You click the link looking for signs. The em dashes. The staccato sentences. The suspiciously balanced ‘It’s not x, it’s y’ phrasing, before you declare: “Aha! I knew it!” And perhaps, that instinct says everything about the moment we are living through.
Trust is teetering. Truth feels increasingly up for grabs. And the breakneck pace of change has us all in a spin. Oh no, the ‘rule of three’ – there’s another obvious tell.
But the use of AI in the workplace has already moved light-years beyond providing a little help typing up meeting notes or piecing together an article. What we are now grappling with is something far bigger and altogether more challenging.
How do we lead teams through a transition moving faster than our structures, governance models and nervous systems were designed for?
How do we lead teams through a transition moving faster than our structures, governance models and nervous systems were designed for?
What does accountability look like when there are fewer humans in the loop and growing networks of agents making decisions for which they can’t be held responsible?
These questions provided the backdrop for this year’s Wisdom & Action Forum ‘Trust in the age of AI’, hosted by Small Giants Academy. In a session titled ‘Leading Organizations Through the AI Transition’, I joined Culture Amp founder and CEO Didier Elzinga, AI strategy and transformation executive Orla Glynn and world-first ‘neuro futurist’ and Director of the Future Minds Lab at University of New South Wales, Professor Joel Pearson, to unpack the tensions rapidly emerging for leaders and boards as AI reshapes our work and our world.
Rather than searching for easy answers or falling into familiar binaries of optimism or doom, we explored five central paradoxes leaders are being asked to hold right now.
Because while agentic systems and large language models may define the technological shift underway, what many organizations are actually experiencing is something far more human: a stress test in capacity and adaptability, alongside a profound recasting of the role of leader itself.
Speed versus judgment
The pressure organizations and boards are feeling to move quickly on AI is immense. And yet, while organizations race to adopt AI, many are still operating inside systems and structures designed for a slower, more centralized flow of decision-making. Fewer still have stopped to ask what they are actually moving faster towards.
Orla Glynn is an AI strategist who works with CEOs and leadership teams on the questions most organizations are avoiding. She is seeing too many organizations still treating AI as “a tool to bolt on” to existing systems, rather than recognizing how fundamentally it redistributes information, authority and decision-making.
“We are in an uncertainty pandemic right now and a lot of people respond to that with anxiety.”
- Joel Pearson
“We have a situation where you have the need to move fast because the market is demanding you to go fast. But … at no point has leadership stepped back and said: ‘Have we actually designed or redesigned our organization for what it means in the AI era?’” Glynn says.
Challenging the assumption that integrating AI is primarily a technological imperative, Glynn argues it is actually a much deeper structural redesign challenge. If we remain overly focused on efficiency and cost reduction, we avoid the harder strategic question: What does this organization become because of AI?
The tension is this: organizations are being rewarded for acceleration at the exact moment they most need judgment, reflection and restraint.
Trust versus accountability
Agentic systems are already shaping decisions across workplaces, often faster than governance structures can keep up. Yet while organizations may delegate tasks or analysis to AI, accountability still lands squarely on humans. So what does responsible governance look like when leaders and boards are required to oversee technologies they do not fully understand?
A significant gap is emerging in AI literacy at the leadership and board level, with many organizations placing growing trust in external vendors and systems without the internal capability to properly interrogate risk or manage accountability.
Culture Amp Founder Didier Elzinga pointed out that AI systems are still far from deterministic – they do not reliably produce the same output every time, which fundamentally challenges traditional ideas around oversight, consistency and control.
Business leaders and boards are now being asked to place trust in systems that are probabilistic, opaque and constantly evolving, while still carrying full responsibility for the outcomes those systems produce.
“Almost every company is looking at their core values right now and going, ‘Do we have to change some of them to lean into this new world of work?’ The question that a lot of them aren’t asking is, ‘Who are those values for?’ Because they’re no longer just for humans,” Elzinga notes.
“Almost every company is looking at their core values right now and going, ‘Do we have to change some of them to lean into this new world of work?’”
- Didier Elzinga
Panelists challenged the growing assumption that AI will somehow deliver perfectly objective decision-making. It won’t. But then again, humans are hardly flawless decision-makers ourselves. Neuroscientist Joel Pearson thinks AI, paired with human oversight, can lead to better decisions overall – while acknowledging that this is also where ethical tension deepens.
“AI can overcome a lot of those fallacies or problems with human decision-making. And so I think soon, whether you’re a medical doctor, a judge or a politician, it’ll probably be illegal to make decisions that are consequential without the aid of AI, because together, you can sort of get the best of both worlds,” Pearson explains.
