AI Software Moat Diligence: Data & Switching Costs

Private equity dealmakers and M&A investment committees are confronting a structural shift in how software assets generate sustainable enterprise value. For over two decades, software due diligence relied on straightforward defensibility proxies: proprietary codebases, intuitive graphic interfaces, and high operational friction that kept customers locked into annual seat licenses. As artificial intelligence evolves from passive, assistive copilots into autonomous, task-oriented agents, those traditional barriers to entry are dissolving at record speed.

The widespread availability of foundation models has lowered the friction required to build functional software. A basic workflow wrapper or point solution that previously required a dedicated engineering team can now be rapidly assembled by small teams using standard APIs and code-generation tools. In this environment, basic application layers and cosmetic user interfaces face immediate commoditization. Investment teams conducting software moat diligence must separate shallow feature sets that are vulnerable to replication from genuine, deeply embedded enterprise platforms.

Public and private equity markets are already pricing in this divergence. According to analysis by PwC, the North American Tech Software Index declined roughly 30% from its mid-September peak as investors reassessed the long-term durability of legacy seat-based business models. Deal teams can no longer underwrite software targets based purely on historical gross margins or top-line ARR expansion without rigorously stress-testing whether the target's underlying moat can survive agentic disruption.

• Erosion of interface lock-in: Graphic user interfaces no longer provide durable barriers when autonomous agents execute workflows programmatically across background APIs.
• Compression of software build cycles: Rapid software prototyping eliminates the defensibility of lightweight point applications and generic productivity tools.
• Divergence in asset quality: Durable enterprise platforms with workflow gravity command premium multiples, while surface-level wrappers experience sharp valuation compression.

A common misconception in growth equity and venture diligence is treating foundation model access or fine-tuning as a proprietary asset. In reality, underlying foundation models are rapidly commoditizing. The cost of running inference and accessing intelligence has cratered over consecutive development cycles, transforming raw model intelligence into a baseline utility rather than a defensible barrier to entry.

Research by Activant Capital reveals that the cost per million tokens of GPT-3.5-class intelligence dropped from $20.00 in late 2022 to just $0.07 in October 2024, representing a price decline of over 280x. When raw model capability is inexpensive and universally accessible, owning a customized fine-tuned checkpoint provides little long-term defensibility against competitors utilizing newer, larger frontier architectures.

Multi-Agent Orchestration and Proprietary Feedback Loops

Real product defensibility in AI-native software sits above the foundation model. Institutional buyers focus their technical audits on multi-agent orchestration architectures: how the system decomposes ambiguous enterprise tasks, routes context between specialized agents, utilizes deterministic toolsets, and executes self-critique routines to correct errors in real time. Defensibility is reinforced when a product captures proprietary usage telemetry, creating a closed-loop feedback mechanism where every executed transaction enhances validation rules and exception handling.

If code is inexpensive to generate and models are commoditized utilities, the primary locus of software value shifts to accumulated context. Deal teams must analyze how an AI application captures, structures, and retains domain-specific knowledge over time. When an enterprise software platform operates within a client environment, it continuously processes structured database records, unstructured communications, edge-case decisions, and explicit user corrections.

This compounding accumulation of operational knowledge creates an institutional memory moat. When properly architected, accumulated memory reflects the precise, historical nuances of how a specific enterprise operates, reconciles disputes, and evaluates risk. Replicating this asset requires months or years of active production usage, establishing a steep operational barrier that deters customers from switching to competing solutions.

Auditing Data Rights, Isolation, and Portability

Technical diligence must look beyond the existence of operational data to verify data provenance and contractual governance. Target companies often claim vast data moats, but deal teams must confirm whether customer contracts grant the vendor explicit, irrevocable rights to extract telemetry and train proprietary system layers. Furthermore, diligence teams must evaluate data architecture: platforms that securely isolate customer context while maintaining a unified, governed semantic layer provide higher resilience against enterprise compliance challenges.

• Contractual data rights: Review master services agreements (MSAs) to ensure unambiguous rights to use anonymized operational telemetry for model refinement.
• Memory schema durability: Audit whether context is maintained as structured knowledge graphs and auditable relational records rather than ephemeral vector embeddings.
• Enterprise security boundaries: Verify multi-tenant isolation, tenant-specific encryption, and compliance with data residency standards to prevent cross-customer leakage.

The resilience of an AI-native software company depends heavily on the risk profile of the workflows it automates. In low-stakes horizontal applications, such as basic content drafting or generic dashboard generation, automated outputs carry minimal operational consequence, allowing buyers to switch vendors with negligible friction. Conversely, mission-critical environments involving capital movement, legal liability, healthcare delivery, or regulatory compliance require extreme accuracy and rigorous auditability.

In these high-stakes verticals, general-purpose foundation models cannot operate autonomously without strict human-in-the-loop oversight and deeply codified domain logic. Compliance requirements and liability boundaries form powerful structural defenses that prevent off-the-shelf AI models from disintermediating specialized vendors. As noted in venture analysis by Ardent Venture Partners, over 70% of banking institutions now utilize agentic AI to some degree, but regulatory authorities are simultaneously tightening requirements around audit trails and human oversight protocols.

