Consequences of Mismanaging Artificial Intelligence

“Treating AI like a tool instead of a transformation…” - has been coming to the forefront this week in a number of great conversations with Paul Gonzalez, Ryan Kurt, John Winsor, and Debbie McCarthy. Here are some #symptoms, #consequences, and #interventions for addressing treating #AI like a tool, a fairly common condition these days. 🔍 SYMPTOMS - Signs that an organization is treating AI as just another tool: 1. Isolated Pilots with No Enterprise Integration: Teams experiment in silos without strategic alignment or cross-functional visibility. 2. Lack of Executive Engagement or Ownership: Leadership delegates AI to IT, innovation, or digital teams rather than championing it as a core shift. 3. Training Focused Only on Features, Not Mindsets: Enablement efforts emphasize prompts and mechanics, skipping over mental models, ethics, and role evolution. 4. No Reexamination of Work, Process, or Strategy: AI is slotted into current workflows rather than prompting a redesign of how work gets done. 5. Success Measured by Usage Stats, Not Business Value: Metrics like prompt counts or log-ins dominate while productivity, creativity, and impact remain unmeasured. 🚨 CONSEQUENCES - What happens when AI is treated as a tool, not a transformation: 1. Low and Superficial Adoption: Employees dabble but don’t deeply embed AI into their daily problem-solving or decision-making. 2. Missed Opportunities for Competitive Differentiation: While others rethink their business models, you're just speeding up status quo tasks. 3. Change Fatigue Without Strategic Progress: Energy is spent experimenting with AI, but there's no visible value or momentum to show for it. 4. Workforce Confusion and Misalignment: Without a coherent narrative, people are unsure whether AI is optional, risky, or central to their future. 5. AI Initiatives Get Sunset Before They Scale: Without framing AI as a transformation, initiatives lose funding, attention, and champions. 💡 INTERVENTIONS - How to reframe and re-energize your AI approach: 1. Anchor AI to Strategic Intent: Define how AI enables your core strategy, mission, and market positioning. Make it a business imperative, not a tech experiment. 2. Develop an AI Integration Approach: Develop an approach to help teams and individuals understand when and where to bring AI to the table. Prosci’s AI Integration Framework provides the foundation anyone needs to identify when to partner with a digital collaborator. 3. Elevate Executive Ownership: Position leaders as the narrators of the AI story, modeling usage, creating urgency, and aligning investments. Prosci’s AI Adoption Diagnostic elevates the AI-sponsor role. 4. Invest in Mindset Shifts, Not Just Skillsets: Train for adaptability, ethical reasoning, prompt literacy, and AI teaming—not just tool proficiency. 5. Measure Transformation, Not Just Activity: Track AI’s impact on outcomes: decision speed, innovation velocity, employee empowerment, and customer value. “To what end!”

🚩 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

The rush to implement AI solutions can lead to significant pitfalls. Here's a provocative thought: the greatest risk in AI isn't just inaction. It's implementing without understanding. Let’s unravel why AI implementation demands careful thought and expertise. The promise of AI is undeniable. But when businesses leap without looking, the consequences can be dire. → Mismanaged data leads to flawed predictions. ↳ Garbage in, garbage out—AI doesn't magically fix bad data. → Overreliance can breed complacency. ↳ AI is a tool, not a crutch. → Lack of understanding can result in ethical oversights. ↳ Algorithms must be checked for bias and fairness. → Insufficient expertise can stall projects. ↳ Proper training and a clear strategy are essential. AI implementation isn't just about tech. It's about aligning with business goals and ethics. So, how do we get it right? Prioritize data quality → Clean, accurate data is nonnegotiable. Invest in education → Equip your team with the knowledge to leverage AI effectively. Engage multidisciplinary teams → Combine tech expertise with business acumen. Embed ethical considerations → Regularly audit models for bias and fairness. Iterate and refine → Continuous learning and adaptation are key. Remember, AI isn't a onesizefitsall solution. It's a journey that requires thoughtful planning and execution. Done right, AI can transform businesses, enabling them to act with foresight and agility. Yet, it's the careful, calculated steps that ensure this transformation is both successful and sustainable. What steps have you taken to ensure AI success in your organization? Share your thoughts below.

