The AI-Native Disruption: The Experience Revolution in Enterprise Observability
Enterprise observability is changing fast.
A few years ago, the market was simple - a handful of big players helped companies monitor their IT systems. Today, everything is different.
Two major forces are reshaping the entire landscape: AI-native architecture and product-led growth strategies.
The Enterprise Tech 30 (ET30) 2025 report confirms what many of us have been feeling. We're not just seeing incremental changes - we're witnessing a complete reinvention of how observability tools work and spread through organizations.
For traditional vendors, this shift creates both threats and opportunities.
Those who adapt will thrive.
Those who don't may find themselves struggling against more nimble competitors.
The Established Observability Landscape: Ready for Change
Until recently, observability platforms followed a predictable pattern. They excelled at collecting data from IT systems and creating dashboards for technical users. Their interfaces were designed for specialists - people comfortable with complex configurations and query languages.
These platforms were typically sold through traditional enterprise sales processes. Long sales cycles led to major contracts, followed by extensive implementation projects. This model worked well when decisions were centralized and technical complexity was expected.
But the ET30 2025 report shows this approach is increasingly out of step with what customers want today.
The Rise of AI-Native Observability Solutions
One of the most striking findings from the ET30 report is that 50% of featured companies are now AI-native - up from zero in 2019. This isn't just adding a few machine learning features. It's a fundamental rethinking of how these systems work.
What makes a platform truly AI-native? Instead of just collecting data for humans to analyze, these systems:
Lead with insights rather than raw data
Build knowledge about your systems over time
Adapt their interfaces to match different user needs
Take autonomous actions to prevent problems
Traditional platforms often try to bolt AI capabilities onto existing products. The result is what we can call "AI-decorated" rather than truly AI-native experiences. Users can immediately tell the difference.
Product-Led Growth Changes Everything
The second major shift in the ET30 report is the dominance of Product-Led Growth (PLG) strategies, now used by 70% of featured companies. This approach flips the traditional enterprise sales model upside down.
Instead of starting with executive buy-in and formal procurement, PLG tools spread through organizations based on their immediate value and superior user experience.
Individual teams can adopt these tools on their own, often starting with free or low-cost tiers.
The report distinguishes between technical PLG (45% of companies) and non-technical PLG (25%). In observability, this means creating experiences that work for both technical users (developers, SREs) and business stakeholders who need insights without complexity.
For established vendors built around enterprise sales cycles, this creates a challenging competitive environment. When both technical teams and business users can independently adopt new tools, the traditional sales advantage weakens significantly.
UX Design Becomes a Competitive Advantage
User experience isn't just about making things look good - it's a critical business advantage. The most successful AI-native, PLG-driven observability platforms share several design principles:
Insight-first interfaces: They lead with actionable information rather than expecting users to find it themselves.
Progressive disclosure: Basic functionality is immediately available, while advanced capabilities are discoverable as users need them.
Cross-functional experiences: They support different user types - from developers to executives - with appropriate views of the same underlying data.
Embedded collaboration: Teams can work together within the tool rather than switching between platforms.
Transparent automation: When AI takes action, users understand what happened and why.
These design principles directly enable the bottom-up adoption that makes PLG strategies work. Companies that invest in these fundamentals see higher adoption rates, faster time-to-value, and stronger competitive positions.
Where Established Players Are Vulnerable
The ET30 2025 report highlights several areas where traditional observability vendors face challenges:
First, legacy architectures designed for manual analysis struggle with true AI capabilities. Adding machine learning to platforms built for human exploration creates disjointed experiences that feel bolted-on rather than native.
Second, enterprise sales models focusing on large deals face pressure from self-service alternatives that deliver immediate value. The report confirms this shift is accelerating, with early-stage companies almost universally embracing PLG approaches.
Third, complex pricing based on data volume or agent deployment creates friction compared to the transparent, consumption-based models of newer entrants. With median early-stage deal sizes now at $19M according to the ET30 report, these well-funded challengers can afford customer-friendly pricing to gain market share.
