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  "path": "/article/4181237/ai-has-a-leadership-problem-not-a-technology-problem-most-organisations-havent-noticed-yet.html",
  "publishedAt": "2026-06-05T09:00:00.000Z",
  "site": "https://www.cio.com",
  "tags": [
    "Artificial Intelligence, IT Leadership, IT Management, Staff Management",
    "research",
    "KPMG and the University of Melbourne",
    "Want to join?"
  ],
  "textContent": "Recently, a senior leader asked me why their people were “resisting” the new AI tools they’d just mandated across the business. As we unpacked it, they admitted there’d been no real narrative about why this mattered, no redesign of processes and no time or support for teams to safely experiment, just licenses, policy and a launch email. The tools were live, but the change never actually started.\n\nI keep seeing the same pattern across organisations in Australia and New Zealand: Significant AI investment, thoughtful technology selection and far less adoption than leaders expected.\n\nAfter 25 years in transformation, I’ve come to believe that AI doesn’t fail organizations. It exposes them. Specifically, it exposes the leadership and change capability gaps that were already there, and it does it faster, more visibly, than most technology shifts before it.\n\n## The numbers don’t lie — but they don’t tell the whole story\n\nProsci recently completed research with 1,107 participants across executives, team leaders and frontline workers examining AI implementation in practice. The headline findings sound encouraging: 94% of organizations say AI is easy to use, and 98% find it valuable.\n\nSo why are so many implementations still falling short of expectations?\n\nThose numbers measure perception, not behavior. Real ROI only shows up when people actually work differently, and that’s where most organizations are stuck.\n\nWhen we looked at the organizations getting real traction from AI compared to those still struggling, the difference wasn’t the technology. It was leadership, trust, culture and change management. More specifically, it was the extent to which organizations treated AI as a human transformation, not simply a technology implementation.\n\n## AI adoption is exposing a leadership gap\n\nOne of the more uncomfortable truths about AI transformation is that it exposes leadership capability gaps faster than most previous technology shifts.\n\nAI changes decision-making, workflows and accountability in real time. That creates uncertainty, especially for middle managers and frontline teams who are being asked to rethink decision-making, productivity and even role design in real time.\n\nEmployees are asking questions many leaders are still struggling to answer themselves: Will AI replace parts of my role? What decisions should humans still own? How do we measure productivity now? What happens if the AI gets it wrong?\n\nIn organisations where leaders avoid these conversations or default to overly optimistic messaging about AI, trust erodes quickly.\n\nEmployees are far more likely to engage with AI when leaders explain how it will be used, where oversight exists and how human judgement remains part of the process. The trust is built through conversation, not comms.\n\nYet the gap between deployment and trust is widening. Research from KPMG and the University of Melbourne found that 65% of Australian employees work for organisations already using AI, yet only 36% say they are willing to trust it. In leadership conversations, the same patterns keep surfacing: Leaders talk about “efficiency” but avoid the real conversation about roles and headcount, offer lofty AI strategy with almost no concrete guidance on how decisions should change, and ask for experimentation while quietly punishing visible failure. Will this replace parts of my role, what do humans still own, what happens if it goes wrong, are ducked or deferred. And when leaders either blindly “trust the data” or run one-way town halls instead of creating ongoing, honest dialogue, people conclude that AI isn’t something being done with them, but to them, which is exactly where trust starts to fray.\n\nThis disconnect matters enormously because the frontline is where the work actually happens. If the people closest to customers, operations and day-to-day decisions don’t trust the tools they’ve been given, adoption stalls.\n\nAnd unlike a software issue, you can’t patch a trust deficit with a product update.\n\n## What struggling organizations have in common\n\nOrganizations that are struggling with AI implementation share a few common traits. Their leaders are taking cautious, incremental steps rather than committing to real transformation.\n\nAI experimentation is unsupported or subtly discouraged. AI capability sits inside a small specialist group instead of being distributed across the workforce. And perhaps most importantly, there’s a growing disconnect between what executives believe is happening with AI adoption and what employees are experiencing day to day.