Challenges Teams Face During AI Adoption

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.

📉 67% of companies fail to scale AI. And nearly half of employees (49%) say their company has done nothing to support them in using it. That’s not an adoption gap...it’s an organizational transformation gap. According to research from Asana’s Work Innovation Lab, AI success depends on crossing 5 critical chasms. From misaligned workflows to missing policies, the teams that fall behind aren't lacking tech—they’re lacking alignment. Here are the key challenges AI leaders must solve: 1️⃣ From AI as a hobby → to AI as a habit 🔹 AI must be embedded into everyday workflows—not treated as an occasional tool. 🔹 Daily AI users report +89% productivity gains; weekly users, +73%. 📊 Insight: Frequency drives fluency. Repetition is what makes AI useful—and usable—at scale. 2️⃣ From top-down buy-in → to all-in adoption 🔹 Leaders are 66% more likely to be early AI adopters than their teams. 🔹 Yet 39% of individual contributors remain skeptical about AI’s benefits. 📊 Insight: Optimism from the C-suite doesn’t guarantee adoption. Teams need role-specific training, clear policies, and space to experiment. 3️⃣ From AI in isolation → to AI in context 🔹 75% of employees report digital exhaustion. 🔹 Workers are 40% more likely to engage with concise AI outputs. 📊 Insight: Low-friction, high-trust workflows are key. AI must reduce—not add to—the noise. 4️⃣ From solo acts → to team sport 🔹 Only 6% of workflows built by individuals scale to peers. 🔹 Co-created AI workflows (the “basketball model”) deliver 651% return on workflow investment (ROWI). 📊 Insight: Centralized solutions scale best early, but long-term success comes from collaborative design and shared ownership. 5️⃣ From acquiring users → to harnessing influencers 🔹 AI workflows built by Bridgers are 96% more likely to be adopted. 🔹 Domain Experts (+27%) and Ops Specialists (+9%) also drive meaningful traction. 📊 Insight: Scale spreads through social influence—not mandates. Find your internal champions early. 💡 So what should People teams do? ➡️ Start tracking AI activity alongside collaboration patterns and workflow performance. ➡️ Segment AI engagement across teams, and surface your internal AI influencers. ➡️ Build habit loops, not just onboarding docs. Make sure to check the comments for the full Asana report. How far along is your organization in crossing these AI chasms? #PeopleAnalytics #HRAnalytics #FutureOfWork #AIAdoption #GenAI

Thought provoking and great conversation between Aravind Srinivas (Founder, Perplexity) and Ali Ghodsi (CEO, Databricks) today Perplexity Business Fellowship session sometime back offering deep insights into the practical realities and challenges of AI adoption in enterprises. TL;DR: 1. Reliability is crucial but challenging: Enterprises demand consistent, predictable results. Despite impressive model advancements, ensuring reliable outcomes at scale remains a significant hurdle. 2. Semantic ambiguity in enterprise Data: Ali pointed out that understanding enterprise data—often riddled with ambiguous terms (C meaning calcutta or california etc.)—is a substantial ongoing challenge, necessitating extensive human oversight to resolve. 3. Synthetic data & customized benchmarks: Given limited proprietary data, using synthetic data generation and custom benchmarks to enhance AI reliability is key. Yet, creating these benchmarks accurately remains complex and resource-intensive. 4. Strategic AI limitations: Ali expressed skepticism about AI’s current capability to automate high-level strategic tasks like CEO decision-making due to their complexity and nuanced human judgment required. 5. Incremental productivity, not fundamental transformation: AI significantly enhances productivity in straightforward tasks (HR, sales, finance) but struggles to transform complex, collaborative activities such as aligning product strategies and managing roadmap priorities. 6. Model fatigue and inference-time compute: Despite rapid model improvements, Ali highlighted the phenomenon of "model fatigue," where incremental model updates are becoming less impactful in perception, despite real underlying progress. 7. Human-centric coordination still essential: Even at Databricks, AI hasn’t yet addressed core challenges around human collaboration, politics, and organizational alignment. Human intuition, consensus-building, and negotiation remain central. Overall the key challenges for enterprises as highlighted by Ali are: - Quality and reliability of data - Evals- yardsticks where we can determine the system is working well. We still need best evals. - Extreme high quality data is a challenge (in that domain for that specific use case)- Synthetic data + evals are key. The path forward with AI is filled with potential—but clearly, it's still a journey with many practical challenges to navigate.

