How to avoid AI adoption pitfalls with change management

AI is no longer a question of capability. It is a question of resilience. Too many organizations remain stuck because they overlook the foundations that make AI sustainable: governance, trust, and disciplined data practices. Succeeding with your AI strategy requires more than technology. Leaders who succeed will balance speed with security, compliance, and transparency because AI leadership is not about being first. It is about building systems that last.   Read more here: https://lnkd.in/gSxBT2ii   Forbes Business Council Hitachi Vantara #DataFoundation #AI #Resilience

In the journey of digital transformation, many enterprises have already laid the groundwork. We've talked a lot about modernizing with AI, embracing APIs and containerization, and building the right cloud-centric frameworks. We've also set the stage for a new kind of workforce, one that includes not just human workers, but also AI agents, coordinators, and overseer systems all working in harmony. But with that foundation in place, what comes next? Over the next three to five years, the real frontier is going to be all about personalization, governance, and keeping the human element front and center. #digitaltransformation #changemanagement #AI #agenticenterprise #governance #humanexperience #csuite #EQengineered https://lnkd.in/g3pYp-s8

💡 AI adoption is accelerating - but governance still lags. According to Collibra’s new survey, most organizations see strong ROI potential from AI (especially “agentic AI”), yet fewer than half have formal governance in place. That gap is where risk lives - bias, data misuse, reputational harm. Takeaway: innovation needs guardrails. Governance isn’t just compliance - it’s confidence. Building clear frameworks, accountability, and auditability now ensures AI scales responsibly later. #DataGovernance #AIGovernance #RiskManagement #DigitalTransformation #DataStrategy #ResponsibleAI #DCSA #KYMAdvisors

💡 What AI Cannot Do in IT Operations & Support AI is transforming how we manage IT Operations — predictive analytics, auto-healing systems, intelligent chatbots, AIOps — the list keeps growing. But amid the excitement, there’s one truth leaders often overlook 👇 AI can assist — but it can’t replace the human element that keeps IT truly operational. Here’s where AI still falls short (and why humans remain the heartbeat of IT): 🚫 1. Understanding Business Context AI can spot anomalies, but it doesn’t understand why they matter. A server alert at 2 a.m. could be noise — or a showstopper before a global product launch. Humans connect technical issues to business impact. 🧩 2. Navigating Complex Dependencies IT ecosystems are a web of interconnected apps, databases, APIs, and networks. AI sees the pattern, but not the relationships. Root Cause Analysis (RCA) often requires intuition, undocumented knowledge, and systems thinking — things AI doesn’t yet master. 💬 3. Showing Empathy in Support When systems fail, users need reassurance, not just responses. AI-generated apologies can sound polite — but lack warmth, credibility, and empathy. In crisis moments, people trust people. 🔒 4. Making Ethical or Security Judgments AI can’t reason through the gray areas: “Should we bypass a control to restore service?” A human must weigh risk, compliance, and context — AI just follows patterns. 🧠 5. Handling the Unknown AI learns from what it’s seen. New incidents, zero-day failures, or vendor-specific quirks often break its logic. Humans innovate; AI imitates. 🛠️ 6. Leading and Building Team Culture AI can automate workflows, but not accountability, ownership, or mentorship. Teams still need human leaders to align, inspire, and grow. ⚙️ 7. Taking Responsibility AI doesn’t “own” decisions. In critical incidents, you still need an Incident Commander — someone accountable for outcomes, communication, and closure. 🌍 The Bottom Line AI is a co-pilot, not a commander. It makes IT faster, smarter, and more predictive — but the human layer gives it purpose, direction, and empathy. In IT Operations and Support, the winning formula isn’t “AI vs. Humans.” It’s AI + Humans. 💬 Your Turn: Where do you think AI still struggles most in IT operations? Have you seen a situation where human judgment made the difference? #AIOps #ITOperations #ArtificialIntelligence #TechLeadership #DigitalTransformation #SupportExcellence

AI governance lags as adoption speeds ahead: Fewer than half of technology decision-makers have formalized governance policies in place, according to a Collibra survey.  #CIODive #Innovation

⚖️ The AI Governance Paradox: Scaling Trust and Velocity Oliver captures the paradox at the heart of AI governance, which is becoming impossible to ignore. The faster we scale capability, the more fragile trust can become. The more we operationalise AI, the harder it is to sustain control. These top 10 challenges show that governance is not about slowing things down — it’s about designing systems that can move fast, safely. Governance should be a conscious design. Creating freedom within a framework, with integrity, oversight, and accountability built into the flow of how AI is designed, delivered and deployed. Because AI amplifies the system it lands in, the question isn’t how much AI can we adopt, but how can we consciously scale. #AIGovernance #ResponsibleAI #AILeadership #DigitalTrust #AITransformation