Why Most Leaders Misfire on AI in the Enterprise A Strategic Briefing for CEOs and Boards 1. AI is Miscast as a Silver Bullet Instead of a Business Lever AI is often treated as a catch-all solution. But AI, in isolation, doesn’t solve business problems — it amplifies your existing capabilities (or dysfunctions). Board takeaway: AI should be explicitly tied to a value driver — whether cost optimization, operational efficiency, or revenue growth. If it doesn’t show up on the P&L, it’s not strategic. 2. Strategy is an Afterthought, Technology Takes the Lead Enterprises frequently prioritize the “how” (tools, platforms, models) over the “why” (business impact). This leads to fragmented pilots with no strategic anchor. CEO mandate: Define the business problem first. Let AI serve as an enabler — not the starting point. 3. Data Foundations Are Overestimated Most enterprises assume they have usable data. In reality, their data is siloed, inconsistent, or lacks the quality needed to power AI reliably. This gap between perception and reality derails execution. Recommendation: Boards should push for a clear data readiness assessment. Without clean, connected, and accessible data, AI will fail to scale. 4. Organizational Readiness is Overlooked Even the best AI models won’t move the needle if they aren’t adopted by operators. Change management, user trust, and cross-functional integration are critical — yet underinvested. Leadership priority: AI requires more than technologists — it needs product, operations, legal, and compliance at the table from day one. 5. Enterprises Get Stuck in "POC Purgatory" Too many AI efforts stall in endless pilot mode. The result: high spend, low impact, and leadership fatigue. Fix: Establish clear success criteria, governance, and a roadmap to scale. If a use case can’t reach production, don’t fund it. 6. AI Initiatives Aren’t Linked to Financial Outcomes Without measurable KPIs tied to business performance, AI becomes a cost center, not a value generator. Board directive: Every AI investment should be linked to quantifiable outcomes — margin improvement, customer retention, risk reduction, etc. 7. AI is Treated as a Technology Initiative, Not an Enterprise Transformation When AI is confined to the IT or innovation team, it lacks strategic sponsorship. It must be led as a cross-functional transformation initiative with C-suite visibility and accountability. CEO imperative: Elevate AI to a core pillar of enterprise strategy — with clear executive ownership. In Summary AI is not a technology project. It’s a business transformation lever — one that requires executive alignment, operational readiness, and disciplined execution. Boards and CEOs that succeed with AI don’t just fund it — they lead

As organizations strive to maintain competitive advantage and looking for opportunities to differentiate, it can lead to ‘Shiny Object Syndrome’ (SOS), the leadership equivalent of chasing squirrels. Business leaders see other company’s making headlines for utilizing buzzworthy technology, such as the latest with AI. This brings a sense of urgency to leverage these same tools without proper foresight. “We've seen how AI can be applied for good, but we must also guard against its unintended consequences. Now is the time to examine how we build AI responsibly and avoid a race to the bottom.” —Satya Nadella. With this new era of AI, if a company does not establish a comprehensive strategy for application of these new technologies, the consequences can be severe. While organizations aim to leverage AI as a mechanism to drive innovation and transform their organization for market differentiation, there is unease and mistrust in this burgeoning technology. Often the focus is on experimenting with AI and less about creating fair, accountable and transparent algorithms. As this technology gets closer to emulating human behavior such as feeling, thinking, and reasoning, the field of AI ethics has grown. Companies developing these systems may face severe consequences if the AI being developed do not produce the intended results. Mistakes in AI governance can have profound legal, financial and social impact on the organizations involved, citizens and even society at large. Governance has a critical role to help organizations monitor and manage their existing AI systems and feel confident embracing this new GenAI technology. With the burgeoning growth of GenAI adoption, it is imperative to consider the responsible application of both structured and unstructured data. Organizations must govern not only the data inputs, but also govern the information generated from the models. The quality of the data input also mirrors the final output of the model. As Malcolm Hawker has discussed, this drives a shift to focus on knowledge management. Evolving the data catalog to inventory and manage contextual insights and outcomes will be transformative in this space. Leadership needs to pause and consider how sound knowledge management enabled with the right technology, will help ensure the responsible use of AI. Build out a strategy for leveraging new technology as part of your data strategy and what governance practice is needed to make this successful. This ensures that AI solutions are both technically sound and ethically robust, enhancing trust and reliability in AI applications across the board. By staying proactive in AI Governance efforts, organizations will foster a culture of ethical AI use that promotes long-term trust and reliability in its AI applications. So now is the time to pause, reflect and adapt your AI strategy to avoid ‘Shiny Object Syndrome’. As leaders, you must evaluate your current strategy and ask yourselves: ‘are your racing to the bottom?’

