Shadow IT. Every organisation has it. Employees adopting new tools without official approval because it helps them get the job done faster. Now we’re seeing its next evolution: shadow AI. And it’s spreading faster than most leaders realise.

As JP Browne, Practice Manager at Talent Auckland, explains:

“The use of AI tools is prolific in every organisation, and it kind of just happened. Executives are scrambling to catch up, to either capitalise on it or put some structure around it.”

From staff pasting data into ChatGPT to marketing teams using generative tools for campaigns, shadow AI is already shaping workflows, decisions, and customer interactions. The question is: will your organisation keep pace, or fall behind?

The upside of shadow AI

It’s tempting to see shadow AI only as a risk. And yes, it comes with security, compliance, and ethical challenges. But there’s also a huge opportunity: grassroots innovation.

“Most of the experiments we’ve seen start with individuals in departments,” JP says. “They’re dabbling to find productivity gains or new insights, and that’s what’s forcing organisations to pay attention.”

In other words, shadow AI is where many of the best use cases are discovered. It’s employees closest to the problem spotting where AI can create value. Ignoring that would be a mistake.

The risks leaders can’t ignore

Of course, there is a dark side. Shadow AI introduces risks that can’t be brushed off:

• Data leaks. Sensitive customer data pasted into public AI tools can breach privacy laws and contracts.
• Security gaps. AI-generated code may introduce vulnerabilities that slip past standard reviews.
• Compliance issues. Using unapproved tools in regulated industries can expose companies to fines or reputational damage.

Jack Jorgensen, General Manager of Data, AI & Innovation at our IT delivery arm Avec, recalls:

“We’ve seen AI drop entire databases and then apologise. If you’re not putting guardrails around this, you’re gambling with your business.”

Why locking it down doesn’t work

Some organisations’ instinct is to ban AI tools outright but that would be a losing battle. Employees will find workarounds if the technology genuinely helps them.

Instead, leaders need to acknowledge shadow AI as a reality and bring it into the light.

“You can’t just bury your head in the sand,” JP stresses. “AI is in everything now, even your phone updates. The only question is whether you create a framework for using it safely.”

Turning shadow AI into an advantage

So, how do you harness the innovation of shadow AI without exposing your business to unnecessary risk? Start with three steps:

1. Listen first. Find out what employees are already using and why. Often these experiments highlight gaps in existing tools or processes.
2. Set clear guardrails. Develop policies around data security, compliance, and acceptable use. Make them practical so employees don’t feel forced underground.
3. Encourage responsible innovation. Provide safe sandboxes or approved platforms where staff can test and share ideas.

Jack’s advice:

“Don’t focus on speed for the sake of it. Focus on building velocity and a foundation that lets you scale safely and keep experimenting.”

The innovation edge

Shadow AI is a sign your people are hungry to innovate. Rather than suppressing it, leaders should channel it. The companies that do will move faster, find better use cases, and keep their competitive edge, and those that don’t risk being left behind by their competitors and their own employees.

Shadow AI isn’t a threat to stamp out. It’s a wave to ride. The businesses that embrace it with the right guardrails will unlock innovation and the ones that don’t will spend the next five years scrambling to catch up.

Discover how other organisations are navigating shadow AI in our AI survey results.

AI is changing the way we work faster than most organisations can adapt. Tools like ChatGPT and Microsoft Copilot are already baked into everyday workflows. But here’s the catch: the more we rely on AI, the more critical thinking becomes the skill that separates the leaders from the laggards.

As Jack Jorgensen, General Manager of Data, AI & Innovation at our IT delivery arm Avec, explains:

“It’s easy to get caught up in the speed AI gives you. But if you don’t understand the problem you’re solving or what happens when the system breaks, you’re setting yourself up for failure.”

AI is powerful, but it’s not infallible. And in a world where outputs can look convincing but still be completely wrong, critical thinking is the safeguard every professional needs.

The productivity trap

Many organisations rush into AI adoption with a focus on speed. How quickly can we roll this out? How much time can we save? That’s short-term thinking.

“Velocity is more important than speed,” Jack notes. “It’s not about being first to market with a shiny AI tool. It’s about building solid foundations so you can scale safely and sustainably.”

Critical thinking shifts the focus from how fast to how valuable. It asks: is this solving the right problem? Is it secure? Is it ethical? Does it actually add value for the business or customer?

Spotting the flaws

For those of us who use AI daily, spotting the flaws can feel obvious; six fingers on a generated image, or a chatbot confidently inventing a source. But as JP Browne, Practice Manager from Talent Auckland points out, it’s not always that simple:

“It’s getting harder and harder to tell what’s AI-generated. And that’s a huge risk if people stop questioning what they see.”

Critical thinkers don’t just accept outputs at face value. They test, validate, and challenge. That mindset is what prevents AI from becoming a liability instead of an asset.

Why this matters for every role

Critical thinking isn’t just for data scientists or IT leaders. It’s for recruiters screening AI-written CVs, finance teams reviewing AI-generated forecasts, and executives reading AI-drafted reports.

JP has already seen how over-reliance on automation can backfire in recruitment:

“Candidates are using AI to craft brilliant cover letters, but the CV doesn’t match the job. If you don’t apply a human lens, you’ll make bad hiring decisions.”

In other words, AI can help filter and accelerate, but without human judgment the wrong calls get made.

