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AI is disrupting enterprise software, and its valuation, by redefining the control plane—the decision layer that governs action, ownership, compliance, and outcomes. As that shift takes hold, value is starting to concentrate in new places. That has real implications for how private equity (PE) sponsors diligence software assets, shape portfolio priorities, and build exit narratives. It’s reshaping how some portfolio companies tell their value story. Many already have the foundations this next phase calls for—operational data, embedded workflows, and compliance infrastructure. But those that move ahead are likely to be the ones that redesign how the business works around adaptive capabilities, rather than simply adding AI to legacy architectures.
To understand where value may move it helps to start with how enterprise software is currently organized. Three archetypes have long defined the landscape.
- Systems of record (SoR) serve as gatekeepers of critical data assets and enforcers of workflow compliance. Think enterprise resource planning (ERPs), customer relationship management (CRMs), product information management (PIMs), core banking software.
- Systems of insight (SoI) support the analysis of disparate data to fuel business intelligence. Examples include data warehouses, business intelligence dashboards, and analytics platforms.
- Systems of engagement (SoE) are interfaces that allow end users to interact and take action, like collaboration tools, field service apps, and point-of-sale (POS) systems.
Each of these systems plays an important role, but none centralizes control on its own. Instead, control is spread across systems and teams. AI is beginning to change that. Whatever language the market uses, the shift points to the same idea: a new layer is emerging called systems of control (SoCs).
SoCs are a genuinely new architectural construct, but they don’t inherently replace existing applications or make them less relevant. They depend on the capabilities and organizational context accumulated in SoRs, SoIs, and SoEs and operate alongside them. This is the argument most overlooked in the AI noise: the control layer needs the layers beneath it to be ready before it can deliver value.
Existing enterprise software assets weren’t built for this shift. The retrofit requirement is real and often underestimated. But most assets still have a credible path forward, with implications that differ across the stack. For sponsors, operating partners, and technology leaders, the question isn’t whether an asset is part of the AI shift. It’s whether the business can put its data, processes, and user insights to work in ways that make it more adaptive and differentiated.
1. The move toward adaptability
Software as a service (SaaS) made enterprise software faster and cheaper to deploy. It didn’t make it more relevant in the control-plane context. SaaS improved on the limitations of legacy, heavily customized systems by accelerating time-to-value and changing unit economics. But the accountability model didn’t change. Software enabled human decision-making; it didn’t take ownership of outcomes.
This continuity with the pre-SaaS model matters. Configuration encodes how a process should work at a point in time and then resists change. Over time, most enterprises learn to work around the gap rather than fix it. The result is an organization running on logic that reflects how it operated two or three years ago, held together by spreadsheets, manual overrides, and tribal knowledge.
The advent of the control plane drives one key functional construct—adaptability. Adaptable systems actively read signals across the enterprise and recalibrate in real-time to improve outcomes. In practical terms, that means retrofitting existing enterprise applications so they can take in signals such as enterprise context, end-user intent, real-time feedback loops, and the ability to automatically recalibrate. That’s a meaningful shift from workflow execution to governance. And each layer of the existing stack has a distinct enabling role to play and a distinct gap to close.
Without adaptable SoRs, SoIs, and SoEs beneath them, SoCs are just sophisticated dashboards. They may be well instrumented, but they’re not really in control.
2. Where each layer faces pressure and opportunity
The SoR, SoI, and SoE framework has long structured how PE sponsors assess enterprise software value. In practice, most enterprise IT landscapes combine all three, each delivering a distinct value proposition. Larger vendors historically sought to own the enterprise by expanding across all three layers. Smaller vendors competed by integrating into the ecosystem around them. As vendors across the stack reimagine relevance in the context of a SoC, each layer faces distinct pressure profiles and opportunities.
Systems of record prioritize quality over volume
SoR platforms are sitting on the asset that matters most for enterprise AI—proprietary operational data that foundation models can’t access and AI-native applications can’t easily replicate. But not all data estates are equal. A platform with clean, well-structured, consistently labeled operational data has a fundamentally shorter path to AI value than one sitting on fragmented legacy schemas.
The data modernization requirement is real and often underestimated. For some platforms, the data that once set them apart may need meaningful investment before it can serve a new purpose. It was built for a specific workflow, not for use across the enterprise. The immediate pressure on SoRs is shifting from simply owning data to owning the ontologies, the shared business definitions that help siloed datasets work together, and knowledge graphs that connect them. In the near-term, that can make data more valuable and drive stickiness. This is a value creation requirement worth testing directly in diligence.
The second opportunity is unstructured data. Most SoR platforms sit on inert data like emails, call logs, contract notes, and claims narratives. AI can now tap into this data. Platforms that can activate that data may have an upside that isn’t showing up in current valuations. For sponsors, diligence should assess not only the quality of structured data but how unstructured data could be used to strengthen differentiation and support future growth. That can help shape underwriting decisions today and help frame a stronger story at exit.
