Common Pitfalls When Scaling AI Solutions

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

After deploying over 200+ AI POCs across my entire career and across a variety of industries, I learned a hard way truth! The biggest threat to AI success has nothing to do with technology — and everything to do with the people. Years ago, we built the perfect AI system. Cutting-edge models (for that time). Impeccable accuracy. Seamless deployment. And then… only 7% of the anticipated user base used it. It sat there — untouched — while the business teams quietly returned to their old, familiar excel and “phone a friend” processes. The system worked. But the people didn’t trust it, didn’t understand it, and didn’t see how it fit into their day-to-day reality. This is how so many organizations get stuck in “Perpetual POC Purgatory” (copyright 2025 Sol Rashidi) — where brilliant proofs of concept never make it into real, scalable use. The Real Lesson: Scale Comes from Adoption, Not Pushing a model into Production After overseeing hundreds of AI initiatives, I developed the 3E Framework — a practical approach to break out of POC purgatory and build AI solutions that people actually use. This framework is copyrighted: © 2025 Sol Rashidi. All rights reserved. 𝟭. 𝗘𝗻𝗴𝗮𝗴𝗲: Don't just announce AI—make stakeholders co-creators from day one. When marketing, operations, and finance help select use cases and metrics, they become invested gardeners rather than skeptical observers. 𝟮. 𝗘𝗱𝘂𝗰𝗮𝘁𝗲: Theory creates anxiety; hands-on experience builds confidence. This isn't about extensive technical training—it's about demystifying AI through guided exposure over months, not days. When done right, deployment day brings curiosity instead of resistance. 𝟯. 𝗘𝗺𝗯𝗲𝗱: The most successful implementations feel like natural extensions of how people already work. For example, integrate that new AI customer segmentation tool directly into the exact dashboards your teams already use daily. Scaling isn't about more sophisticated algorithms—it's about human adoption at every level. Think of AI systems like exotic trees in your organizational garden—you can select perfect specimens and use cutting-edge cultivation techniques, but if your local gardeners don't know how to nurture them, those trees will never flourish. The next time you face resistance to AI scaling, remember: technical hurdles are often the easiest to overcome. The real transformation happens when you nurture the human ecosystem around your AI. That is how you scale AI across the workforce.

80% of enterprise AI projects are draining your budget with zero ROI. And it's not the technology that's failing: It's the hidden costs no one talks about. McKinsey's 2025 State of AI report reveals a startling truth: 80% of organizations see no tangible ROI impact from their AI investments. While your competitors focus on software licenses and computing costs, five hidden expenses are sabotaging your ROI: 1/ The talent gap: ↳ AI specialists command $175K-$350K annually. ↳ 67% of companies report severe AI talent shortages. ↳ 13% are now hiring AI compliance specialists. ↳ Only 6% have created AI ethics specialists. When your expensive new hire discovers you lack the infrastructure they need to succeed, they will leave within 9 months. 2/ The infrastructure trap: ↳ AI workloads require 5-8x more computing power than projected. ↳ Storage needs can increase 40-60% within 12 months. ↳ Network bandwidth demands can surge unexpectedly. What's budgeted as a $100K project suddenly demands $500K in infrastructure. 3/ The data preparation nightmare: ↳ Organizations underestimate data prep costs by 30-40%. ↳ 45-70% of AI project time is spent on data cleansing (trust me, I know). ↳ Poor data quality causes 30% of AI project failures (according to Gartner). Your AI model is only as good as your data. And most enterprise data isn't ready for AI consumption. 4/ The integration problem: ↳ Legacy system integration adds 25-40% to implementation costs. ↳ API development expenses are routinely overlooked. ↳ 64% of companies report significant workflow disruptions. No AI solution can exist in isolation. You have to integrate it with your existing tech stack, or it will create expensive silos. 5/ The governance burden: ↳ Risk management frameworks cost $50K-$150K to implement. ↳ New AI regulations emerge monthly across global markets. Without proper governance, your AI can become a liability, not an asset. The solution isn't abandoning AI. It's implementing it strategically with eyes wide open. Here's the 3-step framework we use at Avenir Technology to deliver measurable ROI: Step 1: Define real success metrics: ↳ Link AI initiatives directly to business KPIs. ↳ Build comprehensive cost models including hidden expenses. ↳ Establish clear go/no-go decision points. Step 2: Build the foundation first: ↳ Assess and upgrade infrastructure before deployment. ↳ Create data readiness scorecards for each AI use case. ↳ Invest in governance frameworks from day one. Step 3: Scale intelligently: ↳ Start with high-ROI, low-complexity use cases. ↳ Implement in phases with reassessment at each stage. Organizations following this framework see 3.2x higher ROI. Ready to implement AI that produces real ROI? Let's talk about how Avenir Technology can help. What AI implementation challenge are you facing? Share below. ♻️ Share this with someone who needs help implementing. ➕ Follow me, Ashley Nicholson, for more tech insights.

