Reasons Automation Fails to Scale in Business

Big consulting firms rushing to AI...do better. In the rapidly evolving world of AI, far too many enterprises are trusting the advice of large consulting firms, only to find themselves lagging behind or failing outright. As someone who has worked closely with organizations navigating the AI landscape, I see these pitfalls repeatedly—and they’re well documented by recent research. Here is the data: 1. High Failure Rates From Consultant-Led AI Initiatives A combination of Gartner and Boston Consulting Group (BCG) data demonstrates that over 70% of AI projects underperform or fail. The finger often points to poor-fit recommendations from consulting giants who may not understand the client’s unique context, pushing generic strategies that don’t translate into real business value. 2. One-Size-Fits-All Solutions Limit True Value Boston Consulting Group (BCG) found that 74% of companies using large consulting firms for AI encounter trouble when trying to scale beyond the pilot phase. These struggles are often linked to consulting approaches that rely on industry “best practices” or templated frameworks, rather than deeply integrating into an enterprise’s specific workflows and data realities. 3. Lost ROI and Siloed Progress Research from BCG shows that organizations leaning too heavily on consultant-driven AI roadmaps are less likely to see genuine returns on their investment. Many never move beyond flashy proof-of-concepts to meaningful, organization-wide transformation. 4. Inadequate Focus on Data Integration and Governance Surveys like Deloitte’s State of AI consistently highlight data integration and governance as major stumbling blocks. Despite sizable investments and consulting-led efforts, enterprises frequently face the same roadblocks because critical foundational work gets overshadowed by a rush to achieve headline results. 5. The Minority Enjoy the Major Gains MIT Sloan School of Management reported that just 10% of heavy AI spenders actually achieve significant business benefits—and most of these are not blindly following external advisors. Instead, their success stems from strong internal expertise and a tailored approach that fits their specific challenges and goals.

𝗪𝗵𝘆 𝗔𝗜 𝗜𝘀𝗻’𝘁 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴: 𝗧𝗵𝗲 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 is today’s 𝗰𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝗼𝗯𝘀𝗲𝘀𝘀𝗶𝗼𝗻. Yet despite $35-$40B invested in GenAI tools and $44B raised by startups in 2025, MIT’s 𝗚𝗲𝗻𝗔𝗜 𝗗𝗶𝘃𝗶𝗱𝗲 report shows 𝟵𝟱% 𝗼𝗳 𝗽𝗶𝗹𝗼𝘁𝘀 𝗳𝗮𝗶𝗹, 𝗮𝗻𝗱 𝗼𝗻𝗹𝘆 𝟱% 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝗿𝗲𝗮𝗹 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗼𝗻. The issue isn’t technology, but a “learning gap”: companies can’t weave AI into workflows, processes, and culture. 𝟭. 𝗧𝗵𝗲 𝗕𝗶𝗴𝗴𝗲𝘀𝘁 𝗜𝘀𝘀𝘂𝗲 𝗶𝘀 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝗮𝗹, 𝗻𝗼𝘁 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 The real barrier to AI adoption isn’t data or algorithms, it is the culture. AI disrupts decisions, power structures, and roles. Projects rarely fail from weak models or messy data; they fail because organizations resist change. When initiatives stall, executives blame accuracy, integration, or data quality, valid issues, but often just smokescreens. 𝟮. 𝗧𝗵𝗲 𝗕𝘂𝗱𝗴𝗲𝘁 𝗙𝗶𝗿𝗲𝗵𝗼𝘀𝗲: 𝗥𝗮𝗻𝗱𝗼𝗺 𝗦𝗽𝗲𝗻𝗱𝗶𝗻𝗴 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆  Companies chase flashy demos like chatbots instead of focusing on repeatable, high-ROI tasks. By skipping basics, business cases, ROI definitions, and success metrics, executives prioritize what looks impressive over what delivers real value, leaving bigger, faster gains untapped. 𝟯. 𝗧𝗵𝗲 𝗕𝘂𝘆 𝘃𝘀. 𝗕𝘂𝗶𝗹𝗱 𝗧𝗿𝗮𝗽 Enterprises waste millions either betting on hyperscalers to “solve AI” or insisting on building everything in-house. Both fail: real workflows span systems and can’t be vibe-coded or fixed with a big check. The winning model is hybrid, external experts to accelerate and de-risk, internal teams to ensure fit. Don’t outsource your brain, but don’t amputate your arms. 𝟰. 𝗣𝗼𝗼𝗿 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻: 𝗪𝗵𝗲𝗿𝗲 𝗚𝗼𝗼𝗱 𝗜𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻𝘀 𝗗𝗶𝗲 Enterprises get swept up in AI mania, flashy dashboards, or pilots that never scale. Shadow AI usage, fueled by weekend ChatGPT experiments, creates the illusion of progress while deepening the chaos. Without a disciplined approach, projects stall in the messy middle, becoming costly theater rather than true enterprise transformation. 𝗧𝗵𝗲 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸 𝗳𝗼𝗿 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝙎𝙩𝙖𝙧𝙩 𝙨𝙢𝙖𝙡𝙡: Automate with clear, measurable outcomes. 𝙋𝙧𝙞𝙤𝙧𝙞𝙩𝙞𝙯𝙚 𝙞𝙣𝙩𝙚𝙜𝙧𝙖𝙩𝙞𝙤𝙣: Fit AI into workflows. 𝘼𝙘𝙠𝙣𝙤𝙬𝙡𝙚𝙙𝙜𝙚 𝙞𝙣𝙚𝙭𝙥𝙚𝙧𝙞𝙚𝙣𝙘𝙚: Partner with experts. 𝙐𝙥𝙨𝙠𝙞𝙡𝙡 𝙖𝙣𝙙 𝙢𝙖𝙣𝙖𝙜𝙚 𝙘𝙝𝙖𝙣𝙜𝙚: Ready people and culture. 𝙎𝙚𝙩 𝙚𝙭𝙥𝙚𝙘𝙩𝙖𝙩𝙞𝙤𝙣𝙨: Distinguish pilots from scaled transformation. MIT’s finding that 95% of AI projects fail isn’t about AI, it is about execution. AI works; enterprises don’t. Winners won’t be those with the biggest budgets, but those willing to change workflows, culture, and habits. Less spectacle, more substance. #AI #GenerativeAI #DigitalTransformation #BusinessStrategy #FutureOfWork