Systems of insight prioritize relevance activation over aggregation
The SoI layer is simultaneously the most defensible and the most architecturally challenged. Its defensibility is rooted in something no other layer has—cross-enterprise decisioning logic, built over years of aggregating data that no single SoR owns. That logic is exactly what adaptive systems need to route intelligently, and it can’t be replicated quickly. As platforms move from automating workflows to governing them intelligently, the SoI layer doesn’t become less important. It’s more important because adaptive systems need to route through it.
SoI platforms, however, are rarely transactional. They have largely served the analytical use case. The shift to operational relevance is SoI’s biggest opportunity. SoI platforms that make this shift can evolve into the orchestrators of the future integrating directly with SoCs.
This is a meaningful shift, but it builds on what many SoI platforms already do well, bringing together data across systems, reading historical patterns, and applying inferential logic. The shift is directional, not a rebuild.
Systems of engagement move from usability to intent capture
Interface quality is no longer a defensible moat. The value shift in the engagement layer is about reducing the distance between a user and the insight they need and ultimately anticipating that need before it’s expressed. Tactically, this implies fewer clicks and fewer menus.
SoEs are now positioned to supply the last missing piece of this puzzle—intent. They can become the critical edge interfaces that capture end-user behavioral signals to infer what users actually need. The result is an evolution from static interface to hyper-personalized, conversational experience with individual intent, organizational context, and compliance requirements converging. This isn’t persona codification. It’s real personalization. It unlocks a new form of compounding stickiness that’s been under-explored in the enterprise context. The mechanism is straightforward. The system’s model of each user deepens with every interaction, making the experience progressively harder to replicate and progressively more costly to abandon.
Technically, most SoEs are already set up to observe these end-user signals, but they haven’t been able to use them because they were deemed strategically irrelevant or constrained by organizational policies. AI is giving SoEs a stronger reason to lean into that product strategy.
3. What this means for PE investors
AI is sorting enterprise software. It’s separating control systems and the layers that support them. They don’t replace each other; they depend on each other. That dependency is reshaping the value narrative for diligence, portfolio transformation, and exit positioning.
For new investments, sharpen the diligence lens
Most vendors have some AI features. The better questions are how far the platform has moved from static configuration toward genuine adaptability, whether the data foundation can support that trajectory and whether the AI investment is being made at the workflow layer or the interface layer.
A platform that’s moving toward real adaptability becomes harder to displace over time. That’s not because traditional switching costs are rising. It’s because the system builds a deeper understanding of individual users, takes on more compliance context, and becomes more embedded in how the organization operates. By contrast, a platform that remains stuck at the configuration stage, with automation layered on top, may still show steady retention and durable expansion economics, but those advantages are less likely to build over time. In many cases, win rates weaken first. Retention pressure tends to show up later.
SoCs represent one of the most compelling white-space opportunities in enterprise software. While hyperscalers and SaaS incumbents are extending their platforms toward AI governance, those efforts remain anchored in deterministic, workflow-centric thinking rather than fully adaptive ones. The opening sits in the gap between what incumbents can retrofit and what the AI era now requires. Platforms purpose-built to govern probabilistic, agent-driven workflows rather than bolt governance onto deterministic ones are where the most durable value is likely to emerge.
Most of that won’t show up in a confidential information memorandum (CIM). That’s making more sophisticated technology and AI-focused diligence increasingly important. The usual deal book may not reveal whether a software asset is truly positioned for this shift in the stack.
For existing portfolios, refresh the value narrative
The AI era is as much an opportunity to reshape the value narrative as it is a mandate to transform the product. The task for existing portfolios is to convert existing strengths into evidence of adaptive potential. The critical distinction is between companies genuinely restructuring around adaptive principles and those layering AI credentials onto an unchanged architecture. The former are building a new value story. The latter are accumulating risk behind a convincing surface.
The right path—build versus partner versus acquire—depends on the layer. SoRs should own the data ontology and look outward for orchestration capabilities. SoIs are natural acquirers of operational routing. SoEs should move quickly to embed intent capture through partnerships before it becomes table stakes. In each case, the test is the same. Does this move deepen the data and workflow moat or just extend the feature set?
The retrofit required is real and uneven. An SoR, an SoI, and an SoE each face a different sequencing logic and capital requirement, and sponsors who triage rather than apply a uniform playbook are more likely to move faster and waste less. The operator mindset is what separates sponsors who arrive at exit with a defensible, forward-looking narrative from those who arrive with a product that looks current but isn’t. For platforms with strong fundamentals, the ingredients of that narrative—proprietary data, process proximity, user-specific context—are largely already there. The work is in activating and articulating them.
How AI is reshaping software valuations for M&A and private equity dealmakers.
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