Wise predicts agentic AI shift in business by 2026

Wise has set out five themes it expects to shape how businesses use artificial intelligence in 2026, as organisations move from experimentation to AI embedded in day-to-day operations.

Its analysis points to wider adoption of "agentic" systems that can carry out multi-step tasks, and a greater focus on integrating AI into existing software rather than relying on standalone tools. It also highlights rising pressure to strengthen oversight as AI is used in areas where errors can carry financial, regulatory or customer consequences.

Wise is best known for cross-border payments and multi-currency accounts, and has been expanding its business offering for small and medium-sized firms that trade internationally. The commentary places the company in a broader debate about how operational teams, finance functions and customer service departments will adapt as automation becomes more common.

Agentic shift

Wise expects AI to move beyond producing answers and summaries and to take on structured work. It describes "Agentic AI" as task-specific assistants that learn from context and operate with more autonomy than today's chat-based tools.

These agents are expected to expand from basic customer service into supply chain and operational processes. Examples include handling complex customer requests or triggering inventory reorders when stock runs low. People would still need to supervise outcomes and handle exceptions.

Wise cites an industry estimate that 40% of company software will use task-specific AI agents by 2026. That shift would make governance and monitoring more important, particularly where systems touch customer accounts, financial data or regulated activity.

Build versus buy

Another theme is a reassessment of reliance on third-party AI tools. Wise argues that rapid changes to underlying large language models, including Gemini, ChatGPT and Claude, will shorten the shelf life of products built on top of them.

As a result, some businesses may consider building their own AI tools rather than buying off-the-shelf services. Control over updates, deployment and fit with internal workflows is central to that argument, alongside costs that can rise as usage scales.

This approach is likely to be more accessible to larger organisations with engineering resources, though smaller firms are also experimenting with customised AI workflows. Many now combine commercial AI subscriptions with internal automation built on their own data and processes.

Where returns sit

Wise highlights three areas where it expects the clearest returns on AI spending in 2026, particularly for work that relies heavily on text and structured data.

The first is content and marketing. Wise expects AI to speed up the production of blog posts, marketing copy and internal documentation. It also forecasts improvements in language translation, while noting today's limits on accuracy.

Wise also expects shifts in how content is discovered. It points to advertising within AI products and suggests this could reshape optimisation strategies. The analysis cites reports that OpenAI is targeting USD $1 billion in "free-user monetisation," which it links to sponsored responses within search products. It also notes that Microsoft Copilot has integrated an advertising auction into a beta release.

The second area is customer service. Wise expects automated systems, including voice-based tools, to handle more enquiries using company scripts and internal guidelines. It identifies escalation as a key design decision, with clear thresholds needed for handing a case to a person.

The third is data and finance. Wise expects AI-driven pattern recognition across large datasets to become more important for accountants, analysts and financial researchers. These tools can surface anomalies and trends, but also raise questions about explainability, audit trails and accountability.

"AI can 'hallucinate,' meaning it can make confident-sounding mistakes based on erroneous assumptions," said Aditya Shrivastava, an AI and automation expert at Wise.

Integration focus

Wise expects more AI to be used inside existing applications rather than through separate websites or tools. It cites a Deloitte prediction that AI embedded in established apps and search engines will be three times more common than use through standalone AI sites.

It also describes a move away from general-purpose models towards domain-specific models trained for sectors such as healthcare and finance. These models can reduce risk and cost in some deployments and improve accuracy when trained and governed to meet narrow requirements.

Human oversight

Human review is a recurring theme. Wise argues that as AI tools move into sensitive areas such as customer data, money movement and rules-based work, businesses must retain responsibility for outcomes and keep checks in place.

This also affects training. Wise expects roles to shift as routine tasks are automated, with staff needing to set direction for AI tools, review outputs and validate results. It frames the change as moving from performing manual tasks to directing automated systems.

Payments context

Alongside these AI trends, Wise links increased automation to more cross-border work, with businesses engaging suppliers, contractors and customers across multiple markets. It positions Wise Business as infrastructure for these workflows, particularly where firms pay software subscriptions, settle invoices or run international contractor payments.

Wise Business provides local bank details in more than eight currencies and lets customers hold and send funds in more than 40 currencies. Wise says it supports payments to more than 160 countries, with 73% arriving instantly. It also offers batch payments for up to 1,000 recipients and integrations with accounting software, including Xero and QuickBooks.

Overall, Wise's predictions reflect a broader industry shift towards applied AI and operational controls, as companies balance automation with governance, data protection and accountability.