Building critical thinking in the AI era

So how do you make critical thinking a core skill in your team? Here are three steps:

1. Teach healthy scepticism. Encourage employees to question AI outputs, not just accept them.
2. Build human-in-the-loop processes. Always pair AI automation with human oversight where decisions impact people, money, or reputation.
3. Normalise checking sources. Whether it’s data, content, or code, make verification a cultural habit.

As Jack says:

“AI should be an enabler, not the thing doing all the work. Human judgment is what makes AI outputs valuable.”

The edge that can never be automated

The irony is that in a world obsessed with automation, the most valuable skills are the ones that can’t be automated. Curiosity. Scepticism. Judgment. Context.

Critical thinking isn’t just another “soft skill.” It’s the hardest edge businesses have in protecting themselves against AI risks and in making sure AI delivers genuine competitive advantage.

In the age of AI, the real superpower isn’t knowing how to prompt a chatbot. It’s knowing how to think critically about what it gives you. The leaders who sharpen that skill and build it across their teams will be the ones who thrive.

Find out how other organisations are navigating the changing AI landscape in our most recent AI report.

When most leaders talk about AI, the conversation is about productivity, cost savings, and innovation. But there’s a blind spot that can’t be ignored: ethics.

As JP Browne, Practice Manager from our Talent Auckland, who’s worked extensively in the insurance sector, warns:

“Nobody wants to end up on the front page because an AI system made the wrong call on a claim. That’s the kind of reputational damage you can’t come back from.”

Yet in many industries, ownership of AI ethics is missing. Governments are slow to legislate, and individual organisations are left to figure it out for themselves. The result? Huge risks hiding in plain sight.

The illusion of control

AI doesn’t just introduce new capabilities; it introduces new vulnerabilities. Jack Jorgensen, General Manager of Data, AI & Innovation at our project delivery arm Avec, highlights one recent example:

“A company built an entire software stack using AI-generated code. When their system was breached, 800,000 passports were leaked. That’s not innovation, that’s negligence.”

The rush to cut costs or speed up delivery often skips over the basics: security audits, human oversight, and clear accountability. Without these safeguards, AI can create more problems than it solves.

Ethics is more than compliance

Many organisations treat AI risks as a compliance issue: tick the right boxes and you’re safe. But as JP points out, ethics goes much deeper.

“In finance and insurance, compliance is the easy part. The harder part is asking whether it’s ethical to let AI decide someone’s mortgage, surgery, or claim outcome. Nobody wants to trust their future to a black box.”

The ethical stakes are high. And unlike sweatshops or environmental practices, consumers can’t easily “see” how companies are using AI. That makes transparency essential.

Jack even suggests that organisations should disclose their AI use openly:

“Imagine a badge on a company’s website saying how much of their service is powered by AI. That level of transparency builds trust and gives consumers real choice.”

The risks you’re probably missing

So, what are the hidden risks? Our recent AI survey surfaced three that too many leaders underestimate:

1. Security breaches. AI-generated code and automated systems can introduce new vulnerabilities, often unnoticed until it’s too late.
2. Bias and fairness. Algorithms trained on flawed data can reinforce discrimination in all process including hiring, lending, or claims processing.
3. Reputational damage. Whether it’s unfair exam results (like the UK’s failed GCSE grading algorithm) or customer data leaks, public trust can vanish overnight.

As Jack notes, “The hype around AI can drown out the noise. But the reality is, these risks are already here and they’re escalating.”

Why leadership matters

The absence of clear ownership is one of the biggest barriers to managing AI risk. In many organisations, executives are excited about AI but pass the responsibility to IT. That’s not enough.

AI ethics requires leadership at the top. It means asking:

• Who is accountable for AI decision-making?
• How transparent are we willing to be with customers?
• What safeguards are we putting in place to avoid harm?

Without executive buy-in, ethics gets sidelined until a crisis forces the issue.

From risk to responsibility

Ethics isn’t about slowing down innovation. It’s about ensuring innovation doesn’t destroy trust. Businesses that lead on AI ethics will stand out not just for their technology, but for their credibility.

JP sums it up well:

“AI is in everything now, from your phone updates to the way companies deliver services. If you don’t set ethical guardrails, you’re leaving your organisation and your customers exposed.”

AI ethics isn’t optional. The risks are real, the costs are high, and the responsibility is yours. Organisations that embrace transparency and accountability now will be the ones consumers trust tomorrow.

Learn more about what else professionals are concerned about around AI in the workplace in our latest report.

If you’re looking to start a new AI or data project, get in touch with Jack’s team to ensure it’s built on a secure and ethical foundation.

Key takeaways

1. Candidate activity is rising fast as job ads remain at low levels
Applications per job ad have jumped sharply as Kiwis search for higher wages amid cost-of-living pressure. With job ads still below pre-COVID levels, TA teams are managing heavier shortlisting loads even as the overall market feels quieter.

2. Hiring conditions are showing early signs of a 2026 turnaround
The economy is beginning to lift, business investment is returning, job ads have ticked up, and most regions reported growth last month. New Zealand is shifting out of stagnation into slow but steady recovery.

3. AI is reshaping skill expectations, but NZ businesses are still early in adoption
Demand for AI-related skills has surged, especially in IT, marketing, and strategy. While only a small share of NZ jobs can be fully automated, most will see tasks change, elevating the importance of capability building and realistic early-career hiring.

Introduction

After two unsettled years for Aotearoa’s labour market, marked by rising unemployment, declining job ads, and cautious business confidence, leaders are looking for clarity to effectively plan for 2026.

