Systems of Intelligence:The Replatforming of Enterprise Software

We're at the beginning of a once-in-a-generation shift in enterprise software. For decades, enterprise value accrued to Systems of Record—rigid databases wrapped in UIs, designed to store structured data and enforce workflows. These systems, born in the 1970s and scaled through the SaaS era, now underpin trillions in global economic infrastructure. But they weren't designed for the world we now live in, one defined by overwhelming data volume, constraints on human bandwidth, and rising expectations for software that doesn't just inform, but acts.

The future belongs to a new software paradigm: Systems of Intelligence. Systems of Intelligence (SOIs) represent a complete rethinking of how software creates value: they ingest unstructured data, learn from context, integrate across silos, and take autonomous action to drive real business outcomes. This transition will unlock new markets, collapse bloated service layers, and compress time-to-impact in ways legacy systems simply can't match.

This shift is not incremental. It is a replatforming. One that replaces dashboards with decisions, workflows with outcomes, and manual middleware with intelligent automation. And it is happening faster than most incumbents, or even most investors, realize.

The implications extend far beyond software itself. We're witnessing the early stages of what may be the largest economic expansion in enterprise technology since the introduction of the personal computer. As SOIs automate not just software workflows but entire service categories, they have the potential to expand software's share of the economy from its current 1-2% of GDP to 10% or more—while simultaneously growing the overall economic pie.¹

In what follows, we unpack this transformation in three parts: why legacy systems are breaking, what the SOI paradigm looks like, and how builders can design for this new reality—and win.

Systems of Record: What They Got Right, and Wrong

Systems of Record (SORs) earned their place in the enterprise stack by solving a first-order problem: digitize the paper trail. Starting in the 1970s and 1980s, software giants like Oracle and SAP introduced revolutionary systems that finally replaced endless filing cabinets and paper-based processes with digital alternatives. ERP, CRM, SIEM, ITSM, HCM, and FMS platforms emerged to capture structured information, enforce compliance, and scale standardized workflows across organizations of unprecedented size and complexity.

And it worked—for a time. SORs provided structure, compliance, and scale in a world where digital alternatives simply didn't exist. They became the backbone of modern business, handling everything from payroll to supply chain management to customer relationships. Today, SORs account for over $400 billion in annual software spend.²

But those very strengths have become their limits. The foundational assumptions that made SORs successful in the 1980s and 1990s—that data would be structured, that workflows would be predictable, that humans would be the primary interface between systems—no longer hold true.

SORs Are Built for Manual Inputs, Not Context

They depend on manual data entry and rigid schemas designed around the limitations of human input and 1980s database technology. Every field, dropdown, and form was defined not by what would produce the richest context, but by what a human could realistically be asked to enter by hand. The richness of reality—calls, conversations, documents, decisions, and the thousand small interactions that actually drive business outcomes—remains invisible to these systems.

Even worse, the narrow band of structured data that SORs are built to capture is patchy at best. Sales teams under-update CRM systems; workflows depend on fields that are blank, outdated, or inconsistently used. Studies consistently show that critical fields in major SORs like Salesforce are incomplete 60-80% of the time. Enterprises today are flying blind, relying on brittle databases that reflect only a fraction of what is actually happening.

SORs Depend on Human Middleware

As a result, SORs are brittle. To make these systems work, entire layers of human middleware have grown up around them, complete with roles, certifications, and business models built on navigating their complexity. That ecosystem has become a powerful constituency with a self-preservation instinct. The harder the system is to use, the more people are paid to make it usable.

It's a fragile equilibrium propped up by inertia, not innovation. The entire consulting-industrial complex, from Accenture to Wipro, owes its scale to the complexity of these systems. When software requires armies of consultants to implement and maintain, something has gone wrong.³

The Result: Sticky Contracts and Price Hikes, Not Innovation

Long contracts, massive switching costs, and years of custom logic keep SORs in place. This stickiness comes from lock-in, not loyalty. Ask any operator, "Do you like your CRM?" and most will pause. The software is tolerated, not loved. Companies like Veeva, the leading life sciences CRM, famously tout 95% gross dollar retention, but this is a testament to how difficult it is for customers to leave once they're embedded, rather than customer satisfaction.⁴

The switching costs are deliberately prohibitive. Migrating from one SOR to another typically requires 12-24 months, costs millions of dollars, and carries significant risk of data loss or business disruption.^{5 6} Vendors have optimized for retention through friction, not through value creation.

