Challenges of Generative AI Implementation

I've been digging into the latest NIST guidance on generative AI risks—and what I’m finding is both urgent and under-discussed. Most organizations are moving fast with AI adoption, but few are stopping to assess what’s actually at stake. Here’s what NIST is warning about: 🔷 Confabulation: AI systems can generate confident but false information. This isn’t just a glitch—it’s a fundamental design risk that can mislead users in critical settings like healthcare, finance, and law. 🔷 Privacy exposure: Models trained on vast datasets can leak or infer sensitive data—even data they weren’t explicitly given. 🔷 Bias at scale: GAI can replicate and amplify harmful societal biases, affecting everything from hiring systems to public-facing applications. 🔷 Offensive cyber capabilities: These tools can be manipulated to assist with attacks—lowering the barrier for threat actors. 🔷 Disinformation and deepfakes: GAI is making it easier than ever to create and spread misinformation at scale, eroding public trust and information integrity. The big takeaway? These risks aren't theoretical. They're already showing up in real-world use cases. With NIST now laying out a detailed framework for managing generative AI risks, the message is clear: Start researching. Start aligning. Start leading. The people and organizations that understand this guidance early will become the voices of authority in this space. #GenerativeAI #Cybersecurity #AICompliance

Today Common Sense Media released their new white paper on "Generative AI in K–12 Education: Challenges and Opportunities." It takes a deep dive into the complexities of AI adoption in education and I was fortunate to share some of our experiences from AI for Education's work in schools and districts with one of the authors, Bene Cipolla . The white paper is definitely worth a read and we love the emphasis on responsible implementation, the importance of building AI literacy, and the need for clear guidelines to ensure AI enhances rather than undermines learning experiences. Key Highlights: Current State of AI in Education: • Though familiarity is increasing, there is still a lack of fundamental AI literacy • Only 5% of districts have specific generative AI policies, which reflects what we have seen in the field • Students are using AI tools, often without clear guidelines Opportunities for AI adoption: •  Student-focused: Adaptive learning, creativity enhancement, project-based learning, and collaborative support •  Teacher-focused: Lesson planning assistance, feedback on teaching, and productivity gains •  System-focused: Data interoperability, parent engagement, and communication Risks and Challenges: •  Inaccuracies and misinformation in GenAI outputs •  Bias and lack of representation in AI systems •  Privacy and data security concerns •  Potential for cheating and plagiarism •  Risk of overreliance on technology and loss of critical thinking skills What Students Want: •  Clear guidelines on AI use, not outright bans •  Recognition of both potential benefits and ethical concerns of the technology •  More education on AI's capabilities and limitations Recommendations: •  Invest in AI literacy for educators, students, and families •  Develop standardized guidelines for AI use in schools •  Adopt procurement standards for AI tools in education •  Use participatory design to include diverse voices in AI development •  Center equity in AI development and implementation •  Proceed cautiously given the experimental nature of the technology Make sure to check out the full report and let us know what you think - link in the comments! And shoutout to all of our EDSAFE AI Alliance and TeachAI steering committee members featured in the white paper. #aieducation #GenAI #ailiteracy #responsibleAI

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.

🔥 Hot off the press! Deloitte’s Q4 report on Generative AI in the Enterprise delivers a deep dive into adoption, scaling, and ROI. Are organizations truly unlocking the power of GenAI—or are they stuck in experimentation? Let’s look at the numbers: ✔️ 74% of advanced GenAI initiatives are meeting or exceeding ROI expectations. ✔️ Cybersecurity is leading the charge—44% of initiatives in this area surpassed ROI expectations. ✔️ 78% of enterprises plan to increase AI spending next year. ❌ Scaling remains a challenge: Over 66% of respondents say only 30% or fewer experiments will scale in the next 3–6 months. The top barriers include: Regulatory uncertainty (38%) Risk management (36%) Data quality issues (30%) 💡 What’s trending? 52% of companies are exploring Agentic AI—autonomous agents designed to accelerate value creation. Multiagent systems (45%) and multimodal capabilities (44%) are also priorities for the future. The verdict? GenAI is moving from hype to real, measurable value, but scaling success requires patience, governance, and disciplined execution. Agree?

