Evaluating Software Businesses in the Age of AI

Category	FY26 Enterprise Value / Revenue Range*	Investment View	Key Diligence Considerations	Value Creation Priorities
Cybersecurity	1.0x – 18.0x (3.0x – 5.5x)	Structurally insulated; AI-native consolidators command premiums while point solutions are repriced for displacement risk	Platform versus point solution architecture; customer concentration; AI-native product roadmap	Pursue platform consolidation plays; expand AI-native detection and response capabilities; cross-sell security solutions
Infrastructure Software	2.0x – 17.5x (3.0x – 6.0x)	Direct AI beneficiary as AI workload complexity increases demand for observability, continuous integration and continuous delivery and cloud cost management; some legacy and on-premises assets may remain undervalued	Revenue tied to AI workload growth; customer expansion tied to usage versus seats; switching cost depth	Convert to usage-based pricing; expand into AI workload monitoring; deepen hyperscaler integrations
Data Infrastructure	1.0x – 12.5x (2.0x – 6.0x)	Strong tailwinds from AI adoption; cloud data warehouses, real-time databases and pipelines underpin AI workloads at scale	Revenue tied to AI workload volume; differentiation from hyperscalers’ native data services; customer expansion trajectory	Position around AI workloads; implement usage-based pricing
Vertical Software as a Service (SaaS)	1.0x – 16.5x (2.0x – 5.0x)	Premium to horizontal SaaS; regulatory complexity, proprietary data and mission-critical workflow depth insulate from AI substitution risk	Depth of regulatory dependency; proprietary data assets; switching costs	Deepen data asset monetization; launch AI-native vertical features; expand into other regulated industries
Back Office Software	1.5x – 4.0x (1.5x – 3.0x)	Premium within horizontal SaaS; payroll data lock-in and compliance complexity reduce substitution risk	Payroll processing dependency; compliance module depth; customer tenure	Deepen compliance and payroll integration; expand into workforce analytics; leverage data assets for product expansion
Workflow and Productivity	0.5x – 3.5x (1.0x – 2.0x)	Highest near-term displacement risk; seat-based models are directly threatened by AI agent substitution	Workflows that are non-replicable by AI agents; NRR steadiness versus deterioration	Identify and fortify non-replicable workflows; accelerate pricing model transition; pursue early exit if AI substitution is imminent
Marketing Technology (MarTech)	0.5x – 3.0x (1.5x – 2.0x)	Broad compression; point solutions are most exposed while platforms with proprietary first-party data are more insulated	Proprietary first-party data ownership; customer dependency on data set	Consolidate around data assets; divest point solution components; frame the business as a data platform rather than a software tool
Tech-Enabled Services	0.5x – 2.0x (0.5x – 1.0x)	AI cost tailwinds, but valuation uplift remains dependent on proven margin improvement	Percentage of delivery labor that is automatable by AI; visible margin improvement	Accelerate AI-driven delivery cost reductions; productize services to reduce labor intensity; build margin improvement evidence

Additional Subsector Nuances to Consider

Beyond the primary investment signals outlined above, several categories carry additional nuances.

Infrastructure Software

The market systematically undervalues certain legacy and on-premises assets relative to their actual fundamentals. High switching costs, entrenched customer relationships and stable recurring revenue create durability that indiscriminate buyer discounting does not reflect. The narrowing valuation gap versus cloud peers signals that sponsors managing on-premises or hybrid assets may have more opportunity than current pricing suggests, provided the exit narrative is built with rigor.

Healthcare Information Technology

Multiple compression in healthcare information technology (HCIT) is policy-driven rather than operational. Federal spending headwinds and reimbursement uncertainty are depressing valuations on assets whose underlying business quality remains intact. Sponsors holding HCIT assets should treat the current disconnect as temporary and construct an exit narrative that isolates macro pressure from fundamental asset durability.

Marketing Technology (MarTech)

The divide between commoditized point solutions and platforms anchored to proprietary first-party data is sharper than the blended category multiple suggests. A MarTech business without a defensible data asset is a point solution. One built around owned, exclusive customer data is a data platform with a software layer. Buyers will price the two very differently.

Reading Financial Signals of AI-Impacted Software Businesses

Regardless of subsector, company leaders must evaluate the profit and loss statement with a critical eye to identify gaps in performance and perception by investors.

NRR composition is a commonly misread signal. While a headline NRR figure can appear healthy on the surface, digging a layer deeper into module attachments, seat expansion and usage growth can reveal true customer dependency strength . Retention built solely on price increases signals that a business’s growth narrative is not sustainable. Buyers will scrutinize each element of NRR as a standard diligence step.