Scaling AI is less about model performance; it's about the infrastructure discipline and data maturity underneath it. One unexpected bottleneck companies often hit while trying to scale AI in production is “data lineage and quality debt.” Why it’s unexpected: Many organizations assume that once a model is trained and performs well in testing, scaling it into production is mostly an engineering and compute problem. But in reality, the biggest bottleneck often emerges from inconsistent, incomplete, or undocumented data pipelines—especially when legacy systems or siloed departments are involved. What’s the impact: Without robust data lineage (i.e., visibility into where data comes from, how it’s transformed, and who’s using it), models in production can silently drift or degrade due to upstream changes in data structure, format, or meaning. This creates instability, compliance risks, and loss of trust in AI outcomes in the regulated companies like Banking, Healthcare, Retail, etc. What’s the Solution: • Establish strong data governance frameworks early on, with a focus on data ownership, lineage tracking, and quality monitoring. • Invest in metadata management tools that provide visibility into data flow and dependencies across the enterprise. • Build cross-functional teams (Data + ML + Ops + Business) that own the end-to-end AI lifecycle, including the boring but critical parts of the data stack. • Implement continuous data validation and alerting in production pipelines to catch and respond to changes before they impact models. Summary: Scaling AI is less about model performance and more about the infrastructure discipline and data maturity underneath it.

Your Automations Are Breaking More Than They’re Fixing. Here’s the uncomfortable reality: For every automation that saves time, there’s another one quietly breaking and costing you hours. You’ve seen it: 👀 A field changes, and suddenly a critical workflow stops working. ⚠️ Nobody catches it until customers are angry, deals stall, or metrics go haywire. 🚒 Firedrill time! A mad scramble to patch things up—until it breaks again. This isn’t a technology problem. It’s a strategy problem. Most automations are built fast and forgotten faster. They’re fragile, disconnected, and no one’s watching to make sure they still work. The result? 🔄 More time fixing than building. 🛠️ More manual work creeping back in. 📉 Less trust in the system you’re trying to scale. Here’s the hard truth: automations fail when no one owns their lifecycle. The fix? Stop building and forgetting. Start managing and evolving. Adopt an Automation Development Lifecycle (ADLC): 🗺️ Plan intentionally: Automations should serve a process, not just a task. Define how it fits into the big picture. 🤝 Build collaboratively: Ops and IT co-create workflows that are scalable and governed. 🚨 Track constantly: Changelogs and alerts flag issues before they become disasters. 🌱 Evolve continuously: Automations should grow with your processes, not stay stuck in the past. When automations are managed like systems—not shortcuts—they work. They scale. They deliver. So, ask yourself: Are your automations working for you, or are you constantly working to fix them? Because if you’re stuck in firefighting mode, it’s not automation—it’s chaos.

“Faster garbage is still garbage.” A client wanted to automate their proposal workflow. Quote speed was slow. Reps were frustrated. We looked under the hood: – 40% of proposals had errors – Legal had no standardized approval triggers – Product bundling logic was legacy and misaligned with pricing They weren’t slow because of inefficiency. They were slow because everyone was stopping to fix things. Automation would have just cemented dysfunction. ✅ What we did instead: – Paused the automation work – Rebuilt proposal templates with version control – Created guardrails on SKUs based on actual margin behavior – Used a custom ChatGPT to test business logic against past deals before rollout 🎯 The mistake we avoided: – Treating speed as a metric of success – Confusing tools with transformation 📌 You don’t need to automate your quoting. You need to make sure what you’re quoting is right. 📥 Follow me for more insights. Repost if this resonated.