🐢 This is the most curious thing in content. A new paper from researchers at Georgia Tech, Duke, and IESE provides more explanation for why enterprise adoption of gen AI is slow — and desperate for strategy. In any model, there are tradeoffs between generality, accuracy, and simplicity. If you want a model to apply to more contexts and to get more accurate, it has to get more complex. On the surface, gen AI models appear to defy the tradeoffs because they work in many contexts with decent accuracy, but the interface stays simple. That’s why individual adoption of AI has been fast. But if you try to deploy gen AI at scale within an enterprise, you realize that the complexity is very much there. It’s just shifting from the individual to the organization through abstraction. Abstraction is layers of infrastructure, process, and talent that remain hidden to the individual using the gen AI. The enterprises that focus on mastering this abstracted complexity will have a lasting competitive advantage. But mastering it isn’t quick or easy. And this jives with two recent reports on the struggle of enterprise AI adoption, which I’ve explained in the past: https://lnkd.in/eAYXBcFh So, this paper calls out ways to manage the complexity that echo what the Content Science team and I have discovered in our research and mentioned throughout this year: ⚒️ Clarifying and defining workflows. 🧠 Involving the right expertise in the right way — domain expertise and content expertise. 🎯 Making strategic choices in deploying gen AI, such as determining the tolerance for cost, latency, and error across potential gen AI tasks. 🔄 Being responsive instead of reactive as the gen AI models evolve. What costs too much to do today might not in a year, for instance. So if your enterprise is stuck in deploying gen AI, it’s not alone. And it’s not too late to turn the situation around with a different, more strategic approach — that now is grounded in even more evidence. One of the best ways to start that turnaround is with an AI audit by a neutral outside partner like Content Science. Start a conversation with us here: https://lnkd.in/esEPSPGF More about the paper: https://lnkd.in/eRZEQY6c More about AI and content in my latest book, The Content Advantage: https://lnkd.in/gqJ85j3z #artificialintelligence #ai #genai #strategy #enterprise #contentstrategy #contentoperations #contentmanagement #digitaltransformation #curiousthingincontent

In the past few months, while I’ve been experimenting with it by myself on the side, I've worked with a variety of companies to assess their readiness for implementing #GenerativeAI. The pattern is striking: people are drawn to the allure of Gen AI for its elegant, rapid answers, but then often stumble upon age-old hurdles during implementation. The importance of robust #datamanagement is evident. Foundational capabilities are not merely helpful but essential, and neglecting them can endanger a company's reputation and business sustainability when training Gen AI models. Data still matters. ⚠️ Gen AI systems are generally advanced and complex, requiring large, diverse, and high-quality datasets to function optimally. One of the foremost challenges is therefore to maintain data quality. The old adage “garbage in, garbage out” holds true in the context of #GenAI. Just like any other AI use case or business process, the quality of the data fed into the system directly impacts the quality of the output. 💾 Another significant challenge is managing the sheer volume of data needed, especially for those who wish to train their own Gen AI models. While off-the-shelf models may require less data, custom training demands vast amounts of data and substantial processing power. This has a direct impact on the infrastructure and energy required. For instance, generating a single image can consume as much energy as fully charging a mobile phone. 🔐 Privacy and security concerns are paramount as many Gen AI applications rely on sensitive #data about individuals or companies. Consider the use case of personalizing communications, which cannot be effectively executed without having, indeed, personal details about the intended recipient. In Gen AI, the link between input data and outcomes is less explicit compared to other predictive models, particularly those with clearly defined dependent variables. This lack of transparency can make it challenging to understand how and why specific outputs are generated, complicating efforts to ensure #privacy and #security. This can also cause ethical problems when the training data contains biases. 🌐 Most Gen AI applications have a specific demand for data integration, as they require synthesis of information from a variety of sources. For instance, a Gen AI system designed for market analysis might need to integrate data from social media, financial reports, news articles and consumer behavior studies. The ability to integrate these disparate data sets not only demands the right technological solutions but also raises complexities around data compatibility, consistency, and processing efficiency. In the next few weeks, we’ll unpack these challenges in more detail, but for those that can’t wait, here’s the full article ➡️ https://lnkd.in/er-bAqrd

According to IBM's latest report, the number one challenge for GenAI adoption in 2025 is... data quality concerns (45%). This shouldn't surprise anyone in data teams who've been standing like Jon Snow against the cavalry charge of top-down "AI initiatives" without proper data foundations. The narrative progression is telling: 2023: "Let's jump on GenAI immediately!" 2024: "Why aren't our AI projects delivering value?" 2025: "Oh... it's the data quality." These aren't technical challenges—they're foundational ones. The fundamental equation hasn't changed: Poor data in = poor AI out. What's interesting is that the other top adoption challenges all trace back to data fundamentals: • 42% cite insufficient proprietary data for customizing models • 42% lack adequate GenAI expertise • 40% have concerns about data privacy and confidentiality While everyone's excited about the possibilities of GenAI (as they should be), skipping these steps is like building a skyscraper on a foundation of sand. The good news? Companies that invest in data quality now will have a significant competitive advantage when deploying AI solutions that actually work. #dataengineering #dataquality #genai