To help map this out, we’ve combined our recruitment experts’ on-the-ground insights with SEEK’s latest market data from our recent webinar with Senior Economist, Blair Chapman, to unpack what the macroeconomic indicators can tell us about what to expect as we head into the new year.

New Zealand’s hiring landscape: National trends to watch for in 2026

Growth is returning after two slow years

After a soft and uneven economic period, New Zealand is finally moving back into growth. GDP is expected to return to pre-COVID norms, supported by stabilising inflation, easing mortgage pressures, and a lift in discretionary spending. This momentum will feed into employment over the next 6-12 months, setting the stage for stronger hiring conditions.

Inflation is easing and business confidence is improving

The RBNZ’s single focus on inflation has paid off: prices are stabilising, mortgage rates have eased, and wage pressures are cooling. With labour costs returning to normal levels, businesses are becoming less hesitant to hire, reversing the uncertainty seen over the past two years.

Job ads remain low, but early signs point to a turning market

New Zealand experienced a much sharper drop in job ads than Australia, but the tide is shifting. Most regions reported job ad growth last month, and ads are beginning to climb across key industries. While this is still below pre-COVID levels, this uptick signals that hiring demand is starting to return.

Surging applications per job ad, driven by local and offshore talent

Applications per ad have risen significantly, but the increase isn’t coming from Kiwis alone. TA teams are seeing a substantial volume of offshore candidates applying for New Zealand roles, adding to already larger shortlists. As a result, hiring teams are processing heavier pipelines even as overall hiring activity remains modest.

Wage growth is easing, reducing hiring hesitation

Wage growth has fallen back to around 2%, and as labour costs stabilise, employers are becoming more confident about adding headcount and planning. With candidates still sensitive to pay, salary benchmarking remains essential while businesses do have more breathing room than they did at the peak of wage inflation.

Consumer spending is lifting again

New Zealand households are beginning to open their wallets, with discretionary spending recovering. This is a positive signal for industries that tend to hire ahead of broader economic recovery, like retail, hospitality, tourism, and logistics.

AI is reshaping skill demand, but NZ adoption is still early-stage

References to AI skills in NZ job ads have surged, particularly in IT, marketing, and consulting. While only a small share of roles can be fully automated, most will see task-level change. Employers are beginning to redesign roles around augmented workflows, but many still lack the internal capability to define what “AI readiness” looks like. With this shift, structured early-career pathways and realistic skill expectations will matter more than ever.

Regional breakdown: Where hiring conditions are shifting most

National snapshot

New Zealand’s labour market recovery is uneven, and the pace varies significantly by region. After a year of rising unemployment and falling job ads, most regions have now recorded an uptick in hiring activity, signalling the early stages of a national turnaround. Employment movements have been mixed, with some areas stabilising while others continue to soften, and the impact of the downturn hasn’t been felt equally across the North and South Islands.

A few regional patterns to note:

• Canterbury/Christchurch: Employment has edged down, but job ad growth suggests recovery is underway.
• Southland & Otago: Both trending towards stabilisation, though not yet fully recovered.
• Central North Island: Some regions such as Taupō saw unemployment ticking down, showing surprising resilience.
• Taranaki: A regional outlier not showing recent job ad growth.

Against this backdrop, Auckland and Wellington sit at the centre of this shift, each with its own drivers: Auckland shaped by broader economic softness and cost pressures, and Wellington shaped by public sector contraction and policy-related hiring freezes. Other regions such as Canterbury, Central North Island, and parts of the South Island are also beginning to show signs of lift, while a handful like Taranaki remain slower to recover.

With this national picture in mind, here’s what employers need to know about the two markets where we see the strongest shifts and the greatest implications for hiring in 2026.

Tāmaki Makaurau / Auckland

Auckland has experienced the sharpest rise in unemployment and one of the biggest drops in employment over the past year. Job ads fell significantly from their post-COVID highs, and hiring demand has been subdued across professional services, construction, retail, and public-facing sectors.

But the turning point is beginning. Early indicators such as a lift in job ads and a gradual return of discretionary spending all point to stabilisation. As mortgage pressures ease and business investment improves, demand for talent is expected to slowly rebuild.

As Kara Smith, New Zealand Country Manager, puts it:

“Auckland has taken some of the biggest hits this past year, but it’s also where we’re now seeing the earliest signs of recovery. Job ads are lifting, spending is picking up, and more movement from employers. With Auckland driving approximately 38% of the country’s GDP, New Zealand as a whole will also benefit.”

For 2026, expect a steady, early-stage recovery with stronger demand in IT, logistics, consumer services, construction and professional roles once activity picks up.

Te Whanganui-a-Tara / Wellington

Wellington’s labour market has been shaped heavily by public sector contraction. Government pullbacks have driven job losses, increased unemployment, and reduced hiring appetite across many adjacent industries.

Even so, Wellington recorded employment growth over the past year. As public sector hiring stabilises and business confidence improves, the private sector is expected to lead the next phase of demand. Candidates remain active and competition for secure roles is high.

In the new year, there will be a cautious rebound driven by consulting, IT, policy-adjacent roles and private sector expansion as government hiring levels out.

Nik King-Turner, Managing Director at Talent Wellington, adds:

“New Zealand absorbed the economic shock of COVID earlier and more sharply than Australia. The view is now that Australia may soon experience some of the same pressures we’ve already worked through, and that could slow the flow of Kiwi talent heading offshore. If that happens, Wellington employers could benefit from greater talent stability at a time when public sector hiring is beginning to level out.”

