There’s a reason enterprise software has taken a beating in financial markets recently: nobody is sure how much value language models are going to destroy.
We are moving toward Generative UI, where the interface doesn't exist until you ask for it. If you need a specific chart, the LLM generates that specific chart in the chat window, for example.
There are going to be lots of business model changes for enterprise and consumer software.
Once the task is done, the interface disappears. This "ephemeral" UI is far more efficient than static dashboards, posing a direct threat to any software whose main value is "organizing data into screens."
Instead of static UI components, Generative UI introduces self-evolving interfaces that dynamically respond to user needs, much like how Generative AI models produce text, images, or code on demand, generating the application’s interface on the fly based on user intent.
By 2026, this technology is shifting the power dynamic from software vendors (who dictate workflows) to users.
In a traditional CRM, sales reps spend up to 70 percent of their time navigating tabs, logging calls, and updating pipeline stages. GenUI replaces the static "account page" with an ephemeral workspace: just ask a question about a customer account.
When a sales manager asks "which deals are at risk due to lack of executive engagement," GenUI doesn't just list them; it builds a temporary interface showing a side-by-side comparison of email sentiment, a "ghost" organizational chart of the client, and a pre-drafted calendar invite for a "check-in" meeting.
The concept of "searching for a record" disappears, as “the UI is the search.”
You talk to the CRM, and the specific fields you need to edit materialize in front of you, then vanish when the task is done.
ERPs have been difficult to navigate. GenUI democratizes the ERP by acting as a translator between complex business logic and human intent.
A procurement officer sees a news alert about a port strike. Instead of digging through Oracle's supply chain module, they ask the GenUI to "visualize the impact on our Q3 inventory."
The system instantly renders a custom map and a "what-if" slider tool that lets the user simulate different shipping routes—functionality that might have taken a developer weeks to build as a permanent feature.
For reconciliation or expense audits, instead of a spreadsheet of 10,000 rows, the interface generates a "review card" for the five most suspicious transactions, with integrated buttons to "Approve," "Flag," or "Ask Employee for Receipt."
Creative software such as Adobe can take years to master. In web or UI design (Adobe XD or Figma), a designer can say, "Create a high-fidelity checkout page for a luxury watch brand." The GenUI generates editable layers, buttons, and cascading style sheets.
Traditional software companies built moats by accumulating features over years. But an AI interface can potentially deliver all these capabilities through natural language, collapsing the feature hierarchy that supported tiered pricing models.
On the other hand, there are cost issues distinct from traditional software as a service, where serving additional users costs almost nothing.
A company providing AI-powered customer service might pay $0.50-$2.00 per complex interaction in application programming interface costs alone. This fundamentally changes unit economics.
Software companies face costs that scale with usage intensity, not just user count. Freemium models become harder to sustain when free users generate actual expenses.
When software products use similar underlying models (Claude, GPT-4 and others), differentiation becomes an issue. Why pay for ten different AI-powered tools when they're all essentially wrappers around the same language model?
So revenue is challenged while costs grow.
A big question is how much enterprise software value language models can displace.
As AI models become more capable, users can increasingly go directly to ChatGPT or Claude instead of using specialized vertical applications.
Enterprise customers may be more tolerant of usage-based pricing since they're accustomed to paying for value delivered.
Consumer products face harsher constraints. Users expect fixed, predictable monthly fees and react negatively to usage limits.
The fundamental question remains: as AI capabilities become more uniform and accessible, how do software companies justify premium pricing? The answer likely involves some combination of specialized data, deep workflow integration, reliability guarantees, and human expertise.
Still, these represent a narrower value proposition than the feature-rich software bundles that defined the previous era, some will argue.
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