Barriers to AI Adoption in Businesses

Last week, a customer said something that stopped me in my tracks: “Our data is what makes us unique. If we share it with an AI model, it may play against us.” This customer recognizes the transformative power of AI. They understand that their data holds the key to unlocking that potential. But they also see risks alongside the opportunities—and those risks can’t be ignored. The truth is, technology is advancing faster than many businesses feel ready to adopt it. Bridging that gap between innovation and trust will be critical for unlocking AI’s full potential. So, how do we do that? It comes down understanding, acknowledging and addressing the barriers to AI adoption facing SMBs today: 1. Inflated expectations Companies are promised that AI will revolutionize their business. But when they adopt new AI tools, the reality falls short. Many use cases feel novel, not necessary. And that leads to low repeat usage and high skepticism. For scaling companies with limited resources and big ambitions, AI needs to deliver real value – not just hype. 2. Complex setups Many AI solutions are too complex, requiring armies of consultants to build and train custom tools. That might be ok if you’re a large enterprise. But for everyone else it’s a barrier to getting started, let alone driving adoption. SMBs need AI that works out of the box and integrates seamlessly into the flow of work – from the start. 3. Data privacy concerns Remember the quote I shared earlier? SMBs worry their proprietary data could be exposed and even used against them by competitors. Sharing data with AI tools feels too risky (especially tools that rely on third-party platforms). And that’s a barrier to usage. AI adoption starts with trust, and SMBs need absolute confidence that their data is secure – no exceptions. If 2024 was the year when SMBs saw AI’s potential from afar, 2025 will be the year when they unlock that potential for themselves. That starts by tackling barriers to AI adoption with products that provide immediate value, not inflated hype. Products that offer simplicity, not complexity (or consultants!). Products with security that’s rigorous, not risky. That’s what we’re building at HubSpot, and I’m excited to see what scaling companies do with the full potential of AI at their fingertips this year!

One in three companies are planning to invest at least $25m in AI this year, but only a quarter are seeing ROI so far. Why? I recently sat down with Megan Poinski at Forbes to discuss Boston Consulting Group (BCG)'s AI Radar reporting, our findings, and my POV.   Key takeaways below for those in a hurry. ;-)   1. Most of the companies have a data science team, a data engineering team, a center of excellence for automation, and an IT team; yet they’re not unlocking the value for three reasons:   a. For many execs, the technologies that exist today weren't around during their school years 20 years ago. As silly as it is, but there was no iPhone and for sure no AI at scale deployed at people’s fingertips.   b. It's not in the DNA of a lot of teams to rethink the processes around AI technologies, so the muscle has never really been built. This needs to be addressed and fast...   c. A lot of companies have got used to 2-3% continuous improvement on an annual basis on efficiency and productivity. Now 20-50% is expected and required to drive big changes. 2. The 10-20-70 approach to AI deployment is crucial. Building new and refining existing algorithms is 10% of the effort, 20% is making sure the right data is in the right place at the right time and that underlying infrastructure is right. And 70% of the effort goes into rethinking and then changing the workflows. 3. The most successful companies approach AI and tech with a clear focus. Instead of getting stuck on finer details, they zero in on friction points and how to create an edge. They prioritize fewer, higher-impact use cases, treating them as long-term workflow transformations rather than short-term pilots. Concentrating on core business processes is where the most value lies in moving quickly to redesign workflows end-to-end and align incentives to drive real change.   4. The biggest barrier to AI adoption isn’t incompetence; it’s organizational silos and no clear mandate to drive change and own outcomes. Too often, data science teams build AI tools in isolation, without the influence to make an impact. When the tools reach the front lines, they go unused because business incentives haven’t changed. Successful companies break this cycle by embedding business leaders, data scientists, and tech teams into cross-functional squads with the authority to rethink workflows and incentives. They create regular forums for collaboration, make progress visible to leadership, and ensure AI adoption is actively managed not just expected to happen.

AI Adoption: Reality Bites After speaking with customers across various industries yesterday, one thing became crystal clear: there's a significant gap between AI hype and implementation reality. While pundits on X buzz about autonomous agents and sweeping automation, business leaders I spoke with are struggling with fundamentals: getting legal approval, navigating procurement processes, and addressing privacy, security, and governance concerns. What's more revealing is the counterintuitive truth emerging: organizations with the most robust digital transformation experience are often facing greater AI adoption friction. Their established governance structures—originally designed to protect—now create labyrinthine approval processes that nimbler competitors can sidestep. For product leaders, the opportunity lies not in selling technical capability, but in designing for organizational adoption pathways. Consider: - Prioritize modular implementations that can pass through governance checkpoints incrementally rather than requiring all-or-nothing approvals - Create "governance-as-code" frameworks that embed compliance requirements directly into product architecture - Develop value metrics that measure time-to-implementation, not just end-state ROI - Lean into understanability and transparency as part of your value prop - Build solutions that address the career risk stakeholders face when championing AI initiatives For business leaders, it's critical to internalize that the most successful AI implementations will come not from the organizations with the most advanced technology, but those who reinvent adoption processes themselves. Those who recognize AI requires governance innovation—not just technical innovation—will unlock sustainable value while others remain trapped in endless proof-of-concept cycles. What unexpected adoption hurdles are you encountering in your organization? I'd love to hear perspectives beyond the usual technical challenges.