Why 95% of Generative AI Pilots Are Failing — And How to Fix It Recently, an MIT report grabbed headlines: 95% of enterprise generative AI pilots fail to deliver measurable business impact. Boardrooms are rushing into AI, budgets are swelling, yet results are lagging far behind expectations. Should this surprise us? Not at all. This isn’t an AI-specific problem. It’s a mindset and value problem. Here’s what every executive needs to know: 1. Put Business First, Not Technology Too many organizations chase AI because it’s trendy — not because they’ve clearly identified where it will create value. Success doesn’t come from applying AI tools for technology’s sake. It comes from starting with a business problem: • Where is value leaking today? • What pain points, if resolved, translate into measurable financial or customer benefits? • How can AI complement execution, not replace it? AI is a capability embedded within a business strategy, not a hammer searching for nails. 2. Build Strong, Connected Data Foundations AI’s power is only as good as the data it learns from. Without quality data governance, breaking down silos, and scalable platforms, AI risks amplifying noise — not insight. The age-old “garbage in, garbage out” rule has never been truer. 3. Invest in People and Change Management AI cannot live in isolated labs. The real ROI comes when frontline teams are empowered, leadership clarifies AI’s role as an enabler, and upskilling and trust-building are prioritized. Change management isn’t optional—it’s the critical lever to scale pilots into profit. 4. Embrace Failure as Part of the Journey A 95% failure rate is not a red flag to stop; it’s a call to learn and iterate deliberately. Responsible experimentation with a value-first mindset builds the organizational muscle to win at AI. Failure uncovers blind spots, sharpens focus, and creates the breakthroughs that ultimately stick. My Takeaway Generative AI isn’t failing business — businesses are failing AI by chasing shiny tools without discipline. The 5% early wins will expand rapidly — but only if we shift the conversation away from tools and hype, and toward clear, tangible business value. Let’s stop trying to make AI succeed for AI’s sake. Let’s make AI succeed because it moves the needle — for customers, for revenue, and for sustainable competitive advantage. If you want to lead AI in your organization — start with the value, build on data, empower your people, and accept failure as the path to real success. https://lnkd.in/g6sk49DA

Today's update on #GenAI adoption and enterprise #scaling raises some critical issues around infrastructure and cybersecurity that often get ignored in the shiny glitz of ever-evolving foundation models and their fancy valuations. --- AI Enterprise Scaling: The Infrastructure Reality Check Beneath the hype, the foundational pillars of enterprise AI—infrastructure, strategy, and security—are cracking under the strain of real-world deployment, preventing organizations from capturing promised value. Update 1: The Infrastructure Preparedness Crisis Challenge: Critical infrastructure gaps are leaving enterprises unprepared for AI workloads. Details: A Cisco analysis reveals only 13% of enterprises are fully prepared to support AI at scale. The issue is not a lack of ambition but a fundamental architectural mismatch; most data centers were not designed for the GPU-dense, data-hungry pipelines that demand high-throughput, low-latency traffic across heterogeneous stacks. Source: https://lnkd.in/gCUDVtV4 Update 2: The Strategic ROI Disconnect Challenge: A massive perception gap on AI strategy is undermining ROI. Details: Research shows that while 73% of executives believe their AI approach is strategic, only 47% of the workforce agrees. This disconnect suggests enterprises are misapplying AI to "old" problems instead of targeting the "dark" business processes where automation can unlock true value—the historically invisible, manual workflows. Source: https://lnkd.in/gUbTuJtR Update 3: Security Governance in the Dark Challenge: Pervasive visibility and control gaps are exposing firms to major AI-driven risks. Details: A staggering 90% of enterprises are unprepared for AI-driven cyberattacks. This is compounded by the fact that only 21% of organizations have visibility into all AI tools being used, and 77% lack AI-specific security practices to protect their models, pipelines, and data from compromise. Source: https://lnkd.in/graipKgU Key Takeaway The path to scalable AI is not paved with better models, but with foundational redesigns of infrastructure, strategy, and security to match the complex operational reality of the enterprise. --- In my upcoming book on Cognitive Chasm, I build upon my research by addressing the "how" of GenAI adoption, i.e., how could the enterprises systematically adopt GenAI and avoid falling into the #cognitivechasm that seems to be rampant in the industry, and "95% failure rate" seems to have been accepted as the de facto constant of cognitive adoption! As I often joke in my talks, most industries, and not just companies, would get outlawed if they even had a 20% failure rate. Think of an airline that says 20% of our flights don't land or reach some other destination!....will you ever travel with them?

I was at Hugging Face during the critical year before and after ChatGPT's release. One thing became painfully clear: the ways AI systems can fail are exponentially more numerous than traditional software. Enterprise leaders today are under-estimating AI risks. Data privacy and hallucinations are just the tip of the iceberg. What enterprises aren't seeing: The gap between perceived and actual AI failure modes is staggering. - Enterprises think they're facing 10 potential failure scenarios…  - when the reality is closer to 100. AI risks fall into two distinct categories that require completely different approaches: Internal risks: When employees use AI tools like ChatGPT, they often inadvertently upload proprietary information. Your company's competitive edge is now potentially training competitor's models. Despite disclaimer pop-ups, this happens constantly. External risks: These are far more dangerous. When your customers interact with your AI-powered experiences, a single harmful response can destroy brand trust built over decades. Remember when Gemini's image generation missteps wiped billions off Google's market cap? Shout out to Dr. Ratinder, CTO Security and Gen AI, Pure Storage. When I got on a call with Ratinder, he very enthusiastically explained to me their super comprehensive approach: ✅ Full DevSecOps program with threat modeling, code scanning, and pen testing, secure deployment and operations ✅ Security policy generation system that enforces rules on all inputs/outputs ✅ Structured prompt engineering with 20+ techniques ✅ Formal prompt and model evaluation framework ✅ Complete logging via Splunk for traceability ✅ Third-party pen testing certification for customer trust center ✅ OWASP Top 10 framework compliance ✅ Tests for jailbreaking attempts during the development phase Their rigor is top-class… a requirement for enterprise-grade AI. For most companies, external-facing AI requires 2-3x the guardrails of internal systems. Your brand reputation simply can't afford the alternative. Ask yourself: What AI risk factors is your organization overlooking? The most dangerous ones are likely those you haven't even considered.