--- title: How AI Is Changing What Companies Offshore description: As agentic AI absorbs routine work, the case for offshore teams moves to judgement, exceptions, data quality and integration. What it means for you. image: https://blog.potentiam.co.uk/hubfs/blog/images/ai-changing-what-companies-offshore-hero-20260925-112613.webp --- [![footer-logo](https://blog.potentiam.co.uk/hubfs/footer-logo.svg "footer-logo")](https://potentiam.co.uk) - [Solutions](https://www.potentiam.co.uk/solutions) [![Technology icon](https://blog.potentiam.co.uk/hubfs/Images/Icons/Technology%20icon.svg) Technology Scale your team with a dedicated advisor who aligns architecture and performance 
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AI agents now carry out defined, multi-step work across business systems: processing documents, reconciling records, resolving routine queries. That puts pressure on the work that used to justify many offshore teams, such as keying data, matching documents and answering scripted queries. The work around the agents grows: handling what they escalate, keeping data clean, integrating systems, checking what they produce, looking after customers and improving the process. A business that runs AI agents still needs people. What changes is which capabilities it needs, and where it builds them. We hear the objection often: "we're using AI now, so we don't need offshoring." We agree with half of it. Offshoring as a way to move headcount somewhere cheaper is losing its logic, and we would not defend it. The useful question is which capabilities a business needs as AI changes the work, and where each belongs: the UK, offshore or the software. Key takeaway Agentic AI is most likely to absorb routine, high-volume tasks, the work the traditional offshore model was built on, because AI agents can now take on whole multi-step tasks. The case for an offshore team rests on capability: judgement, exception handling, data quality, integration, AI oversight, customer interaction and continuous improvement. Where that work continues and your team cannot absorb it, the model is hybrid: AI handles volume, an embedded offshore team runs and improves the process within limits you set, and your UK team sets those limits and takes escalated decisions. Start with who owns the exceptions. ## Why the old offshoring model is changing The model AI challenges is the purely arithmetic one: move a repeatable, high-volume process to a lower-cost location and measure success in heads moved and cost per full-time employee. Under that model, data entry, document processing and first-line query handling were natural candidates to move. That is the work AI is best placed to absorb. The [World Economic Forum's Future of Jobs Report 2025](https://www.weforum.org/publications/the-future-of-jobs-report-2025/digest/) (January 2025, still the latest edition) expects clerical and secretarial workers to see the largest decline in absolute numbers by 2030, and lists data entry clerks among the fastest-declining roles. The [International Labour Organization's global exposure index](https://www.ilo.org/publications/generative-ai-and-jobs-refined-global-index-occupational-exposure) (May 2025, its most recent) finds that clerical occupations remain the most exposed to generative AI. Sourcing strategies are already shifting. In [Deloitte's Global Outsourcing Survey 2024](https://www.deloitte.com/global/en/issues/work/global-outsourcing-survey.html), its most recent, 70% of more than 500 executives had brought some previously outsourced work back in-house over the previous five years, and 78% were using global in-house centres. We read that as a move towards owned capability: teams that work inside your business and build knowledge you keep. It is the distinction we draw between [an outsourcing arrangement and an embedded offshore team](https://blog.potentiam.co.uk/bpo-vs-embedded-offshore-team), and AI sharpens it. ## What AI is likely to automate, and what it will augment AI automates tasks, and most roles are bundles of tasks. The ILO estimates that one in four workers worldwide is in an occupation with some exposure to generative AI, rising to 34% of employment in high-income countries. Its conclusion is that "transformation of jobs is the most likely impact", because most occupations consist of tasks that still require human input. UK evidence agrees. The [Office for National Statistics](https://www.ons.gov.uk/businessindustryandtrade/business/businessservices/articles/artificialintelligenceinukbusinesses/2023to2026) (July 2026) found that around 35% of UK businesses with ten or more employees used at least one AI technology in June 2026, up from around 12% in late 2023. Adoption is still shallow: only 10% of businesses using AI report using it extensively, and most report no change in overall headcount so far. Treat the table below as a starting point to test process by process: how much can be automated