The next value frontier for enterprise software

Enterprise software has always been defined by where it creates value. Early systems concentrated on organizing information, later ones on enabling collaboration. Both are still necessary, but neither offers differentiation. The point of differentiation and advantage has shifted.

The relevant question for enterprises today is not whether they have reliable records or smooth collaboration tools, but how software contributes directly to outcomes. With automation, agentic AI, and new coordination protocols, software is no longer just a passive tool. It performs work, connects across environments, and adapts over time.

This shift reframes the boundaries of value. Information and collaboration have become commoditized. Execution, coordination, and learning are now the frontiers where enterprises can build new advantages, and where choices on architecture, procurement, and culture will define performance over the long term.

The trajectory of enterprise software has been shaped by shifts in how value is defined between vendors and their customers. In its early phase, software was sold much like physical equipment. Value was measured in features and licenses, and the economic model depended on upfront payments and annual maintenance contracts. This led to a market misalignment: vendors were incentivized to expand feature sets, while customers accumulated unused “shelfware” [1]. The focus was on capability rather than outcomes - what we now recognize as an Information advantage.

The rise of cloud delivery in the 2000s introduced the subscription model. Software became accessible on demand, and vendors shifted to metrics such as Annual Recurring Revenue (ARR) [1]. While this lowered deployment friction and standardized delivery, pricing was still disconnected from consumption or impact. Companies paid the same subscription fee regardless of realized outcomes. This model powered the Collaboration advantage, where usability and team adoption became the selling points. Yet differentiation eroded as enterprises converged on similar SaaS platforms, often with limited ability to stand apart competitively [2], [5].

The current transition, we argue, is more profound. AI and automation have turned software into an active participant in business processes rather than a passive tool. Vendors are experimenting with outcome-based pricing, tying compensation to measurable results. Salesforce’s Agentforce charges per AI-handled conversation, and Intercom’s Fin AI prices per resolved query [3]. This approach addresses the inefficiencies of idle licenses and aligns revenue with delivered value. For vendors, it creates a compounding advantage, as successful execution produces proprietary data that reinforces competitive strength [3].

This backdrop explains why enterprises are moving beyond the commoditized information and collaboration layers toward the higher-value frontiers of Execution, Coordination, and Learning. Procurement is no longer about acquiring licenses; it is about securing results [4]. ROI is no longer measured by proxy engagement metrics like click-through rate or impressions [6], but by operational outcomes such as cycle time reduction, defect rates, or overall equipment effectiveness [7]. And critically, this transition requires a cultural realignment, as employees and leaders adapt to working alongside digital colleagues [8], [9], [10].

In short, the story of enterprise software is the story of shifting value differentiation: from features, to access, to outcomes.

From information to learning: five advantage points

Enterprise software has always been judged by where it creates value. Systems of record and engagement defined earlier phases, but these are now standardized and offer little competitive advantage. The point of differentiation has shifted. The emerging frontiers; execution, coordination, and learning, are where enterprises now compete, and where choices on architecture, procurement, and culture will shape long-term outcomes.

Value	Definition	Economic model	Value created	Strategic status
Information	Structuring, storing, and securing data	Features and licenses	Trust, compliance, single source of truth	Necessary but commoditized
Collaboration	Making information accessible and enabling interaction	Access (SaaS, ARR, NDR)	Adoption, faster collaboration, productivity gains	Necessary but commoditized (a UI layer for humans and AI agents)
Execution	Software that performs work, not just supports it	Outcome-based pricing (per resolution, per output)	Direct productivity gains, digital colleagues delivering results	Strategic shift from “tools” to “participants”
Coordination	Orchestrating multiple agents, systems, and workflows	Composability and interoperability as differentiators	Reduced vendor lock-in, distributed execution, protocol-driven ecosystems	Ongoing debate: monolithic platforms vs open protocols
Learning	Adaptive systems that improve continuously through feedback loops	Long-term compounding advantage tied to proprietary data and self-improvement	Continuous adaptation, resilience, enterprise intelligence that evolves with use	Requires governance, culture, and human-AI workflow design

The five “frontiers” offer a map of how enterprise software creates value today. Information and collaboration remain essential foundations, but they no longer differentiate. Competitive advantage now lies in execution, coordination, and learning. Enterprises that can embed these capabilities into their operating models will not just adopt new tools, they will redefine how work is done.

Information and collaboration are prerequisites for operating, but they no longer shape market advantage because they follow well-established models and economics.

Execution marks the first break, where software shifts from enabling tasks to completing them. Coordination then tackles the complexity of connecting agents, systems, and workflows in a distributed environment. Learning pushes further, embedding feedback and adaptation so that enterprise systems evolve alongside the business itself.

