Will AI Replace Customer Service Jobs in Papua New Guinea? Here’s What to Do in 2025

As Papua New Guinea accelerates its digital transformation - finalising a National AI Adoption Framework and rolling out SevisPass - customer service jobs are already shifting from repetitive phone queues to supervising AI-powered assistants that can answer SMS, Facebook and WhatsApp queries 24/7; globally, consumers expect this change (see the Zendesk 2025 AI customer service statistics report) and PNG's policy push shows the change will arrive with local force (read the PNG National AI Adoption Framework progress at ICT.gov.pg).

That means routine ticket work is most exposed, while roles that combine judgment, local language skills and ethical oversight become more valuable - skills that can be learned in practical courses like the AI Essentials for Work bootcamp syllabus (Nucamp), which teaches prompt-writing and real-world AI tool use for non‑technical learners.

For PNG teams, the smart move in 2025 is to treat automation as a productivity partner: test outcome-based pilots, retrain for higher-value tasks, and keep customer trust front and centre.

“SevisPass will serve as a Digital Public Infrastructure, enabling secure authentication across banking, telecommunications, and government systems,” said Minister Masiu.

Customer service jobs in Papua New Guinea are especially exposed because the tasks most likely to disappear - routine ticketing, FAQs and simple order queries - are exactly the ones AI already automates at scale, with a chatbot able to “handle thousands of inquiries simultaneously” and companies planning AI across every touchpoint (see Zendesk 59 AI customer service statistics).

Global uptake is rapid: many customers prefer instant bot responses and CX leaders expect AI to be embedded everywhere, yet firms often fail to align that shift with data and training plans; only 45% of agents report receiving AI training and just one‑third understand their department's AI strategy, which raises the risk that PNG teams will see roles erode before workers gain the skills to supervise or augment AI. The danger is amplified where data infrastructure and governance lag: IoT Analytics stresses that “there's no AI strategy without a data strategy,” so organisations without robust data practices or clear transparency (a top consumer concern) will find automation substitutes cheaper and faster to deploy than locally‑tailored reskilling, making routine customer service jobs the first to feel the squeeze.

“There's no AI strategy without a data strategy.” - Frank Slootman

AI turns the raw stuff of customer support - call audio, chat logs, SMS and social posts - into searchable, actionable signals: machine learning and NLU can produce automated live transcripts from voice calls (making every conversation searchable and faster to triage) while LLMs, when paired with graph-style analytics, can map sentiment over time to reveal recurring pain points and cross‑channel patterns (see the call center automation explainer at call center automation explainer - GetVoIP and the advanced sentiment approach at advanced sentiment analysis with LLMs and graphs - SearchUnify); LLMs also triage, detect sarcasm and emotional tone, and draft context-aware replies that speed resolution.

For Papua New Guinea teams this means a practical payoff - automated transcripts plus sentiment timelines let supervisors spot a sudden surge of negative emotion (imagine a red pulse on a timeline after a network outage) and escalate human help where it matters most - but only if models are measured and monitored.

Build simple evaluation metrics (topic relevancy, negative‑sentiment alerts, faithfulness tests) and an LLM monitoring loop so outputs stay accurate and safe in production, following the LLM evaluation best practices outlined by Datadog (LLM evaluation framework and best practices - Datadog), and pair those systems with human review for critical or local‑language cases.

Papua New Guinea's immediate challenge is less about whether AI can answer FAQs and more about making sure people, policy and power keep pace: global policy debates - like the U.S. House committee wrestling with how to safeguard AI in health care (PoliticoPro report on safeguarding AI in health care) - and new federal procurement and use memos summarized by legal experts (Wiley Law summary of U.S. federal AI procurement memos) stress governance, pre‑deployment testing and worker upskilling, all of which matter for PNG's rollout; GAO findings flagged in those reviews also warn of substantial energy and human impacts from large AI systems, so infrastructure and operational costs are real constraints.

On the ground, awareness gaps and limited training mean many agents may not yet understand how to supervise LLM outputs or spot hallucinations - so a single network outage or misconfigured model can quickly turn a 24/7 promise into radio silence.

Practical local responses pair tighter procurement and monitoring with focused reskilling: PNG teams should pilot outcome-based trials and learn tool‑level workflows from practical resources like the Nucamp AI Essentials for Work syllabus - Complete guide to using AI in PNG customer service, while building simple data governance and energy plans before scaling.

Timing matters: PNG sectors with the heaviest exposure are those that already run high volumes of routine enquiries and can plug straight into AI - banks and fintech, telcos, government services (immigration and land), retail/e‑commerce, hospitality and utilities - and many of these shifts are imminent rather than distant.

Global CX research shows 59% of consumers expect AI to change interactions within two years and CX leaders plan broad integration on a similar timetable (Zendesk 2025 AI customer service statistics), while contact‑centre studies find AI is already enabling 24/7 omnichannel support for most organisations; that means PNG firms with high query volumes face near‑term automation pressure.

Homegrown examples underline the point: NiuPay's March 2025 AI visa processor now makes real‑time decisions in minutes and can handle up to 1,000 applications a day - transformations that turn weeks‑long queues into instant outcomes and free staff for complex cases (NiuPay / APNGBC report).

Practically, expect banking, government portals and telco support to be first in line, retail and tourism to follow as e‑commerce and visitor flows grow, and utilities/manufacturing to adopt AI where automation cuts repeat field visits; planning for retraining and phased pilots now will catch that wave rather than be swept aside.

