Challenges Presented by AI Today

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

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

📉 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.

On May 28, 2024, the Science, Innovation and Technology Select Committee, appointed by the UK House of Commons, published a report on the governance of AI, reviewing developments in AI governance and regulation since an earlier interim report in August 2023: https://lnkd.in/gX4nZrk9 The report underscores the necessity of fundamentally rethinking the approach to AI, particularly addressing the challenges posed by AI systems that operate as "black boxes" with opaque decision-making processes. It stresses the importance of robust testing of AI outputs to ensure accuracy and fairness when the internal workings of these systems are unclear. The report also highlights challenges in regulatory oversight, noting the difficulties faced by a newly established AI Safety Institute in accessing AI models for safety testing, as previously agreed upon by developers. It calls for future government action to enforce compliance and potentially name non-compliant developers. The document concludes by emphasizing the need for an urgent policy response to keep pace with AI's rapid development. It noted that optimal solutions for AI's challenges aren't always clear. In this context, the report identified "Twelve Challenges of AI Governance" and proposed initial solutions (see p. 89ff): 1. Bias Challenge: Addressing inherent biases in AI datasets and ensuring fair outcomes. 2. Privacy Challenge: Balancing privacy with the benefits of AI, particularly in sensitive areas like law enforcement. 3. Misrepresentation Challenge: Addressing the misuse of AI in creating deceptive content, including deepfakes. 4. Access to Data Challenge: Ensuring open and fair access to data necessary for AI development. 5. Access to Compute Challenge: Providing equitable access to computing resources for AI research and development. 6. Black Box Challenge: Accepting that some AI processes may remain unexplainable and focusing on validating their outputs. 7. Open-Source Challenge: Balancing open and proprietary approaches to AI development to encourage innovation while maintaining competitive markets. 8. Intellectual Property and Copyright Challenge: Developing a fair licensing framework for the use of copyrighted material in training AI. 9. Liability Challenge: Clarifying liability for harms caused by AI, ensuring accountability across the supply chain. 10. Employment Challenge: Preparing the workforce for the AI-driven economy through education and skill development. 11. International Coordination Challenge: Addressing the global nature of AI development and governance without necessarily striving for a unified global framework. 12. Existential Challenge: Considering the long-term existential risks posed by AI and focusing regulatory activity on immediate impacts while being prepared for future risks. Thank you, Chris Kraft, for posting - follow his incredibly helpful posts around AI Gov, and AI in the public sphere.

This is the reality of most AI projects. At AtliQ Technologies, we've worked with multiple clients across industries — and a clear pattern is emerging: The majority of ongoing AI initiatives are still just Proof of Concepts (POCs). Why? Because while companies want to ride the AI wave, they’re still figuring out how to use it to actually generate revenue and profit. So they experiment. They invest in building POCs — not full-fledged products — just to stay in the game. But turning these POCs into scalable, revenue-generating production systems is hard. Here are the biggest challenges we see: 1) Hallucination and Compliance AI models, especially LLMs, still hallucinate. Take the case of Air Canada: Their AI chatbot gave a completely wrong answer to a customer asking about bereavement policy. The case went to court — and the company had to admit fault. In regulated environments, such mistakes are costly. 2) Data Quality and Governance We often get well-curated, cleaned data for model training. But once the model meets real production data, performance drops. At this time "Shiny AI project" quickly turns into a "Cumbersome Data Engineering Project" which takes forever to implement 3) Lack of Explainability In industries like finance and healthcare, “black-box” models don’t cut it. You need to explain why the model made a prediction. Unless you use simple statistical models (e.g., linear regression), this explainability is often lacking — stalling production deployment. 4) Legacy Systems In the U.S., major corporations like Costco and Delta Airlines still run on mainframes and other legacy tech. Integrating modern AI solutions into these systems is slow, complex, and often not worth the immediate ROI. Share your thoughts on POCs in the case you are an AI engineer working in the industry 👇🏼