Infrastructure cost attribution is a gap in many software businesses. Cloud and infrastructure spend is not always tracked by product line, customer segment or usage pattern. As AI workloads increase, unattributed infrastructure cost can become a margin risk. If a buyer is unable to get clarity from management on infrastructure cost, the buyer may want to build a more conservative model.

AI adoption figures also demand deeper scrutiny. Buyers now expect feature utilization rates, retention lift in AI-using cohorts and support cost deflection data. Management presentations that claim material AI-driven cost savings without supporting metrics could face credibility issues and earnout risk.

These metrics are just a few of the variables buyers will review closely during diligence. Sponsors must address any gaps before launching a process.

Developing a Playbook for Action, Whether Exposed or Defensible

Once sponsors and management teams have conducted an honest assessment of where their assets sit – defensible or exposed – they must act swiftly before the market acts for them.

Defensible Assets

Defensible assets are positioned to become category leaders. Launching agentic products on top of the existing platform could unlock new revenue streams with usage-based or outcome-based economics. Businesses that leverage proprietary customer data as a differentiator can create an advantage that is extremely difficult for competitors to replicate.

Companies that own a proprietary model of customer workflows, including labor and non-labor cost mapping, cash flow modeling or industry-specific process data, often have yet to fully monetize or embed those assets in the customer relationship. Businesses that generate new AI-enabled revenue while reducing delivery costs can achieve growth in both top-line performance and EBITDA.

Exposed Assets

Exposed assets require tougher decisions. The playbook for these businesses centers on preserving value by concentrating resources on the most defensible customer cohorts rather than defending the entire base equally. Sponsors should identify cohorts with the deepest workflow dependency, highest switching costs and longest tenure and concentrate retention investment there.

Exposed businesses should evaluate a shift from seat-based pricing toward usage-based, outcome-based or consumption pricing that ties revenue to demonstrated value rather than license count. This transition requires careful sequencing but can materially reframe the valuation narrative.

Similarly, customer acquisition cost (CAC) payback periods that made sense at peak multiples become unsustainable in a compressed valuation environment. Sponsors should evaluate CAC payback by segment, reduce investment in low-probability new logo acquisition and redirect resources toward expansion within existing accounts.

When top-line growth is constrained, leaders should also consider restructuring the cost base to protect margins. Infrastructure, delivery and customer support are areas where AI creates the most immediate cost reduction opportunities.

A credible AI response roadmap is essential for exit positioning. Buyers will ask whether management has identified specific AI displacement risks and articulated a product and operational response. A roadmap built on assertion without utilization data, feature adoption metrics or cost deflection evidence will not withstand diligence. A management team that has identified exposure and articulated a measurable response will be in a stronger negotiating position.

For some exposed assets, the most value-preserving decision is also the most difficult: a near-term exit may preserve more value than extended operational intervention. Sponsors should evaluate this option with the same rigor applied to any other value creation lever. The market does not wait and delayed processes in deteriorating assets rarely improve.

Software Asset Transaction Readiness

For every asset, regardless of AI exposure, exit preparation has become a source of competitive advantage. Buyer scrutiny around AI has intensified and gaps between what is claimed and what can be demonstrated are now a primary diligence focus. Sellers who cannot close that gap face the possibility of structured outcomes, earnouts or failed processes. Buyers risk underwriting AI upside, durability or defensibility without sufficient evidence.

Sponsors on both sides of the transaction must surface AI risk proactively and ground their investment thesis in durable, current-market performance. For sellers, that means building a credible exit narrative supported by evidence. For buyers, it means pressure-testing whether AI expands the value creation opportunity, accelerates competitive threat or changes the asset’s long-term defensibility. As AI reshapes the software landscape, boards and investors are focusing diligence on a specific set of considerations. Sponsors should expect to address these topics in any process and ensure management can support each with evidence.

• Whether net revenue retention is driven by price, expansion, new module adoption or new logos and how that mix has shifted over the past four quarters?
• Which components of the product AI agents could replicate today and how quickly that threat could mature?
• Whether AI is a demand tailwind, substitution headwind or both across the core revenue model?
• Which proprietary data assets the company owns and whether they are embedded deeply enough in the customer relationship to create durable switching costs?
• Whether the company has demonstrated AI-driven cost savings in its own operations and where those savings are visible in the financial statements?
• Which AI roadmap features have demonstrated measurable customer adoption, retention lift or revenue impact?
• Retention exposure over the next 18 months if a well-funded AI-native competitor entered the market today?