depends on how consistent each process is and how clean its data is. | How AI is likely to affect the task | Typical examples | What the people around it do | | --- | --- | --- | | **Most likely to be automated** | Keying and extracting data from documents, matching invoices to orders, routing tickets, answering standard queries, producing first drafts and routine reports | Handle what the automation cannot match or answer, check samples of its output, and fix the rules and data that cause errors | | **Most likely to be augmented** | Analysis and reporting, software development and testing, customer support conversations, research, month-end and reconciliation work | Do the work faster with AI assistance, and remain responsible for whether the result is right | | **Most likely to stay human** | Decisions with financial, legal or customer consequences, complaints and difficult conversations, supplier and client relationships, deciding what to change in a process | Use AI for preparation and information, and keep the judgement and the accountability | ## What remains human, and why ![Customer operations specialist in a Cape Town office reviewing a customer case escalated from an automated system and taking notes before responding](https://blog.potentiam.co.uk/hubfs/blog/images/ai-changing-what-companies-offshore-exception-handling-20260925-112622.webp) When AI handles routine execution, the human role moves to the edges of the process. Four responsibilities stay with people in almost every process we see. **Exceptions.** The unusual invoice and the record that fails validation need a person who understands the process. **Judgement.** Where a decision carries financial, legal or customer consequences, a named person has to own it, whatever tool informed it. **Quality.** AI output has to be checked, sampled and corrected, and the errors fed back into the rules and the data. **Customers.** Complaints, escalations and conversations where trust is at stake still need a person who can listen and decide. The evidence supports this division. In a study of 5,172 customer support agents published in the [Quarterly Journal of Economics](https://academic.oup.com/qje/article/140/2/889/7990658) (May 2025), an AI assistant raised issues resolved per hour by 15% on average and by around 30% for less experienced agents. [McKinsey's State of AI 2026](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai/) (August 2026) reports that 80% of respondents say AI has improved their individual productivity, yet only 37% attribute any impact on earnings to it. In our experience the gap usually sits in the operating model: who owns the process, the data and the exceptions. Read more on [why most AI implementation fails](https://blog.potentiam.co.uk/why-most-ai-implementation-fails-potentiam). So who owns exceptions, judgement and quality when AI is embedded in a process? The business does, through named people. Before automating, decide who handles what the system rejects, who signs off above which threshold, and who reviews output and how often. Much of that ownership can sit offshore. ### What AI agents change The main change of the past year is that AI now carries out work as well as answering questions. AI agents take on defined, multi-step tasks, such as reading a supplier invoice, matching it to the order, posting it and raising a query. When an agent should stop and hand a case to a person is something the business has to define and test. In McKinsey's 2026 survey, 40% of respondents from organisations with revenue above $1 billion said they were scaling AI agents, up from 27% a year earlier; at smaller organisations the figure held at 22%. [Deloitte's survey](https://www.deloitte.com/us/en/about/press-room/state-of-ai-report-2026.html) (fieldwork August to September 2025, published January 2026) found close to three-quarters of companies planning to deploy agentic AI within two years, but only 21% of them with a mature model for governing agents. That strengthens the case for the people around the technology. An agent that acts on its own needs clean data, connections into the systems it touches, and people to handle what it escalates and to catch the errors it does not flag. In a [Gartner survey](https://www.gartner.com/en/newsroom/press-releases/2026-05-05-gartner-says-autonomous-business-and-artificial-intelligence-layoffs-may-create-budget-room-but-do-not-deliver-returns) of 350 executives at large organisations piloting or deploying AI agents or similar autonomous technology (fieldwork third quarter of 2025, published May 2026), around 80% reported workforce reductions, yet reductions were almost as common where returns were modest or negative as where they were higher. Gartner predicts autonomous business will create more work for people and be a net creator of jobs by 2028 to 2029.   ## The new skills and roles emerging around AI ![Data engineer and colleague in an offshore team office reviewing records that failed a data quality check before they flow into reporting and AI tools](https://blog.potentiam.co.uk/hubfs/blog/images/ai-changing-what-companies-offshore-data-quality-20260925-112620.webp) Demand is growing for the capabilities that make AI reliable, and overseeing AI is becoming work in its own right. [Stanford's AI Index 2026](https://hai.stanford.edu/ai-index/2026-ai-index-report) (April 2026) recorded 17% growth in AI-specific governance roles in 2025. In [Microsoft's 2026 Work Trend Index](https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization) (May 2026), a survey of 20,000 AI users in ten countries including the UK, the two human skills most often named as becoming more important were quality control of AI output (50%) and critical thinking (46%). Not all of this is technical. Much of the change is in operational roles whose content shifts: finance teams spending less time keying and more resolving mismatches, service desk analysts handling what automated triage escalates, sales operations staff keeping CRM data clean enough for AI tools to use. The capabilities we see businesses building most often fall into seven groups. | Capability | What the work involves | Typical roles we build | | --- | --- | --- | | **Data quality** | Cleansing, enriching and validating the records AI tools read and write | Data cleansing and enrichment specialists; data quality specialists | | **Data engineering and integration** | Connecting finance, sales and operational systems so data flows reliably into AI tools | Data engineers, system architects and DevOps engineers | | **AI and machine learning** | Building, tuning and maintaining models and AI features | AI and machine learning specialists; data scientists | | **Quality assurance** | Testing software and AI-enabled workflows before and after changes | QA engineers, manual and automation | | **Operations and support** | Keeping systems running, monitoring them and resolving what users and automation escalate | Application support analysts, site reliability engineers, and L1 and L2 service desk analysts | | **Exception handling and analysis** | Resolving the cases automation cannot, and turning output into decisions | AP and AR clerks, management accountants, data analysts and BI developers | | **Continuous improvement** | Mapping processes, finding where automation fails and changing the process | Business analysts and project managers | We can build any of these capabilities in any of our hubs; which hub is right depends on a conversation with each business. ## Where global talent fits Most of the AI tools a mid-sized business buys are available to its competitors too. What differs is whether a business has the people to make them work day to day. In our experience those people are hard to hire quickly in the UK, where a specialist role often takes six to twelve weeks or longer to fill, so access to AI-proficient talent matters as much as the choice of tools. We build these teams in five hubs in four countries: Cape Town and Johannesburg in South Africa, Iași in Romania, Bengaluru in India and São Paulo in Brazil. We built [Open Energy Market's development team](https://blog.potentiam.co.uk/how-open-energy-market-accelerated-development-offshore-team-bangalore). When Opinium split its data processing away from its UK insight consultants, we built an offshore team focused on data cleaning and processing, and it has returned about 20 days a month to those consultants; read [how Opinium built its offshore data analytics team](https://blog.potentiam.co.uk/how-opinium-research-built-offshore-data-analytics-team-cape-town). Time zones shape which work goes where. Bengaluru is four and a half hours ahead of the UK in summer and five and a half in winter, so a 9 to 6 day there runs from 4.30am to 1.30pm UK time in summer and 3.30am to 12.30pm in winter. The overlap falls in your morning, and with scope agreed, overnight queues can be cleared before your UK team starts. South Africa is one hour ahead in summer and two in winter, so its day runs from 8am to 5pm UK time in summer and 7am to 4pm in winter, which suits live collaboration. Full-day or out-of-hours cover needs agreed shifts in any hub. See also [the best offshore location for a technology team](https://blog.potentiam.co.uk/best-offshore-location-technology-msp-team). Cost still matters, and the savings in every hub are substantial, but the deciding question is whether you can build, and keep, the capability you need. ## What a hybrid onshore, offshore and AI operating model looks like A hybrid model gives each part of a process to the layer that does it best. AI, including agents, handles volume. An embedded offshore team runs the process, handles exceptions and makes day-to-day decisions within limits you set. Your UK team sets those limits, takes escalated decisions, stays accountable for the outcome and keeps the key relationships. We have run large