Will AI "Eat Enterprise Software?"

If you are an investor in enterprise software, you are aware there is a fear that language models are going to disrupt the traditional enterprise software market and firms. And that fear seems to be playing out in equity prices.

At one level there is concern that the traditional pricing model (per-seat licenses) will be challenged.

At another level there is concern that increasingly-capable AI models will displace the need for many enterprise software functions. 

Investors are essentially moving from views that “software eats the world” (so invest) to “software is dead” (so stay away). Near-term turbulence is inevitable. 

But it also is possible to argue that long term, there will be more enterprise software, even as AI adoption accelerates. 

More to the point, though language models enable natural language interfaces, automate routine tasks and generating insights from vast datasets, they arguably cannot replace enterprise software. 

Enterprise systems are engineered for reliability, security, scalability, and regulatory compliance in high-stakes environments. Moreover, enterprises often deal with proprietary data silos, strict data privacy laws and mission-critical uptime that general-purpose models cannot easily replicate. 

Aspect	Role of Enterprise Software	Role of General-Purpose Models	Coexistence Example
User Interface and Interaction	Provides structured dashboards, forms, and workflows for consistent, role-based access.	Enables natural language querying and conversational interfaces for ad-hoc exploration.	Models integrated as chatbots within enterprise resource planning systems (querying inventory via plain English without navigating menus).
Data Management and Security	Handles secure storage, compliance (audit trails, encryption), and integration with legacy databases.	Analyzes unstructured data or generates summaries, but relies on external data feeds.	Enterprise tools feed sanitized data to s for insights, while maintaining control over sensitive information ( GDPR-compliant AI assistants in CRM).
Automation and Workflow	Executes rule-based, repeatable processes like approvals or batch processing with high reliability.	Automates creative or variable tasks, such as generating custom reports or code.	Models suggest workflow optimizations within HCM platforms, but the core execution remains in the enterprise system (auto-drafting performance reviews in Workday).
Analytics and Insights	Delivers predefined key performance indicators, business information tools, and real-time dashboards with deterministic accuracy.	Provides probabilistic predictions, trend spotting, or scenario modeling from natural language prompts.	Hybrid BI where enterprise software runs core analytics, and models enhance with exploratory queries ("What if" simulations in financial planning tools).
Customization and Scalability	Supports enterprise-grade customization via APIs, modules, and cloud scaling for thousands of users.	Offers flexible, on-demand generation but struggles with consistent scaling or versioning.	Models used to generate custom code snippets for enterprise integrations, deployed within the platform (auto-building plugins for Salesforce).
Compliance and Auditing	Ensures regulatory adherence with built-in logging, versioning, and certification	Lacks inherent auditability; outputs can be opaque or inconsistent.	Enterprise systems log  interactions as auditable events, using AI for efficiency while meeting standards (fraud detection in banking software).

It’s a bit analogous to the traditional choices between general-purpose and application-specific processing. Sometimes one makes more sense than the other, but both coexist. 

AI-enabled or AI-centric software is moving up the stack of what a product is. So consumer experiences of products include vastly more software content than in prior years.

Sometimes a general-purpose approach will suffice. But not always. ASICs still make sense as well. 

And AI will often allow software to become more capable, rather than replacing it, which is the common concern today. 

Domain experience, codified in enterprise software, arguably will be just as important tomorrow as it is today. 

But investors, at the moment, seem more focused on the near-term negative impact on enterprise software company fortunes. 

In some cases, that concern is exacerbated by huge new capital spending requirements for AI infrastructure. 

An adage suggests "markets can stay irrational longer than you can stay solvent." And that is the reality some investors might be facing in the near term.