𝗬𝗼𝘂𝗿 𝗔𝗜 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲𝘀 𝗮𝗿𝗲 𝗳𝗮𝗶𝗹𝗶𝗻𝗴. 𝗔𝗻𝗱 𝗶𝘁'𝘀 𝗻𝗼𝘁 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝘆𝗼𝘂𝗿 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆. 70-85% of AI projects fail to deliver value. But here's the thing: → Your algorithms work fine → Your data is clean   → Your APIs connect perfectly So why are you still stuck? 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝘆𝗼𝘂'𝗿𝗲 𝘁𝗿𝘆𝗶𝗻𝗴 𝘁𝗼 𝘀𝗼𝗹𝘃𝗲 𝗮 𝗽𝗲𝗼𝗽𝗹𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝘄𝗶𝘁𝗵 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆. The real blocker isn't your tech stack. It's your culture. 𝗧𝗵𝗲 3 𝘀𝗶𝗹𝗲𝗻𝘁 𝗸𝗶𝗹𝗹𝗲𝗿𝘀 𝗼𝗳 𝗔𝗜 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻: 𝗧𝗵𝗲 𝗘𝘅𝗶𝘀𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗧𝗵𝗿𝗲𝗮𝘁 "If AI can do my job, what happens to me?" (Employees resist what they can't control) 𝗧𝗵𝗲 𝗠𝗶𝗱𝗱𝗹𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 𝗦𝗾𝘂𝗲𝗲𝘇𝗲 You're asking them to implement tech that threatens their role (While still judging them by old metrics) 𝗧𝗵𝗲 𝗜𝗻𝗰𝗲𝗻𝘁𝗶𝘃𝗲 𝗠𝗶𝘀𝗺𝗮𝘁𝗰𝗵 Your AI recommends preventative shutdowns Your managers get rewarded for uptime (Guess which one wins?) 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀: • Elevate people, don't eliminate them • Create safe-to-fail zones for experimentation   • Put domain experts in control of AI implementation • Align incentives with AI-enhanced productivity • Address career anxieties with concrete transition plans 𝗧𝗵𝗲 𝗯𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: - Technical advantages last weeks. - Cultural advantages last years. Your competitors can copy your algorithms. They can't copy your culture. 𝗪𝗵𝗮𝘁'𝘀 𝗵𝗮𝗿𝗱𝗲𝗿 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Building a chatbot or getting people to actually use it? Your answer says it all. I just published a deep dive on this in The AI Journal: "The Hidden Barrier to AI Success: Organizational Culture" It breaks down exactly how to build a culture that makes AI adoption inevitable (not just possible). 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗮𝗿𝘁𝗶𝗰𝗹𝗲→ 𝗵𝘁𝘁𝗽𝘀://𝗮𝗶𝗷𝗼𝘂𝗿𝗻.𝗰𝗼𝗺/𝘁𝗵𝗲-𝗵𝗶𝗱𝗱𝗲𝗻-𝗯𝗮𝗿𝗿𝗶𝗲𝗿-𝘁𝗼-𝗮𝗶-𝘀𝘂𝗰𝗰𝗲𝘀𝘀-𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝗮𝗹-𝗰𝘂𝗹𝘁𝘂𝗿𝗲/ Want more insights on the human side of AI transformation? 🔔 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 for weekly posts on AI + organizational psychology 📧 Join other informed leaders getting my "AI + Human Edge" newsletter for frameworks like this 𝘞𝘩𝘢𝘵'𝘴 𝘣𝘦𝘦𝘯 𝘺𝘰𝘶𝘳 𝘣𝘪𝘨𝘨𝘦𝘴𝘵 𝘣𝘢𝘳𝘳𝘪𝘦𝘳 𝘵𝘰 𝘈𝘐 𝘢𝘥𝘰𝘱𝘵𝘪𝘰𝘯? 𝘛𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘺 𝘰𝘳 𝘱𝘦𝘰𝘱𝘭𝘦? 𝘋𝘳𝘰𝘱 𝘢 𝘤𝘰𝘮𝘮𝘦𝘯𝘵 𝘣𝘦𝘭𝘰𝘸 👇

Everyone wants AI at scale. But here's what really happens when you try to make it work across your company ↓ 1. Excitement turns to confusion Initial hype gives way to the realization that AI isn't a magic wand. It's a tool that requires careful integration and strategy. 2. Data becomes your biggest hurdle You quickly discover your data isn't as clean, organized, or accessible as you thought. Garbage in, garbage out. 3. Skills gap emerges Your team's current skillset might not align with AI needs. Upskilling becomes crucial, but takes time and resources. 4. Ethical concerns surface AI decisions impact real people. Ensuring fairness and transparency becomes a major challenge. 5. Integration issues arise Existing systems don't always play nice with new AI tools. Legacy tech can be a major roadblock. 6. ROI questions loom Stakeholders want results, fast. But AI often requires long-term investment before showing significant returns. 7. Culture shift struggles Employees may resist AI-driven changes. Change management becomes as important as the tech itself. 8. Scalability challenges appear What works in a pilot doesn't always translate company-wide. Infrastructure and processes need rethinking. The reality? AI at scale is a journey, not a destination. It requires patience, investment, and a willingness to fail and learn. Success comes to those who approach AI with eyes wide open, ready for the challenges ahead.