🚩 Up to 50% of #RPA projects fail (EY) 🚩 Generative AI suffers from pilotitis (endless AI experiments, zero implementation) 𝐃𝐈𝐓𝐂𝐇 𝐓𝐄𝐂𝐇𝐍𝐎𝐋𝐎𝐆𝐈𝐂𝐀𝐋 𝐍𝐎𝐒𝐓𝐀𝐋𝐆𝐈𝐀 𝐘𝐨𝐮𝐫 𝐑𝐏𝐀 𝐩𝐥𝐚𝐲𝐛𝐨𝐨𝐤 𝐢𝐬 𝐧𝐨𝐭 𝐞𝐧𝐨𝐮𝐠𝐡 𝐟𝐨𝐫 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 In the race to adopt #GenerativeAI, too many enterprises are stumbling at the starting line, weighed down by the comfortable familiarity of their #RPA strategies. It's time to face an uncomfortable truth: 𝐲𝐨𝐮𝐫 𝐩𝐚𝐬𝐭 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐬𝐮𝐜𝐜𝐞𝐬𝐬𝐞𝐬 𝐦𝐢𝐠𝐡𝐭 𝐛𝐞 𝐲𝐨𝐮𝐫 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐨𝐛𝐬𝐭𝐚𝐜𝐥𝐞 𝐭𝐨 𝐀𝐈 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧. There is a difference: 1.    𝐑𝐎𝐈 𝐅𝐨𝐜𝐮𝐬 𝐈𝐬𝐧'𝐭 𝐄𝐧𝐨𝐮𝐠𝐡 AI's potential goes beyond traditional ROI metrics. How do you measure the value of a technology that can innovate, create, and yes, occasionally hallucinate? 2.    𝐇𝐢𝐝𝐝𝐞𝐧 𝐂𝐨𝐬𝐭𝐬 𝐖𝐢𝐥𝐥 𝐁𝐥𝐢𝐧𝐝𝐬𝐢𝐝𝐞 𝐘𝐨𝐮 Forget predictable RPA costs. AI's hidden expenses in change management, data preparation, and ongoing training will be a surprise and can be non-linear. 3.    𝐃𝐚𝐭𝐚 𝐑𝐞𝐚𝐝𝐢𝐧𝐞𝐬𝐬 𝐈𝐬 𝐌𝐚𝐤𝐞-𝐨𝐫-𝐁𝐫𝐞𝐚𝐤 Unlike RPA's structured data needs, AI thrives on diverse, high-quality data. Many companies need complete data overhauls. Is your data truly AI-ready, or are you feeding a sophisticated hallucination machine? 4.    𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐂𝐨𝐬𝐭𝐬 𝐀𝐫𝐞 𝐚 𝐌𝐨𝐯𝐢𝐧𝐠 𝐓𝐚𝐫𝐠𝐞𝐭 AI's operational costs can wildly fluctuate. Can your budget handle this uncertainty, especially when you might be paying for both brilliant insights and complete fabrications? 5.    𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 𝐈𝐬 𝐨𝐧 𝐀𝐧𝐨𝐭𝐡𝐞𝐫 𝐋𝐞𝐯𝐞𝐥 RPA handles structured, rule-based processes. AI tackles complex, unstructured problems requiring reasoning and creativity. Are your use cases truly leveraging AI's potential? 6.    𝐎𝐮𝐭𝐩𝐮𝐭𝐬 𝐜𝐚𝐧 𝐛𝐞 𝐔𝐧𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐚𝐛𝐥𝐞 RPA gives consistent outputs. AI can surprise you – sometimes brilliantly, sometimes disastrously. How will you manage this unpredictability in critical business processes? 7.    𝐄𝐭𝐡𝐢𝐜𝐚𝐥 𝐌𝐢𝐧𝐞𝐟𝐢𝐞𝐥𝐝 𝐀𝐡𝐞𝐚𝐝 RPA had minimal ethical concerns. AI brings significant challenges in bias, privacy, and decision-making transparency. Is your ethical framework robust enough for AI? 8.    𝐒𝐤𝐢𝐥𝐥 𝐆𝐚𝐩 𝐈𝐬 𝐚𝐧 𝐀𝐛𝐲𝐬𝐬 AI requires skills far beyond RPA expertise – data science, machine learning, domain knowledge, and the crucial ability to distinguish AI fact from fiction. Where will you find this talent? 9.    𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞 𝐈𝐬 𝐒𝐡𝐢𝐟𝐭𝐢𝐧𝐠 Unlike RPA, AI faces increasing regulatory scrutiny. Are you prepared for the evolving legal and compliance challenges of AI deployment? Treating #AI like #intelligentautomation, in learning about it and in its implementation is a path devoid of success. It's time to rewrite the playbook and move beyond the comfort of 'automation COE leadership'. #AIleadership

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.

Leadership in the Loop: Navigating AI Beyond the Technology 🚌 In the rapidly evolving landscape of artificial intelligence, it's easy to get caught up in the sheer potential of the technology itself. However, as leaders, our focus must extend beyond the bits and bytes, diving deep into the strategic integration of AI within our organizations. A recent piece by Venkataraman Lakshminarayanan and Amir Hartman sheds light on a critical challenge many of us face: the leap from AI proof-of-concept to full-scale deployment, or as they aptly term it, "POC paralysis." The research, conducted in collaboration with the Experience Alliance, reveals that a staggering ✳ 74% of companies are in the throes of engaging with AI technologies. Yet, a significant majority are ensnared in the POC stage, unable to transition to impactful, organization-wide implementations. This phenomenon isn't just a minor hiccup; it reflects a broader issue within AI adoption. The article delineates three distinct approaches to AI adoption—shadow AI, Embedded AI, and Sanctioned AI—each with its own set of challenges and opportunities. The nuanced exploration of these categories provides a roadmap for leaders to navigate the complex terrain of AI integration. Moreover, the authors introduce an intriguing real estate metaphor to describe strategic profiles in AI deployment: Stager, Remodeler, Builder, Architect, and Syndicator. This metaphor simplifies the complexity of AI strategies and guides organizations in identifying their current stance and envisioning a path forward. As we delve into the intricacies of AI deployment, it becomes evident that leadership plays a pivotal role. It's not merely about choosing the right technology but about fostering an environment where AI can truly flourish. This involves aligning stakeholders, assessing organizational readiness, and, most importantly, maintaining a clear vision of the problems we aim to solve through AI. The journey of AI adoption is indeed a marathon, not a sprint. It requires patience, strategic foresight, and an unwavering commitment to navigating the challenges that lie ahead. As leaders, we must remain in the loop, not just as overseers but as active participants in shaping the future of AI within our organizations. For those intrigued by the strategic depth and insightful analysis offered by Lakshminarayanan and Hartman, I highly recommend diving into the full article. The link to this must-read piece is in the first comment below. 📌 Let's embrace this journey, knowing that our leadership can and will make a difference in the AI-driven world. #AI #Leadership #Innovation #FidereAI