What employers should prioritise in 2026

With hiring conditions beginning to turn, employers should be preparing now for a more competitive and fast-moving 2026.

Rebuild hiring confidence and capability

As growth returns and job ads lift, competition for talent will increase again, especially in IT, professional services, logistics, construction and health.

Manage surging application volumes

High application numbers mean more administrative pressure on internal TA teams. Streamlined screening, clear criteria, and fast communication will be essential to avoid losing quality candidates.

Refresh early-career pipelines

AI-related roles are evolving quickly, and demand is outpacing supply. Cutting junior pathways only widens the capability gap, and businesses need structured graduate and early-career programs to build future-ready teams and long-term sustainability.

Upskill for AI-enabled roles

Most NZ jobs won’t be automated, but many will be augmented. Employers should focus on practical AI tools, workflow redesign, and building comfort and familiarity with new technologies across teams.

Be realistic about salary dynamics

While wage pressures have eased, candidates remain salary-driven due to cost-of-living pressures. Competitive benchmarking and transparent ranges will help secure talent without inflating budgets.

Plan regionally

New Zealand regions are recovering at differing paces. Tailor hiring strategies, salary positioning, and timelines to local market conditions.

Moving forward

After a challenging few years, New Zealand’s labour market is finally shifting back toward growth. The recovery will be gradual, but the direction is clear. With rising candidate activity, early job ad growth, and easing wage and inflation pressures, 2026 is shaping up to be a more positive year for both employers and jobseekers.

As you plan your workforce strategy, having a clear view of market movement and salary expectations will be essential. Our online More than Money Salary Guide offers searchable rates and salaries to support confident, data-driven hiring decisions across Auckland, Wellington, and beyond.

AI is not a strategy, but you still need one

When ChatGPT first hits the scene, it felt like magic. You typed in a question and out came paragraphs of seemingly human responses. That “wow” moment sparked a wave of experimentation across industries.

However, Jack Jorgensen, General Manager of Data, AI & Innovation at our IT delivery arm, Avec, points out:

“There’s a big difference between punching in a search query and building something deterministic and robust enough to run in production systems.”

And that difference is exactly where many businesses get stuck. According to our latest AI survey, nearly half (47.6%) of organisations are still in the experimental pilot stage. This isn’t inherently bad. Testing is critical, but it highlights a bigger issue: too many companies are running pilots without a clear strategy.

The hammer and nails problem

One of the most striking survey responses captured the mindset perfectly: “AI is a solution to some business needs. It’s not an objective or self-evident value proposition in its own right.”

Jack expands on this:

“What we’re seeing is a shift from the traditional IT delivery model, where you start with the value proposition and business case, then source the right tool. With AI, too many leaders are saying, ‘We’ve got this new hammer, now where are the nails?’”

That approach leads to wasted investment, disjointed projects, and technology that doesn’t deliver value. AI may not be the strategy, but without a strategy, you’re setting yourself up to fail.

Why “no strategy” is not an option

Some executives have argued that AI doesn’t need a dedicated strategy, comparing it to something as basic as staplers or office chairs. But as Jack explains, this is dangerously short-sighted:

“AI is a tool, yes. But it’s a tool that comes with new cybersecurity threats, compliance challenges, and ethical considerations. Ignoring it leaves your business exposed.”

From phishing attacks to vulnerabilities in AI-generated code, the risks are real. Without a roadmap, companies open themselves up to reputational damage, compliance breaches, and spiralling costs.

As JP Browne, Practice Manager from Talent Auckland puts it bluntly:

“Burying your head in the sand is not an option. AI is here, one way or another, and every organisation will be affected by it.”

The IT department squeeze

Another dynamic uncovered in our research is the unusual role IT departments are playing in AI adoption. Traditionally, IT has been a service function, enabling strategy set elsewhere in the business. But with AI, the tables have turned.

“Executives are excited about AI and pushing hard to adopt it, but IT leaders are often the ones hitting the brakes,” JP notes. “They’re saying: yes, this is powerful, but we need to address security, infrastructure, and compliance first.”

That tension is leaving many organisations in limbo. The money is there. The executive interest is there. But without a strategic framework to prioritise use cases, align with business goals, and manage risk, progress stalls.

Building an AI strategy that works

So, what does an effective AI strategy look like? It doesn’t have to be a 50-page blueprint. In fact, Jack recommends starting simple:

1. Define the business problem. Don’t adopt AI for the sake of it. Be clear about the challenge you’re trying to solve.
2. Set guardrails. Establish data security, compliance, and ethical guidelines before scaling experiments.
3. Start small, but with intent. Pilots are valuable, but only if they feed into a roadmap for production-ready solutions.
4. Assign ownership. Decide who is accountable for AI adoption across the business. Avoid the “hot potato” problem where no one owns it.
5. Review and adapt. A strategy isn’t fixed. As AI evolves, so should your approach.

“Having no AI strategy is worse than having the wrong one,” says Jack. “At least a flawed strategy can be corrected. No strategy leaves you wide open.”

From fear to opportunity

Much of the fear surrounding AI, from job loss to ethics and compliance, stem from uncertainty. And uncertainty thrives where there’s no plan.

With the right strategy, AI becomes less of a threat and more of a force multiplier. It can streamline workflows, surface insights, and free people up from repetitive tasks to focus on higher-value work. But those benefits only come when you align AI projects with business objectives and set the right foundations.

As JP concludes:

“AI can absolutely change the game for productivity and competitiveness. But only if you stop reacting, start planning, and make it part of your business strategy.”