This new white paper "Steps Toward AI Governance" summarizes insights from the 2024 EqualAI Summit, cosponsored by RAND in D.C. in July 2024, where senior executives discussed AI development and deployment, challenges in AI governance, and solutions for these issues across government and industry sectors. Link: https://lnkd.in/giDiaCA3 * * * The white paper outlines several technical and organizational challenges that impact effective AI governance: Technical Challenges: 1) Evaluation of External Models:  Difficulties arise in assessing externally sourced AI models due to unclear testing standards and development transparency, in contrast to in-house models, which can be customized and fine-tuned to fit specific organizational needs. 2) High-Risk Use Cases: Prioritizing the evaluation of AI use cases with high risks is challenging due to the diverse and unpredictable outputs of AI, particularly generative AI. Traditional evaluation metrics may not capture all vulnerabilities, suggesting a need for flexible frameworks like red teaming. Organizational Challenges: 1) Misaligned Incentives: Organizational goals often conflict with the resource-intensive demands of implementing effective AI governance, particularly when not legally required. Lack of incentives for employees to raise concerns and the absence of whistleblower protections can lead to risks being overlooked. 2) Company Culture and Leadership: Establishing a culture that values AI governance is crucial but challenging. Effective governance requires authority and buy-in from leadership, including the board and C-suite executives. 3) Employee Buy-In: Employee resistance, driven by job security concerns, complicates AI adoption, highlighting the need for targeted training. 4) Vendor Relations: Effective AI governance is also impacted by gaps in technical knowledge between companies and vendors, leading to challenges in ensuring appropriate AI model evaluation and transparency. * * * Recommendations for Companies: 1) Catalog AI Use Cases: Maintain a centralized catalog of AI tools and applications, updated regularly to track usage and document specifications for risk assessment. 2) Standardize Vendor Questions: Develop a standardized questionnaire for vendors to ensure evaluations are based on consistent metrics, promoting better integration and governance in vendor relationships. 3) Create an AI Information Tool: Implement a chatbot or similar tool to provide clear, accessible answers to AI governance questions for employees, using diverse informational sources. 4) Foster Multistakeholder Engagement: Engage both internal stakeholders, such as C-suite executives, and external groups, including end users and marginalized communities. 5) Leverage Existing Processes: Utilize established organizational processes, such as crisis management and technical risk management, to integrate AI governance more efficiently into current frameworks.

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!

Every CEO I know is trying to figure out AI. But here’s the real challenge—adoption takes time. Just getting Microsoft Copilot or ChatGPT Premium isn’t the solution. The biggest struggle? Mindset. You can’t apply the same approach to everyone, and shifting the way people work takes effort. Recently, Akshata Alornekar (HR Manager) and Lidya Fernandes (Assistant Finance Manager)—who have a combined 30 years at SJI visiting NYC as part of our company policy to bring employees into different offices, helping them understand our culture and way of working. But what happened? → Every conversation turned into an AI hackathon. Spending time with us, we focused on showing them how @Shahera and I actively use AI in our daily work, not just talking about it, but demonstrating its impact. Seeing this firsthand shifted their perspective. “Before coming here, we were seeing AI from a 60 degree angle. But watching how you and the NYC team use it , it’s a full 180 degree shift!” This is why exposure and experience drive AI adoption. But many companies struggle because they treat AI like a tech upgrade. It’s not. AI adoption is a behavioral shift. How Companies Can Drive AI Adoption Effectively: → Lead from the Front AI is Not Just an IT Project C-level executives need to actively use AI in their own workflows. If leadership treats AI as an “IT tool” instead of a core business function, adoption will stall. Employees follow what leaders do, not just what they say. → Make AI a Part of Daily Workflows, Not Extra Work Employees resist AI when they see it as something “extra.” The best way to drive adoption? Embed AI into existing tasks automate reports, summarize meetings, or assist in decision-making. AI should feel like a time-saver, not another tool to manage. → Create AI Champions Inside the Organization Identify team members who are curious about AI and empower them to guide others. These AI champions can test new use cases, train colleagues, and help build momentum. AI adoption is easier when it spreads peer-to-peer, not just top-down. → Focus on Habit-Building, Not Just Training One-off AI workshops don’t work. AI adoption happens when employees use it consistently. Introduce small, daily challenges to get them comfortable just like Akshata and Lidya experienced in NYC. Seeing AI in action changed their perspective. → Repeat, Repeat, Repeat! AI adoption isn’t a one-time rollout—it’s a continuous process. Companies that embed AI into their culture, not just their technology, will be the ones that thrive. The companies that embrace AI culturally, not just technologically, will win. Are you leading AI adoption the right way? What’s been your biggest challenge? Let’s discuss.

