Introduction: 2026 is not about AI being usable, but about reorganizing around AI
In 2026, AI's performance will improve gradually, but it will also rewrite the very guidelines for work procedures, product design, and how organizational roles are allocated. In 2024–2025, it became common to consult via chat and to generate text or code. The next stage is that AI will move on its own (agentification), connect with company data to enter frontline decision-making, and that costs and regulations will determine adoption success.
This article summarizes, from a practical perspective, 10 points about what will change in 2026. Some terms may be difficult, but we'll try to explain them clearly.
Change 1 in 2026: AI moves from 'conversation' to 'execution' (agents become standard)
As of 2025, the mainstream usage is AI being consulted with humans executing. In 2026, it will take a step further: AI will decompose tasks, call tools, verify results, and take the next step — making the agents more common.
- Example: In inquiry handling, AI performs FAQ search → references customer information → drafts → escalates according to rules → records the interaction history in the CRM, all in a semi-automatic loop
- Key point: The important thing is not cleverness but permission design (what can be executed and to what extent) and rollback when something goes wrong
Change 2: Multimodal becomes widely adopted (text, images, audio, and video on the same footing)
Text-only AI has limits. In 2026, multimodal AI that handles images, audio, screen interactions, and video will become normal UI rather than a rare feature.
- Examples: Screen sharing while AI guides operation steps or analyzes an error screen to infer causes
- Examples: On-site work captured with a smartphone, AI detects safety risks or deviations from procedures, and automatically generates a report
As a result, AI will be easier to apply not only to white-collar work but also to frontline roles such as on-site operations, retail, healthcare, and manufacturing, which have heavy paper and verbal communication reliance.
Change 3: From 'RAG' to 'data productization' (search connectivity alone won't differentiate)
Retrieval-Augmented Generation (RAG) for searching internal documents and having AI answer is becoming basic equipment in 2026, and it's not enough for competitive advantage. The differentiator will be preparing company data in a form that AI can use through data productization.
- Specifically: data dictionaries, ID design, update frequency, permissions, audit logs, and quality metrics (e.g., data completeness)
- Outcome: AI's answers will connect to KPIs and decision workflows, not just provide responses