𝐓𝐡𝐞 $𝟗𝟎 𝐌𝐢𝐥𝐥𝐢𝐨𝐧 𝐋𝐞𝐬𝐬𝐨𝐧: 𝐖𝐡𝐲 𝐒𝐭𝐚𝐧𝐥𝐞𝐲 𝐁𝐥𝐚𝐜𝐤 & 𝐃𝐞𝐜𝐤𝐞𝐫’𝐬 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐃𝐢𝐝𝐧'𝐭 𝐖𝐨𝐫𝐤 𝐐𝐮𝐢𝐜𝐤 𝐒𝐮𝐦𝐦𝐚𝐫𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐅𝐚𝐢𝐥𝐮𝐫𝐞: Stanley Black & Decker, Inc.'s ambitious $90 million automation project in Fort Worth, Texas, aimed to revive the Craftsman brand by producing tools domestically with unprecedented efficiency. However, equipment issues, slow production, and the impact of COVID-19 led to the closure of the factory 3½ years after its inception. 𝐋𝐞𝐬𝐬𝐨𝐧𝐬 𝐋𝐞𝐚𝐫𝐧𝐞𝐝: 1. 𝐒𝐞𝐥𝐞𝐜𝐭 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞𝐝, 𝐋𝐨𝐜𝐚𝐥𝐥𝐲 𝐒𝐮𝐩𝐩𝐨𝐫𝐭𝐞𝐝 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐁𝐮𝐢𝐥𝐝𝐞𝐫𝐬: • SBD selected a machine builder from Belarus. Although the company demonstrated its machines could forge with minimal waste, the machines didn’t work properly when installed and were difficult to fix. • SBD had to wait weeks for overseas parts and tooling to arrive for repairs. 2. 𝐓𝐡𝐨𝐫𝐨𝐮𝐠𝐡 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧: • The automation technology wasn't fully tested before scaling up, leading to persistent production issues. This was likely due to pressure to finish quickly to support the increased demand during COVID. 3. 𝐇𝐮𝐦𝐚𝐧 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 𝐯𝐬. 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧: • The loss of senior experienced workers, mostly due to retirements during COVID, and over-reliance on untested automation systems underscored the value of human expertise. 4. 𝐔𝐧𝐬𝐭𝐞𝐚𝐝𝐲 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩: • SBD had two CEOs and five Presidents of the Global Tools Group during this period. • Leadership focus is critical for complex projects. 5. 𝐑𝐞𝐚𝐥𝐢𝐬𝐭𝐢𝐜 𝐓𝐢𝐦𝐞𝐥𝐢𝐧𝐞𝐬 𝐚𝐧𝐝 𝐄𝐱𝐩𝐞𝐜𝐭𝐚𝐭𝐢𝐨𝐧𝐬: • Overly aggressive timelines, disrupted by the pandemic, compromised the project's success. • Setting realistic goals and being adaptable to unforeseen challenges are essential for complex projects. Leadership was critical here. 𝐅𝐮𝐭𝐮𝐫𝐞 𝐃𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧𝐬 𝐚𝐧𝐝 𝐏𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐌𝐢𝐬𝐬𝐭𝐞𝐩𝐬: Stanley Black & Decker may have learned the wrong lessons from this experience. The company's recent consideration towards manufacturing parts in Mexico, rather than further investing in automation within the USA, suggests a retreat from the challenges faced rather than a strategic approach to overcoming them. 𝐏𝐨𝐬𝐢𝐭𝐢𝐯𝐞 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Snap-on gradually integrated automation into its U.S. factories, evolving from a 100-to-1 ratio of workers to robots in 2010 to an 8-to-1 ratio over twelve years. This phased approach allowed Snap-on to identify optimal roles for both humans and machines. Snap-on's CEO, Nick Pinchuk, emphasized the importance of understanding the intricacies of the product and the manufacturing process. ATI Industrial Automation supports reshoring #manufacturing with the help of #robotics and #automation.  #robotrevolution https://lnkd.in/eaGwt2V5