Finally, tools addressing specific observability domains (metrics, logs, traces) increasingly compete with integrated platforms that unify these perspectives. The surge in horizontal/vertical applications (35% of ET30 companies) reflects this trend toward more comprehensive solutions.
Agentic AI: The Next Frontier in Observability
Perhaps the most transformative trend in the ET30 report is the rise of agentic AI, featured in four of the five top early-stage companies. For observability platforms, this fundamentally changes how users interact with their systems.
Traditional tools require users to actively hunt for information - building queries, creating dashboards, setting alerts. Agentic AI flips this model. Users express goals in natural language, and AI agents explore data, generate hypotheses, test them, and present conclusions. Advanced systems can even take corrective actions autonomously.
This shifts the interface from data exploration to agent collaboration. Users spend less time analyzing data and more time reviewing, refining, and directing AI-generated insights and actions.
For UX leaders in established companies, this requires rethinking fundamental interaction patterns. We need to design for collaborative intelligence rather than just data visualization. This includes clearly communicating what agents can do, providing appropriate control mechanisms, and creating feedback loops that help both users and AI systems learn from each interaction.
Geographic and Talent Considerations
The ET30 report's findings on talent distribution offer additional strategic insights. Despite the rise of remote work, innovation in enterprise tech remains geographically concentrated, with 88 out of 167 founders based in the San Francisco Bay Area.
For observability platforms, this creates both challenges and opportunities. Established players must consider whether their physical presence aligns with these talent hubs, particularly as they seek AI expertise. The report also identifies emerging secondary hubs that could offer access to specialized talent with less competition.
Europe deserves special attention in this landscape. Cities like Berlin, London, Amsterdam, Linz, Vienna, Barcelona, and Stockholm have developed vibrant observability ecosystems with unique strengths. European teams often bring valuable perspectives on data privacy, security, and compliance - critical considerations for enterprise observability solutions. The region has produced notable observability companies and open-source projects, with engineering talent that combines strong computer science fundamentals with practical distributed systems experience.
While the ET30 report shows US dominance in funding, European observability talent often comes with lower acquisition costs and higher retention rates. For established vendors looking to build AI-native capabilities, strategic investment in European talent hubs can provide access to specialized skills in areas like time-series databases, distributed tracing, and machine learning operations - all essential components of modern observability platforms.
The increased funding environment (median early-stage deal size at $19M) means startups can attract top talent in critical AI specializations. This puts pressure on established vendors to create compelling environments for AI researchers and engineers beyond just competitive compensation.
Adapting UX Strategies for the AI-Native Era
For established observability vendors, navigating this transition requires bold action rather than incremental change. Based on the ET30 2025 findings and my experience leading UX in this space, I recommend several strategic priorities:
Commit to true AI-native architecture, even if it means significant platform redesign. Surface-level AI integration is immediately apparent to users and creates technical debt that becomes increasingly difficult to address.
Develop dual PLG motions targeting both technical and non-technical users. This requires rethinking everything from product design to pricing to customer success models.
Build design systems for insight-first experiences rather than just data visualization. This means creating components that effectively communicate AI-generated insights and recommendations.
Create frameworks for agentic experiences that maintain appropriate human oversight while leveraging AI capabilities. Users need to understand what happens autonomously and where human guidance is required.
Form cross-functional teams with design, engineering, data science, and product management working collaboratively on AI-native experiences rather than treating AI as a separate workstream.
The Future of Observability
The Enterprise Tech 30 (ET30) 2025 report confirms we're at the beginning of a fundamental shift in how organizations gain visibility into their systems.
The combination of AI-native architecture and Product-Led Growth creates both challenges and opportunities for established vendors. Those that transform their platforms, go-to-market approaches, and user experiences to embrace these trends will thrive. Those that attempt to add new capabilities to legacy foundations will increasingly struggle against more agile, purpose-built competitors.
For UX leaders, this represents our moment to drive strategic transformation. By championing truly AI-native experiences, designing for diverse user personas, and reimagining interaction models for agentic systems, we can help our organizations navigate this pivotal shift in enterprise technology.
The observability platforms that succeed won't just monitor systems more effectively - they'll fundamentally change how organizations understand, optimize, and evolve their digital capabilities.
