AI in 2026 – what to expect?

In 2026, artificial intelligence will increasingly function as a practical information-work system, rather than just a chat for generating text. The most important change is expected to be the shift to natively multimodal models, meaning ones that simultaneously understand text, images, audio and video. At the same time, reasoning stability will improve thanks to planning and the use of tools (e.g. a search engine or a code interpreter), although errors and hallucinations will still remain a real risk. In organisations, the “answer only on the basis of sources” approach and implementations with RAG and fragment citation will gain importance. Instead of one “biggest” model, an increasingly common approach will be to use a set of specialised models selected according to cost, quality and data sensitivity. Below is a practical overview of what to expect and how to translate these changes into implementation decisions.

Multimodality as the standard in 2026

In 2026, multimodality will become the standard, so most leading AI models will natively combine text, images, audio and video within a single experience. This means that a question such as “what is in this video?” may result simultaneously in a description, a summary and a list of possible actions. Such a model does not need to be “bolted on” to separate tools for each format, because understanding different media is built into the way the system works. In practice, this translates into faster grasping of context and less manual preparation of input data.

The most practical consequence of multimodality will be the automatic drawing of conclusions from materials that today require manual review. One example is analysing footage from a shop camera, where AI can detect queues, out-of-stock shelves and suspicious behaviour without manually tagging frames. For businesses, this means a simpler move from “we have video” to “we have operational signals” that can be used in processes. At the same time, the importance of precisely defining the aim of the analysis increases (description, summary, list of actions), because that determines what result the model should deliver.

1. 01One experienceText, images, audio and video natively connected.
2. 02Built-in understandingThe system natively understands different media.
3. 03Automatic conclusionsFast analysis without manual review.
4. 04Less manual workFaster context, less preparation.

Key takeaway: in 2026, AI integrates everything natively, automating analysis and accelerating action.

Better reasoning and specialisation of AI models

In 2026, AI models will reason more effectively, while the market will shift towards specialisation instead of relying on one universal “biggest” model. Improvements in quality will result, among other things, from more frequent use of planning and tools such as a calculator, a search engine or a code interpreter, thanks to which tasks like “calculate this and show the steps” will be handled more reliably. However, in areas based on incomplete data (e.g. law, medicine), hallucinations may still appear, which is why response modes based solely on sources will become crucial. In practice, this means more frequent implementations with RAG verification and an emphasis on quoting fragments and versioning sources.

The safest and often most cost-effective approach will be a model mix: a large one for planning, a smaller one for classification, and a local one for sensitive data. This setup matches real needs well: one model will be enough for 80% of repetitive tasks, while another will handle difficult cases better, where higher quality matters. This is directly linked to API costs as well, because an architecture with a smaller 7–13B model for most tasks and a larger one run only “for escalations” often works out more favourably financially. Regardless of the choice, quality testing in specific use cases and clear rules for when the result should be checked against sources and/or by a human will become standard.

Work automation thanks to AI agents

In 2026, work automation thanks to AI agents will involve the system itself planning the steps, using tools (e.g. a browser, CRM or email) and finally presenting a report with the result, rather than limiting itself to carrying on a conversation. In practice, this applies to requests such as “can AI book a business trip?”, where the answer is: yes, but best within clearly defined policies (price limits, preferred airlines, approval). The most widespread implementations will cover office work, such as inbox triage, meeting summaries and preparing versions of contracts and applications. A typical time saving on repetitive tasks is 20–40%, but only with organised templates, dictionaries and approval flows.

To stop an agent from “breaking” the process, in 2026 the standard will be granular permissions, read-only mode, sandboxes and human-in-the-loop for high-risk actions (e.g. a transfer or a production deployment). Multi-agent orchestration with role division is also becoming more common: an analyst gathers data, an executor makes changes, and an auditor checks compliance, which reduces the risk of priorities getting lost during longer tasks. Frameworks such as AutoGen, CrewAI and LangGraph (the LangChain ecosystem) are used for such setups. At the same time, the importance of measuring agent effectiveness is growing through metrics such as the % of tasks completed successfully, time to result, number of escalations to a human and cost per task, supported by observability tools (e.g. LangSmith, OpenTelemetry, dashboards in Grafana).