As a result, with customers effectively locked in, legacy providers have raised prices, rather than delivering productivity improvements. SaaS pricing has decoupled from broader economic indicators, rising at 8-12% annually, roughly four times the rate of inflation.⁷ When incumbents can raise prices faster than they ship features, the market is ripe for disruption.

AI is the Breaking Point

Enterprise software users are running out of patience. Three structural shifts are exposing the brittleness of legacy systems and accelerating the timeline for replacement.

AI Reset the Baseline

The ChatGPT moment wasn't just a technological breakthrough—it was a reset in user expectations. The consumer bar is now the enterprise bar. The disconnect is stark: employees use sophisticated AI tools in their personal lives, then return to work to fill out forms in systems that feel decades out of date.

The productivity costs are staggering. Employees lose 80 hours per year to underperforming IT and 180 hours per year to slow, legacy software.^{8 9} Across 100 million US knowledge workers, that's approximately $1 trillion in lost productivity annually.^{10 11} AI-powered optimizations could drive savings of up to $240 billion from software and hardware asset management alone, according to Activant benchmarks.

No wonder boards everywhere are demanding AI strategies—and consultants are cashing in to help people implement them. But now they need results.

Costs Are No Longer Justifiable

The economic model of SORs is breaking down. Why pay $2,000 per terabyte per day for Splunk when AWS offers comparable storage for cents? Why maintain armies of system integrators just to keep a Salesforce instance alive when data quality is poor and user adoption is low? Why license 1,000 seats when only 100 users are active—and only five are getting meaningful value?

Enterprise software spend has reached $8.7K per employee in 2024-25, up 20-30% in just one year.^{12 13} For large enterprises, that's hundreds of millions in annual costs. CFOs and boards are asking harder questions about ROI, especially when compared to the immediate productivity gains visible from AI-native tools.

Data Moats Are Eroding

Legacy systems once benefited from having all the data. But AI-native tools are catching up fast—both by living alongside existing SORs and by generating and inferring context in real time from email, Slack, video calls, PDFs, and logs.

The dirty secret about enterprise data is becoming impossible to ignore: what once looked like a moat is increasingly a liability. The numbers tell the story: nearly 50% of data workflows are still manual, 87% of data science projects never reach production, and only half of available data is being used to extract value.^{14 15 16} Meanwhile, 33% of business users lack confidence in the quality of data provided to them—hardly the foundation needed for AI automation.¹⁷

As data generation accelerates, any system depending on manual input is fighting a losing battle. Data creation is expected to grow 80% over the next three years, to become nearly 100x the amount generated in 2010.^{18 19} The winners will be systems that can ingest this flood of data in real time, while legacy moats drown in their own complexity. More on this below.

Targeting Services, Not Just Software

SOIs go beyond just replacing software, and are actually encroaching on the much larger services layer that sits on top of it.

Software represents only 1-2% of GDP.²⁰ Services represent 10%+. The $541 billion IT outsourcing market, the armies of system administrators, the endless integration projects—all exist because legacy SORs are too brittle to operate without human intervention.²¹

SOIs flip this model. These systems resolve issues at the source, eliminating the need for manual tickets and workarounds. They surface the right connections across tools without relying on consultants to wire them together. And they begin delivering measurable value immediately—long before a traditional rollout would even be complete.

The economic case is clear: replace expensive service providers while improving efficiency. And unlike traditional rip-and-replace projects, SOIs can start by living alongside legacy systems, capturing services spend while gradually taking over workflows. This isn't just about better software. It's about AI expanding software's footprint from a niche slice of the economy into critical, GDP-scale infrastructure.