AI is not the strategy. But without a strategy, AI is just hype. Organisations that take the time to define their approach, even if it starts small, will be the ones that cut through the noise, manage the risks, and realise real business value.

If you’re ready to source in-house AI capability, get in touch with our team. Or, if you’re looking to kick off a data project, reach out to Jack’s team at Avec.

While government agencies have to carefully navigate AI changes while maintaining dependability, the private sector can move like a high-speed bullet train in comparison; faster, more agile, and ready to change direction. However, when it comes to AI, speed without strategy can be as dangerous as standing still.

Our latest AI survey with 864 business leaders and tech professionals shows that 48% of organisations overall are still in the experimental or pilot stage of AI adoption. In the private sector, this can be exciting with tools being trialled, data flows unlocked, and quick wins celebrated… But without clear ownership and governance, experimentation can quickly spiral into risk.

The private sector’s AI advantage

Private organisations have more flexibility than government agencies, which means they can:

• Pilot AI use cases without length approval processes
• Redirect budgets and talent more quickly
• Partner with vendors or start-ups to accelerate capability

In-house AI expert Jack Jorgensen, General Manager of Data, AI & Innovation at Avec, explains, “In the private sector, leadership can decide today that AI is a priority, and tomorrow there’s a project team in place.” This agility allows them to capitalise on emerging opportunities, from automating repetitive tasks to improving customer experience.

The strategy gap

It’s important to note that speed is an advantage, until it isn’t. Our survey data shows that:

• 41% of organisations cite “no strategy” as a major obstacle to AI adoption
• 41% say “unclear goals” are holding them back
• 34% cite “unclear ownership”

“We’ve seen this before with automation. Without a cross-business strategy, AI gets walled into a single department and it never reaches its full potential,” says Jack.

In many cases, the enthusiasm is there at the executive level, but ownership is unclear. Is AI a technology initiative? A business transformation project? A data function? Without a clear answer, adoption can stall or become fragmented.

Security and governance risks

Organisations in the private sector are split in their approach to AI security:

• 3% have restrictions or policies limiting the use of external AI tools
• 9% use tools like ChatGPT with minimal governance
• 9% are exploring secure, fit-for-purpose AI solutions
• 11% have implemented secure, in-house AI capability

Practice Manager from our office in Auckland, JP Browne observes, “You either lock it down completely or let it run free, and the private sector is doing both, often within the same organisation.”

The role of talent in AI maturity

AI success in the private sector is often tied to talent strategy, and the current roles in highest demand according to our recruitment experts include:

• Data engineers and analysts
• Systems engineers to build infrastructure
• Change managers to drive adoption across business units

But while technical capability is critical, so is critical thinking and the ability to bridge technical and commercial priorities.

What private sector leaders should do next

• Define ownership and accountability for AI strategy
• Prioritise secure data infrastructure before scaling
• Pilot AI projects with clear and measurable goals
• Invest in cross-functional teams that blend technical skill with business insight
• Develop a company-wide AI policy that balances innovation with risk management

The private sector’s ability to move quickly is a strength, but only if it’s guided by clear strategy, governance, and talent. The leaders in AI adoption will be those who can balance the hype and excitement of rapid innovation with the discipline to scale it safely and sustainably.

If you’re looking to hire AI and data talent, get in touch with our team. Or if your business is planning a high-impact data, AI or innovation project, drop a message to Jack’s team at Avec.

The insurance industry has long been a pioneer in automation. Fraud detection, claims processing, and risk modelling all lend themselves to technology, and AI is simply the next layer. However, it brings with it new complexities, risks, and opportunities.

In our recent AI survey, 40.3% of financial services respondents (including insurance) said their organisation is still in the experimental or pilot stage of AI adoption. And while early wins are clear, there’s a universal truth in insurance: you can’t take humans out of the loop entirely.

From automation to AI: An evolution, not a leap

JP Browne, Practice Manager from Talent Auckland says, “Insurance has been using automation for years and AI just extends what’s possible, from approving low-value claims instantly to extracting insight from thousands of documents.”

Examples of early AI adoption in insurance include:

• Automating claims approvals for low-value, low-risk cases
• Using AI to scan and summarise large volumes of customer documents
• Generating insights from call centre transcripts to improve service quality

These targeted use cases reduce cost, save time, and free human experts for more complex work.

Why human oversight still matters

AI may be fast, but it can’t (yet) replace human judgement in high-stakes decisions.

“If somebody’s house is on fire, you can’t let a bot decide whether to let the claim go through,” says JP.

In regulated industries like insurance, compliance, ethics, and customer trust demand human sign-off for:

• Large or complex claims
• Disputed cases
• Situations with incomplete or ambiguous data
• Potential fraud indicators

The security and compliance factor

As part of the broader financial services sector, insurance organisations share similar AI adoption challenges, particularly around security and compliance.

Our survey findings show:

• 2% said security or compliance concerns are their biggest barrier to regular AI use
• 3% said their organisation has restrictions or policies in place limiting the use of external AI tools
• 9% are exploring secure, fit-for-purpose AI solutions
• 11% have developed or implemented their own secure, in-house AI capability

Some insurers are even moving back to on prem to maintain tighter control of sensitive data and meet stringent regulatory requirements.

The data quality challenge

Insurance leaders know that AI is only as good as the data it’s fed. “We’re seeing a big rise in demand for data engineers and analysts, because poor-quality data kills AI performance,” observes JP.