1. 01Step planningThe system plans by itself
2. 02Automatic actionUses tools (e.g. CRM)
3. 03Office implementationsTriage, summaries, contracts
4. 04Savings and order20–40% with organised processes

In 2026, clearly defined policies will be standard so that an AI agent can carry out tasks effectively without risk.

Integration of AI in the software lifecycle

In 2026, the integration of AI in the software development lifecycle (SDLC) will move from simple code suggestions towards handling tasks such as creating PRs, tests, migrations and documentation with references to the repository. The answer to the question “will AI replace a senior?” will remain no, but AI will shorten the time needed for routine work, provided the organisation has standards (lint, tests, review) and does not allow code without verification. In practice, this means AI becomes part of the development process, rather than an add-on run “on the side”. A consistent approach to quality will also be crucial, because automation without control increases the risk of errors in changes.

Operationally, SDLC with AI in 2026 will require an LLMOps approach: versioning prompts, regression testing, logging sources (for RAG) and controlling token costs in CI/CD pipelines with evals. In companies, a “router” architecture for models will also increasingly appear, selecting a model for the task, language, data sensitivity and cost limit, rather than relying on one solution for all use cases. The success of deployments will largely depend on integration with the data and permissions ecosystem (e.g. access to SharePoint/Confluence/Drive, data warehouses and ERP/CRM), and tools such as LlamaIndex/LangChain and cloud connectors will shorten the time needed for the technical integration, although access audits will remain the organisation’s responsibility. Where training a proprietary model is being considered, it will often be more sensible to start with RAG + instructions + company data, and fine-tuning only makes sense once there are thousands–millions of examples, a stable task definition and a measurable KPI.

AI on devices: privacy and personalisation

In 2026, AI on devices (phones and laptops) will increasingly perform tasks locally, which directly strengthens privacy because not everything has to go to the cloud. In practice, this includes transcription, summaries and basic question-and-answer tasks, run on compressed models with a limited context. This direction is being driven by NPU chips, such as Apple Neural Engine, Qualcomm Hexagon and Intel NPU. At the same time, “on-prem” and VPC deployments will become more common when an organisation wants greater control over its data.

The most common deployment pattern will be a hybrid: local for privacy and initial processing, and the cloud for more difficult tasks. As a result, sensitive data can first be anonymised and summarised on the device, and only then – if needed – passed to a cloud model. Personalisation will be achieved without training “from scratch”: through preference profiles, lightweight adaptations (e.g. LoRA) or retrieval from a private user database. A safer approach to style (“can AI write like me?”) is to use examples and a vocabulary, rather than uploading private archives for external training.

As AI moves to the edge, the importance of protection against data leakage and attacks on models running on devices will also increase. Companies will implement automatic classification and masking of PII (e.g. PESEL, addresses, account numbers) before content reaches the model, supplemented by log retention policies. There will also be edge-specific risks, such as model extraction or attacks via injected inputs, so signatures, encryption and hardening will be needed. To maintain portability between environments, formats and optimisation tools will become crucial, e.g. ONNX and toolchains such as TensorRT and OpenVINO.

1. 01Local processingFaster tasks, less cloud.
2. 02Enhanced privacyData stays on the device.
3. 03Hybrid modelsLocal anonymisation, cloud for tasks.
4. 04Data control (on-prem)Full control within the organisation.

Key direction: local anonymisation and cloud for difficult tasks, combining security with performance.

AI regulation in Europe: AI Act and copyright

In 2026, in Europe the AI Act will, in practice, affect the way AI systems are designed and deployed, because it introduces risk classes and obligations, especially for high-risk use cases. Whether a “chatbot is subject to the AI Act” depends on the context of use. Different requirements will apply to marketing than to recruitment, scoring or education. In critical processes, the standard will become a model where AI recommends, a human approves, and the system records the basis of the decision. Consequently, the organisation must take responsibility for system errors and prepare a transparent audit trail.

