The enthusiasm for artificial intelligence (AI) adoption is at its highest, and rightly so. AI is already transforming how software is created, delivered, and operated, unlocking extraordinary productivity and efficiency gains. The organisations embracing this shift are positioning themselves to lead.
The market shows different speeds of adoption. Some organisations are adopting large language models (LLMs) and generative AI to improve existing processes and workflows, while others are working with an AI-first approach, leveraging agentic AI to reimagine how software is built and operated through new ways of working. Both paths are valid, so long as momentum continues to accelerate.
What we see across the board is a healthy progression. Organisations are learning, iterating, and building confidence in how to introduce AI into operational environments in a safe and scalable way. As adoption grows, companies gain valuable insights into the safeguards and data foundations required for consistent results - and those insights are compounding fast.
As investment in AI-driven monitoring and automation grows, executives are recognising that reliable AI-powered observability is the bridge from human-driven operations to human-supervised, autonomous digital ecosystems.
The Scientist and the Artist: Two faces of AI
The generative AI everyone is talking about is a probabilistic system. It's a creative artist which is brilliant for brainstorming, generating novel content, and accelerating ideation. Probabilistic approaches shine where complexity exceeds deterministic reach. But for critical IT operations, you also need a scientist: a deterministic, fact-based system that is reliable and comprehensible. Just as a calculator is deterministic, operational systems need that same precision, so outputs are accurate and reproducible. The scientist is AI that reasons from verified data such as real-time topology, causal dependencies and precise metrics to deliver answers teams can act on with confidence.
This is where the promise of Agentic AI - systems that can independently reason and act - needs the right foundation. While 50% of organisations have agents in production for limited use cases, only 23% have scaled these projects to mature, enterprise-wide integration. The gap isn't a failure of AI; it's a signal that the ecosystem around AI agents still needs to mature. Organisations that solve for context, accuracy, and feedback loops will scale fastest.
One of the key challenges is that generative AI can hallucinate and push agents off course. Chief Technology Officers (CTOs) rightly prioritise ensuring that agentic systems have instant access to high-quality information, so multi-step agent workflows can execute quickly and reliably. Hallucinations aren't minor errors, they can trigger wrong actions leading to outages and security risks. In agentic chains, inaccuracies can accumulate and amplify, resulting in financial exposure.
But here's the exciting part: these are solvable problems. The combination of contextual observability, deterministic AI, and real-time dependency mapping is already making agentic AI more reliable and actionable.
The journey to true autonomy
So, how do we move forward? By establishing an AI ecosystem that encompasses AI-powered observability for reliable results. Organisations start with clearly defined use cases, implement strong oversight, and gradually expand AI's role as confidence in its outputs grows. The key is to combine the creative power of generative AI with a deterministic foundation grounded in the hard facts of operational data. This creates a system where humans set strategic goals while reliable AI handles tactical execution with precision, guided by established policies and guardrails.
The journey from automation to autonomy is an evolution - and it's moving faster than ever before. Each organisation progresses through its own maturity stages on the journey to autonomous operations. For the majority, it takes shape across three distinct stages.
The first stage is automated. The journey begins by moving beyond simple, brittle scripts. In this stage, the system performs well-defined tasks based on AI-generated answers rooted in real-time, contextual data. It's about reliably automating responses to known problems. Many organisations are striving to reach this stage or are already in it, and that progress is accelerating.
The second stage is supervised autonomous. As trust in the system grows, it graduates to handling more complex scenarios. AI can analyse a novel situation, understand its business impact, and generate a ready-to-implement action plan. However, this plan is not executed until a human expert gives their approval. This keeps humans in the loop for critical decisions while offloading the cognitive burden of the initial analysis. Key principles here are reliability, transparency, and a precise feedback loop.
The final stage is fully autonomous systems which operate independently to achieve business goals - dynamically managing environments, optimising costs and performance, and remediating issues before they impact users. The system continuously observes itself to self-optimise, ensure compliance, and provide insights that help people refine their goals. People still play a crucial role: they review outcomes, adjust strategies, and set direction. Think of the human role as an entrepreneurial-minded architect working alongside AI - focused on knowledge management, defining goals, and shaping what the system should deliver. As a result, organisations deliver and operate software with higher resilience, happier customers, and lower cost.
Why observability is mandatory for reliable AI
The truth is that AI agents are powerful - they can code faster, refactor at scale, and process information beyond human capacity. But AI has no awareness of what's happening in production. It's blind to the real world without observability feeding it context.
This is why observability is mandatory for reliable AI. And this is where the real opportunity lies - not in collecting more data, but in making that data immediately usable for precise decisions.
Organisations need systems built on a unified AI data lakehouse that continuously ingests and structures telemetry, combined with a real-time dependency graph that maps every service, transaction, and infrastructure component in context. Such a foundation allows AI to be reliable and capable - providing the memory that AI needs, at scale.
This level of contextual precision is critical because LLMs cannot directly process petabytes of heterogeneous observability data. Their context windows are limited, and performance degrades as input approaches the maximum length. Curating and structuring only the most relevant information yields better results than providing everything at once.
To overcome these constraints, it's essential to rapidly distill vast amounts of data into short, high-quality context. If AI agents need one thing, it is great, accurate, crisp context - delivered at speed. This is where contextual analytics, dependency graphs, and an AI-optimized data lakehouse become critical differentiators.
The business impact of autonomous operations
Building this future is not just an innovation; it's a business necessity. Spending on AI-optimised infrastructure to power these systems reached US $82 billion in a single quarter in 2025 and is projected to hit US $758 billion annually by 2029. The rewards for getting this right are immense.
The fusion of deterministic and agentic AI represents the next chapter for enterprise IT. It creates a system where AI observes and manages other AI-driven systems, fostering a new standard of digital resilience and superior customer experiences.
What excites us most is the speed of this transformation - and we are moving at that speed ourselves, building observability that is native to AI-first ways of working as well as AI-powered. Technology is evolving into a trusted, strategic partner, empowering organisations to confidently navigate the complexities of the digital world and build a truly autonomous future.