Chief AI Officers and other tech leaders reveal challenges…. I recently moderated roundtable discussions with over 125 Chief AI officers and leaders responsible for AI across both regulated and unregulated industries. A few key themes surfaced around the barriers to successful AI adoption: • Budget constraints and demonstrating clear ROI • Executive buy-in: Leadership alignment remains a major hurdle • Setting realistic expectations: AI is not an overnight solution, but a long-term strategy • Employee fear: Concerns about AI’s impact on jobs create resistance • Data: Access, quality, and governance issues continue to slow progress • Governance and regulatory compliance: Navigating the complex landscape of rules and regulations presents additional challenges • Hype vs. reality: There is a lot of AI hype to combat, and managing expectations around what AI can truly deliver is essential It’s clear that the job for chief AI officers, CTOs, and others leading these efforts is extremely challenging, requiring a delicate balance of technical knowledge, leadership, and strategy. Despite these obstacles, the energy and innovation in the AI space are undeniable. What did we miss? #AIAdoption #ChiefAIOfficer #ArtificialIntelligence #AILeadership #EthicalAI #TechLeadership #AIInBusiness #AIInnovation #AIRegulation #DataGovernance #ExecutiveBuyIn #FutureOfAI #AITransformation #AIChallenges #AIForGood

These 3 gaps stop AI initiatives in their tracks. Here’s how to break through. We're too focused on tech challenges, and not devoting enough focus + energy to work through the human challenges blocking us from AI value. Here are 3 gaps worth digging into (I see these in most orgs right now). >>>> Leaders who don’t use AI <<<< It's nearly impossible to lead teams toward a bold AI vision if you haven't experienced meaningful value from the technology yourself. Unfortunately, I see this in all kinds of organizations (including some you would not expect). The good news is that with a shift in mindset it doesn’t take long to not only get leaders hands-on, but to do it in a way that leads them to immediate value in their own work. I know because I have a workshop that guides them right there, and it’s magical to see this unlock. The secret is: don’t start by talking about AI. Start by asking business questions that really matter. Prioritize an area to tackle and partner closely with execs to demonstrate how AI can deliver answers that move the business forward. >>>> Your tools vs. their tabs <<<< Employees bypass internal tools for more powerful public ones. Enterprise tools often lag in capability, so people turn to shadow AI use.  It’s about perceived usefulness vs. actual availability. To unblock it, develop a holistic, nuanced, and shared understanding of how your organization defines risk, considering different kinds of risk: 1. Operational risk: People will keep using unapproved AI tools in the shadows if approved ones don’t meet their needs. 2. Competitiveness risk: Falling behind peers or rivals who adopt AI more effectively, faster, and with greater real-world impact. 3. Compliance risk: Sensitive data and workflows may leak outside safe channels, creating exposure for privacy, IP, or regulatory breaches. From THIS lens, open dialogue: build feedback channels, create safe spaces to surface gaps, and prioritize where “better AI” drives “better business”. >>>> Using AI does not = AI value <<<< Most teams are experimenting but struggle to unlock meaningful value. Too often, AI learning programs focus on mechanics over helping people practice applying AI to real problems or incorporate AI into their day to day work. How to unblock it? Stop teaching tools in isolation — reshape learning programs to tackle real problems side-by-side with employees, showing how to connect new AI capabilities to the work that matters most to them. ______ We always tend to underestimate what it takes to make change happen. With AI moving so fast (and feeling so chaotic in many orgs), this is especially dangerous. _____ What do you think??? What other human barriers to AI success should we be talking about here? What other tactics have you found help to break through these gaps? ____ If this is helpful, ♻️ repost to help someone in your network! ____ 👋 Hi, I'm Alison McCauley. Follow me for more on using AI to advance human performance.

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

We hear all about the amazing progress of AI BUT, enterprises are still struggling with AI deployments - latest stats say 78% of AI deployments get stall or canceled - sounds like we’re still buying tools and expect transformation. But those that have succeeded? They don’t just license AI, they redesign work around them. Because adoption isn’t about the tool. It’s about the people who use it. Let’s break this down: 😖 Buying AI tools just adds to your tech stack. Nothing more, nothing less! Stat you can’t ignore: 81% of enterprise AI tools go unused after purchase. (Source: IBM, 2024) 🙌🏼 But adoption, adoption requires new workflows, new roles, and new routines - this means redesigning org charts, updating SOPs, and rethinking “a day in the life.” Why? Because AI should empower decisions—not just automate tasks. It should amplify human strengths—not quietly sideline them. That’s where the 65/35 Rule comes in! 65% of a successful AI deployment is redesigning business processes and preparing the workforce. Only 35% is tools and infrastructure. But most companies still do the reverse. They invest 90% in tech and 10% in training… and wonder why they’re stuck in “perpetual POC purgatory” (my term for things that never make production. It’s like buying a Formula 1 car and expecting your team to win races—without ever learning to drive. Here’s the better way: Step 1: Start with the “day in the life” Map how work actually gets done today. Not hypothetically. Not aspirationally. Just reality. Step 2: Identify friction points Where do delays, errors, or bad decisions happen? Step 3: Redesign with intent Now—and only now—do you introduce AI. Not to replace the human. But to support and strengthen them. Recommendation #1: Design AI solutions with your workforce, not just for them. Co-create roles, rituals, and reviews. Recommendation #2: Adopt the 65/35 Rule as your north star. If your AI strategy doesn’t spend more time on people and process than tools and tech… it’s not ready. ⸻ AI doesn’t fail because it’s flawed. It fails because the org using it is unprepared. #AI #FutureOfWork #DigitalTransformation #Leadership #OrgDesign #HumanInTheLoop #AIAdoption #DataDrivenDecisions #Innovation >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Sol Rashidi was the 1st “Chief AI Officer” for Enterprise (appointed back in 2016). 10 patents. Best-Selling Author of “Your AI Survival Guide”. FORBES “AI Maverick & Visionary of the 21st Century”. 3x TEDx Speaker