Industry insights
SaaSpocalypse: How AI is reshaping B2B software businesses and valuations
SaaSpocalypse: How AI is reshaping B2B software businesses and valuations
Since the beginning of 2026, around $280 billion in market value has been wiped from the global software sector. Investors fear that AI will materially erode traditional SaaS economics and, in some segments, eliminate entire layers of existing business models.
Recent launches of agentic AI systems, like Anthropic’s Claude Cowork, provide tangible proof points for this shift. From legal agents to finance and sales automation, AI is beginning to execute workflows that previously required human interaction within software interfaces.
A simple example: If a sales manager can instruct an AI agent to update deal probabilities, log interactions, and generate forecasts automatically, why navigate multiple menus in a CRM system? If AI increases productivity fivefold, a company will need only a fraction of its current sales force to generate the same revenue. For seat-based SaaS providers, such efficiency gains can materially compress subscription demand.
Dr. Stefan Sambol, Founding Partner at OMMAX:
“What we are observing across software transactions, vendor due diligence projects, and portfolio reviews is valuation-level anxiety. AI exposure is now a core investment committee discussion point.”
Over the last decade, cloud delivery, UX improvements, and broad feature portfolios distinguished market leaders. But this no longer moves the value needle in a meaningful, sustainable way. Today, growth and strategic advantage come from autonomous execution: software that not only supports workflows but actively runs them.
AI as operational power, not a software feature
This transition represents a structural re-orientation of the economics of enterprise software. Cloud adoption was defensive: a necessary migration to protect customer retention and modernize delivery. Agentic AI is expansive, enabling platforms to collapse effort, intelligence, and outcomes into a single operating layer that organizations depend on every day.
At OMMAX, our engagements with software executives and investors globally reveal that the biggest disruption isn’t AI as a feature. It is AI as operational authority. The distinction between incremental enhancement and structural redesign is becoming decisive. Companies that treat AI as an add-on risk marginal gains. Companies that redesign their architecture, workflows, and monetization logic around AI redefine their competitive position.
A new category: Systems of action
Historically, enterprise software fell into two categories: systems of record and systems of engagement. Systems of record store and structure data. Systems of engagement enable collaboration around that data. In the AI era, a third category is emerging: systems of action. A system of action does more than show data. It sits as an orchestration layer on top of systems of record: integrating with them, reading and writing live operational data, contextualizing information, making decisions, triggering workflows across systems, and continuously refining execution as business context grows.
A system of action can arise in two ways: by advancing an existing system of record into an execution layer, or by a newcomer building on top of existing records. The latter is materially harder: it requires deep cross-system integration, trusted write-back, domain-specific harmonization, and governance to reach the same level of execution authority.
Pure reporting is unlikely to remain defensible. Basic dashboarding and text-to-SQL functionality will increasingly be commoditized by ERP vendors and AI-native tools. Sustainable differentiation lies in deeper capabilities: cross-system consolidation, vertical mapping logic, proprietary harmonization layers, and the ability not only to surface insights but to trigger corrections, reconciliations, or actions directly within operational workflows.
Daniel Soujon, Partner & CTO at OMMAX:
"Agentic AI does not simply automate tasks; it redistributes decision authority within organizations. Agents increasingly act as operational counterparts rather than passive tools, requiring new governance and accountability models."
This shift has profound financial implications:
- Revenue models move from per-seat licensing to outcome-based monetization. Customers pay for measurable impact, not just access.
- Growth levers shift from adding features to expanding automation depth and scaling autonomous agents.
- Total addressable markets expand beyond IT into operational budgets and P&L categories that software did not previously control.
- Competitive advantage increases over time as continuous data improves the system and makes automation more effective.
Software value shift: From migration uplift to operational OPEX
Agentic platforms expand TAM up the stack through outcome monetization and drive structurally higher ARR and margin expansion. The economics of software are moving away from a defensive “migration” uplift that captures IT spend toward an expansive model in which agentic workflows capture operational OPEX and P&L line items. The practical effect is twofold: the traditional growth pockets at the bottom of the inverted pyramid (seat-based licensing, reporting, and interface layers) are unlikely to be the primary drivers of future expansion. At the same time, value is being re-created, and often at a larger scale, at the top by platforms that own execution. In short, on-prem and cloud solutions may continue to capture revenue, but the principal growth opportunity will accrue to agentic platforms that deliver measurable operational outcomes.
Paradigm shift in B2B software: New TAM opportunity and competitive dynamics
Execution authority as the core competitive moat
In the AI era, ownership of operational execution is the moat that drives differentiation. The platforms that will lead are those that control proprietary, structured domain data reflecting real-time business activity, and those that are deeply embedded in mission-critical workflows across finance, operations, sales, and service. These platforms orchestrate actions autonomously. They initiate tasks, enforce business rules, and continuously adapt based on feedback. As they operate, they generate ongoing data loops that improve their models and make automation more precise over time. This creates compounding advantages.
Equally important is direct customer ownership, because execution creates dependency: When software runs essential workflows, organizations expand their use over time. This drives additional revenue per customer and makes the platform harder to replace. This is particularly relevant in competitive environments where ERP providers and hyperscalers are moving aggressively into AI-enabled functionality. While they can replicate surface-level features, they often lack the depth of vertical domain logic and cross-system orchestration required in complex environments. Platforms that embed proprietary integration layers and automate real operational tasks, rather than merely aggregating data, create a level of stickiness that is difficult to displace.