This focus on data readiness is driving workforce changes in:

• Systems engineering
• Data engineering and analytics
• Data governance and compliance roles

What insurance leaders should so next

• Identify low-risk AI use cases that deliver measurable ROI
• Maintain human oversight for complex or high-value claims
• Strengthen data governance and quality
• Build secure infrastructure for AI deployment
• Create clear policy frameworks for AI use across teams

AI can process claims in seconds and surface insights no human could spot, but it can’t replace the trust built through human expertise. In insurance, the leaders won’t be those who hand decisions over to machines, but those who combine AI’s speed with human empathy, ethics, and accountability. The winning formula? Let AI handle the heavy lifting, while people make the calls that truly matter.

Want to find out what else our AI survey revealed? Access the full report.

If you’re looking to build internal AI capability or make your first AI hire, get in touch with our team. Or if your business is ready to kick off a data, AI or innovation project, drop a message to Jack’s team at Avec.

When budgets tighten and sustainability goals loom large, most councils zero in on headcount, procurement, and property costs.

But what about your fleet?

For many organisations, the fleet is the ultimate blind spot, an invisible cost centre quietly draining millions. Yet, with the right data and meaningful insights, it can become a powerful lever for savings, sustainability, and smarter decision-making.

That was the key message from our recent webinar with Fleetonomics™ experts Karen Whitehouse and Melvin Worth, who joined our Head of Government here at Talent, Steve Tompkins, to unpack how councils can transform their fleet from a hidden expense into a strategic advantage.

The hidden value sitting in your data

GPS logs, activity reports, booking systems… Most councils are swimming in vehicle data, but few are truly using it. Karen and Melvin call this the “untapped goldmine” of fleet management.

“We’ve helped councils uncover an average of 20% optimisation opportunity in their fleets, without disrupting business-as-usual,” said Karen.

The trick isn’t to collect more data, but to make sense of what you already have. When you connect your telematics, finance, and asset management systems into one source of truth, patterns emerge: underused vehicles, inefficient routing, even “ghost” cars sitting idle for months.

Busting fleet myths that cost you millions

The Fleetonomics team often sees the same misconceptions play out again and again:

• “We need more vehicles.”
• “If it’s depreciated, it’s free to keep.”
• “Our Hiluxes are essential.”

Sound familiar?

In reality, many fleets are overcapitalised and under-utilised. One council discovered their vehicles were only used a handful of times a week, yet were fully assigned to individuals.

Another realised that peak summer “demand periods” didn’t actually exist once they analysed utilisation data.

“The operational voice can be loud,” Melvin noted. “Without evidence-based analysis, it’s easy for anecdotes to drive costly decisions.”

Where to start: Your ‘why’

Before you optimise anything, start by asking: why now?

• Is it cost reduction?
• Sustainability goals?
• Public perception or compliance pressures?

Getting alignment on the ‘why’ across leadership is critical. Fleet optimisation is a change program, not a procurement exercise. Once that purpose is clear, you can bring your people, and your data, on the journey.

Turning data into action

Good fleet data tells a story: where vehicles go, how often, and why. When that story is clear, conversations shift from assumptions to actions.

Karen and Melvin recommend:

1. Consolidate your data – Create one version of truth that includes GPS, finance, booking, and maintenance records.
2. Interrogate the patterns – Identify waste (idle vehicles, over-spec’d models, duplicate assets).
3. Engage your stakeholders early – Optimisation only works when fleet users are part of the solution, not the surprise.

“When data meets dialogue, that’s when real change happens,” Karen said. “Once users understand the ‘why,’ you get faster adoption, less pushback, and better long-term results.”

Case in point: One Council’s $4.5m wake-up call

A district council approached Fleetonomics after senior leaders realised they couldn’t answer basic questions like: “How many vehicles do we have?” or “Are they fit for purpose?”

After a full fleet audit and utilisation review, the results spoke for themselves:

• 27% fleet overcapacity identified
• 17% reduction achievable with no operational impact
• $4.5M in long-term savings unlocked
• 87% transition to EVs planned, plus infrastructure fully funded from savings

By challenging assumptions and unifying data, they turned confusion into confidence and built a blueprint for others to follow.

Keep the conversation moving

Fleet optimisation isn’t a one-and-done project. It’s a living process.

As technology evolves, staff change, and sustainability targets accelerate, your strategy should too. Karen and Melvin suggest revisiting your data quarterly, especially in the early stages.

Because the councils that stay agile, those that question entrenched thinking and act on evidence, are the ones turning fiscal waste into measurable progress.

You can’t manage what you can’t see

But when you make your fleet visible, you don’t just save money, you create capacity for innovation, sustainability, and smarter decision-making.

So, is your fleet costing you more than you think?

There’s only one way to find out: start with the data.

Want to discuss how we can help? Reach out today.

New Zealand’s infrastructure and energy pipeline is booming but the people needed to deliver it are in short supply. With record numbers of skilled Kiwis moving overseas, and Australia’s own talent shortage intensifying, organisations here face unprecedented competition for technical and project delivery expertise.

This isn’t just a numbers issue. It’s a race for knowledge, capability, and experience.

The great Kiwi outflow

Over the past year, almost 72,000 Kiwis have relocated overseas — more than half (58%) heading to Australia. For sectors like energy, utilities, and infrastructure, this isn’t a marginal shift. Every engineer, project manager, or digital specialist leaving the workforce takes with them years of institutional knowledge and practical experience.