The New Stack: Systems of Intelligence

The SOI stack represents a fundamental reimagining of how enterprise software should work, built around three foundational layers that work together to create truly intelligent systems.

1. DataOps: The Foundation Layer

Data is simultaneously the #1 moat for SORs and the #1 blocker for AI in the enterprise. This paradox defines the current moment: legacy systems hoard data behind rigid schemas and proprietary formats, while AI systems require rich, accessible, real-time context to function effectively.

In a recent study, 96% of analytics leaders reported being held back by data management challenges, including storage, access, quality, state, and flow of the data.²² 33% of business users are not confident in the quality of data provided to them, and 44% don't have visibility into their data pipelines. But this challenge is also the opportunity.²³ The best SOIs excel in the data layer through two key capabilities that traditional SORs cannot match.

A. Platforming: Stitching Disparate Systems

The modern enterprise uses thousands of tools—up 2.5x in seven years. Large organizations manage over 2,000 different software systems, each with its own data model and integration requirements. The average security team resolving alerts requires gathering context across 50 different security tools.²⁴

SOIs excel at reducing these silos. Consider shipping a laptop to a new employee—a workflow that spans HR systems, IT asset management, procurement, facilities, identity management, and financial systems. Traditional SORs force manual handoffs at each step. SOIs unify this into a single, intelligent process.

Sardine, an Activant portfolio company, demonstrates this in financial services, unifying the previously siloed fields of fraud, risk, and compliance. Their platform combines payment flows, device intelligence, behavioral signals, and third-party risk indicators into a dynamic context graph that enables real-time, autonomous decision-making. Sardine creates a unified view of what previously would have been managed by separate teams, managing different tools, to allow for faster, more accurate decisions.

B. Unstructured Data

Traditional SORs were designed around structured data entry—forms and fields that humans could populate. But the most valuable information exists in unstructured formats: emails, documents, video calls, chat messages, and informal interactions that actually drive business outcomes.

The scale is massive. Internal company data is growing 63% monthly from 400+ sources, yet only half is being used to extract value.²⁵ 90% of internal data is unstructured, limiting knowledge sharing across organizations.²⁶ SOIs unlock this previously invisible data to build a far richer picture of operations than traditional SORs ever could. And this is even before we layer on the mountains of new unstructured data (and context) being created in AI-native tools from note-takers like Granola, to outbound calling platforms like Stuut, an Activant portfolio company.

The Voice AI Revolution

Voice is emerging as both a critical data source and interface. In many industries, especially SMBs, the phone remains the dominant channel for customer interactions and internal handoffs. Recent advances in transcription and speech synthesis have made 24/7 voice agents economically viable—transforming inaccessible streams into rich automation opportunities.

We're seeing this across verticals: Stuut uses voice AI to automate accounts receivables, a highly manual process today. Studies show voice AI prevents the loss of the 83% of customers who will move to other restaurants after hitting voicemail more than once.²⁷ Healthcare systems deploy voice agents for appointment scheduling. Legal firms generate case notes from client consultations. Tools like Vapi and Bland have made this infrastructure accessible even for nontechnical teams.

Moving Beyond the Office

SOIs are extending beyond the "office world" into physical environments with video and IoT data. Over 1.5 billion video surveillance cameras generate 5,500 petabytes daily—a largely untapped data source.^{28 29} Vision-language models now enable SOIs to process video as structured, queryable input for real-time insights into safety, equipment failures, and operational inefficiencies.

Companies like Teton embed computer vision in healthcare patient rooms, detecting falls and monitoring vital signs automatically. In manufacturing, cameras confirm physical arrivals while sensors verify package contents, triggering actions only when real-world state is verified. This isn't just digitizing workflows, it's making environments themselves observable and responsive. And while this space remains early, some of the best founders and engineers are developing critical infrastructure to bridge the gap between software and physical assets. A great example is Realworld, which is building a perception and reasoning engine for physical systems. Think DataDog for the real world.

