The growing role of AI in B2B productivity software

For a long time, AI in B2B software meant a chatbot tucked into a corner of the screen, or a "generate with AI" button bolted onto an existing feature. That's no longer the case. AI agents are starting to embed directly into the workflow — not as an optional module, but as the default way a task gets done.

From an extra feature to a layer that runs through everything

The difference isn't just technical, it's structural. An AI feature gets added to a product; an agentic layer runs through it — search, drafting, categorization, reporting, and automation become capabilities AI can orchestrate end-to-end, rather than independent checkboxes scattered across a menu.

The number that sums up the shift

Gartner predicts that 40% of enterprise applications will feature task-specific AI agents by the end of 2026, up from less than 5% in 2025 — an eightfold increase in a single year. Few technology transitions in B2B software have moved this fast.

Toward multimodal, not just conversational

The next shift isn't only about natural-language conversation. Gartner predicts that 80% of enterprise software and applications will be multimodal by 2030, up from less than 10% in 2024 — able to handle text, voice, image, and structured data within the same interface, rather than confining AI to a chat window isolated from the rest of the product.

What it changes for the teams buying software

When every vendor bolts an AI layer onto its product within a few months, comparing two tools on a spec sheet becomes less and less reliable — the feature advertised today may be outdated by the time the contract is signed. The useful question is no longer "does this tool have AI?" but "what does this AI actually do, on what data, at what reliability measured in real usage?"

The risk: mistaking the presence of AI for real value

A "powered by AI" badge on a marketing page guarantees nothing about a tool's actual impact. The companies getting the most out of this wave aren't the ones stacking up the most AI features — they're the ones pinpointing exactly where automation moves a measurable business outcome, and staying able to compare what they pay against what the market actually pays for an equivalent level of value.