\n\nOn a recent transformation in a large financial services organization, these patterns were almost textbook. The exec team believed they were “playing it safe” with AI, limiting access to a small innovation group and asking everyone else to “watch and learn”, but on the ground, teams quietly read that as a signal that AI was risky, optional and not really for them. The result was a widening gap: Reports to the board painted a picture of steady AI progress, while frontline staff were still copy‑pasting into old templates, unsure what they were allowed to try and increasingly cynical about all the AI talk because nothing meaningful in their day-to-day work had actually changed.\n\nToo many organizations still approach AI as a technology deployment exercise rather than a business transformation effort. ess.\n\nThere’s an assumption that once the tools are available, adoption and value will naturally follow.\n\nBut technology doesn’t change behavior on its own. People do.\n\n## What organisations getting AI right are doing differently\n\nThe organisations making real progress with AI tend to behave differently in four important ways.\n\nFirst, their executives behave as though AI adoption is a business priority – not an innovation side project. They are visible, engaged and clear about what success looks like.\n\nSecond, they manage the human side of change deliberately — with structured change management, clear communication, manager enablement, feedback loops and active resistance management. The technology rollout is only one workstream. The people workstream runs alongside it. That’s where trust is either built or lost.\n\nThird, they build trust through transparency. High-performing organizations explain how AI tools work, what data they use and where human oversight still matters. They don’t expect employees to blindly trust a black box.\n\nFinally, they democratize AI capability. Rather than isolating AI knowledge inside IT or innovation teams, they actively spread capability across the organization through experimentation, learning and peer support.\n\nIn one recent transformation with a multi-national, adoption only started to accelerate when the executive team stopped treating AI as an innovation experiment and rewired how they led day to day. The COO began every weekly ops call by showing one concrete way they had personally used AI in their own workflow that week, then asked each manager to share an example from their teams, creating visible norms and peer pressure around real usage rather than abstract support.\n\nAt the same time, they ran a dedicated “people workstream” alongside the tech rollout, equipping managers with talk tracks, running open Q&A forums about data and oversight and inviting frontline employees to co-design how AI would support core processes like rostering decisions. Trust, transparency and capability all rose together instead of lagging behind the technology.\n\n## The question I’d ask leaders\n\nIf your AI investment isn’t paying off the way you expected, before revisiting the vendor contract or platform choice, I’d encourage you to ask a harder question:\n\nHave we actually managed the change? Not simply announced it. Not just trained people on where to click. Managed it — in the full sense of that word.\n\nHave we built the case for change at every level of the organization? Have we addressed the trust gap between the boardroom and the frontline? Have we created permission to experiment, and supported people when things don’t go perfectly the first time?\n\nBecause AI adoption only happens when people understand how the technology supports their judgement, not replaces it.\n\nMost organisations already have access to capable AI technology. What will separate successful organisations from everyone else over the next few years won’t be access to tools. It will be leadership’s ability to build trust, clarity and confidence around how work changes.\n\nFor ANZ organizations, what’s at stake here is more than just a few missed efficiency gains, it’s the risk of quietly falling behind in competitiveness while telling themselves they’re “doing AI.” If we don’t address the human side of AI transformation, we’ll end up with expensive tools sitting on the shelf, increasingly cynical workforces and a widening gap between glossy strategy decks and what actually happens in offices, sites and frontline teams.\n\nBut if leaders lean into the hard work of trust, clarity and genuine engagement, AI becomes something very different: A catalyst for better decision-making, more meaningful roles and workplaces where people feel empowered, not threatened by new technology. In that future, the organizations that win in ANZ won’t just be the ones with the best models or biggest budgets; they’ll be the ones whose leaders had the courage to bring their people with them, not just their platforms.\n\nBecause AI transformation isn’t primarily a technology transformation.\n\nIt’s a human one.\n\n**This article is published as part of the Foundry Expert Contributor Network.**\n**Want to join?**",
  "title": "AI has a leadership problem, not a technology problem. Most organisations haven’t noticed yet"
}