MachineGPT: Speaking the Language of Machines to Shape the Future of AI

At .conf25, we took a bold step forward—introducing the concept of MachineGPT, which brings the power of generative AI to one of the most overlooked resources: machine data. MachineGPT speaks the language of machines. Just like ChatGPT learned the grammar of words and sentences to understand questions and respond in human language, MachineGPT can learn the hidden “grammar” of how systems behave through machine data.

Machine data is the digital exhaust of every system, application, and device. These logs, metrics, traces, and events are digital heartbeats—the signals that power our businesses and shape our economy. As the first wave of AI unlocked human language, the next wave will unlock the language of machines giving IT teams a game-changing tool.

Our heritage at Splunk is deeply rooted in the mastery of machine data, and as data is increasing at an unprecedented rate, our foundational expertise has never been more critical.

Turning Data into Fuel for Digital Resilience

Machine data has long been the backbone of troubleshooting—we use it to retrace steps after an outage and prove compliance—but it’s more reactive than proactive, looking at what happened over time rather than what’s about to occur. MachineGPT changes the equation. By learning the hidden grammar and structure of machine language, it transforms noise into knowledge.

Mastering data as the heartbeat of the digital world is a game changer. Consider sensor readings that keep vehicles safe, transaction streams that keep retail moving, the authentication patterns that secure global banking. MachineGPT can take that data and detect subtle anomalies, correlate time-series signals across domains, simulate scenarios, and even orchestrate automated responses. In short, it gives enterprises an AI that doesn’t just listen to machines—it understands them and can work alongside humans to deliver real business value for customers.

Beyond AIOps: from Rules to Reasoning and Action

AIOps is a start. Using mostly predefined models and specific use cases, it can deliver insight, but usually after the fact. MachineGPT lets us shift from hindsight to foresight. It can connect data across silos and apply reasoning. By embedding agentic AI into the operational fabric of the enterprise, MachineGPT enables organizations to detect and diagnose, to plan and act—at machine speed and extreme scale. This is more than operational efficiency. It is resilience powered by the language of machines.

Weaving an Intelligent Data Fabric

To accelerate the adoption of MachineGPT, we developed the Cisco Data Fabric—an AI-ready architecture that unifies telemetry across infrastructure, applications, security, and business operations at massive scale. It enriches machine data with context and gives organizations the ability to fine-tune foundation models on their own proprietary data.

The term “fabric” is important, in that the approach must integrate diverse data sources into a unified view. Not a traditional centralized repository, but an adaptive, AI-ready foundation that connects data wherever it resides and makes it actionable in real time. We do this by intelligently weaving data together wherever the data lives. This ‘federated’ data becomes a strategic asset with a single, trusted layer of visibility across security, IT, and business operations. Cisco Data Fabric will also provide advanced pattern analysis and temporal reasoning on time series data, enabling advanced anomaly detection, forecasting, and automated root cause analysis.

These capabilities drive proactive operations and ensure fewer blind spots, faster decision-making, and the ability to react in real time. It also provides governance and trust by ensuring that AI agents can operate with context and precision through quality checks and security compliance. Ultimately, it’s what allows leaders to move from reacting to incidents to proactively shaping business outcomes.

Unlocking New Insights through Partnership

For MachineGPT to reach its full potential, it must reach beyond operational insights and connect with business intelligence. Understanding operational impact is valuable; understanding business impact is transformative. Customers that can do this will see the benefits in the bottom line.

That’s why our partnership with Snowflake is so important. With Splunk Federated Search for Snowflake, organizations can query and combine machine data in Splunk with business data in Snowflake AI Data Cloud, all without moving or duplicating information.

This federation use case creates a new plane of intelligence. Imagine an anomaly detected in authentication logs that can immediately be correlated with customer transactions, or a system slowdown linked directly to promotional activity. Detection becomes richer, planning becomes broader, and responses become more precise. Ultimately, all this positively impacts business and customer outcomes.

The Business Impact

The true power of MachineGPT is in the outcomes it unlocks. Retailers can build self-healing checkout systems that fix issues before customers notice. Automakers can predict warranty failures before they cascade into recalls. Financial institutions can spot fraud patterns invisible to siloed detection systems. These are not futuristic scenarios. They are examples of what happens when machine data is no longer treated as exhaust but as a strategic asset—when AI can not only describe the past but anticipate and shape the future.

We are entering a new era of AI. The first wave was about making sense of human language. The next is about speaking the language of machines.

With MachineGPT, Cisco Data Fabric, and our partnership with Snowflake, we are shaping the foundation of tomorrow’s digital economy. We are turning insight into foresight, empowering organizations to act with speed, innovate boldly, and create lasting value in a world where data drives possibility.

When teams move boldly, practical AI delivers digital resilience in the agentic era. We’re proud of the incredible work our teams and customers are doing to unlock the untapped potential of machine data—and we’re just getting started.