Global software spending continues to grow at a brisk pace. Gartner expects total software spending to reach $1.4 trillion in 2026, roughly 15% higher than in 2025. Artificial intelligence is a key driver of that growth, accelerating innovation, expanding use cases, and fundamentally reshaping how value is created and sustained across the software ecosystem.

But AI is also changing how these businesses should be evaluated. As development becomes faster and cheaper, traditional code‑based software moats can weaken. In this new paradigm, capital providers are increasingly focused on more durable sources of defensibility, such as proprietary, governed data, deep workflow entrenchment, and mission-critical control points.  

For capital providers, this shift is not just technological; it requires a fundamental rethink of how software businesses are evaluated.

Historically, software companies gained a competitive advantage by developing complex, hard‑to‑replicate code and advanced product features. Today, AI is narrowing parts of that advantage, making feature‑level products increasingly easy to replicate, compressing pricing power and, in some cases, displacing entire categories of feature-level software.

The strongest software moats are now built around:

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  Proprietary, governed data with embedded permissions, lineage, and historical context

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  Deep workflow entrenchment within core business processes

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  Mission‑critical control points, such as ERP systems, billing platforms, or compliance software

These elements are both harder for competitors to replicate and more difficult to displace. They also matter to capital providers. For lenders, they offer downside protection by supporting predictable, resilient cash flow. For investors, they signal upside durability and long‑term relevance.

From a capital provider’s perspective, the key distinction is whether a software platform is augmenting a workflow or acting as the authoritative system that governs it. AI may enhance many applications, but it is far less likely to replace systems that serve as the source of truth for financial, operational, or compliance-critical processes.

As AI reshapes both how software is built and how it is monetized, these shifts also have important implications for revenue models and long-term value creation.

Software companies that produce recurring, usage‑based, or transaction‑based revenue are more attractive to capital providers. From a lending perspective, these companies provide predictable cash flow that can comfortably support taking on debt. For investors, recurring revenue tied to customer activity creates confidence in long‑term value generation.

In an AI-enabled efficiency environment, revenue models tied to customer activity or outcomes may prove more resilient than seat-based pricing, particularly when automation allows customers to do more with fewer people.

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  Customers pay per user or license

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  Revenue grows as headcount grows

Risk: By automating much of the work, AI leads to headcount reductions, thereby reducing software revenue

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  As customers receive more value using the product, they pay a higher price for the software

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  Activity drives revenue growth even if staffing levels remain flat or decline

Upside: Revenue model is better aligned with customer outcomes and automation trends

As AI changes how customers consume software, companies without strong moats or outcome-aligned pricing models face real revenue pressure and, in some cases, declining relevance. That’s why software companies whose products are embedded deeply into workflows have become more attractive for capital providers.

Software that’s “sticky” is embedded in the organization itself rather than individual users. These solutions, such as software for regulatory compliance, financial records, billing, and risk management, become part of the operational backbone and are far harder to remove or replace. In many cases, removing these systems would disrupt financial reporting, compliance obligations, or revenue generation.

Because embedded software solutions function as a system of record, they tend to be resilient to change and are not easily replaced by another software solution or by AI alone. Removing them would break core operational, financial, or compliance processes, resulting in high switching costs.

While AI is expanding the overall software market, it is also increasing risk for certain categories of products. Businesses most exposed to disruption tend to:

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  Rely primarily on feature-level differentiation without owning underlying data or workflows

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  Price primarily on user seats rather than outcomes or activity

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  Offer capabilities that can be replicated or bypassed by horizontal AI platforms

These models face increasing risk of pricing compression or disintermediation as AI adoption accelerates.

Not all software is created equal, and some products are more exposed as AI adoption accelerates. When evaluating software companies, capital providers should focus on a few critical questions:

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  If an organization removed the product, would core operations, financial reporting, or compliance processes break?

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  Would revenue decline sharply because of customer headcount reduction?

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  Does the solution control a true system of record, or is it an add‑on feature?

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  Is customer retention driven by switching costs or convenience?

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  Can the solution be easily replicated?

Companies with durable software moats tend to answer these questions convincingly. Their solutions have meaningful control points, are deeply embedded in workflows, and operate on recurring revenue models that scale with activity.

Ultimately, as AI reshapes the software landscape, the key question for capital providers is not simply whether a company is growing, but whether its role remains essential within the customer’s operations.