He also warned that as AI becomes more embedded in consequential decision-making, there is a risk that humans begin distancing themselves from moral culpability simply because an AI system was involved. We cannot outsource responsibility simply because we have outsourced the workflow.
Perhaps that becomes one of the defining leadership inquiries of the AI era. It’s not simply what these systems are capable of doing, but whether we are still willing to fully own the consequences of the decisions they help us make.
Profit versus prosperity
AI is creating value at the firm level, but disrupting value distribution across broader systems. While much of the current business conversation centers on productivity, efficiency and cost reduction, the harder question is: What happens when the economic value generated by AI no longer flows through people, wages and communities in the same way?
What does dignity and prosperity look like in an economy increasingly shaped by agents and distributed intelligence? Where does the value flow? What obligations do we have to workers, communities and future generations during the transition?
For Culture Amp, cost cutting is the easiest and least imaginative application of AI:
“The easy thing to chase is the cost saving, because it’s the business case that writes itself. But that is, at best, an arbitrage. It lasts maybe a year, and then everybody else will do the same thing. So the question for every business is not, ‘how do you rip 30 percent of your cost out?’ The question is, ‘how do you build something that uses AI for great growth?’’ Elzinga says.
The challenge for leaders, particularly within an ethical and purpose-driven framework, is ensuring the gains created through AI are shared in ways that strengthen people, communities and the planet – rather than concentrating and consolidating power and resources even further at the top of the chain.
Competition versus collective responsibility
One of the strongest themes throughout the session was how many of the opportunities and risks emerging around AI are both systemic and shared. Workforce wellbeing, surveillance, privacy — these are neither challenges individual organizations face, nor will be able to solve in isolation, particularly when competitive pressure is pushing entire industries in the same direction at once.
What happens when ethical choices create commercial disadvantages? How do we prepare the next generation for a future so uncertain, especially when many of the junior, entry-level and administrative roles that have traditionally acted as pathways into industries are now the first to go?
“We keep asking whether organizations are ready for AI. A better question might be whether leaders have the capacity to navigate what AI is about to demand of them.”
- Orla Glynn
“We are in an uncertainty pandemic right now and a lot of people respond to that with anxiety,” Pearson acknowledges. “When AI comes into a business, they worry about the future of their job. What about their mortgage? And so there is a fundamental human support piece to navigating change that is more important now than basically anything else at any kind of scale.”
Pearson spoke to the need for AI-specific change management models – not just for businesses, but families and whole countries. We need more spaces to socialize and verbalize the information, more ways to share stories about what works and what terrifies us, as a way to reduce collective uncertainty.
As leaders, we also need to resist forcing a binary between ‘pro-AI’ as ‘anti-people’ and vice versa, and instead create environments where people can build shared language, ask difficult questions and process change together.
Authority versus capacity
The final paradox is what happens when the very leaders responsible for ushering teams through the AI transition are the same people whose authority, expertise and roles are being fundamentally reshaped by it.
Traditional leadership has long been built on expertise, authority, decisiveness and having the answers. What happens when information flows faster than hierarchies can process it? What does leadership look like when certainty itself becomes increasingly scarce?
For Pearson, one of the most important shifts leaders can make is adopting a beginner’s mindset and creating environments where learning can move freely across organizations.
For Glynn, the imperative is for leaders to ready themselves and their organizations for the complexity of what lies ahead.
“We keep asking whether organizations are ready for AI. A better question might be whether leaders have the capacity to navigate what AI is about to demand of them,” she suggests.
For Elzinga, honesty is best.
“Organizations would be better placed to actually try and engage people and take them on the journey, and to be honest about the fact that they can’t give them the certainty they want,” he advises.
Perhaps the reimagined role of leader is less about providing certainty and having all the answers, and instead about a willingness to be transparent and involve teams in shaping what comes next.
No neat answers, but that’s the new normal
There is still so much we don’t know about what business and leadership will look like in the age of AI. What we do know is we need more leaders guided by ethical frameworks and robust standards, prepared to wade into its murky waters and moral implications.
We also suspect the organizations best positioned to prosper through the transition may not be the ones moving the fastest – but the ones building the capacity to navigate change and doing what they can to stay connected and human while the world around us continues to shift.
What does accountability look like when there are fewer humans in the loop and growing networks of agents making decisions for which they can’t be held responsible?
Perhaps that is why the Māori whakataukī shared during the session felt so resonant: Kia whakatōmuri te haere whakamua – I walk backwards into the future with my eyes fixed on the past.
Our challenge as leaders is to slow down, at least occasionally, so we may carry forward the lessons and values that make progress worth pursuing in the first place.
