A key M&A dynamic that is playing out across software in 2026: AI appears to be commoditising certain horizontal SaaS segments, while vertical software with deep domain integration is getting repriced upward. A notable inflection point for horizontal SaaS was reached in February 2026, when Anthropic’s release of new AI tools contributed to a $285 billion off software and financial services stocks in a single day. That repricing has since become more structural repricing and we can observe more SaaS companies aiming for vertical integration since vertical SaaS valuations remain well anchoured.
Key takeaways:
- Market shift: AI tools can now replace simple project management and CRM tools, “one-shotted” with a single prompt.
- The vertical advantage: Vertical software that owns proprietary data and lives inside regulated workflows that can’t be easily replicated are demonstrating strong M&A defensibility.
- Due diligence evolution: Prospective buyers rightfully want to understand ‘AI defensibility’, Gross revenue retention (GRR) and proprietary data moats over simple headline growth.
- Founder action: SaaS founders planning an exit should pivot their narrative from generic AI features to quantifiable data and regulatory moats.
The numbers behind the inflection
Something counterintuitive is unfolding in software M&A. The same AI wave disrupting project management tools and generic CRM platforms is making certain founders extraordinarily attractive to acquirers:
McKinsey estimates that generative AI could add $2.6 trillion to $4.4 trillion in annual economic value, with about 75% of that value concentrated across customer operations, marketing and sales, software engineering, and R&D. These are historically the workflows that horizontal SaaS has monetised.
The velocity is measurable. Bloomberg reported that the February 2026 selloff erased roughly $285 billion from the global software and services complex following the release of agentic AI capabilities. Atlassian, one of the big horizontal collaboration vendors, reportedly cut approximately 10% of its workforce in March 2026, with CEO Mike Cannon-Brookes stating the company was restructuring to “self-fund further investment in AI and enterprise sales.”
The divergence is a thesis-level structural shift for any investor or founder operating across software M&A.
Why AI commoditises horizontal SaaS
Large language models are trained on codified workflows like project management, note-taking, basic CRM, time tracking and typical web-based services with light collaboration that can easily be described in natural language and executed by an agent.
Bain & Company’s Will Agentic AI Disrupt SaaS? report introduces an explicit framework: workflows are scored on AI’s potential to automate user tasks and AI’s potential to penetrate SaaS workflows. Horizontal categories typically score high on both axes, making them structurally vulnerable. Regulated vertical workflows score lower on penetration because of the depth and specificity of the surrounding regulatory and data context.
Table 1: AI disruption risk by software category (illustrative)
| Software category | AI task automation potential | AI workflow penetration risk | Structural defensibility | 2026 valuation trend |
| Horizontal PM tools | High | High | Low (workflow commoditisation) | Compression |
| Generic CRM | High | High | Low (unless proprietary data moat) | Compression |
| Note-taking/collaboration | Very high | Very high | Very low (directly replicable) | Compression |
| Vertical pharma compliance | Medium | Low | High (regulatory + data moat) | Stable to expansion |
| Vertical construction management | Medium | Medium | Medium-high (proprietary workflows) | Stable to expansion |
| Vertical healthcare interoperability | Low | Low | Very high (regulatory + trust) | Stable to expansion |
Source: Illustrative analysis based on Bain & Company insights, and observed market dynamics.
The moat that vertical SaaS has in the AI era
The moat in vertical SaaS has four layers that compound in value precisely as horizontal alternatives face commoditisation pressures:
1. Proprietary industry data
If a company spent years accumulating anonymised, aggregated, domain-specific data across hundreds of valuable data points, it possesses something no single competitor or AI model can reconstruct and no foundation model trained on public data can replicate. As an example, a pharma compliance vendor sitting on five years of audit trail data across 200 distributors, covering over a million serialised product movements, holds an asset that’s genuinely irreplaceable.
2. Regulatory complexity as a feature, not a bug
Compliance software for pharmaceutical distributors is the archetypal example. As an example, the FDA’s Drug Supply Chain Security Act (DSCSA) requires manufacturers, distributors, and dispensers to implement interoperable electronic systems to trace prescription drugs at the package level. A large language model simply cannot “do” compliance natively. Vendors with validated, audited, traceable systems of record hold the keys, and buyers understand this now. Regulatory complexity that once felt like a burden is being repriced as a competitive feature.
