Enterprise AI Platforms as an Operational Modernization Layer
An enterprise AI platform is a software layer that connects to existing business systems, interprets their data and logic, and then uses artificial intelligence to automate high‑value decisions and transactions without replacing core infrastructure. In the latest wave of enterprise AI platform funding, investors are backing startups that focus less on generic productivity gains and more on measurable financial and operational outcomes. Two recent examples, Rivvun AI and Conduct, target painful gaps that traditional software left unresolved: lost spend and revenue on one side, and opaque, over‑customized systems on the other. Rather than promoting a full systems overhaul, these enterprise AI startups position their products as AI spend recovery software and AI‑ready software systems, adding an intelligent execution layer on top of ERPs, CRMs, and other tools. Their seed and growth rounds highlight how AI is becoming a modernization layer for legacy system modernization.
Rivvun AI Turns Contract Obligations into Spend and Revenue Recovery
Rivvun AI has raised USD 7.55 million (approx. RM35.0 million) in an oversubscribed seed round to build an enterprise spend and revenue recovery platform focused on uncollected commercial obligations and settlement inefficiencies. Founded by former Icertis leaders Anand Veerkar and Niranjan Umarane, along with serial entrepreneur Patrick Linton, the company identifies gaps between negotiated terms and actual settlements. McKinsey research cited by Rivvun shows that procurement organizations lose up to one‑third of planned savings during execution, and another 3% to 4% of external spend disappears through transaction inefficiencies and noncompliance—over USD 2 trillion (approx. RM9.3 trillion) in unrealized value across Fortune 2000 firms. Rivvun’s AI spend recovery software integrates with ERP, CRM, and procurement systems, then uses specialized agents to trigger recovery actions. Its Spend Assurance product targets supplier rebates and pricing commitments, while Margin Defense finds revenue leakage on the sell side, all without demanding a core systems replacement.
Conduct Makes Deeply Customized Systems AI-Ready
Conduct, built by former Palantir engineers, has secured €51 million (approx. RM260.0 million) in Series A funding to make large enterprises’ existing software estates AI‑ready. The platform acts as an AI operating system that ingests custom code, configurations, dependencies, and integrations across SAP, Salesforce, Oracle, MES, WMS, and other tools. Over decades, these systems have been customized tens of thousands of times to reflect real‑world procurement rules, manufacturing workflows, approval chains, and supply‑chain dependencies. That customization makes them powerful but hard to understand, slowing change and blocking AI agents that depend on clear, well‑mapped logic. Conduct maps this buried business logic and turns it into something understandable, actionable, and executable, so the time between a business decision and its execution in software shrinks. According to ICONIQ, Conduct “helps make the decades of business logic trapped inside those systems understandable and executable for the first time.”
Why Investors Are Backing AI-Native Infrastructure Over Rip-and-Replace
The backing of Rivvun AI and Conduct signals a broader shift in enterprise AI platform funding toward AI‑native infrastructure that works with existing systems rather than against them. In the same funding environment, investors are also supporting ERP transformation tooling, workflow automation, knowledge infrastructure, and governance layers for AI agents. Capital is flowing into software that makes core applications AI‑ready and economically sharper, instead of betting on wholesale system replacements. For CFOs and CIOs, the appeal lies in direct, measurable impact: Rivvun ties its value to recovered spend and revenue, while Conduct cuts the manual labor and delay in transformation projects, improving time‑to‑value by 30% or more for its users. Together, these enterprise AI startup stories show AI being applied as a modernization layer that recovers lost value, speeds up change, and extends the life and usefulness of legacy systems.