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗳𝗼𝗿 𝗮𝗻𝘆𝗼𝗻𝗲. (But it won’t be the tech that’s failing you...) In fact, you will face these 6 challenges when introducing AI agents in your business (and quickly move from excitement to disillusionment): 1) Lack of clear business objectives Rushing into AI without defining why you need it. Without clear KPIs, AI becomes a costly experiment instead of a game-changer. 2) Overhyped expectations, underwhelming reality Expecting AI agents to replace entire workflows overnight. Instead, these systems require continuous tuning, monitoring, and human oversight. 3) Poor data quality and access AI is only as good as the data it learns from. Fragmented, biased, or outdated data leads to unreliable outputs and a loss of trust in AI-driven decisions. 4) Resistance from employees Team members fear job displacement or find AI tools frustrating to use. Without proper change management and training, adoption suffers. 5) Lack of human-AI centric process design True autonomy is still a bit off. AI agents need human-in-the-loop workflows, but many organizations fail to design effective collaboration models. 6) Scaling without strategy Your company starts with flashy AI pilots but struggles to scale due to technical bottlenecks, lack of cross-functional buy-in, or unclear ROI. How to avoid these challenges and turn Agentic AI into success? - Pursue AI projects as enablers of business strategy - Tie AI projects to measurable business value - Invest in data readiness & governance - Build AI literacy across teams - Design for human-AI collaboration The leaders who focus on practical implementation over hype will drive tangible value for their business. 𝗪𝗵𝗮𝘁 𝘄𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗮𝗱𝗱? #ArtificialIntelligence #GenerativeAI #AgenticAI #IntelligenceBriefing

Now That the Management Consulting Firms Have Raked In Millions And Applied Their Industrial Age Automation Playbook, What Will Their Clients Do Next? Who is going to clean up the mess? The same consultants who charged so much to create it? Over the past year, management consulting firms have rolled out a familiar playbook. They promised to “future-proof” businesses with Generative AI and automation. They analyzed operations into atomic “tasks,” optimized workflows through automation, and proposed staff reductions as a path to “efficiency.” They cited “productivity gains” in PowerPoint decks filled with industry jargon and untested assumptions. But none of it works According to a 2024 Gartner survey, 72% of organizations implemented “off-the-shelf” GenAI solutions without customizing them for business needs, leading to integration challenges and low ROI.¹ Pilot failures: Gartner projects that 80% of GenAI projects will fail to scale by 2025, primarily due to poor change management and unrealistic expectations.¹ Erosion of quality and trust: Forrester reports that 56% of customers notice a decline in service quality from companies aggressively pursuing GenAI cost-cutting measures.⁵ Hidden costs: IBM’s 2025 survey of CFOs found that over 50% of AI investments delivered lower-than-expected ROI due to retraining, error corrections, and compliance fines.⁶ Talent drain: PwC found that 42% of employees at companies with aggressive automation initiatives feel disengaged, with a 25% increase in talent attrition.⁷ In all honesty, I am not sure how any business can implement new technologies by working with consultants who have been using the same playbook Henry Ford would recognize? ******************************************************************************** The trick with technology is to avoid spreading darkness at the speed of light Disclosure: I’m the Founder & CEO of Curiouser.AI, a Generative AI platform and strategic advisory focused on elevating organizations and augmenting human intelligence through strategic coaching and values-based leadership. I also teach Marketing and AI Ethics at UC Berkeley. If you're a CEO or board member committed to building a stronger, values-driven organization in the age of AI, reach out, we’d welcome the conversation. Visit curiouser.ai, DM me, or connect on Hubble: https://lnkd.in/gphSPv_e Footnotes Gartner, “AI Transformation in Enterprises: Reality Check 2024–2025,” 2024. Boston Consulting Group, “The Risks of GenAI Implementation: Oversight and Governance,” 2025. McKinsey & Company, “Generative AI and the Future of Work: A Reality Check,” 2025. Deloitte Insights, “Digital Transformation or Workforce Reduction? Managing Culture in AI Transitions,” 2024. Forrester, “The Customer Cost of GenAI-Driven Efficiency Plays,” 2025. IBM Institute for Business Value, “AI ROI: Hidden Costs and Compliance Challenges,” 2025. PwC, “Employee Sentiment in the Era of AI and Automation,” 2025.