3. Workflow integration that goes beyond a chat interface
There is a meaningful difference between a project management tool and a construction management platform that orchestrates procurement, subcontractor coordination, progress billing, lien waiver tracking, safety incident reporting, and billing in a single auditable workflow. The first may be replaced by a chat interface, whilst the second cannot, because it is a system of record for a heavily regulated, multi-stakeholder process that took years to build and validate.
4. Trust relationships with domain-specific buyers
Enterprises experimenting with AI tools are simultaneously becoming more cautious about mission-critical systems. Domain-trusted vendors with multi-year customer relationships and regulatory credibility are sticky because the cost of switching is institutional, and the risk of getting it wrong is existential for the customer.
How this is reshaping M&A: the flight to quality in 2026
Industry reports from advisors such as KPMG’s spell out the SaaS-specific implication: AI agents can replicate or reduce many horizontal workflows, which often manifests as multiple compression in valuation, but specialised, embedded software gets rewarded. Similarly, PwC’s deals practice lists what dealmakers want from a vertical target in the AI era: AI defensibility, durable pricing power, organisational embedding, and high gross revenue retention. Weakness on any of these can trigger a multiple haircut.
M&A advisors specialising in technology transactions report that buyer diligence in 2026 now quite frequently includes an “AI defensibility analysis” as a standalone workstream, sitting alongside financial, legal, and technical due diligence.
Practical guidance for founders: articulating an AI defensibility story
Founders preparing for a business exit in 2026 should not simply anchor the pitch on “we use AI.” but anchor it on why AI makes the business more defensible. The distinction is everything.
Showing the data flywheel, not the feature
Founders should be able to show, with metrics, the data assets that cannot be reconstructed from public information or competitor scraping. That means quantifying:
- How many years of domain-specific operational data the firm has accumulated
- How many discrete customer interactions or transactions have been captured
- The proprietary taxonomies and benchmarks that can be derived from pooled data
- The contractual or regulatory barriers that keep customers from exporting and aggregating that data elsewhere
Quantify the regulatory cost to replicate
Founders should be able to show:
- Any specific compliance frameworks the software natively supports
- An estimated financial and time investment required for a competitor to build and certify a similar compliance engine from scratch
- The frequency of automated regulatory updates embedded into the workflow
- The cost of non-compliance for the end customer, highlighting the risk-mitigation value of the platform
Show workflow integration depth
Bain’s two-by-two for SaaS executives is a defensive framework founders can flip into offence: mapping the workflows against AI penetration risk and demonstrating that the riskiest ones are not the business value sits:
- How many distinct systems of record the software integrates with (ERP, TMS, etc.)
- How many discrete workflow steps it orchestrates end-to-end
- The decision points that require domain judgement, regulatory interpretation, or, cross-system data reconciliation
- The audit and compliance checkpoints embedded in the workflow
Reframe metrics for AI-era diligence
AI add-ons can inflate NRR whilst seats contract. Founders should be prepared to report:
- Gross revenue retention: the percentage of recurring revenue retained from existing customers, excluding upsells and cross-sells
- Logo retention: the percentage of customers that renew, period over period
- Seat retention: the percentage of seats retained within renewing customers (to surface any seat compression masked by upsell)
- AI-attributed upsell: the share of NRR expansion driven by AI features, isolated from organic expansion
This level of granularity allows buyers to assess whether growth is durable or whether it is masking underlying commoditisation. Understanding why valuation multiples do not tell the full story becomes critical in this context.
Pre-empt the agentic AI repricing question
Buyers are carrying AI-related risk into the data room. The founder’s job is to show why the margin sits outside that risk corridor. That requires showing:
- The percentage of revenue derived from workflows that score low on AI penetration risk (using Bain’s framework)
- The percentage of contract value tied to regulatory compliance, audit trail generation, or data pooling rather than user-interface access
- Customer cohort analysis showing that retention is stable or improving even as customers experiment with AI tools elsewhere in their stack
Acquinox Advisors is a Swiss M&A advisory firm specialising in tech transactions. If you are a vertical SaaS founder considering your strategic options in the future, we are happy to have an early conversation. Contact us today.