onshore and offshore operating models ourselves: our Founder-Directors, Christopher Lydiard-Wilson and Charles Fenton, scaled [EnergyQuote JHA to more than 300 people, about 60% of them offshore](https://blog.potentiam.co.uk/how-a-ceo-delivered-21m-of-value-and-an-exit-to-accenture-by-building-offshore-teams-part-1-of-6), before its acquisition by Accenture in 2015. | Layer | What it does | Example: supplier invoice processing | | --- | --- | --- | | **AI** | Routine execution at volume: extraction, matching, routing, first drafts | Reads invoices, matches them to purchase orders and queues clean matches for payment | | **Embedded offshore team** | Runs the process within agreed limits: exceptions, data quality, output checks, integration, support and improvement | AP clerks resolve mismatches and new-supplier queries; a data specialist fixes supplier records; a business analyst tracks why exceptions occur and removes the causes | | **UK team** | Sets the limits, takes escalated decisions, stays accountable, keeps key relationships and designs the process | Your finance lead approves payments above a threshold, handles disputes with key suppliers and decides what the automation may do | The same pattern applies elsewhere. In IT support, AI triages tickets, [an offshore service desk](https://blog.potentiam.co.uk/offshore-service-desk-teams-for-msps) resolves what is escalated, and the UK team owns major incidents. [Offshore finance teams](https://blog.potentiam.co.uk/offshore-finance-accounting-teams-uk) spend more time on reconciliations and exceptions, and less on data entry. Under [the Embedded Offshore Team Model](https://blog.potentiam.co.uk/embedded-offshore-team-model), the offshore layer works in your systems and reports into your managers, while we handle employment, workspace and local infrastructure. With AI in the process this matters more: the people handling your exceptions learn how your process fails and why, and in an embedded team that knowledge stays with you. ## Practical questions for a business planning its next phase 1. **Which processes do we expect AI to handle, and where will a human layer still be needed?** Map by task, and mark where people interpret, check or act on AI output. 2. **Who owns exceptions, judgement and quality in each process?** Name the role, the approval thresholds and the review frequency before any process or agent goes live. 3. **Is our data ready?** If records are incomplete or systems do not connect, data quality and integration come first. 4. **Which capabilities do we have, which do we lack, and which need augmenting?** Compare the seven groups above with your current team. 5. **Which work must stay close to UK customers and decision-makers, and which can run offshore?** Accountability and time of day are better guides than how difficult a task looks. 6. **What will we do with the capacity AI frees up?** Decide in advance whether it goes to growth, service or cost. [Book a discovery call](https://www.potentiam.co.uk/contact-us) to discuss how we build dedicated offshore capability around the functions and skills your business needs. ## Frequently asked questions **Will AI replace offshore teams?** AI is likely to take over some offshore tasks, particularly data entry, document matching and scripted query handling. The evidence on what happens to overall job numbers is still emerging. Demand is growing for people who handle exceptions, keep data clean, integrate systems, check AI output and look after customers. Those capabilities can be built offshore in embedded teams. **If we are using AI, do we still need an offshore team?** Some businesses will not. If AI absorbs your routine volume and your existing team can handle the exceptions, data and oversight, you may not need one. AI also creates continuing work: preparing data, integrating systems, checking output, handling exceptions and improving the process. Where that work exceeds what your team can absorb, an embedded offshore team is one way to build the capability. **Which offshore roles are growing because of AI?** We see growing demand for data engineers, data quality specialists, data analysts and BI developers, QA engineers, application support analysts, site reliability engineers, AI and machine learning specialists and business analysts. Operational roles such as accounts payable and service desk analysts are changing too, with more time on exceptions and less on data entry. **What is a hybrid onshore, offshore and AI operating model?** It divides each process between three layers. AI handles routine execution at volume. An embedded offshore team runs the process, resolves exceptions, maintains data quality and improves the workflow. The UK team sets the limits, takes escalated decisions, stays accountable for outcomes and keeps key relationships. **Who should own exceptions when AI runs a process?