Navigating AI Disruption: How Software Companies Should Invest to Win

Recent advancements in agentic AI—most recently Anthropic’s expansion into legal and financial workflows—only accelerated an already-active reset in the software market. Public software valuations reacted, private equity portfolios and debt are under review, and boards are on high alert about their own exposure.

This is not a moment for panic, it is a call for decisive action.

As software companies’ valuations are reshaped by AI advancements, software leaders are being pulled between two loud, competing narratives:

On one side: AI will be the end of SaaS as we know it. Agentic systems, legal and financial plugins, and horizontal orchestration layers will automate workflows end‑to‑end, collapse seat counts, and turn today’s enterprise software into commoditized infrastructure.

On the other: Incumbent enterprise software companies are best positioned to win. They own proprietary data, deeply embedded workflows, and long‑standing customer relationships that startups and model providers simply can’t replicate.

Both narratives carry some truth—but they are also incomplete. The future lies somewhere in between. 

Software products with real competitive advantage do have time. But that time is finite. Moats buy options and time, not endless immunity. Companies that use this window to innovate can extend their advantage. Those that stand still or wait too long will see their relevance and revenue erode.

Why AI-Led Disruption Is Reshaping Software

AI is not just another feature wave. It is reshaping the fundamentals of how enterprise software creates and captures value—and the market is noticing.

Remember: Customers cannot change overnight. Switching core enterprise systems is risky, slow, and expensive. Our research shows 77% of B2B buyers are highly confident in the ROI of their existing software investments, which should provide a sense of relief. This confidence creates a real window for incumbent software firms to adapt—but the window won’t be open for long.

That tension defines the opportunity—and explains why today’s debate has become so polarized.

Which Software Can Withstand AI Disruption?

Not all enterprise software faces the same level of disruption risk.

Software companies tend to be better positioned when their products exhibit one or more of the following characteristics:

• Highly deterministic workflows that demand precision and auditability
• Heavy regulatory and compliance requirements
• Deep integration across multiple third‑party systems
• Dependence on proprietary or long‑lived historical data
• Tight coupling across multiple enterprise workflows

These attributes create friction for horizontal AI agents and delay full displacement. But even the most defensible software is vulnerable if it doesn't move fast enough.

Generative AI is steadily shifting value away from tools and toward autonomous workflows. Over time, software that fails to evolve risks becoming a passive system of record—useful, but interchangeable.

West Monroe’s 3 Strategic Priorities to Evolve Software for AI Disruption

The path forward is not one-size-fits-all. Many incumbents have already begun responding to AI. Others are still assessing exposure. The right moves depend on where a company’s true moat sits today.

What follows is a pragmatic playbook with three non-negotiable steps designed to meet incumbents where they are—and help them move faster.

Building Long-Term Competitive Advantage (aka “Moat”)

As AI reshapes enterprise workflows, tomorrow’s winners will be defined less by features and more by control points.

The emerging moat belongs to companies that can:

• Own the system of record and evolve it into a system of authority
• Convert proprietary data into intelligence by combining context, rules, and history
• Orchestrate workflows safely across systems in regulated environments
• Maintain ownership of the customer relationship—even as AI does more of the work

In practice, this means evolving beyond systems that simply record data into platforms that guide outcomes.

As work becomes more automated, the most valuable asset isn’t raw data. It’s context—knowing what inputs matter, when exceptions occur, where human judgment is required, and how decisions are approved.

That context rarely lives in one place. It’s spread across core systems, collaboration tools, documents, emails, and third-party platforms. Software companies that can reliably connect that information—while maintaining security, governance, and trust—become essential to how AI-powered work gets done.

Over time, value accrues to platforms that can:

• Bring together decision-relevant information from across tools
• Apply rules and guardrails consistently
• Learn from outcomes and improve performance over time

This is why control over how information moves between systems is just as important as the application itself. Investors are increasingly focused on companies that can expand into adjacent workflows, connect fragmented context, and play a larger role in how decisions are made, ultimately increasing the value they can deliver.