2. Context & Control: The Intelligence Layer

Intelligence requires context, and unleashing intelligence requires controls. While legacy systems were designed around departmental silos, SOIs bridge organizational boundaries and reason across previously disconnected domains.

This capability is critical as organizational knowledge walks out the door daily. 14.8 million workers are expected to retire between 2024 and 2029, cutting 10% of the workforce in 18 major sectors.³⁰ Even worse, 57% of Baby Boomers say they have shared less than half the knowledge needed to do their jobs, and 21% admit they have shared none of it.³¹

Traditional knowledge transfer methods—documentation, training programs, mentorship—simply aren't keeping pace with knowledge loss. SOIs fight back by capturing, connecting, and preserving institutional knowledge automatically.

A. Knowledge Graph: The Organizational Brain

A strong DataOps foundation can be leveraged to create something far more powerful than a traditional database: a dynamic knowledge graph that serves as the organizational brain: a living model of how the organization actually works, learns, and evolves.

Modern SOIs build rich, contextual maps that connect people, processes, data, and outcomes in ways that reflect true organizational complexity. XOPS, an Activant portfolio company, exemplifies this in IT infrastructure management. When a server experiences issues, the system understands which applications depend on it, which business processes might be affected, which teams should be notified, and what remediation steps are most likely to succeed based on historical context.

This knowledge graph becomes more valuable over time. Every interaction and outcome feeds back into the system's understanding, creating a compounding advantage that traditional SORs cannot match. The more an SOI is used, the more powerful—and intelligent—it becomes.

Most importantly, SOIs can reason across departmental boundaries. A customer complaint might reveal a product issue, supply chain problem, or training gap. A security incident might indicate fraud patterns or compliance risks. SOIs identify cross-divisional patterns that no single system could detect and take proactive action before problems escalate.

B. Controls: Security and Governance at Scale

Security is the #2 blocker to AI adoption, demonstrated by 84% of companies that see cybersecurity as the primary roadblock to implementing AI systems.³² This concern is well-founded: 64% of companies report being "under pressure" to adopt Generative AI, creating a tension between competitive necessity and security prudence.³³

The stakes are higher than ever. New data is growing rapidly—estimates suggest that the number of sensitive data files organizations must manage will increase approximately 5x by 2028.³⁴ Even well-architected security policies can become inundated with this growing stock of data to manage over time, yet 66% of staff believe that their data has already outpaced their ability to secure it.³⁵

Organizations understandably fear the leak of sensitive data into AI models, which are environments into which they have minimal visibility and which they can't control. That's why they need robust access controls and security built into their platforms from the ground up.

Traditional permissioning systems were designed for humans accessing discrete applications. SOIs operate differently—they access data across multiple systems, reason about complex relationships, and take autonomous actions on behalf of users. This requires reimagining identity and access management. Consider that 84% of organizations have experienced identity-related security breaches, with an average cost of $4.9 million, while 99% of cloud identities are "excessively privileged" with only 2% of granted permissions actually used.^{36 37 38 39}

SOIs require sophisticated controls that grant contextual permissions based on current needs, audit all actions with full traceability, implement dynamic access controls that adjust based on risk levels, provide kill switches for anomalies, and ensure compliance with data protection legislation in 137 countries where GDPR penalties can reach up to 4% of global turnover.⁴⁰ Financial services, healthcare, and government sectors require complete audit trails and demonstrated compliance with evolving regulations.

This auditability is essential not just for compliance, but for building trust. When AI systems make decisions affecting real business outcomes, organizations need to understand how and why those decisions were made.

3. Autonomous Systems: The Execution Layer

The ultimate goal of SOIs is not just to understand, but to act. This is where they differ the most fundamentally from traditional SORs, which are limited to generating reports, dashboards, and alerts. SOIs close the loop by taking autonomous action to drive real business outcomes.