"If organizations and enterprises are serious about AI transformation up and down the organization, both in terms of Agents doing big buckets of new work but also their existing employees being more productive, their going to have to pony up for skills training, enablement, and broader change management." 🖐🎤🔥 at 20:48 I've now listened to this episode three time over the weekend, especially the back half with Nathaniel Whittemore's 15 Reasons Why AI Pilots Actually Fail. In the front half, he examines the headline-making, market-moving MIT research declaring a 95% failure rate for AI pilots. After exploring the dubious math, he digs more deeply into the root causes and rationale behind real AI pilot challenges. What makes the episode a must listen? The mic drop line above, to start. The discussions about the people side/technical side challenge proportion resonated - his head of research estimated 80/20, our research found about 75/25. And while not naming it, he hits on the process debt, data debt, and technical debt I just wrote about. I was also impressed / not surprised by the significant overlap between the list on the AI Daily Brief and the findings in Prosci's "AI Adoption Across the Enterprise" research. Here is his list: 1. Leadership Buy-In 2. Team Buy-In  3. Problem Value Fit  4. Success Criteria and Baselines  5. Lack of Enterprise Context 6. Data Readiness 7. Data Access  8. Poorly Documented Workflows 9. Skills Enablement and Support 10. Overzealous Risk 11. Org Fragmentation  12. Existing Vendor Lock-In 13. Pilot Ownership  14. No Strategic Plan or Next Steps  15. Technology Problems By comparison, here are a few of the 20 factors we evaluate in the Prosci AI Adoption Diagnostic - a research-based tool for evaluating organizational readiness on the people side - that line up directly to the root causes from the podcast and the study: 1. Senior Leadership Commitment: “Our senior leaders clearly articulate the value AI brings to transforming our organization.” 2. Strategic Communication: “Leadership regularly communicates the strategic importance of AI initiatives and their expected impact.” 5. Structured Change Approach: “We use a formal, proactive change management approach (e.g., readiness, impact analysis, engagement) to guide AI adoption.” 8. Effective Training & Support: “Employees promptly receive training, coaching, and support to adapt to new AI-enabled processes.” 12. Shared AI Trust: “Executives, managers, and frontline employees generally trust AI outputs, showing minimal (but healthy) skepticism.” Aside: Are you about to launch an AI initiative, or move from pilot to scale? Already have something in flight that is facing low adoption or utilization? Looking to lay the foundation for being an AI-first organization? Reach out to learn about putting the Prosci AI Adoption Diagnostic to work for you. https://lnkd.in/gPyiPf-u

AI is a force multiplier, not a fix-it fairy. “How do we scale AI across the enterprise?” Wrong question. Start with this one instead: “Are we automating dysfunction?” I’ve seen this play out often: A company spends millions on a GenAI project to reduce support tickets. But their documentation was outdated and the product UI broken. No AI can fix that. Another company deploys agents to accelerate sales ops. But their reps don’t trust the data, and the CRM is a mess. The AI becomes a very expensive intern, ignored by everyone. Here’s the pattern: 1. Leaders think AI will save them. 2. They pour money into tools and pilots. 3. They ignore the root problem: broken process, bad data, or unclear accountability. Then they blame the tech. AI is a force multiplier and it scales what’s already there; good or bad. Before you scale AI, ask: - Are our people, data, and incentives aligned? - Will AI augment excellence, or automate failure?

The hardest part of automation isn’t starting small, it’s scaling thoughtfully. Many organisations fall into two traps: 1. Over-customising too early by building solutions so rigid that they can’t evolve. 2. Chasing complexity by adding layers of integration before the foundations are in place. The companies that succeed take a different path. They design for compatibility and modularity from day one. They think about how a simple sensor, chatbot, or AI workflow can connect to other systems in the future, even if today it runs stand-alone. We’ve seen this mindset make the difference. Projects that begin with focused automation, whether in healthcare, logistics, or finance become the foundation for scalable digital ecosystems. And because they’re built on open standards and flexible architecture, they grow as the business grows. For leaders, the lesson is clear: • Don’t just ask what can we automate today? Ask how do we ensure today’s solution doesn’t limit tomorrow’s potential? Automation is not about replacing people or rushing into complexity. It’s about creating the kind of infrastructure where both people and technology can do their best work, now and in the future. #Automation #Leadership #DigitalTransformation #smartData

74% plan to scale AI. Few are ready. But what happens when automation outpaces your team? AI doesn’t fail. People fail to prepare for it. People resist what they don’t understand. People stall what they’re not trained to lead. People fear the tools they can’t control. The truth is: * Operational efficiency is no longer a tech problem. * It’s a skills and culture readiness issue. I’ve seen mid-sized firms invest millions in automation—only to hit a wall because staff couldn’t keep up. Legacy mindsets. Limited upskilling. No integration playbook. Meanwhile, competitors with leaner teams and sharper training programs? They’re moving fast. Delivering more. Scaling smoother. The gap isn’t in your AI stack. The gap is in leadership readiness, team capability, and trust in transformation. Upskilling isn’t a support task. It’s the strategy. And those who lead it? They won’t just survive this shift—they’ll own it. How is your organization preparing its people for the AI-powered future? #healthcare #Cellbot #AI