The People Power Behind AI Adoption and Why Organisations Often Fall Short 

The adoption of AI is an exciting prospect for most organisations, but it can also be unsettling. AI is transforming work faster than most anticipated, leaving some worried they’ll fall behind or that automation will make their roles redundant. 

Perhaps the bigger challenge is there’s still exponential growth we’re yet to see. Right now, AI feels like the internet in 1997, it’s only just beginning.

The fear is understandable, especially with recurring headlines about disruption and job losses, and generational perspectives that fuel uncertainty about where AI is heading. But fear doesn’t have to be the default. People need to feel confident to engage with AI, and that comes through learning and experimentation, not fear. 

AI will continue to be seen as a threat rather than an opportunity until organisations put in place the right governance to build confidence and give employees the chance to learn and build skills and capability.

ELMO’s 2026 HR Industry Benchmark report reinforces this reality. While 34% of organisations across Australia and New Zealand have at least half their workforce actively using approved AI tools, only 16% of HR leaders report a transformative impact from AI in the past 12 months, revealing a critical gap between adoption and strategic value.

If you’re reading this, you’re likely already using AI in your work, perhaps driving wins on your own, or starting to build infrastructure for your team. But adoption without impact is a warning sign. The gap between tool usage and transformation isn’t closing by accident. It requires intentional investment in people capability, not just technology deployment. Here’s what we’ve learned from closing that gap ourselves.

Why adoption doesn’t equal impact

The HRIB research reveals why this gap persists. When we asked HR professionals about the biggest barriers to AI adoption, three stood out: lack of skilled people (33%), lack of expert guidance (30%), and lack of time to experiment (29%).

But here’s what those stats really mean: organisations are deploying tools faster than they’re building capability. People want to use AI, 28% say it’s one of the skills they need most to drive impact, but they don’t feel equipped, supported, or given permission to learn.

That’s the gap. And closing it requires a deliberate, human-centred approach to building capability, not just rolling out technology.

Building capability, not just rolling out tools

This adoption-impact gap is precisely what ELMO set out to address when we committed to leading with AI across our entire organisation. We recognised that simply rolling out AI tools wouldn’t drive transformation, we needed to shift how our people understood and approached AI fundamentally. 

That’s why in May 2025, we launched ELMO Educate Day: a company-wide event designed to create a shared foundation for our AI-first future.

This event created a critical shift: employees realised AI is no longer a nice-to-have, it’s embedded in everything we do. That’s the moment AI literacy and fluency moved to the centre of our strategy. Because the gap between adoption and impact? It closes with understanding, not just usage.

From there, we built a structured training plan that builds on itself to raise the collective level of capability across the organisation. Our training journey unfolds in three stages:

• Foundational: The essential building blocks, what AI is, how to use it safely, how to prompt effectively, and where guardrails exist
• Advanced: Solving real business problems, building custom solutions, optimising workflows
• Transformative: Strategic use cases like workforce planning, scenario modelling, and process redesign

The key: each level builds on the last. You can’t skip foundations.

We started with a foundational module because you can’t build advanced capability on shaky ground. Employees needed to understand what AI could and couldn’t do, how to prompt effectively, and where guardrails existed, before we could expect them to solve complex problems with it.

Our refreshed onboarding programme now equips new starters with a clear understanding of our AI policies and AI-specific training to embed AI literacy from day one. Importantly, we’ve highlighted success stories from our early adopters to inspire others, and we back this with OKRs and recognition programmes that celebrate employees who are leading the change.

While leadership’s voice and action really matters, a top-down approach alone won’t result in AI adoption. Employees must have a strong voice to shift the dial, because when AI feels done to them, they resist, but when it’s done with them, they inevitably lean in and come out the other side more engaged and optimistic than before.

That’s why our recent Glean implementation started with employees identifying the problems they wanted to solve, not IT dictating the solutions. Teams across the organisation built custom agents tailored to their specific workflows, from automating tender prep to streamlining compliance checks, without needing to write code. The key wasn’t the tool, it was the approach: we gave people permission to experiment, support to build capability, and recognition for sharing what worked.