The most practical compliance strategy is to map use cases to the risk level and implement documentation, human oversight and auditability where required by the regulations.

• for high-risk systems: documentation, data management, human oversight and auditing
• data policies: decisions on log retention and anonymisation, as well as separating production data from data used to improve models
• operational security: robustness testing (including against prompt injection) and the principle of least privilege in the tools used by the agent

In the area of copyright and training data, the risk will not disappear, because disputes over licences and fair use will still be settled in court. Companies asking about safe generation of materials for campaigns will more often choose models with a clearer licensing regime, use stock libraries (e.g. Shutterstock/Adobe Stock) and keep a provenance register for materials. At the same time, purchasing AI solutions will start to resemble purchasing in the IT security space. Questions will arise about ISO 27001, SOC 2, data location, subcontractors and incident procedures. As a result, checklists that cover SLA, VPC/on-prem options, declarations that data will not be used for training, and results of tests on their own use cases will become increasingly important.

Changes in the labour market with AI and new skills

In 2026, AI will change the labour market most strongly by taking over fragments of tasks, rather than by “disappearing” entire professions. In practice, models will prepare first drafts of texts and analyses, as well as code drafts, while people will more often focus on verification and decision-making. That is why the question “will I lose my job?” will increasingly give way to “which tasks will I automate and how will I turn that into growth?”. The biggest advantage will go to people who can delegate tasks to agents and consistently control the quality of the outputs.

New roles combining business, data and risk will appear in companies, such as AI product owner, LLMOps engineer, model auditor or specialist in red-team. At the same time, “prompting” will stop being a differentiator, because workflow design, permission policies, testing and acceptance criteria will matter more. In education and training, the emphasis will shift to measurable outcomes, such as shorter cycle times, fewer errors and improved customer satisfaction, rather than just “tool workshops”. As automation increases, the need for checklists, spot audits of outputs and training in recognising hallucinations will also rise, because quality control can be tedious.

Competitive advantages will more often come from whether a team is “AI-native”, meaning it has standards for AI use, better data and shorter decision paths. At the same time, AI will improve the accessibility of work tools through live captions, translations, summaries and screen-reader support, which will make it easier for people with disabilities and in international teams to function. However, this requires clear confidentiality policies, because conversations and working materials often contain sensitive data. In educational institutions, GDPR and consent will be an additional constraint, which is why instances with training on data disabled or local deployments in larger organisations will become more popular.

AI application sectors: health, finance and industry

In 2026, the most practical AI applications in health, finance and industry will focus on working with documents, compliance and event prediction, rather than fully “replacing” experts. In medicine, AI will most often support documentation, including creating appointment notes, summaries of medical histories and coding procedures, which can improve patient service. In finance, automation of compliance, monitoring and customer service will dominate, while maintaining rigorous logs and audits. In industry, the key will be combining sensor data with maintenance history in order to predict failures and plan downtime.

In these sectors, the standard approach will become “AI supports the decision”, rather than “AI makes the decision”, because the risk of error and audit requirements are high. In banking, models can summarise information and simulate scenarios, while recommendations will be surrounded by constraints and risk controls. In insurance, analysing damage photos and documents will speed up valuations and initial decisions, and anomaly detection (e.g. repeated photos) will help reduce fraud, although attempts at deepfake fraud will also emerge. In industry, real benefits will come from reducing unplanned downtime and improving parts planning, rather than from the model’s “accuracy” alone.

In energy, AI will help forecast demand and renewable energy generation, and support decisions on controlling energy storage, while respecting hard constraints arising from system safety. In healthcare, the scope of diagnostic support will increase, but in practice it will remain under a doctor’s supervision and within regulatory approvals, especially for imaging and triage. Across sectors, deployments will require validation procedures and clear rules of accountability, because the cost of mistakes in critical processes is usually greater than the time saved. For this reason, solutions that genuinely deliver value will combine automation with quality control, logging and a transparent audit trail.