What an Enterprise AI OS Is—and Why It Matters Now
An enterprise AI OS is an AI-native control layer that turns scattered, customized business systems into a single environment where software, data, and AI agents can be understood, orchestrated, and changed safely without rebuilding everything from scratch. As enterprises race to show concrete AI results, they collide with legacy system modernization barriers: systems of record like ERP and CRM were built for stability, not rapid change. Years of custom code, integrations, and one-off workflows make these platforms opaque to both humans and AI agents. AI infrastructure startups are now moving beyond point tools toward operating systems and AI agent orchestration layers that sit on top of existing stacks. Conduct and Zaro.ai are early examples of this new category, aiming to make enterprise software integration AI-ready instead of replacing core systems outright.
Conduct: Mapping Opaque Systems into an AI-Ready Operating Layer
Conduct calls itself an enterprise AI OS that helps organisations understand, operate, and change their software systems by making them legible to both humans and agents. The company has raised €51 million in Series A funding to expand engineering and go-to-market teams, deepen SAP capabilities, and extend across Salesforce, Oracle, MES, WMS, and other platforms. Conduct ingests custom code, configuration, dependencies, and integrations, then maps each technical component to the business logic it serves—procurement rules, manufacturing workflows, approval chains, and supply-chain dependencies. Teams can then ask what depends on a workflow, which objects a migration touches, or what breaks if a field changes, and have Conduct generate the code, tests, and implementation steps. According to ICONIQ’s Seth Pierrepont, Conduct “helps make the decades of business logic trapped inside those systems understandable and executable for the first time.”
Zaro.ai: A Unified Context Layer for AI Agents, Data, and Apps
Where Conduct focuses on making legacy stacks understandable, Zaro.ai targets the fragmentation of enterprise AI deployments. The startup has emerged from stealth with USD 5.1 million (approx. RM23.5 million) in pre-seed funding led by Cherry Ventures. Zaro’s platform offers a single workspace where company data, AI agents, and custom applications share one unified context layer owned by the business, not the vendor. This model-agnostic enterprise AI OS routes simpler tasks to cheaper models and reserves frontier models for complex work, which Zaro claims can cut costs by around ten times versus frontier-only approaches. The founders, who previously worked on AI agent products at Convergence and later Salesforce’s Agentforce, designed the system so that intelligence compounds over time rather than being scattered across tools. This places Zaro squarely in the AI agent orchestration and enterprise software integration layer, rather than as yet another standalone agent product.
Ex-Palantir DNA and the Rise of AI Infrastructure Startups
Both Conduct and Zaro.ai are led by teams with deep experience in complex enterprise environments, a pattern increasingly visible across AI infrastructure startups. Conduct’s founders are former Palantir engineers who spent years working on data-intensive, mission-critical systems. Zaro’s founders and early team built AI agents at Convergence before its acquisition by Salesforce, then contributed to Agentforce. This background matters for legacy system modernization: these builders have seen how brittle ERP, CRM, and operational systems can be, and why direct rewrites often fail. Their answer is an AI-native operating system sitting on top of existing systems—an orchestration layer where AI agents, data, and workflows interact safely with legacy software. The funding Conduct and Zaro have secured signals investor belief that enterprise AI OS platforms are becoming a distinct infrastructure category, not a feature of standalone apps.
From Point Solutions to AI-Native Operating Systems
Recent funding into Conduct and comparable platforms for ERP transformation, agent governance, and workflow automation shows capital moving toward software layers that help enterprises deploy AI against existing systems rather than replace core infrastructure. Conduct reports that customers see more than 30% acceleration in transformation workstreams and time-to-value for new features, suggesting that AI-native control layers can remove long-standing execution bottlenecks. Zaro.ai, meanwhile, tackles the sprawl of AI tools by offering a single context layer where intelligence improves as agents operate within the organisation. Together, they mark a shift from isolated AI pilots to integrated enterprise AI OS architectures that treat legacy system modernization and AI agent orchestration as the same problem. For enterprises, the message is clear: the path to effective enterprise software integration with AI runs through operating systems that can understand and coordinate what is already in place.
