Madhu Gurumurthy, former Google executive and founder, recently captured a critical insight: “AI has a product problem. Not a model problem.” He’s right, while AI models have evolved, AI product innovation has been slower. New products force AI into existing patterns rather than designing from first principles. The famous AI wrappers… By the way, this isn’t a design problem either. What we are seeing is a shift from traditional software to autonomous systems. Systems that don’t just store data or facilitate communication, but actually take action…
Let’s talk about the evolution of enterprise software
- Systems of Record (1960s-2000s)
Traditional business software had a simple goal: replace paper with digital data. Systems of Record emerged as the single point of truth. This was useful when information is created and processed by multiple users. As companies grew these systems became the backbone of the enterprise. Examples include ERP systems like SAP, or Oracle, CRMs, and applicant tracking systems.
- Systems of Engagement (2000s-2020s)
As the internet appeared, a new category emerged. Systems of Engagement prioritized user experience and real-time interaction over data management. The need to improve collaboration at companies led to real-time communication, mobile design… Examples include Slack, Teams, Zoom etc.
- Systems of Action (2020s-Present)
Today, systems of action don’t just store data or help communication. They execute processes. AI agents take action, intelligent workflows adapt based on context and outcomes. This isn’t about adding AI features to existing software. It’s about reimagining what software does when intelligence is built in, not bolted on.
Why Legacy Platforms Cannot Transform
Legacy systems are built around a single assumption: Human users. In legacy software Humans input data, make decisions, and execute actions.
- Design
They are point and click systems for Human data entry rather than AI information processing. Their workflows can’t adapt to AI’s processing capabilities. They have manual decision points involving Humans at every stage. And they all feature screen-based interaction that limits AI’s data processing abilities.
- Technical Debt
Some platforms carry decades of technical debt making AI-native transformation unfeasible. Their monolithic codebases can’t be rebuilt. Their database schemas predate modern AI data requirements. Their security is not designed for AI agents data access needs.
- Interface mismatch
Traditional software uses form-based data entry, menu-driven navigation, dashboard, and workflow approval chains. AI-native systems have conversational interfaces, autonomous monitoring, predictive suggestions, and dynamic workflows. Foundation Capital’s recent analysis reveals the fundamental shift: “Software interfaces you know today – forms, buttons, dashboards… AI agents will shatter these constraints.”
- User resistance
Existing users who invested in current workflows will resist changes. Large enterprises can’t rip out mission-critical systems for experimental AI features. Their development teams focus on maintaining existing systems rather than rebuilding from scratch.
Industry example in Talent Acquisition
We see this in our industry where applicant tracking systems are glorified Excel worksheets. Features include structured fields, static candidate profiles, keyword matching, and calendar integration. Their Human-centric workflows require recruiters to review candidates using form-based interaction. AI-native recruiting platforms reimagine talent acquisition. Semantic search understands skills and experience contextually rather than matching keywords. Proactive sourcing identifies passive candidates. Voice-based evaluation reveals capabilities that CVs cannot capture through natural conversation. Autonomous agents provides 24/7 candidate engagement. This allows Humans to focus on what matters most: strategic decision making, relationship building, etc.
Tough choice for Technology Leaders
Organizations face a choice between incremental AI enhancement and AI-native transformation. The incremental approach adds AI features to existing systems, offering immediate implementation advantages. The transformation approach adopts Systems of Action that reimagine business processes entirely.
Early adopters of AI-native platforms are building moats that will be difficult for late movers to overcome. Foundation Capital identifies the shift. “As agents become the de facto point of data entry, traditional Systems of Record are reduced to commoditized storage solutions.”
Successful transition will require phased implementation.
- Initial pilots should identify use cases, select AI-native platforms building autonomous systems rather than AI-enhanced legacy tools.
- Strategic transformation will require re-imagining processes around AI rather than humans, develop autonomous systems, and build AI-native technology stacks
The problem: how do you select vendors?
Evaluating AI-native platforms requires different criteria than traditional software. Selection criteria should include autonomous operation, contextual intelligence capabilities and integration with systems. Capabilities need to include autonomous execution,Hhuman collaboration, visibility of AI decision-making, and scalability
Conclusion
The transformation from systems of record to systems of action represents more than technological evolution. It’s a reimagining of how business operates. Legacy platforms are constrained by human-centric design assumptions, technical debt makes transformation unfeasible. On the other hand, AI-native platforms deliver outcomes that incremental innovation cannot match.
Most companies are retrofitting old with new technology instead of reimagining what’s possible. Organizations that recognize this distinction and act on it will win . Those that don’t will find themselves operating legacy systems in an AI-native world.