Even more challenging, the migration overlaps with an ageing workforce. A large portion of technical talent is nearing retirement, leaving gaps that can’t be filled by headcount alone. More than just “hiring”, organisations must think strategically about knowledge transfer, capability rebuilding, and workforce renewal.

Australia’s market pressure

Australia is facing similar talent supply issues, particularly in high-voltage energy, specialised civil engineering, and digital infrastructure. Organisations there are competing fiercely for people with Transmission Extra High Voltage (EHV) experience — skills that are scarce in both countries.

And when Australian employers can’t find talent locally, New Zealand becomes a “hunting ground” for engineers and specialists. Beyond that, consultancies are increasingly tapping Singapore, Malaysia, and the Philippines to support regional projects. For New Zealand, this adds both opportunity and risk: demand for skilled talent is now regional, not local, and competition is growing fast.

Skills scarcity isn’t sector-specific

The convergence of multiple industries competing for the same skill sets is creating a national talent pressure point. Energy, utilities, telco, transport, and water infrastructure projects are all vying for engineers, digital specialists, and project managers.

It’s no longer enough to focus on sector-specific pipelines and companies are competing across industries and borders for the people who can make projects happen. This highlights the importance of strategic workforce planning, capability development, and early talent engagement.

The opportunity: plan for capability, not just headcount

While the pressures are real, they also create an opportunity to rethink workforce strategy. Organisations that proactively capture knowledge, upskill existing teams, and design career pathways will be better positioned to navigate both the local and regional talent landscape.

By viewing workforce challenges as a strategic issue, New Zealand can move from a reactive hiring approach to building sustainable capability that ensures projects are delivered efficiently, safely, and to future-proof standards.

Learn more about the talent shaping New Zealand’s delivery future and download our Infrastructure & Utilities Snapshot for insights on workforce trends, cross-sector competition, and the skills needed to meet the country’s infrastructure ambitions.

AI is no longer a tech-sector story, but a project and infrastructure one.

As New Zealand doubles down on energy transition and network modernisation, the same organisations managing critical utilities are also navigating AI transformations. The result is a compounding effect with energy and water demand surging, digital transformation accelerating, and the skills needed to deliver both converging faster than the market can keep up.

Energy is now the bottleneck in the AI race

Globally, AI innovation is driving unprecedented demand for electricity. Anthropic’s Build AI in America report warns that the US AI sector will require at least 50 GW of electric capacity within three years, which is more than New Zealand’s entire generation output several times over. Data centre buildouts are now competing directly with renewable generation projects for power, land, and transmission access.

By contrast, China added over 400 GW of new power to its grid in 2024, creating a massive infrastructure advantage and is a reminder that the AI race isn’t just about algorithms, but about who controls the energy supply that powers them.

The irony? While global power demand soars, AI itself is getting more efficient. Google reports that energy consumption per prompt has decreased 33 times in the past year, and water consumption per interaction has fallen to a fraction of earlier predictions. While AI is learning to use less, the systems supporting it still need more.

For New Zealand, this tension creates a unique challenge: modernising networks fast enough to keep pace with global digital demand while pursuing sustainability goals at home.

AI adoption is accelerating unevenly

According to newzealand.ai, 82–87% of New Zealand businesses now use AI tools for productivity, and 69% of consumers do the same. Adoption is led by transport, media, tech, and public services, but energy, utilities, and telco aren’t far behind.

Our own research shows that 83.3% of leaders in the energy and resources sector and 69.3% in telco believe AI will positively impact their roles within two years. However, half of these organisations are still in the experimental or pilot stage. The shift from exploration to enterprise-level integration is only just beginning.

Encouragingly, around 64% of energy and resources organisations and 75% of telcos already consider AI a strategic priority. For most, transformation is starting in areas where automation can drive immediate efficiency and cost benefits (such as customer service and operations) before expanding into asset management, project delivery, and predictive maintenance.

It’s clear that the conversation has moved beyond “if” to “how fast”.

Where opportunity meets capability

As AI becomes embedded in the backbone of how we build, maintain, and manage infrastructure, the demand for digital talent across engineering and energy is exploding. Data scientists, automation engineers, and AI project leads are now as critical to delivery as civil or electrical engineers.

And these skills don’t stay in one lane. The same professionals helping utilities modernise their networks are being hired by telcos, energy companies, and even transport authorities. And the overlap continues to grow.

For hiring leaders, this means rethinking workforce strategy. It’s not enough to source talent from your own sector anymore. The organisations staying ahead are those building cross-industry capability, creating hybrid roles, and embedding AI literacy across all levels of the workforce.

The next delivery advantage: human + machine

AI won’t replace the workforce that builds New Zealand’s future, but it will augment it.
And it’s about amplifying a person’s impact, freeing specialists to focus on critical decision-making while machines handle data-heavy analysis and monitoring.

The next delivery advantage will belong to organisations that combine technical capability with digital intelligence, and those who see AI not as a standalone initiative but as an enabler of better, faster, and more sustainable outcomes.

Get the insights on what’s shaping New Zealand and download our latest Infrastructure & Utilities Snapshot.

When it comes to AI adoption, government is in no hurry. And that’s exactly the point.

In our latest AI survey, 50% of respondents working in the public sector said their organisation is still in the experimental or pilot stage of AI use. Compared to many private-sector industries, where early adoption is already shifting workflows and job design.

While at first glance, it might look like governments are falling behind, there’s good reason they move differently.