Is your GenAI strategy missing a key ingredient? Successful AI adoption is about change on three fronts: 1) operational development, 2) people, and 3) tech change, not just tech upgrades. Successful AI adoption needs a two-pronged approach LLM + HLM (Large Language Model + Large Human Model): 1. Operational Development Change: Adapt workflows, processes, and IT infrastructure for AI. Think of it as preparing soil for a new plant. Examples: streamline data collection, redesign workflows, train employees on AI tools, and upgrade IT systems. 2. Cultural Change: Shift mindsets to embrace AI. Create an environment where people are comfortable and excited about AI. Examples: address employee concerns, communicate benefits, and foster a culture of experimentation and learning. >> Why Both Matter: Implementing the latest AI tech alone won’t guarantee success. Your operations, including IT infrastructure, must support it. Without employee buy-in, AI investments may go to waste. Think of it as building a house: Operational changes lay the foundation. While cultural changes ensure employees feel comfortable and fully utilize AI. Both are essential for successful AI adoption. Thoughts? ------------------------------- 👋 I'm Mariana Saddakni. I help businesses grow with AI by enhancing business efficiency and keeping teams up-to-date with tech evolution.

Generative AI’s Dirty Secret... 🤫 ....the Challenges That Hold Enterprises Back What’s really holding them back from achieving the transformative results they’ve been promised? The answer lies not in the technology itself, but in the hidden challenges that companies face when trying to implement it at scale. The Challenges of Generative AI While the potential is huge, there are quite a few obstacles standing in the way of widespread adoption. 📊 What are businesses struggling with? 1️⃣ Messy Data (46%): AI needs clean, reliable data to perform well. If the data isn’t right, the results won’t be either. 2️⃣ Finding the Right Use Cases (46%): Businesses often don’t know where AI can make the biggest impact. 3️⃣ Trust and Responsibility (43%): Companies need strong guidelines to make sure AI is used ethically and doesn’t cause harm. 4️⃣ Data Privacy Concerns (42%): Keeping sensitive information secure while using AI is a constant worry. 5️⃣ Lack of Skills (30%+): Many teams don’t have the expertise needed to develop and manage AI systems effectively. 6️⃣ Data Literacy (25%+): Employees often don’t know how to interpret or work with the data AI relies on. 7️⃣ Resistance to Change (25%): Adopting AI means rethinking workflows, and not everyone is on board with that. 8️⃣ Outdated Systems (20%): Legacy technology can’t keep up with the demands of advanced AI tools. How to Overcome These Challenges Generative AI works best when companies have the right foundation: clean data, modern systems, and a team ready to embrace the change. Here’s how businesses can tackle the challenges: 1️⃣ Improve Data Quality: Make sure your data is accurate, clean, and well-organized. AI thrives on good data. 2️⃣ Find Real Use Cases: Talk to teams across your company to figure out where AI can save time or create value. 3️⃣ Build Trust with Responsible AI: Set up rules and guidelines to ensure AI is used fairly and transparently. 4️⃣ Upskill Your Team: Invest in training programs so your team can learn how to build and manage AI systems. 5️⃣ Upgrade Technology: Move to modern, scalable systems that can handle the demands of generative AI. Why This Matters Generative AI isn’t just a fancy new tool—it’s a way for businesses to work smarter, solve problems faster, and drive innovation. 🔑 What you can gain: Better Accuracy: Clean data leads to better AI results. Scalability: Modern systems make it easier to grow and take on bigger AI projects. Faster Results: Streamlined processes mean you can see the value of AI sooner. 💡 What’s next? AI will become a part of everyday workflows, helping teams make decisions faster. Cloud-based AI tools will give businesses more flexibility to innovate. Companies will put a bigger focus on ethical AI practices to build trust with customers and stakeholders. The real question isn’t whether businesses will adopt generative AI—it’s how quickly they’ll embrace it to stay ahead of the curve. ♻️ Share 👍 React 💭 Comment