Deloitte¡¯s Pip Dexter joins Future of Work: Australia to share why leaders need to spend time on the AI 'dance floor' to keep human skills front and centre.
Bel Bennett
Partner Marketing Director
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Audio also available on and .
, Chief People and Purpose Officer at Deloitte Australia, is on a mission to make work better for humans, and humans better at work.
She has her work cut out for her, leading an 11,000-strong workforce across the country. But she is steadfast in her vision for what truly matters in 2026 and beyond.
"In this AI era, it will be the human skills that be the differentiator," she says when we meet to record the latest episode of .
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Unlocking those human skills means solving a problem that ²ÝÝ®ÊÓƵ has dubbed ''.
Our research shows that while ANZ employees believe in the power of AI, around 30% are losing more than seven hours a week reconciling conflicting data between systems that don't talk to each other.
In other words, we have no shortage of technology in our workplaces, but until we redesign how work gets done, people will continue acting as the 'glue' between disconnected systems.
When attempting to solve this problem, there is a tendency to focus solely on capability. How do we build AI fluency? Which tools should we buy? How do we train our people?
Those things matter, but as Pip points out, the more urgent challenge showing up right now isn't capability¡ªit¡¯s capacity.
report shows that over the past year, one-third of workers experienced up to 15 major changes, sparking widespread 'change exhaustion'.
When employees spend time copying, pasting, and learning new tools while managing their day job, it can create an 'AI tax' that leaves them fatigued.
"When I come back to this concept of an AI tax, I think that also comes down to your wellbeing and the conditions that you have to embrace new things," Pip tells me.
Getting hands on with the tools and building AI fluency can role-model credibility and curiosity among teams.
For leaders, this means looking closely at capacity and asking whether teams actually have the space and mental bandwidth to adapt.
It requires stepping back to ask: what outcomes are we trying to drive? What problems are we actually trying to solve? How do we bring people, process, and technology together to do that?
Without that reflection, technology just adds to the noise.
So, what can leaders do to make sure their teams have the capability and capacity to thrive? According to Pip, they need to balance their time on the 'dance floor' with time on the 'balcony'.
"I think in this environment, it's super important that leaders spend time basically on the front line, truly understanding what the issues are of their organisation, the people that they're working with. And so that's what I mean by getting on the dance floor," Pip says.
Getting hands on with the tools and building AI fluency can role-model credibility and curiosity among teams. But there's a risk.
"The flip side is if you are always on the dance floor, you don't necessarily get perspective," Pip says. "And so hence the metaphor of the balcony. It's super important that leaders make that time to get on the balcony, to get perspective, get outside-in thinking."
According to Pip, the best leaders move fluidly between both. They step onto the balcony to get quiet reflection time, look at the big picture, and set strategy. Then, they step back onto the dance floor with fresh context to get the team moving.
One of the most interesting parts of our conversation was around what tasks AI should do, versus what tasks humans should do.
Pip shared a great example of the hidden costs of automating tasks that should be preserved for humans.
If we automate away the foundational tasks that build this judgment early in someone's career, we risk undermining our long-term capability.
"When you take notes and listen in a meeting, you're really trying to discern what is important and what's not," Pip says. "That's a really critical human skill and it helps you develop judgment."
She shared how one legal firm recognised that taking notes was actually how junior lawyers learned to listen, synthesise information and pull out what really mattered.
As a result, they decided to bring junior employees back into certain meetings to take notes, instead of using AI. As Pip put it: "Just because AI could take notes, it doesn't mean it should."
It's a good point. Critical judgement and taste are becoming more important in a world of 'AI slop', not less.
If we automate away the foundational tasks that build this judgment early in someone's career, we risk undermining our long-term capability.
Younger employees may be digital natives who pick up new AI tools effortlessly, but they often lack the commercial context developed from experience.
Senior leaders may have years of strategic experience, but may feel less comfortable diving into new tech.
To bridge this gap, Deloitte has introduced a reverse-mentoring program where executive team members get paired with younger employees as 'AI buddies'.
"What I love about this is it's a digital native who really understands technology, and they will spend more time with us as leaders," Pip says.
The learning flows both ways. Leaders build practical AI fluency, while junior team members get direct exposure to executive thinking, commercial context, and leadership skills.
It turns technology adoption from a stressful top-down mandate into a genuine two-way connection across .
7 in 10 business leaders say their primary competitive strategy over the next three years is to be fast and nimble.
In report, 7 in 10 business leaders say their primary competitive strategy over the next three years is to be fast and nimble.
This requires more than technology investment. It's about multiplying human potential with AI to achieve deeply integrated human-machine collaboration.
The human edge¡ªcritical judgment, clear communication, genuine empathy, and the ability to collaborate effectively¡ªwill always be the real differentiators.
As leaders, our goal is to build the right environment so employees can use technology to scale their impact, while doubling down on the human qualities that make work meaningful.
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Why task-oriented AI is failing the enterprise in Australia and New Zealand.