Why governments move slowly on AI

Government agencies aren’t built to “move fast and break things”. According to our in-house AI expert, Jack Jorgensen, General Manager of Data, AI & Innovation at our project delivery arm Avec, “There’s a big difference in the way governments need to operate versus private enterprise. They’re designed to be stable, reliable, and robust.”

A government body’s core responsibilities of public services, infrastructure, safety and regulation demand caution, reliability, and trust. So, when your ‘customer’ is the entire population, the stakes are high. Errors can impact millions, data breaches can threaten national security, and AI decisions must stand up to legal and public scrutiny.

The reality on the ground

In many agencies, AI is still in the exploratory stage:

• Small, controlled pilots
• Internal tools tested in low-risk areas
• Strong focus on compliance and security requirements
• Longer approval cycles for procurement and deployment

“Policy-making roles are challenging to automate and in highly regulated environments, finding relevant and safe use cases understandably takes time,” says Jack.

Security and compliance non-negotiables

Government respondents ranked “security and compliance concerns” on par with financial services and is no surprise given the sensitivity of the data they hold.

Some agencies are also grappling with:

• Lack of relevant applications – 20.2% said AI doesn’t apply to their current work
• Ownership uncertainty – it’s widely unclear who should lead AI initiatives
• Siloed operations – meaning slow cross-department collaboration

Why this pace makes sense

Jack says, “If anyone’s surprised government is slow on AI adoption, they don’t understand the role. The systems are meant to be dependable, not bleeding edge.”

While speed matters for the private sector in competitive markets, stability matters more than anything in public service. AI in government must work every time, be explainable and auditable, serve the public interest, and align with legislation and policy.

So, what can government do next?

• Continue piloting in low-risk and high-value areas
• Invest in AI literacy for leadership and frontline teams
• Create clear ownership and governance frameworks
• Learn from private-sector implementations without importing their risk appetite
• Build secure, compliant infrastructure before scaling

The private sector can afford to experiment, and government can’t, so caution at this stage isn’t failure. In an era where public trust is fragile, deliberate and well-governed AI adoption is the only responsible path.

Want to explore the sector-by-sector data? Access the full report.

If you need to hire talent for AI or data roles in public service, get in touch with our team. Or if you want to plan a secure AI pilot, partner with Jack’s team at Avec.

New Zealand is at the start of one of its biggest delivery decades and the next two years will determine how well we adapt. Billions are being invested across energy, utilities, and transport, but the workforce to deliver it all is stretched thin. Project backlogs are growing, skills are overlapping across industries, and the people who built the last generation of infrastructure are starting to retire.

Not just another investment cycle, this is potentially a once-in-a-generation test of delivery capability.

A nation under construction

Across New Zealand, major infrastructure and energy projects are ramping up. From the City Rail Link to the Central Interceptor, from solar and wind developments to regional water upgrades, the country’s delivery pipeline is swelling, and so are expectations.

The scale of the challenge is clear: all of our current systems, networks, and capability were built for a different era. Transmission and distribution networks weren’t designed for today’s pace of renewable energy generation. Billions are being committed to new assets, but integrating them into existing grids remains slow, complex, and underfunded.

The result? A delivery window packed with both opportunity and risk. Organisations must not only deliver projects faster but do so while modernising the very systems they depend on.

Workforce renewal is now a critical risk and opportunity

A significant portion of New Zealand’s infrastructure and energy workforce is nearing retirement age, taking decades of institutional knowledge with them. And isn’t just a numbers game. Workforce renewal is about capturing experience, rebuilding capability, and making these industries attractive to the next generation of specialists.

At the same time, there’s a record number of almost 72,000 Kiwis moving overseas — 58% of them to Australia — adding pressure to already scarce talent pools. Competing for skilled project managers, engineers, and digital specialists has become a national challenge.

The good news? This challenge also creates space for innovation in how we train, partner, and attract talent. Workforce renewal can be a powerful driver of transformation if leaders act early and strategically.

The skills race isn’t industry-specific anymore

When every major project needs engineers, data specialists, project managers, and digital delivery talent, competition for skills stops being an industry problem. It becomes an economy-wide one.

Energy companies are competing with telcos for automation talent. Utilities are hiring the same project delivery specialists as major transport programmes. Data centres are drawing from the same electrical and civil engineering pools as renewable developers.

The line between technical industries has blurred and companies that understand this overlap are the ones building smarter, faster, and more sustainably.

At Talent, we see this shift every day. The most successful organisations are those treating workforce strategy as a competitive advantage by planning early, building flexibility into delivery teams, and investing in partnerships that blend capability and capacity.

Rebuilding capability for the next 30 years

Adapting to what’s next will be the hardest and most rewarding thing New Zealand’s infrastructure and utilities sectors do.

Delivering tomorrow’s projects will take more than hiring replacements. It means rebuilding technical capability, embedding digital fluency, and creating career pathways that attract diverse, emerging talent — from women in engineering and technology, to meaningful Māori and Pasifika representation across technical and leadership roles.

That’s how you build resilience, not just for the next two years, but for the next two decades.

The opportunity ahead

The next two years will define how well New Zealand delivers — not just its projects, but its future workforce. The infrastructure race is on, and every leader faces the same challenge: how to scale and deliver fast without compromising quality, safety, or capability.

The organisations that’ll win are the ones that act now, by treating workforce planning as strategy, not an afterthought.

Get the full picture. Download our latest Infrastructure & Utilities Snapshot for insights on workforce trends, investment priorities, and the roles critical for project