🚨 𝗕𝗲𝘄𝗮𝗿𝗲 𝘁𝗵𝗲 𝗔𝗜 𝗛𝘆𝗽𝗲: 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗧𝗮𝗸𝗲𝘀 𝗠𝗼𝗿𝗲 𝗧𝗵𝗮𝗻 𝗦𝗽𝗲𝗰𝘂𝗹𝗮𝘁𝗶𝗼𝗻! A new WIRED piece by Ethan Mollick suggests that in 2025, organizations will start to fundamentally restructure around human-AI collaboration, driven by GenAI. While it paints an exciting picture, this vision is too speculative and overlooks some critical realities: 𝗧𝗵𝗲 𝗖𝗼𝘀𝘁 𝗼𝗳 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 Reorganizing an enterprise around AI isn’t as simple as flipping a switch. It requires massive investments in infrastructure, retraining teams, and overhauling processes. Add to that the operational challenges of scaling AI and the cultural resistance to change, and it’s clear that 2025 is an optimistic timeline. Some of the barriers slowing this transition include: 👉 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘀𝘁𝘀: Developing, implementing, and maintaining AI systems at scale can be prohibitively expensive. 👉 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗡𝗲𝗲𝗱𝘀: Many companies lack the IT infrastructure to support large-scale AI deployment. 👉 𝗖𝗵𝗮𝗻𝗴𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Reorganizing a company around AI requires massive retraining, restructuring, and cultural shifts – all of which are slow and costly. 👉 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗢𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁: Operationalizing AI at an organizational level introduces challenges around data privacy, ethical use, and regulatory compliance. Put simply, change, especially for large enterprises, takes years due to inertia, resistance to change, and the complexity of systems already in place. These deeply ingrained structures (as well as the barriers and operational costs mentioned above) don’t disappear overnight. 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗔𝗜 𝗜𝘀𝗻’𝘁 𝗥𝗶𝘀𝗸-𝗙𝗿𝗲𝗲 Emerging AI technologies bring serious pitfalls: 👉 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝗜𝘀𝘀𝘂𝗲𝘀: GenAI can hallucinate or make biased decisions, making it unreliable for high-stakes tasks. 👉 𝗗𝗮𝘁𝗮 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆: AI relies on clean, high-quality data, which is a significant challenge for many organizations. 👉 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗥𝗶𝘀𝗸𝘀: Integrating AI at scale increases the attack surface for malicious actors. 👉 𝗘𝗺𝗽𝗹𝗼𝘆𝗲𝗲 𝗣𝘂𝘀𝗵𝗯𝗮𝗰𝗸: Concerns about redundancy and mistrust in the technology can create resistance. 𝗔𝗜 𝗛𝗮𝘀 𝗕𝗲𝗲𝗻 𝗛𝗲𝗿𝗲 𝗳𝗼𝗿 𝗬𝗲𝗮𝗿𝘀 Finally, Mollick’s piece reflects a narrow focus on GenAI, as if it represents the future of all AI applications. GenAI is exciting, but it’s just one part of the broader AI landscape. Treating it as the sole driver of transformation risks oversimplifying AI’s true potential and limitations. 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 AI has incredible potential, but real change requires grappling with the operational, cultural, and ethical realities. The focus should be on building sustainable, impactful solutions, not chasing hype! https://lnkd.in/eju6gCbc

🚩 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

Generative AI Adoption Grows, but Boards and CISOs Must Pay Attention Recent data indicates a significant rise in the adoption of generative AI across various demographic groups. The technology offers multiple benefits but also presents substantial cybersecurity and legal challenges. Study Highlights - Data indicates that 29% of Gen Z, 28% of Gen X, and 27% of Millennials now use generative AI in their daily work. - The increase in adoption is notable; projections suggest that large-scale adoption will increase from 23% to 46% by 2025. Board’s Oversight Role - Boards have a duty to monitor technological risks, including those from generative AI. - Boards may expose themselves to derivative shareholder suits under Caremark claims if they ignore these risks, as these claims require them to monitor corporate risk and legality. Challenges and Controls: A CISO’s Perspective Data Security - The use of generative AI tools with sensitive organizational data can lead to security breaches. - CISOs should recommend controls to disable chat history and to specify the types of data allowed in the system. Copyright Issues - Employing generative AI may result in unintentional copyright infringements. - Controls could include strict policies specifying permissible types of content, especially around citations. Bias and Discrimination - Due to their training data, generative AI tools might generate biased or discriminatory information. - CISOs can suggest controls that prohibit the use of generative AI for employment decisions to minimize discrimination risks. Inaccurate Information - Generative AI might provide incorrect information. - Company policy should enforce that employees validate the information generated by AI, a control that CISOs should advocate. Additional Considerations for CISOs and Boards - Data Classification: Consider focusing on classifying and protecting data, rather than on the AI tools themselves. - Regular Risk Assessment: Boards and CISOs should conduct comprehensive risk assessments to evaluate both the negative and positive impacts of using generative AI. Boards and CISOs play critical roles in assessing and mitigating the risks associated with generative AI. Although the technology offers promising capabilities, implementing it demands careful planning and effective controls. #cybersecurity #risk #AI