AI has very quickly become a standard part of the working day. Seventy-one per cent of professionals now use it at least a few times a week, while 53% say AI has been implemented in workflows or embedded into their organisation’s strategy.

But widespread use doesn’t automatically mean an organisation (or its workforce) is ready.

In our recent webinar, From use to impact: How is AI really changing the way we work?, JP Browne from Talent Auckland was joined by Emily Zhang, Founder and Director of The HumAIn Impact, and Jack Jorgensen, General Manager – Data, AI & Innovation at Avec.

Drawing on insights from 1,505 business leaders and technology professionals across Australia and New Zealand, they explored what organisations and professionals need as AI moves beyond experimentation.

Here are five key takeaways for employers, managers and technology professionals.

1. Using AI is no longer the differentiator

As access to AI expands, simply listing an AI tool as a skill will carry less weight. The stronger differentiator is whether someone can use AI to improve the work while retaining ownership of the outcome.

That means really understanding the problem, assessing the quality of the output and knowing when human judgement is required.

As Jack puts it:

“Don’t outsource understanding.”

This matters for employers assessing AI capability and for professionals demonstrating it. Evidence of good prompting may be useful, but evidence of better decisions, improved workflows or measurable outcomes is much stronger.

2. Human judgement becomes more valuable

At this point, you would have encountered ‘AI slop’ in one way or another.

We all know AI can produce polished work quickly, but we also know it can produce work that’s inaccurate, generic or poorly suited to its audience.

As AI-generated outputs become harder to distinguish on presentation alone, professionals need to demonstrate that they can evaluate what the technology produces. Is it accurate? Is it appropriate? Does it solve the right problem? What risks or assumptions need to be challenged?

For hiring managers, this creates a need to look beyond the finished output. Interviews, work samples and role-relevant assessments can help reveal how a candidate arrived at an answer, what decisions they made and where they applied judgement.

The same principle applies inside organisations: people remain accountable for the work, even when AI contributes to producing it.

3. Managers are central to workforce readiness

Only 25% of managers surveyed feel ‘very prepared’ to manage AI use within their teams, which raises alarms as managers sit where organisation-wide AI strategy meets everyday work, helping interpret policies, identify appropriate use cases, maintain quality and decide when something needs to be escalated.

They’re also closest to the workflows being changed.

Employers can’t expect managers to carry that responsibility without clear guidance and need to understand:

• Which tools and data are approved
• Where their decision-making authority begins and ends
• What their teams remain accountable for
• When human review is essential
• Where to seek support when a use case falls outside existing guidance

Preparing managers is therefore not a secondary part of AI adoption but one of the very practical conditions for making it work.

4. Training should build capability, not just tool familiarity

Almost a quarter of respondents have never received AI training or had a policy refresh.

And, unfortunately, even where training is available, completing a course doesn’t necessarily mean someone can apply AI effectively in their role.

As Emily explained:

“Training is an input. Capability and adoption are the outcomes.”

Useful training should be grounded in real work and should help people understand where AI can support their role, what it should not be used for, how to check its output and how their responsibilities may change.

It also needs to evolve. Tools, vendor policies and organisational use cases are changing way too quickly for one-off training to provide lasting readiness.

5. Workforce planning should start with the work

AI is likely to change the scope of some roles, the balance of tasks within them and the capabilities organisations need to retain.

However, that doesn’t mean workforce planning should begin with headcount reduction. Instead, a more useful starting point is to examine the work itself:

• Which workflows create the most friction?
• Which tasks can be automated safely?
• Where does specialist expertise remain essential?
• What will people do with the capacity AI releases?
• Which new responsibilities, skills or review points will emerge?

Involving the people doing the work helps organisations answer these questions with a clearer view of operational reality. It can also reveal opportunities to redirect capacity towards higher-value work rather than treating time saved as the final outcome.

AI maturity is a workforce question

A mature AI organisation isn’t defined by how many people use the technology or how many tools it deploys, but by whether its people understand the work, apply sound judgement and use AI to improve outcomes without losing human accountability.

For employers, that means building the right mix of specialist expertise, manager capability and practical AI literacy. For technology professionals, it means being able to show not simply that they use AI, but that they know where it adds meaningful value.

Explore what organisations and professionals should focus on next. Watch the full webinar on YouTube.