Codifying Domain Edge Cases and Guardrails

Software vendors that succeed in regulated industries build defensibility through specialized guardrails: deterministic exception routing, role-based approval thresholds, and comprehensive provenance tracking that links every AI output directly back to source evidentiary records. These product choices may seem tedious compared to general AI demonstrations, but they represent the exact operational capabilities required for enterprise compliance teams to approve production deployments.

Software moats in the age of autonomous agents are heavily defined by ecosystem connectivity and network effects. Standalone point solutions that operate in isolation risk obsolescence as enterprise buyers consolidate their technology stacks around unified platforms. Conversely, applications that function as essential data hubs and API nodes across core systems of record establish durable structural entrenchment.

The vulnerability of shallow switching costs was highlighted in an extensive equity research review conducted by Morningstar. Morningstar reevaluated the economic moats of 132 companies where it judged AI could be disruptive; of those, 22 wide-moat ratings were downgraded (20 to narrow, two to none) and 18 narrow moats were downgraded, with close to half of the downgraded companies having been classified as switching-cost moats at some point. Payroll services, IT services, and enterprise software felt the most pressure, which is intuitive given that AI hits hardest where vendors monetize human labor, simple workflow automation, and seat-based licenses. Crucially, Morningstar found that companies supported by network effects saw the fewest relative downgrades, and that competitive advantage was usually secure where a firm controlled infrastructure or proprietary data, operated deep ecosystems, or sat behind high regulatory barriers.

Interoperability Protocols and Rip-and-Replace Friction

Deal teams evaluating a target's ecosystem entrenchment must audit its integration density. Platforms that maintain bi-directional synchronization with core enterprise systems (such as ERPs, CRMs, and core banking engines) and adopt emerging agentic interoperability protocols become difficult to displace. When autonomous agents rely on a target software's API to access structured context and execute actions, the cost and operational risk of ripping and replacing that node creates an enduring competitive advantage.

• Integration depth: The target software maintains verified, bi-directional connectors into primary enterprise systems of record.
• Write-back capability: The system not only queries third-party tools but safely executes and logs transactions back into host databases.
• Protocol adoption: The product supports standardized agent communication frameworks, positioning itself as an indispensable orchestration hub.

The transition from seat-based software tools to outcome-driven autonomous agents requires growth equity and buyout investors to adapt their core financial underwriting frameworks. Historical SaaS metrics can deliver false signals when applied to AI-native assets. In traditional SaaS, Net Revenue Retention (NRR) served as the primary indicator of account expansion and product stickiness. However, in an AI environment, high NRR can temporarily mask underlying seat contraction if short-term software upsells or model usage fees offset declining user counts.

To gain an unclouded view of customer health, diligence teams must prioritize Gross Revenue Retention (GRR) and perform cohort-level seat utilization analysis. A software target that delivers true automation may intentionally compress its customer's headcount requirements; if that software remains priced on a per-seat basis, it creates an adverse economic incentive that ultimately erodes the target's customer lifetime value.

Transitioning to Outcome-Based and Value-Based Monetization

Underwriters must evaluate whether a target company is proactively modernizing its monetization structure. Sustainable AI-native businesses are shifting away from pure per-user subscriptions toward outcome-based, consumption-tiered, or value-aligned pricing models, such as charging per resolved claim, completed audit, or processed transaction. This pricing alignment ensures that as the software's agentic capabilities automate larger operational workloads, revenue expands in direct proportion to the business value delivered rather than human seat counts.

Evaluating an AI software target across data rights, multi-agent orchestration, compliance guardrails, and shifting unit economics requires deal teams to process thousands of technical documents, customer contracts, and product architecture specifications. Conducting this multidisciplinary analysis manually under compressed deal timelines introduces significant diligence risk, particularly when verifying whether claimed AI capabilities reflect genuine technical defensibility.

Modern M&A advisory firms, private equity sponsors, and corporate development teams increasingly execute technical and commercial due diligence using specialized AI data room analysis platforms. These institutional tools are designed specifically to ingest virtual data rooms, cross-reference technical architectures against contract terms, and structure comprehensive defensibility evaluations.

Within Plausity, the Data Room Ingestion pipeline rapidly scans confidential documentation, code repositories, and vendor agreements. The core AI-Analysis Engine then evaluates the target's operational context, tracing system dependencies and validating proprietary data claims. To surface vulnerabilities, the Risk Radar identifies disclosure gaps, flags potential intellectual property encumbrances, and quantifies material risks across technology, commercial, and regulatory workstreams.

Deal teams coordinate findings across advisors using the Collaboration Hub and automatically synthesize diligence workstreams into verifiable deliverables with the Report Builder. By grounding every observation in verified source citations, Plausity equips investment committees to accurately price true AI software defensibility, discard cosmetic wrappers, and execute transactions with institutional confidence.

Plausity is an AI-native due diligence platform that helps M&A advisory firms, VC and PE funds, and corporate development teams structure evidence, findings and questions across a data room. It does not replace human advisers, does not guarantee deal outcomes, and does not provide legal, tax, audit or regulatory advice — all AI-generated findings, especially regulatory ones, require confirmation and advisor review by qualified professionals.

To explore the underlying capabilities, see the Plausity AI analysis engine and the findings and risk intelligence product page. For team-level workflows, see how VC and PE funds and M&A advisory firms use Plausity across live deals.

1. pwc.com
2. activantcapital.com
3. ardent.vc
4. morningstar.com