CRM Isn't Dead, Autonomous IT Is Here & Your 2026 Infrastructure Problem

The latest episode of ConstellationTV features co-host analysts Holger Mueller and Liz Miller, unpacking developments in enterprise AI, marketing automation, CRM evolution, and vertical-specific technology solutions. This episode provides a roadmap for navigating today’s fast-moving, competitive tech landscape.

#1 Enterprise News Spotlight: Marketing Automation and AI Take Center Stage

Constellation analyst Liz Miller predicts a tidal wave of announcements signaling an evolutionary shift in how small and medium businesses access and use customer data platforms (CDPs) and AI. Companies like HubSpot, Canva, and Adobe are driving this change, with notable announcements such as HubSpot’s AI tools and AI-driven automation enhancements integrated into Canva’s newly acquired CDPs.

Miller anticipates a new marketing landscape: one no longer revolving around customer relationship management systems (CRMs) as the centerpiece, but instead anchored by CDPs that empower businesses to use AI-enhanced tools for real-time data and actionable insights.

“We’re gonna suddenly see the marketing automation space change from a very CRM-centric point of view... shifting to AI-powered platforms that prioritize flexible data accessibility.”

This signal shift is underpinned by broader trends, including the growing necessity for personalization, automation, and data-driven decision-making at scale. It represents a significant departure from traditional CRM-centric solutions towards platforms designed to harness the power of generative AI in everyday business processes.

CRM Redefined: Is CRM Dead or Evolving?

The often-repeated claim that CRM is “dead” is akin to the enduring existence of instant coffee and paper. Nevertheless, the role of CRM is undoubtedly changing as the center of gravity shifts away from reliance on capturing transactional data alone.

Miller notes that AI is redefining CRM by extending its capabilities to manage more flexible data and generate real-time insights. While CRMs remain critical for transactional records, AI is stepping in to drive contextual understanding and actionable intelligence that goes beyond traditional use cases. She asserts that platforms are finally settling into their true roles: “The right sizing of CRM is actually starting to happen.”

This evolution marks a broader trend in enterprise tech: the relationship between AI and legacy platforms. Rather than undermining CRM, AI complements these established systems, creating new value through dynamic decision support and deeper customer engagement opportunities.

The Enterprise AI Model Wars: Responsible Deployment and Competitive Strategy

Holger Mueller shifts the focus to the intense competition between enterprise AI providers, particularly OpenAI and Anthropic. He identifies major developments in Anthropic’s release process for its powerful AI models as an approach resembling safety standards for autonomous vehicles. By prioritizing responsible innovation, Anthropic demonstrates how enterprise AI needs guardrails to align cutting-edge technology with real-world operational demands.

With the introduction of Cursor, a coding-specific model designed for enterprise use, Holger notes OpenAI is racing to catch up with Anthropic in developing capabilities suited to closed systems—a dynamic that is sparking innovation and competition across the enterprise AI vendor ecosystem.

This rivalry underscores a broader theme: the demand for trustworthy AI systems tailored to operational realities. Mature deployment strategies, paired with an industry-wide shift towards responsible innovation, are crucial for ensuring AI becomes an enabler rather than a disruptor in enterprise contexts

Enterprise Infrastructure Evolution: Oracle’s Moves Toward Agentic AI

Holger discusses Oracle's advancements in agentic AI, cloud systems, and backend operational capabilities. These include the company’s cutting-edge technology for sub-second availability, which is a feat driven by its emphasis on massive scale and high reliability.

This is a proactive step by Oracle to prepare backend systems for the success of agentic AI. At its core, Oracle’s vision aligns with the idea that enterprise systems must evolve to accommodate emerging AI capabilities, providing robust infrastructure that supports automation, scalability, and real-time decision-making.

As enterprise vendors increasingly integrate AI into their offerings, Oracle’s forward-looking approach stands out for its focus on readiness, reliability, and cloud-first architecture.

Redefining AI Success Metrics and the OpenAI Conundrum

It is important to question how AI success is currently defined. Metrics must be rooted in practical use, rather than stock-market interpretations of tech hype. Highlighting OpenAI’s challenges, including the recent shutdown of its Sora platform, Liz warns against using unstable and overhyped platforms as industry benchmarks.

“We have to stop using the room that has no adult as the yardstick for every vendor’s success.”

Enterprises must remain skeptical of fleeting trendsetters and focus instead on sustainable, scalable, and value-driven AI innovation.

#2. Autonomous IT and Elastic’s Observability Innovations

Elastic has emerged as another significant player reshaping IT operations. In the next ConstellationTV segment, Constellation analyst Chirag Mehta describes Elastic's transition from traditional observability systems to newer autonomous IT platforms powered by AIOps and generative observability. The reason Elastic made Constellation's 2026 ShortList for top Autonomous IT Platforms.

These platforms go beyond displaying data. They actively produce insights and recommendations, enabling faster decision-making during incidents.

• Speed and Control: Elastic’s agent model reflects the balance organizations need. “Teams want speed, but they also want control. Autonomy grows around guardrails and trust,” explains Mehta. Such frameworks will appeal to businesses seeking greater operational efficiency without sacrificing oversight. Elastic’s approach exemplifies how organizations can balance automation with human judgment.
• Cost-Efficiency and Scalability: Elastic’s use of Elastic Search is a standout innovation. By efficiently organizing massive telemetry data, they mitigate the challenges posed by sprawling observability needs. As digital transformation continues to expand telemetry and AI usage, such cost-effective systems will be indispensable.

#3. Verticalized Platforms Lead the Way at Infor Analyst Summit

Finally, the episode shifts gears to examine the future of verticalized enterprise platforms, building on observations from the Infor Analyst Summit. A discussion with analysts R "Ray" Wang, Michael Ni, and Holger Mueller highlights the impact of vertical AI capabilities and industry-specific tools on enterprise growth.

Ni explains the importance of process transformation over data consolidation: “AI is getting a job, and that job is improving processes.” This evolution highlights the need for industry-specific solutions that go far beyond generic data tools to deliver transformative results. Wei adds that industry verticals, with their distinct needs and value chains, are increasingly driving AI strategies.

For example, to serve manufacturing, airlines, and other niches effectively, AI platforms—such as Infor’s Intelligent Applications—are focusing on integrating data lakes and transactional systems into unified conversational user experiences. As Muller notes, "The vertical depth matters more than breadth when it comes to digital transformation."

AI Applications and Speed to Value

Ni and Wang highlight the speed at which AI transformations are translating into real-world outcomes. Within three weeks of deployment, organizations can achieve game-changing efficiency. The strength lies in platforms that are built with built-in models and process mining capabilities.

For enterprise leaders navigating AI adoption, the lesson is twofold:

1. Focus on platforms with pre-baked models that interconnect seamlessly.
2. Prioritize rapid implementation to achieve speed-to-value metrics

Final Thoughts: Balancing Optimism with Responsibility

There is a vital balance in enterprise tech: optimism for transformative AI and automation on one side, tempered by the need for maturity, responsibility, and context in deployment strategies.

Whether through marketing automation, CRM evolution, enterprise AI model wars, or verticalized platforms, the key lies in leveraging robust technologies while exercising caution amid hype-driven narratives.

For enterprises looking to stay ahead, the takeaways are clear: adopt AI and automation thoughtfully, focus on reliable infrastructure, explore industry-specific solutions, and redefine success metrics to reflect operational impact rather than hype.

Stay tuned to Constellation Research for more insights on emerging trends in enterprise tech, as these dynamic conversations set the stage for continuous innovation.