Why it’s imperative for HR to be involved in AI’s roll out - and not just left to IT
For New Zealand HR leaders, artificial intelligence is no longer a distant wave rolling in from the U.S. and Europe. It is already lapping at the doors of local offices and contact centres – and researchers have put a confronting number on what today’s systems can do.
MIT’s Project Iceberg, a large-scale simulation of the U.S. labour market, finds that current AI tools are already capable of performing tasks worth 11.7% of total wage value, or around US$1.2 trillion a year. The model represents about 151 million workers across 923 occupations, mapped to more than 32,000 distinct skills.
New Zealand’s economy is much smaller, but similarly reliant on services: banking and insurance, professional services, public administration, health, education, tourism and logistics. If AI can already do that much work in the U.S., it is reasonable to assume a similar order of exposure here as the same cloud tools and platforms roll across the Tasman.
What Project Iceberg actually measures
Project Iceberg doesn’t simply ask which jobs might be affected one day. It tracks how existing AI tools line up against real work.
Each worker in the simulation is modelled as an “agent” with a bundle of skills and tasks, then matched to thousands of deployed AI systems. The central metric, the Iceberg Index, measures technical exposure: the share of an occupation’s wage bill that comes from skills where AI has already demonstrated usable performance in at least one context.
The authors stress that the Index “captures technical exposure, where AI can perform occupational tasks, not displacement outcomes or adoption timelines.”
For HR, that distinction matters. The study is not a prediction that 11.7% of jobs will disappear on a set date. It is a map of where organisations now have a choice about whether work is done by people, machines, or a mix of both.
The visible layoffs are only the tip
Most public attention has focused on headline tech redundancies and AI code assistants. Iceberg’s message is that this is just the exposed tip of a much larger structure beneath the surface.
Looking only at current adoption in computing and technology roles, the researchers find exposure of about 2.2% of wage value — roughly US$211 billion. They call this the “Surface Index” and note that it is “only the tip of the iceberg.”
Below the waterline lies a far bigger opportunity for automation. Technical capability “extends far below the surface through cognitive automation spanning administrative, financial, and professional services,” totalling about 11.7% of wage value, or around US$1.2 trillion.
In other words, AI’s quiet strength is in routine white‑collar work: financial analysis, document handling, back‑office administration and coordination. One summary notes that high Iceberg scores in several U.S. states are driven by “cognitive work—financial analysis, administrative coordination, and professional services.”
New Zealand employers have similar layers of work sitting in banks and insurers, local councils, ministries, DHBs and health organisations, universities, law firms, engineering consultancies, freight and tourism operators.
Which skills are easiest for AI to pick up?
The report does not publish a simple list of doomed roles, but it is quite specific about the kinds of work AI handles well today.
The clearest near‑term exposure is in structured, rules‑based tasks that are already digital:
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Processing and extracting data from documents and forms
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Routine administrative and clerical work in offices and health services
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Standard financial analysis, reconciliation and reporting
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Workflow coordination, triage and scheduling across large teams
The researchers note that financial services firms are already using AI for “document processing and analytical support,” and healthcare systems are automating “administrative tasks” to free clinical staff for patient care.
These capabilities line up closely with the day‑to‑day tasks of claims officers, payroll and billing clerks, junior analysts, paralegals, contact‑centre staff, coordinators and many early‑career “assistant” roles across the Kiwi labour market.
Importantly, Iceberg is skills‑based. Many jobs will be partially automated: the AI takes the repetitive, high‑volume components; humans focus on exceptions, judgement calls, whānau and customer relationships, and local context.
Why this matters for New Zealand employers
Project Iceberg was built so governments can “identify exposure hotspots, prioritize training and infrastructure investments, and test interventions before committing billions to implementation.” That’s directly relevant to Wellington and to large NZ employers planning their own technology and people investments.
Three takeaways stand out for HR.
1. Traditional metrics won’t catch this in time: The researchers show that standard indicators such as GDP, income and unemployment explain “less than 5% of this skills-based variation” in exposure. If New Zealand employers only watch vacancy rates and turnover, they will miss where AI is quietly reshaping work well before it turns up in official stats.
2. The productivity race will reach Aotearoa: The report emphasises that AI’s impact depends on how quickly organisations package capabilities into tools and integrate them into workflows. As local banks, telcos, public agencies and global firms with NZ operations chase productivity and service‑level improvements, the temptation to automate a chunk of routine work will grow — particularly in a tight labour market.
3. HR will be judged on how exposed workers are treated: Iceberg shows where leaders have options. It does not say those options must be used to cut jobs. That puts HR at the centre of decisions about redeployment, retraining and how openly organisations communicate with staff whose work is highly exposed.
A practical playbook for NZ HR
So how should New Zealand HR and people leaders respond?
Map your own exposure, task by task: Follow Iceberg’s lead and go beyond job titles. Break key roles into the actual tasks people perform — processing invoices, updating records, drafting standard letters, basic analysis and reporting — and identify which ones look like the document processing, analytical support and administrative work the report highlights. Even a rough map of high‑exposure tasks will put you ahead of most organisations.
Design an AI transition plan, not just pilots: Before large‑scale AI projects kick off, work with the C‑suite to agree on principles: how redeployment will be handled, what retraining budget will be set aside per exposed full‑time equivalent, and what consultation will occur with staff and unions. Project Iceberg exists so policymakers can test scenarios “before committing billions”; HR should demand similar discipline around workforce decisions.
Shift from jobs to skills and pathways: The Iceberg Index is deliberately “a skills-centered metric that measures the wage value of skills AI systems can perform.” New Zealand employers can mirror this by building skills inventories, funding targeted learning, and creating internal pathways that help people move from high‑exposure tasks into work that is harder to automate — complex case management, relationship roles, change and implementation, or work that requires deep cultural and local knowledge.
Update what goes to the board: Alongside standard people metrics, boards should start seeing a simple AI‑era indicator: what share of the organisation’s wage bill is tied to highly exposed tasks? In an economy wrestling with productivity and skills shortages, that number will be hard to ignore.
The MIT team is blunt: “The window to treat AI as a distant future issue is closing.” For New Zealand HR leaders, that window is narrower still. The same cloud tools that underpin Project Iceberg’s findings are only a login away for Kiwi employers.
AI is already capable of doing a noticeable share of the work New Zealanders are paid for. Whether that becomes a story of managed transition — or of avoidable dislocation — will depend on the choices organisations and their HR leaders make now, before the iceberg fully breaks the surface in Aotearoa.