This approach addresses what the HRIB data reveals as the real barriers to AI adoption. When you create the space for experimentation and skill-building, rather than just deploying tools, those barriers around skilled people, expert guidance, and time to experiment start to dissolve.

Education isn’t a nice-to-have. Leadership knows it. 78% say it’s extremely or very important when operationalising AI, and HR is being held accountable for it, with 78% of HR teams reporting they’re fully or very accountable for education and training.

That accountability is precisely why we took an iterative approach to building AI capability.

From pilot to practice: Learning as we go

We started small: piloting a foundational training module designed to give employees the essential building blocks of AI, then scaling based on what worked. There’s no blueprint or north-star for AI. We’re learning as we go and evolving training in real time, with AI itself cutting workshop design from days to hours, a major benefit for time and resource-stretched teams!

But here’s what became immediately clear: putting that pilot into practice demanded close partnership between People & Culture and our Technology team. Neither side could drive transformation alone. Success requires both to have an equal voice.

The HRIB research backs this up. AI adoption is rarely owned by HR alone (12%), it’s typically driven by IT (39%) or C-suite (27%), with shared HR-IT ownership accounting for just 17%. The fragmentation is real. That’s why intentional partnership isn’t optional, it’s the only way to move from isolated pilots to organisation-wide capability.

Addressing job security concerns head-on

Even with dedicated AI learning opportunities in place, I occasionally speak with people who feel uneasy about their job security. Unfortunately, I can’t guarantee anyone’s job, not even my own. But I can show how AI has the potential to make roles richer and more interesting, and set our people up for long-term success.

My go-to approach is to listen to concerns and then reframe the conversation: ‘by having strong AI skills, you can future-proof your career’; ‘with AI, you can reduce the time it takes to do repetitive admin tasks’; ‘using AI will enable you to do more meaningful, strategic and impactful work’.

Ultimately, the goal is to replace the uncertainty with opportunity, by showing that AI is not here to take work away, but to create new ways for people to grow and add value.

The data supports this optimism. ANZ HR professionals are already seeing tangible improvements across their functions. 43% report performance and development support has improved due to AI, 38% point to reporting automation, and 37% to predictive analytics. 

And looking ahead, the use cases expected to have the greatest impact in the next 12 months are workforce forecasting and planning (44%), predictive analytics (39%), and performance and development support (42%). 

These aren’t tasks being eliminated. They’re being elevated.

Understanding where you sit on the AI spectrum

The HRIB research revealed that AI readiness and effectiveness vary dramatically across individuals and organisations. When we asked HR professionals to assess themselves, six distinct profiles emerged, each representing a different combination of infrastructure and capability:

• Explorers: Curious and experimenting, but without consistent habits or strong infrastructure
• Doers: Delivering results individually, but struggling to scale without organisational support
• Mavericks: High-impact results despite limited infrastructure, building solutions through determination
• Builders: Strong foundations in place, but AI adoption hasn’t kept pace with readiness
• Architects: Solid infrastructure and growing momentum, ready to accelerate
• Masters: AI fully embedded, driving measurable strategic impact

The point isn’t to label yourself. It’s to recognise that different stages require different support:

• If you’re an Explorer or Doer: Start with foundational training, build your personal capability and prove quick wins
• If you’re a Maverick or Builder: Focus on infrastructure and governance, so your wins can scale
• If you’re an Architect: Push into advanced use cases, workforce forecasting, scenario modelling, strategic planning

Not sure where you sit? Take our AI Maturity Assessment to benchmark yourself against the HRIB data and get tailored guidance on your next steps.

Efficiency and curiosity

I’ve experienced firsthand how AI streamlines my own work, cutting through admin and freeing up time for big-picture thinking. For those still on the fence, I encourage you to just jump in, whether it’s applying AI to professional tasks or experimenting casually, like I do with my custom GPT for footy tips (which might need refining for next year).

From my perspective, careers have always been about continuous learning and development. AI just makes that reality more urgent. If people engage now, they’ll future-proof themselves, not just for today, but for what comes next.