AI-Native Operations: From Optional Add-On to Core Enterprise Software
AI-native operations tools are software platforms that combine AI agents, systems mapping, and automation to understand and run complex enterprise infrastructure with minimal human intervention, turning observability and systems management data into actions that maintain, modernize, and optimize core applications at scale. This shift is transforming AI enterprise software from a set of bolt-on assistants into a layer of AI infrastructure management that touches every part of the stack. Instead of adding another dashboard, enterprises are asking for SRE artificial intelligence that cuts incident times, and for enterprise ops automation that can modify workflows across ERP, CRM, and industrial systems. The result is a wave of acquisitions and funding rounds focused on AI-native tooling that can operate legacy systems, not replace them outright, and that can embed intelligent automation directly into the systems of record that run the business.
Elastic Buys DeductiveAI: AI SRE Becomes Table Stakes
Elastic’s agreement to acquire DeductiveAI for up to USD 85 million (approx. RM391 million) shows how quickly AI-native SRE artificial intelligence is turning into acquisition currency in observability. DeductiveAI built AI agents that plug into code, logs, metrics, traces, and events, then reason over a live knowledge graph to find root causes and propose fixes. The company reported up to 90% reductions in incident resolution time at customers like DoorDash and Foursquare, with DoorDash alone saving more than 1,000 engineering hours a year through automation. Elastic plans to fold these agents into its observability platform, extending earlier moves such as its purchase of AIOps startup Keep and an agentic Kubernetes investigation workflow. The goal is clear: shift from passive monitoring to active AI infrastructure management that can diagnose and remediate incidents without waking an on-call engineer.
Conduct Targets AI-Ready Enterprise Systems, Not Greenfield AI
While observability platforms race toward autonomous incident resolution, Conduct is attacking a quieter but equally large problem: making existing enterprise systems understandable to AI. The company, founded by former Palantir engineers, raised €51 million to expand its AI OS, which maps the business logic buried in decades of customisation across SAP, Salesforce, Oracle, MES, WMS, and related systems. CEO Jan Philipp Haas argues that today, “the systems AI needs to work on cannot be fully comprehended by humans,” and that this opacity also blocks agents from acting. Conduct turns these opaque systems into legible models, letting enterprises connect AI enterprise software and agents to workflows inside their current stack instead of rewriting it. Customers such as Daimler Truck, Heidelberg Materials, Fraport, and DHL report 30% or more acceleration in transformation workstreams and faster time-to-value for new features.
Why Enterprises Want AI to Operate, Not Only Observe
Both Elastic and Conduct reveal the same demand signal: enterprises want AI that operates core systems, not only analyzes them. In operations, that means SRE artificial intelligence that can move from smarter alerting to autonomous incident response. In business systems, it means an AI layer that can read, change, and coordinate workflows across ERP, finance, logistics, and industrial software. Investors are responding by backing AI enterprise software that sits close to production systems, such as ERP transformation tools, industrial AI, and governance for AI agents. Importantly, capital is flowing into tools that help AI work against existing systems rather than replace them. This is shifting enterprise ops automation from scripts and playbooks to continuous, AI-driven control loops that shorten the gap between a decision and its execution inside the software that runs the business.
Consolidation Signals AI Ops as Essential Infrastructure
The acquisition spree signals that AI-native ops tooling is moving into the category of essential infrastructure. Elastic’s purchase of a dedicated AI SRE startup shows incumbents believe they cannot wait to build full agentic capabilities on their own. At the same time, Conduct’s backing from SAP and partnerships with major consulting firms show that AI infrastructure management for existing systems is being treated as a strategic layer, not an experimental add-on. Startups that combine deep systems understanding with automation are becoming prime targets for platforms that want to control the full lifecycle of monitoring, change, and transformation. For large software vendors, owning these AI-native layers is becoming a competitive advantage: it lets them promise not only visibility into complex estates, but active control, faster modernization, and fewer humans tied up in manual operational work.
