to integrate AI into the way work actually gets done. Enterprise GenAI fails when treated like a the shiny new toy. It wins when treated like infrastructure. https://lnkd.in/g8UXmMKM

MIT's State of AI in Business 2025 report confirms what we observe in government technology implementations: 95% of enterprise AI pilots fail not due to technology limitations, but workflow integration challenges. The study's finding that vendor partnerships succeed twice as often as internal builds validates our approach of strategic technology partnerships rather than attempting to build everything in-house. Key insight for government contractors: specialized, domain-specific AI solutions consistently outperform generic productivity tools in complex regulatory environments. The lesson isn't to avoid AI, but to focus on purpose-built solutions that embed into existing operational frameworks rather than creating additional administrative overhead. https://lnkd.in/eEiXCteC

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

MIT’s NANDA findings show a sharp divide in enterprise GenAI outcomes: only about 5% of AI pilots deliver rapid revenue acceleration, while the vast majority stall with little P&L impact. The gap isn’t model quality but an enterprise learning and integration challenge—tools that work for individuals don’t automatically adapt to workflows, and many budgets go to marketing tools while back-office automation yields the biggest ROI. Success favors tools purchased from specialists or paired with strong partnerships, empowered line managers driving adoption, and deeply integrating solutions that can evolve over time. As organizations navigate shadow AI and workforce changes, the horizon points to agentic AI—systems that learn, remember, and act within boundaries—as the next phase of enterprise impact. https://lnkd.in/g5fsucwf

“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

95% of enterprise AI pilots fail to deliver ROI, per MIT’s 2025 report. Billions are spent chasing trends like sales and marketing tools, yet back-office automation quietly drives value. Strategy, integration, and cultural alignment—not just tech—determine success. External partnerships double deployment rates (67% vs. 33% for internal builds). Misaligned execution amplifies flaws, not fixes them. AI isn’t a shortcut; it’s a mirror of your business’s strengths and weaknesses. What’s your take on building AI that delivers? Share your thoughts below. Read more- https://lnkd.in/g8j7qxXi #ai #roi #openai

MIT study revealing that 95% of enterprise AI pilots fail highlights a crucial distinction: the failure is largely due to execution problems, not technology shortcomings. The 5% of successful AI projects share certain common traits and practices that differentiate them from the majority:⁣ ⁣ 𝐊𝐞𝐲 𝐑𝐞𝐚𝐬𝐨𝐧𝐬 𝐁𝐞𝐡𝐢𝐧𝐝 𝐭𝐡𝐞 𝟗𝟓% 𝐅𝐚𝐢𝐥𝐮𝐫𝐞 𝐑𝐚𝐭𝐞⁣ ⁣ - Poor integration into existing workflows and culture: AI tools often fail to adapt to or learn from the specific operational contexts, leading to low adoption and impact.⁣ ⁣ - In-house development struggles: Internal AI builds have a much lower success rate compared to solutions purchased from specialized vendors or developed in partnership.⁣ ⁣ - Lack of clear focus: Companies that spread efforts too thin or fail to solve a high-value, specific problem tend to have stalled pilots.⁣ ⁣ - Misallocation of resources: Most AI budgets go toward sales and marketing tools, yet the highest ROI tends to come from back-office automation like process outsourcing reduction.⁣ ⁣ 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐒𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥 𝟓% 𝐀𝐫𝐞 𝐃𝐨𝐢𝐧𝐠 𝐑𝐢𝐠𝐡𝐭⁣ ⁣ Focused execution: They pick one clear pain point and execute well around it, avoiding spreading efforts over numerous fragmented initiatives.⁣ ⁣ Smart partnerships: Successful companies often leverage AI tools and expertise from specialized vendors, rather than solely relying on building solutions internally.⁣ ⁣ Deep workflow integration: AI projects that deeply integrate with business processes and empower line managers generally outperform those confined to centralized AI labs.⁣ ⁣ Agile, iterative scaling: Taking an agile approach by starting small, setting measurable benchmarks, and scaling successful pilots helps mitigate risk and optimize impact.⁣ ⁣ Cross-functional collaboration: Effective use of AI involves coordination between IT, business units, and security teams to ensure quality and compliance.⁣ ⁣ 𝐎𝐯𝐞𝐫𝐚𝐥𝐥 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬⁣ ⁣ The failure rate is less about the AI technology itself—which is advancing rapidly and proving powerful for specific tasks—and more about the complexities of organizational adoption and execution. Enterprises face a “learning gap” in adapting AI tools to their unique contexts and needs. The companies in the successful 5% show that success depends on business-aligned strategies, clear value definitions, strong organizational buy-in, and choosing the right execution partners.⁣ ⁣ In short, the key to crossing from the 95% failure side to the 5% success side lies in focusing on practical, measurable business value through well-integrated, targeted AI applications rather than chasing hype or trying to build everything internally without clear ROI or adoption plans.⁣ ⁣ This aligns with broader findings that AI efforts often fail due to leadership readiness gaps and inadequate change management, reinforcing the importance of executive AI literacy and strategic adoption roadmaps in driving successful enterprise AI.

20 strategies to make AI faster, leaner and more effective — from quantization to federated learning to orchestration. What stood out most to me? The insight from Charles Crouchman, Chief Product Officer at Redwood Software: without unifying automation tools and processes, AI gets stuck managing silos instead of delivering outcomes. The question isn’t just “How do we make AI smarter?” It’s “How do we clear the path for AI to perform at its best?” #AI #Automation #DataDriven

20 strategies to make AI faster, leaner and more effective — from quantization to federated learning to orchestration. What stood out most to me? The insight from Charles Crouchman, Chief Product Officer at Redwood Software: without unifying automation tools and processes, AI gets stuck managing silos instead of delivering outcomes. The question isn’t just “How do we make AI smarter?” It’s “How do we clear the path for AI to perform at its best?” #AI #Automation #DataDriven