🔧 𝗡𝗲𝘄 𝗧𝗼𝗼𝗹, 𝗦𝗮𝗺𝗲 𝗢𝗹𝗱 𝗣𝗮𝗶𝗻 - 𝗗𝗿𝗶𝘃𝗶𝗻𝗴 "𝗕𝘂𝘀𝘆𝗻𝗲𝘀𝘀" 🗡️ 𝗪𝗲’𝘃𝗲 𝗮𝗹𝗹 𝘀𝗲𝗲𝗻 𝗶𝘁: a shiny “𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝗯𝗼𝗼𝘀𝘁𝗲𝗿” is rolled out across the company with great fanfare—only for teams to spend hours figuring it out, grafting it onto existing workflows, or simply abandoning it altogether. 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗸𝗲𝗲𝗽 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴—especially with the latest wave of AI / Gen AI platforms? 1️⃣ 𝗧𝗼𝗼𝗹-𝗳𝗶𝗿𝘀𝘁 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴: Many organizations fall in love with a popular LLM stack, then hunt for a business challenge that might fit it. The result? Misaligned pilots that never reach scale. 2️⃣ 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗼𝘃𝗲𝗿𝗹𝗼𝗮𝗱 ≠ 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻: Swapping a dozen point AI apps for another dozen doesn’t simplify work; it fractures attention and forces employees into constant context switching. 3️⃣ 𝗜𝗻𝘃𝗶𝘀𝗶𝗯𝗹𝗲 𝗳𝗿𝗶𝗰𝘁𝗶𝗼𝗻: Every new login, data-privacy checkbox, or “learning curve” minute chips away at the very productivity the tool was meant to unlock. 4️⃣ 𝗨𝗻𝗶𝗻𝘁𝗲𝗻𝗱𝗲𝗱 𝗰𝗼𝗻𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀: Poorly integrated Gen AI agents can trigger duplicate processes, shadow workflows, or data-quality issues—quietly eroding trust in the tech and the team’s morale. 𝗔 𝗕𝗲𝘁𝘁𝗲𝗿 𝗣𝗮𝘁𝗵 🔍 Start with the problem, not the platform • Map real pain points and measurable outcomes before evaluating vendors. 🧩 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲, 𝗱𝗼𝗻’𝘁 𝘀𝗰𝗮𝘁𝘁𝗲𝗿 • Build (or buy) solutions that plug into core systems—CRMs, knowledge bases, ticketing—not a siloed chatbot on the side. 📊 Define adoption metrics early • Track activation, frequency of use, and time saved; celebrate wins and rapidly iterate when metrics stall. 👥 Design for humans, govern for scale • Pair Gen AI experimentation with clear guardrails: data security, version control, and change-management plans. ⚡ Ship small, learn fast • Deploy a narrow use-case (e.g., automated RFP drafting) to one team, gather feedback, then expand—with the same tech only if it proves ROI. Generative AI is transformational, but only when matched to the right challenge, culture, and workflow. Let’s retire knife-handles-without-blades and start delivering solutions that truly cut through busywork. What’s your biggest lesson learned in rolling out new AI tools? Drop your stories below 👇

AI Agents can be tasked to find the "most efficient way" to deliver on a specific challenge. One problem with that is that sometimes the most efficient way is illegal, for instance colluding on price fixing, or insider trading. There is a significant risk that the current legal and regulatory frameworks cannot handle this issue, that companies will be able to "blame the algorithm" when caught, while reaping the profits. Everyone would agree if that is "deliberate" then the person who did that should be prosecuted, but what if it was "just an omission" or where "controls were not clear"? AI demands a different view of accountability, because it cannot be punished and it will not learn from regulatory admonishment. As people delegate their personal agency to AI, they shouldn't be able to delegate their responsibility. #EthicalAI #AI #TrustedAI #AIRegulation James Wilson, Oliver Stuke, Bikash Dash, Victoria Madalena Otter, Dr Maya Dillon

We need to stop talking about the risks of AI and start talking about its impacts. Risk is the possibility of something bad happening. Impact is the consequences. So, what are the future consequences that companies will be facing with AI? 𝟭. 𝗟𝗮𝘄𝘀𝘂𝗶𝘁𝘀: From using unlicensed data to train models to not informing users that AI is collecting, processing, and training on their data. This is happening today, and we’re just starting to see lawsuits pop up. 𝟮. 𝗥𝗲𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗮𝗺𝗮𝗴𝗲: A customer chatbot goes off script and starts spewing toxic content, which goes viral on social media. The chatbot is pulled offline and now you're struggling to figure out your next move while managing a PR nightmare. 𝟯. 𝗗𝗮𝘁𝗮 𝗟𝗲𝗮𝗸𝗮𝗴𝗲: You overshare data to your enterprise search solution, and now employees can access employee salaries via their chatbot. Or a malicious actor hacks your external chatbot and steals secrets that can be used to log into your cloud infrastructure, starting a full-on cloud compromise. 𝟰. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗢𝘂𝘁𝗮𝗴𝗲𝘀: Today ransomware targets critical servers to cripple a business. As companies lean into AI agents and use them for core business functions, we’re one rogue agent away from a new type of ransomware…one that doesn’t even have to be malicious, it’s just an agent going off script. I wrote about this in more detail in my latest newsletter. Check out the full article here: https://lnkd.in/eUCHb6bf