The New Role of AI in Operational, Business Decisions

The digital landscape is rapidly evolving, driven by the adoption of cloud-native platforms and the scaling of AI technologies. According to Gartner, over 95% of new digital workloads are expected to run on cloud-native platforms by 2025, up from 30% in 2021. As organizations accelerate these initiatives, a unified approach to managing complexity, risk, and performance becomes essential, says Carmen Nava, Senior Strategic Enterprise AE, Dynatrace, during the Mexico Business AI, Cloud & Data Summit 2025.

“Digital acceleration has outpaced our ability to manage complexity,” says Nava. “Observability is no longer a technical nice-to-have; it is the foundation that connects data, AI, and business outcomes.” 

For cloud and AI workloads, effective observability delivers three critical business outcomes:

• Accelerating Deployments: Speeding up the rollout of cloud-native workloads

• Ensuring Success and Governance: Supporting AI initiatives while maintaining compliance and control

• Enhancing Developer Experience: Improving productivity and overall developer satisfaction.

Data alone is not enough; the real challenge lies in integrating it intelligently to generate insights that drive decision-making, automation, and innovation, Nava explains. In multicloud environments, every layer of the technology stack tells a different story. “Observability gives you a single, unified narrative — one source of truth that allows leaders to innovate without losing control,” she adds.

AI observability ensures that Generative AI, LLMs, and advanced AI initiatives achieve their intended objectives. Key metrics include total requests, average request duration, costs, and guardrail executions to detect issues such as PII leaks or model toxicity. Beyond infrastructure monitoring, observability is crucial for application performance and end-user experience, explains Nava. Simplifying complex applications eliminates blind spots, enhancing both operational efficiency and business outcomes.

Operating AI systems without observability can be similar to “flying a plane without radar,” says Nava. Organizations must know where they are headed, identify risks such as toxic prompts or inefficiencies, and ensure that every model is governed and aligned with strategic goals, she adds.

End-to-end observability and security is the final cornerstone, enabling organizations to reduce complexity, optimize costs, and safeguard against threats across modern environments. The unified approach transforms business operations through innovation, risk reduction, and cost savings. It also optimizes cloud and vendor expenditures, while mitigating risk with stronger vulnerability management and compliance.

A consolidated platform delivers real-time insights for executives, continuous compliance for IT teams and SREs, and automated root cause analysis for engineers. Developers benefit from autonomous intelligence tools like Davis CoPilot, which provide guidance directly in the IDE, resolve root causes, and suggest code fixes to accelerate productivity, reports Dynatrace.

“Technology should enable people, not overwhelm them,” Nava says. “When developers spend less time troubleshooting and more time creating, that is when innovation truly scales.”

Looking ahead, observability is expected to evolve into something far more powerful. “The future of observability is not just visibility — it is intelligence,” says Nava. “When systems can see, understand, and act in real time, organizations gain the confidence to evolve faster than ever before.”