Human and organizational transition

The move from information and collaboration to execution, coordination, and learning is not primarily a technical change. It is an organizational and cultural transition. Software no longer sits in the background as a passive tool. It operates as a digital colleague; performing tasks, coordinating with others, and improving over time.

To unlock value from these new frontiers, enterprises need more than integration projects. They need a shift in how work is designed and governed:

Leadership commitment: clarity from the top that AI-enabled systems are not pilots or experiments, but part of the operating model.
Employee involvement: adoption depends on trust and participation, not just deployment. Workers need to see digital colleagues as support, not threats.
Continuous learning and upskilling: people and systems must evolve together; static training programs are insufficient.
New governance models: balancing autonomy and oversight, ensuring that digital colleagues act within clear boundaries while still creating room for adaptation.

Moving into execution, coordination, and learning requires more than technology deployment. It demands new ways of leading, involving, and governing. Enterprises that succeed treat adoption as a staged process where leaders, employees, and systems evolve together.

The gladiator approach ensures adoption is not imposed but co-created. It brings leadership commitment and employee engagement into a shared process, making the transition cultural as much as technical.

We already see this transition in action. In healthcare imaging, AI systems support clinicians by taking on routine analysis. In customer service, resolution rates improve as AI colleagues handle first-line queries. In IT service management, automated triage reduces delays and frees experts for complex issues. Each case shows that the barrier is less about technology and more about redesigning how humans and AI colleagues work together.

What does this mean for enterprise software

For software vendors, the move toward execution, coordination, and learning creates both opportunity and risk. The basis of competition is no longer defined by features or user adoption alone, but by how directly a product contributes to outcomes and how easily it fits into a broader ecosystem.

Vendors face a strategic fork:

Monolithic platforms offer convenience and integration but struggle with differentiation. They risk tying customers into closed environments that may feel efficient today but limit long-term adaptability.
Protocol-based architectures prioritize openness and composability. As argued in Designing the AI-native enterprise, protocol-driven coordination enables enterprises to orchestrate human and digital colleagues, creating resilience and avoiding platform lock-in.

Emerging strategies show how players are repositioning themselves:

Embedding AI agents into product roadmaps: Software is no longer a passive tool but becomes a participant in work execution, directly influencing outcomes .
Outcome-based monetization: Moving beyond licenses and seats, buyers are increasingly questioning “cubicle-era” vendor logic and seeking models tied to throughput, resolutions, or uptime .
AI-driven efficiency in vendors’ own operations: Vendors themselves are adopting automation in development, support, and delivery to stay competitive.

The competitive risk is clear: incumbents tied to legacy monetization and incremental upgrades will be squeezed. As argued in Enterprise IT was built for standardization, AI-native challengers benefit from architectures built for variability, not conformity, and can undercut on cost while innovating faster .

The next market leaders will be those who can balance customer trust with new forms of openness, adaptability, and measurable impact, moving from tool providers to ecosystem participants.

The road ahead: hybrid workforce that execute, coordinates and learn together

The emerging frontiers of enterprise software are not abstract; they reflect practical differences in how systems are designed and how value is created. Two contrasts are especially useful in clarifying what is at stake.

Automation vs execution: Traditional automation relies on rule-based workflows that execute predefined tasks. Execution systems go further: they act as process managers, capable of making context-dependent decisions, reallocating resources, and driving work to completion. The difference is between scripting repeatable tasks and delegating real responsibility to digital colleagues.
Optimization vs learning: Optimization is about finding the best solution within fixed parameters, usually through static models. Learning systems, by contrast, adapt over time. They incorporate feedback loops, update their own parameters, and evolve as conditions change. The leap is from one-time efficiency gains to compounding advantage through adaptation.

The five steps of value creation offer a map for enterprises entering the next phase of software-driven work. Information and collaboration remain essential, but they are now table stakes, providing reliability without differentiation.

The real advantage lies in execution, coordination, and learning. These frontiers shift software from a supportive role into an active one; performing tasks, orchestrating systems, and adapting through feedback. They redefine how outcomes are achieved and how organizations compete.

For leaders, this is not a matter of adopting new tools but of re-architecting business processes, redesigning procurement models, and shaping cultures where human and digital colleagues operate together. The imperative is strategic: enterprises must position themselves at the new frontiers to sustain advantage.

Those who succeed will not view software as static infrastructure but as a living system; one that learns, evolves, and reshapes the way work gets done.

From information to learning: five advantage points

Human and organizational transition

What does this mean for enterprise software

The road ahead: hybrid workforce that execute, coordinates and learn together

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