** A named person or team inside the business. Before automating, decide who handles what the system rejects, who approves decisions above a set threshold and who reviews AI output and how often. That work can sit with an embedded offshore team, with UK managers owning the thresholds and final decisions. **How long does it take to hire an offshore data or AI team?** In our experience, hiring typically takes four to six weeks in Cape Town, Johannesburg, Iași and Bengaluru, and four to eight weeks in São Paulo, from starting the search to the candidate accepting the offer. We see the UK equivalent take six to twelve weeks or longer. That applies to AI and data roles too. Notice periods and onboarding follow the offer. Sources: [World Economic Forum, The Future of Jobs Report 2025 (January 2025)](https://www.weforum.org/publications/the-future-of-jobs-report-2025/digest/); [International Labour Organization, Generative AI and Jobs: A Refined Global Index of Occupational Exposure (May 2025)](https://www.ilo.org/publications/generative-ai-and-jobs-refined-global-index-occupational-exposure); [Office for National Statistics, Artificial intelligence in UK businesses: 2023 to 2026 (July 2026)](https://www.ons.gov.uk/businessindustryandtrade/business/businessservices/articles/artificialintelligenceinukbusinesses/2023to2026); [Deloitte, Global Outsourcing Survey 2024](https://www.deloitte.com/global/en/issues/work/global-outsourcing-survey.html); [Brynjolfsson, Li and Raymond, Generative AI at Work, Quarterly Journal of Economics (May 2025)](https://academic.oup.com/qje/article/140/2/889/7990658); [McKinsey, The State of AI in 2026 (August 2026)](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai/); [Deloitte, The State of AI in the Enterprise (January 2026)](https://www.deloitte.com/us/en/about/press-room/state-of-ai-report-2026.html); [Gartner, autonomous business survey (May 2026)](https://www.gartner.com/en/newsroom/press-releases/2026-05-05-gartner-says-autonomous-business-and-artificial-intelligence-layoffs-may-create-budget-room-but-do-not-deliver-returns); [Stanford HAI, AI Index Report 2026 (April 2026)](https://hai.stanford.edu/ai-index/2026-ai-index-report); [Microsoft, 2026 Work Trend Index (May 2026)](https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization). 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The evidence on what happens to overall job numbers is still emerging. Demand is growing for people who handle exceptions, keep data clean, integrate systems, check AI output and look after customers. Those capabilities can be built offshore in embedded teams." }, "name" : "Will AI replace offshore teams?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "Some businesses will not. If AI absorbs your routine volume and your existing team can handle the exceptions, data and oversight, you may not need one. AI also creates continuing work: preparing data, integrating systems, checking output, handling exceptions and improving the process. Where that work exceeds what your team can absorb, an embedded offshore team is one way to build the capability." }, "name" : "If we are using AI, do we still need an offshore team?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "We see growing demand for data engineers, data quality specialists, data analysts and BI developers, QA engineers, application support analysts, site reliability engineers, AI and machine learning specialists and business analysts. Operational roles such as accounts payable and service desk analysts are changing too, with more time on exceptions and less on data entry." }, "name" : "Which offshore roles are growing because of AI?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "It divides each process between three layers. AI handles routine execution at volume. An embedded offshore team runs the process, resolves exceptions, maintains data quality and improves the workflow. The UK team sets the limits, takes escalated decisions, stays accountable for outcomes and keeps key relationships." }, "name" : "What is a hybrid onshore, offshore and AI operating model?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "A named person or team inside the business. Before automating, decide who handles what the system rejects, who approves decisions above a set threshold and who reviews AI output and how often. That work can sit with an embedded offshore team, with UK managers owning the thresholds and final decisions." }, "name" : "Who should own exceptions when AI runs a process?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "In our experience, hiring typically takes four to six weeks in Cape Town, Johannesburg, Iași and Bengaluru, and four to eight weeks in São Paulo, from starting the search to the candidate accepting the offer. We see the UK equivalent take six to twelve weeks or longer. That applies to AI and data roles too. Notice periods and onboarding follow the offer." }, "name" : "How long does it take to hire an offshore data or AI team?" } ] } ```