This dynamic creates an asymmetric threat environment for horizontal versus vertical software. Horizontal SaaS (applications managing generic, cross-industry workflows) faces rapid commoditization, as general-purpose AI agents can increasingly replicate these broad use cases. Conversely, Vertical SaaS possesses a natural defensive moat. Because these platforms govern highly specific, often regulated industry workflows, they hold the proprietary, structured domain data that AI agents require to function accurately. For vertical leaders, AI is an engine for expansion; for horizontal players without deep execution authority, it is an existential threat.
What incumbents do better
Incumbents that are best positioned to become true execution authorities share a set of durable advantages: deep vertical positioning along the customer’s value chain, a tech-stack role as the system of record, proprietary live domain data that cannot be recreated easily, regulatory or audit requirements that raise switching costs, and an already-sticky customer base from which expansion revenues flow. These factors let incumbents convert automation into measurable P&L impact and defend margins. By contrast, many AI-first challengers lack enterprise customer footprints, carry very high CAC, and can run out of capital before achieving durable expansion, a structural weakness in capital-intensive B2B markets. Mid-market software providers are especially exposed: without deep execution embedding, they risk being squeezed between AI-native entrants and platform ecosystems that integrate automation directly into core systems.
Konstantin Kugler, Partner & Head of Software Advisory at OMMAX:
“In transactions today, AI readiness and maturity are becoming a structured diligence dimension, especially in the B2B software sector. Buyers increasingly assess automation depth, execution embedding, monetization resilience, and dependency on seat-based expansion. AI is no longer a product roadmap topic; it is a multiple protection topic.”
Investor lens: What matters now
Share prices across large-cap and mid-cap B2B software and information services companies have fallen sharply in recent months. Capital markets are already repricing software businesses based on their AI execution maturity. The new benchmarks reflect structural growth quality, not superficial adoption signals.
At the same time, this repricing signals a very large economic opportunity for platforms that win the execution layer. Companies that convert operational OPEX into recurring ARR through outcome monetization expand TAM up the stack, improve unit economics, and capture materially larger pools of enterprise spend. As platforms climb the AI maturity ladder, we see meaningful ARR uplift potential. Crucially, this upside is not only top-line: execution authority drives higher retention and expansion, compresses marginal cost as automation scales, raises LTV/CAC, shortens payback periods, and strengthens EBITDA leverage, exactly the characteristics that justify premium valuations and more attractive exit outcomes.
High-performing AI-driven platforms consistently demonstrate:
- Organic revenue growth above 15%
- EBITDA margins sustained above 25–35%
- Gross retention rates above 95%
- Net revenue retention exceeding 110–120%, driven by expansion
- Expansion revenue as the primary driver of ARR uplift
In summary, investors increasingly ask: “Does this software control execution flows, or is it dependent on others who do?” Beyond current metrics, investors are evaluating exposure to seat-based revenue compression, resilience of expansion logic, and the structural depth of AI integration. Valuation premiums increasingly correlate with execution authority, not feature velocity.
Integration and differentiation potential through AI
From pilot to scale: What separates software leaders from the rest
The number of AI pilots launched across enterprises far outstrips those that have delivered measurable ROI. This gap is not technical alone: Scaling agentic AI demands organizational and operational readiness. What is changing now is tempo and orientation. 2025 was the testing ground; from 2026 onward, we see AI move from pilot to product-first development. Incumbents and legacy players are no longer observers; they are launching AI-first product roadmaps and small, fast development “speed boats” that use the same agentic toolchains as start-ups, closing the time-to-market gap. At the same time, product strategies are following a clear maturity path (from embedded AI to workflow AI, to cross-system orchestration, and ultimately autonomous AI operations), and leaders explicitly design roadmaps to progress up that maturity curve.
Leaders prioritize these areas:
- Data foundations and engineering excellence that can serve high-quality, governed inputs to AI systems
- Governance, accountability, and cross-domain orchestration frameworks that ensure automated actions align with business policies and risk tolerances
- Strategic alignment at the executive level with clear P&L responsibilities tied to AI outcomes, not just proof of concept results
- Time-to-value and implementation simplicity, ensuring that AI delivers measurable operational impact quickly and embeds seamlessly into existing workflows
The difference lies not in model sophistication, but in operational integration. In our experience, when these elements align, the impact is quantifiable: double-digit revenue growth in sales productivity, significant operational cost reduction, and triple-digit ROI over multi-year horizons.
The core message
Software leaders face a clear choice: use AI as a feature to stay relevant or use it to run execution and lead the market. Winning requires more than better engineering. It means redesigning architecture, pricing, and go-to-market to move from embedded AI toward cross-system orchestration and autonomous operations, shifting to outcome-based pricing, and selling execution authority into operational P&L with fast time-to-value. Platforms are increasingly evaluated through a single lens: execution authority density; the degree to which they control, automate, and embed themselves into mission-critical workflows across functions. The core question is simple: Does your platform merely support decisions or does it execute them?
Now is the time for every enterprise software business and investors to:
- Understand the disruption risk, and
- Develop an AI strategy to strengthen the Equity Story.
Take action
If your organization is redefining its software strategy for the age of agentic AI, speak with our software and AI leadership team. We help executives and investors move beyond AI experimentation to scalable execution strategies that unlock structural growth.
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