This capability is desperately needed. Organizations are drowning in alerts and overwhelmed by operational decisions. Take security for example. A typical Security Operations Center faces 4,500 alerts daily, with Fortune 100 companies seeing millions.^{41 42} Most are repetitive, low-fidelity "noise" requiring manual clearing. Between 50% and 99% of these alerts are false positives, yet analysts believe up to 100% of routine alerts could be automated.^{43 44 45}

The human cost is unsustainable. The security industry employs 5.5 million people but declares a workforce gap of 4 million. Every security employee is doing the work of 1.7 people.⁴⁶ Analysts get desensitized, demotivated, and miss true threats—up to 67% of alerts are completely ignored.⁴⁷

SOIs solve this by automating routine responses and escalating only when human judgment is required. They go beyond detection and actually resolve issues—the ultimate outcome that organizations care about.

A. Systems Designed for Autonomous Outcomes

Building truly autonomous systems requires rethinking how systems should be designed when they don't depend on human intervention for routine decisions. Activant portfolio company Tines is an excellent example of how the foundational layers of the stack enable this in cybersecurity, automating threat detection, investigation, and response across some of the world's largest enterprises. The platform stitches together inputs from disparate systems, including SIEM logs, endpoint detection tools, and threat intelligence feeds, injecting intelligence only at points where it can operate reliably and avoiding compounding effects where minor hallucinations snowball into major issues. The system understands when it has sufficient context to act autonomously and when it needs human judgment. And then it takes proactive action—blocking malicious IPs, isolating compromised devices, and escalating critical incidents to human analysts. Critically, Tines closes the loop. It executes remediation autonomously when appropriate, documents actions and reasoning, and learns from outcomes to improve future responses, creating a virtuous cycle that reduces burden on human analysts.

Building systems that operate autonomously requires balancing AI flexibility with enterprise predictability. The best SOIs achieve this by being deterministic where it matters and generative where it adds value. They use AI to interpret complex inputs and reason about responses, but execute through reliable, well-tested workflows that produce consistent outcomes.

B. AI-Native UI/UX

SOIs represent a fundamental shift from deterministic to non-deterministic software, from static dashboards to real-time intelligence, from rigid UIs to headless platforms capable of multi-modal interaction. The experience moves from human-in-the-loop to human-on-the-loop, from human middleware to autonomous outcomes, from non-intelligent software to AI agent colleagues with memory of our styles and preferences.⁴⁸

SOIs co-exist alongside existing SORs and generalist AI platforms, connecting multiple SORs across domains like ServiceNow, Workday, SAP, and Splunk into unified knowledge graphs. They run autonomously in the background or surface in front-ends, scaling headlessly across multiple form factors. XOPS exemplifies this: 92% of their platform operates autonomously. The remaining 8% surfaces only what truly requires human expertise and judgment, keeping humans on-the-loop rather than in-the-loop.

The illustration above is purely to highlight that SOIs will exist at various depths of the stack, and as headless platforms, will surface on the front-end to the user in a variety of ways.

Model Context Protocol (MCP) connectivity has accelerated the gravitational pull away from traditional SOR UIs by opening systems to model providers. You can now connect HubSpot to OpenAI and query knowledge directly—why ever log into your CRM if you can query in Claude? This creates an opening for best-in-class SOIs to own customer engagement. While OpenAI or Anthropic could compete, the best SOIs are positioned to drive reliable outcomes through specialized, secure design for enterprise context. SOIs will be critical infrastructure whether owning full-stack UI/UX, accessed via Claude, or embedded in vertical SORs.

Scaling SOIs: Execution That Matters

While the conceptual advantages of SOIs are compelling, actually executing on this vision is extraordinarily difficult. The gap between concept and successful implementation is littered with well-funded startups that understood the opportunity but couldn't execute effectively.

First-Contact Magic: Overcoming Buyer Inertia

Software inertia is one of the most powerful forces in enterprise technology. Even when the technical case for replacement is overwhelming, organizational inertia can delay adoption for years. Switching systems means retraining teams, rewriting workflows, migrating data, and taking personal risk. For organizations to endure that disruption, the new system must deliver a step-change improvement that feels instantly superior—something that feels like magic.

