Defining the New Wave of Enterprise AI Modernization
Enterprise AI modernization funding refers to investment rounds backing startups that turn decades-old, customized business systems into platforms where AI agents and data-intensive applications can operate safely, quickly, and at scale without replacing core infrastructure. PhoenixAI and Conduct are emblematic of this wave. PhoenixAI raised USD 80 million (approx. RM368 million) in Series B funding to build an agentic AI database that can handle thousands of unplanned queries from autonomous agents. Conduct secured €51 million (USD 60 million, approx. RM276 million) to create an AI operating system that makes existing software environments understandable and controllable. Both companies focus on the same pain: enterprises cannot integrate AI into legacy software without faster data access, clear governance, and a way to interpret the custom logic buried in old systems. Their traction shows enterprise AI funding is shifting from pilots to core infrastructure.
PhoenixAI: Building an Agentic AI Database for Live Enterprise Data
PhoenixAI, formerly known as CelerData, is positioning its agentic AI database as the data backbone for autonomous enterprise agents. Its platform combines real-time and historical data in a single engine, delivering sub-second responses on live, normalized datasets for AI-driven workloads. According to PhoenixAI President Rick Underwood, “agents now fire off thousands of unplanned, real-time queries,” a pattern that strains traditional databases built around pre-modeled questions. PhoenixAI’s design targets this mismatch by making streaming updates from systems like Kafka queryable within seconds, while preserving governance required by regulated industries. Customers such as AppLovin, Coinbase, Conductor, and Demandbase already run production workloads on the platform, using it as a real-time analytical database layered on top of existing data lakehouses. The USD 80 million (approx. RM368 million) Series B funding led by Sky9 Capital underlines investor belief that an agentic AI database is becoming essential infrastructure for enterprise AI.
Conduct: An AI Operating System for Opaque Enterprise Software
Conduct takes a different but complementary route, building what it calls an AI operating system for enterprise software. Instead of replacing ERP, CRM, or industrial systems, Conduct maps the business logic buried inside decades of custom code, integrations, and undocumented dependencies. CEO Jan Philipp Haas argues that many systems “cannot be fully comprehended by humans,” and that this opacity blocks AI agents from acting. Conduct’s platform makes those systems legible and operable, reducing the time between business decisions and execution in software. Founded by former Palantir engineers, the company already works with enterprises such as Daimler Truck, Heidelberg Materials, Fraport, and DHL. Across customers, transformation workstreams reportedly accelerate by 30% or more. Its €51 million (USD 60 million, approx. RM276 million) Series A, co-led by Index Ventures and ICONIQ with strategic investment from SAP, will deepen support for SAP, Salesforce, Oracle, MES, and WMS environments.
A Converging Market for Legacy Software Modernization
Despite different technical approaches, PhoenixAI and Conduct converge on the same strategic problem: legacy software modernization. Enterprises have systems customized tens of thousands of times to encode procurement rules, manufacturing flows, approval chains, and supply-chain dependencies. These environments are hard to change and even harder for AI to use. Conduct focuses on understanding and orchestrating these systems through an AI operating system, while PhoenixAI focuses on feeding AI agents with fast, governed access to live and historical data via an agentic AI database. Together, they show how enterprise AI funding is shifting toward platforms that make existing software AI-ready instead of ripping and replacing systems of record. Their customer logos, from financial technology to industrial logistics, suggest wide demand for AI that respects current infrastructure while shortening the path from insight to action. That alignment is drawing investors toward this emerging infrastructure layer.
Why Agentic AI and AI OS Platforms Form a New Category
The approaches taken by PhoenixAI and Conduct highlight a new category distinct from traditional AI assistants: platforms designed for agentic AI and AI operating systems. Traditional assistants answer questions for humans; agentic platforms support software agents performing tasks end-to-end, from querying data to executing changes in complex systems. PhoenixAI’s agentic AI database is tuned for unpredictable, high-volume queries from AI agents, combining data at rest and in motion with strong governance. Conduct’s AI operating system focuses on understanding and controlling the logic inside ERP, CRM, and industrial systems so agents can execute safely. Both respond to a broader 2026 funding trend where capital flows into enterprise AI infrastructure, agent governance, ERP modernization, workflow automation, and industrial AI. This signals that the market sees long-term value not in standalone chatbots, but in the AI-native foundations that let enterprises modernize legacy environments without losing stability.