The data point to a mixed picture for Papua New Guinea: globally, the World Economic Forum and PwC scenarios show that technology can both displace routine roles and lift demand for higher‑value skills, and the WEF's 2025 findings suggest about 22% of jobs will be affected worldwide - with roughly 14% of roles created and 8% displaced - meaning PNG's repetitive ticketing and FAQ work is most at risk while supervisory, language‑savvy and judgement roles could grow; practical action is straightforward and local: run outcome‑based pilots to measure net impact, prioritise upskilling where the World Economic Forum's scenarios predict advantage (problem‑solving, empathy, creativity), and link training to measurable redeployment targets so displaced agents move into oversight, quality and escalation lanes rather than out of work (see the WEF/PwC futures framing at the World Economic Forum Future of Jobs report and the PwC Ireland Future of Work summary).

For teams in PNG, pairing small automation pilots with guaranteed retraining pathways - and using targeted guides to deploy chatbots on SMS, Facebook and WhatsApp - turns a displacement risk into a net job transformation opportunity.

“By replacing workers doing routine, methodical tasks, machines can amplify the comparative advantage of those workers with problem-solving, leadership, emotional intelligence, empathy and creativity skills.”

Customer service workers in Papua New Guinea should focus on three practical, high‑value skills to stay indispensable in 2025: become a knowledge curator who captures clear, localised answers and builds a shared, searchable handbook for the team; level up as a personal guide who maps customer personas, solves mismatches between expectations and what systems can deliver, and writes empathetic, brand‑aligned replies; and learn to be a community co‑creator who moderates peer help, nudges productive backchannels, and scales C2C support for recurring issues (these are the core skills in the upskilling playbook at MetricsHerpa - see the Knowledge Curator, Personal Guide and Community Co‑Creator framework).

Practically, use the simple Tell–Show–Do training loop to practice new behaviours in short roleplays, then document outcomes so every successful interaction becomes team knowledge (Jeff Toister's Tell, Show, Do method is a fast way to make training stick).

Pair those human skills with AI workflows - try a Project Buddy copilot for complex tickets to learn how prompts and handovers work in real cases - and run small outcome‑based pilots so new routines are measured, not hoped for.

Picture one well‑tagged transcript turning dozens of repeated calls into a single, reliable answer: that's the kind of everyday efficiency that protects jobs by making people the essential link between AI and Papua New Guinea's customers.

“Let's get smarter with every customer interaction.”

Employers in Papua New Guinea should redesign customer‑service roles as task bundles - moving repetitive ticket work into controlled pilots while creating new human lanes for escalation, quality and local‑language judgement - by making HR lead a clear reskilling roadmap, running micro‑credentials and AI hackathons, and partnering with universities and telcos to shore up infrastructure and cybersecurity.

Start small with outcome‑based pilots that test automation without large upfront cost, use talent‑mapping to match people to new oversight roles, and fund short, industry‑aligned credentials so teams learn to supervise LLM outputs and focus on higher‑value activities; these steps reflect regional guidance that AI can augment jobs if employers build learning cultures and targeted training (see the PNG employer and skills analysis at Islands Business PNG employer and skills analysis), coordinate with higher‑education drives for AI literacy and stronger cybersecurity measures (PNG higher-education AI integration - Pacific Islands AI), and align reform, workforce skilling and monitoring to capture productivity gains highlighted by regional studies (World Bank 2025 East Asia and Pacific employment findings).

The practical aim is simple: use pilots to prove that augmentation raises productivity, then scale with measurable retraining guarantees so automation becomes a pathway to better jobs, not a quick layoff.

“Today's innovations, from AI to robotics, can enhance productivity and create better jobs. Realizing these benefits will require a skilled workforce, competitive markets and policies to mitigate transition costs,” said Manuela V. Ferro, World Bank Vice President for East Asia and Pacific.

Split the next 90 days into three focused 30‑day sprints so Papua New Guinea teams move from risk to results: Sprint 1 - prepare governance, pick one high‑volume channel (SMS, Facebook or WhatsApp), and build a “golden dataset” and simple evaluation metrics so success isn't guesswork (NewDay's proof‑of‑concept started this way).

Sprint 2 - run fast Build‑Measure‑Learn loops to ship a lightweight RAG prototype, invest in data parsing (NewDay's bespoke parser lifted accuracy ~20 points) and aim for an 80%+ accuracy threshold before wider testing; serverless stacks and tight observability keep costs and ops simple.

Sprint 3 - pilot with a small group of experienced agents, collect feedback weekly, fix knowledge‑base gaps, and only then scale (NewDay expanded from 10 agents to 150 after proving the model and cutting lookup time from ~90 seconds to 4 seconds).

Throughout, lock in procurement and safety checks so models meet emerging standards and workforce commitments - for example, follow guidance on model selection and workforce training in the new AI Action Plan - and treat every pilot as a retraining opportunity so automation augments local jobs rather than replaces them (Infor and industry case studies show 90‑day managed services can deliver measurable insights and momentum).

“truthful” and “ideologically neutral” large language models

Ready-to-use resources and clear next steps make the difference between being disrupted and staying in demand: start with curated learning (ISACA's practical ISACA AI training and resources) to understand governance and risk, then move to short, applied programs - Nucamp's Nucamp AI Essentials for Work bootcamp teaches prompt-writing and on-the-job AI skills in 15 weeks, while targeted guides like

Top 10 AI Tools for PNG customer service guide

show low-risk pilot ideas for SMS, Facebook and WhatsApp channels; combine a short course with an outcome-based pilot (one tested transcript can collapse dozens of repeat calls into a single verified answer) and fund the path with Nucamp's payment plans or available scholarships so training is affordable.

For teams, prioritise a 90-day pilot, log golden datasets, and use these resources to build measurable retraining routes that keep local language and judgment where machines can't.