The most successful SOI companies ensure that this magic is experienced by the customer during their first experience using the platform—the "first-contact." This typically manifests as pre-populated intelligence (Activant portfolio company Pallet combines TMS with AI workflows that automatically pre-fill data from emails and calls), instant problem resolution (Lightyear reduces months-long telecom projects to days through AI coordination), or invisible automation (Sardine makes thousands of fraud decisions per second without human intervention) that are highly superficial in the UI.

These are more than just powerful features, they are experiences that immediately reset expectations. Once customers experience that leap, switching becomes obvious. Critically, this creates a narrow timing window: the first SOI to deliver step-change experience often locks in relationships, as later entrants feel incremental rather than transformative.

Velocity: The Only Moat

AI has created massive advantages in development speed. AI pair-programming cuts coding time by 20-55% and can double speed on routine tasks.^{50 51 52} Production-branch throughput surged 68% since generative AI tools went mainstream.⁵³ Yet fewer than 1 in 5 public tech companies have rolled out AI coding tools enterprise-wide, creating measurable shipping advantages for nimble startups.⁵⁴

Shipping speed and a culture of ruthless execution is non-negotiable. The energy is palpable in San Francisco and NYC today—peer pressure and feature commoditization are driving startups to move at breakneck speed. And thankfully, existing SORs are (for the most part) incapable of replicating this culture.

But velocity must be coupled with aggressive GTM motion. Activant portfolio company Owner demonstrates this. The winners will be those who can both ship features and articulate the need at scale.

And the most successful SOI companies think beyond individual features to platform effects. Owner started helping restaurants manage online ordering but rapidly expanded into marketing automation, customer communication, and financial analytics. Each capability leverages the same underlying intelligence infrastructure, creating synergies where the whole becomes more valuable than the sum of its parts.

Outcome-Based Business Models: Aligning Incentives

Legacy systems charge for access regardless of value; SOIs meter actual impact—problems solved, processes automated, outcomes achieved. When software autonomously closes security incidents or prevents fraud in real-time, traditional seat-based pricing becomes redundant.

This creates another challenge for SORs and opportunity for SOIs. Not only are their technical moats under pressure but now they must rethink their business models, disrupting GTM motions and potentially cannibalizing revenue. But the writing is on the wall: usage-based pricing is accelerating, with 41% of SaaS companies now offering or testing it.⁵⁵ Even Salesforce, the king of seats, is making the shift.

Outcome-based models require infrastructure most SaaS companies lack: real-time value metering, outcome tracking, and dynamic cost management. Metronome, an Activant portfolio company, already powers this very billing infrastructure for OpenAI, Databricks, Confluent, and NVIDIA.

For SOI builders, outcome-based models provide competitive advantage: immediate monetization, natural expansion revenue, direct paths to undercut per-seat incumbents, and aligned customer incentives. It also opens the potential for SOIs to adopt the Palantir playbook of forward-deployed employees—embedding teams on-site to drive outcomes. We've seen more and more SOIs embrace this philosophy in the last year: every employee at Pallet is required to spend one week every month on-site with customers. Companies that capture value proportional to business impact build the most defensible businesses.

Conclusion

The shift from Systems of Record to Systems of Intelligence isn't just another software upgrade. It's a fundamental replatforming of enterprise operations. While SORs trapped organizations in rigid workflows dependent on human middleware, SOIs automate entire categories of work previously impossible to digitize.

Timing is good. Legacy systems are buckling under data volumes, pricing models, and user expectations they can't meet. Meanwhile, AI can now reason across unstructured data, maintain context across domains, and take autonomous action with enterprise-grade reliability.

SOIs represent something bigger than "better software." They're targeting the $541 billion services layer that exists because legacy systems are too brittle to operate without human intervention.⁵⁶ By automating entire business processes and eliminating armies of consultants and manual processors, SOIs can expand software's economic footprint, delivering outcomes instead of just features. This transition is happening faster than incumbents realize. AI-native companies ship 10x faster and demonstrate immediate ROI that makes switching costs irrelevant. The question isn't whether this transformation will happen—it's how far it will go, and who will lead it.