This guidance is so relevant, and I want to highlight and thank Oliver Patel, AIGP, CIPP/E, MSc for overview that outlines and summarizes the Top 10 Challenges for AI Governance Leaders in 2025. It emphasizes the critical realities of overseeing AI governance as it becomes increasingly widespread across enterprises. Key points include: Establishing effective governance is essential for maintaining competitiveness and ensuring compliance in a landscape where AI is increasingly accessible. Leaders must find ways to manage a growing number of AI initiatives without becoming overly restrictive. Key challenges include focusing governance efforts on the small percentage of AI systems and use cases that pose the most significant risks, protecting confidential and proprietary business data, and ensuring proper vendor due diligence for externally provided AI applications. There is also a need to promote AI compliance by design and by default throughout the enterprise while encouraging responsible human oversight of AI systems—particularly since the goal of agentic AI is to minimize human involvement. Finally, it is crucial to streamline digital governance and risk management processes to accelerate AI innovation while strengthening compliance. Additionally, I also recommend reading Oliver Patel, AIGP, CIPP/E, MSc newsletter and the resource "Practical Solutions: The Three Gs for AI Use." Guidance: Train employees on their responsibilities when using AI and the applicable compliance rules. Any AI system deemed high-risk under the EU AI Act must undergo careful review, regardless of the technology employed. Guardrails: Implement safety measures for accessible AI platforms to prevent high-risk use cases from advancing. Regularly review prompts, documents, and model outputs to identify and address potential compliance issues. Greenlight: Clearly specify which AI platforms, tools, data sources, and workflows are allowed. This will help everyone understand safe practices and improve AI governance. Thanks Oliver Patel, AIGP, CIPP/E, MSc #AI #AI governance

Top 10 Challenges for AI Governance Leaders in 2025 Working in AI governance is not for the faint hearted. Every day you are confronted with knotty legal, compliance, ethical, technology, and people-related challenges—or usually a blend of all five! To further complicate things, there is no established blueprint or institutional memory for AI governance success. Working at the enterprise AI governance coalface for over 3 years has been rewarding and relentless in equal measure. Reflecting on the state of the field today, this post summarises my personal take on the Top 10 Challenges for AI Governance Leaders in 2025. They key message is that we have no choice but to be agile and adaptive, so that we do not apply AI governance designed for the AI of 3 years ago to the AI of today. You can read the full analysis in my new 2-part series on Enterprise AI Governance (link in comments). This covers each of the 10 Challenges in detail and offers 3 practical and actionable solutions for each one. 1. The Democratisation Dilemma 2. The Volume and Velocity Challenge 3. Refining the risk-based approach 4. Streamlining digital governance silos 5. AI engineering: building with foundational models 6. Protecting confidential business data 7. Vendor due diligence and oversight 8. Open-source AI model oversight 9. Engineering compliance by design 10. Agentic AI governance: taking the human out of the loop

Earlier today as I was preparing for a panel discussion on what else #AI, I shared with the group, that the pace of change is faster than it's ever been, and the sobering thought is, it's unlikely to ever be slower than it is today. To that end, I think it's more than fair to say that AI is moving fast. What may not be moving fast is the governance or guard rails for ensuring AI is used responsibly, safely and consistent with how a company builds a reputation and brand. It begs the question while AI adoption is racing ahead, is governance? Or is it lagging behind? Or worse, not even an after thought? The results of this survey would seem to indicate that fewer than half of technology leaders have formal AI governance frameworks in place. Yet most organizations are already exploring or deploying agentic AI. As we let that sink in, it creates an interesting dynamic... certainly we’re eager to capture the ROI of AI innovation, but are the majority still building the guardrails to manage its risks? Again, taking from the article and survey, three in five enterprises are monitoring for bias, fairness, and transparency, yet only 47% provide governance or compliance training. Wow, that's really thought provoking as without structure, even the best intentions risk becoming ad hoc governance. Maybe this is fine for pilots, but unsustainable at scale. As I mentioned prior, what is at stake here is the brand and credibility companies work so hard to build. At a high level, if we release new technology (AI) into the wild and it outputs mean, wrong, just plainly stupid things, it's not necessarily a technical failure, it’s a reputational one. Technology leaders have a critical leadership opportunity on our doorstep. We must bring discipline to innovation to ensure that AI governance evolves as quickly as AI adoption. That will certainly come with tradeoffs between speed, risk management. Where ever we land, it must be intentional, explicit, and accountable. I would submit in the AI era, trust isn’t a compliance checkbox, it’s going to be a competitive advantage. #CIO #AIGovernance #DigitalTrust #Leadership #AIEthics #AIMaturity #Transformation #RiskManagement