Hybrid IT Usage Doubles

Companies prefer a mix of on-prem and cloud environments, according to the 2024 Global State of IT Automation Report from Stonebranch. In only one year, hybrid IT usage has doubled from 34% to 68%.

Organizations are deciding where to store data — either on-prem or cloud — on a case-by-case basis. This approach is efficient and flexible but poses a challenge when it comes to automating and orchestrating these environments.


Source: Stonebranch

Other key automation trends from the report include:

Automation is Evolving into Orchestration

Driven by a desire to orchestrate across complex hybrid IT environments, 82% of respondents plan to replace legacy IT automation tools or add new automation tools to the mix.

Democratization of Automation Continues to Grow

88% of respondents enable end-users across the business with self-service access to IT automation. Those who plan to add or replace IT automation tools with a more modern solution cited a desire to add self-service as the leading reason for change.

Machine Learning Pipelines are Evolving and Growing in Importance

A significant 74% of respondents have already incorporated data and ML pipelines to operationalize their AI-driven initiatives. This is an indicator of many companies' strong commitment to the pivotal role of AI in shaping their future success.

Peter Baljet, Stonebranch CTO, concludes, "Our findings show that companies are adapting their IT automation and orchestration strategies to meet the specific challenges that come with cloud technologies. In the complex world of today's hybrid IT environments, automation is crucial to enhance performance, secure systems, and control costs."

Methodology: The report is based on a survey of IT automation professionals and executives, conducted by Censuswide between January 24 – February 5, 2024.

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Hybrid IT Usage Doubles

Companies prefer a mix of on-prem and cloud environments, according to the 2024 Global State of IT Automation Report from Stonebranch. In only one year, hybrid IT usage has doubled from 34% to 68%.

Organizations are deciding where to store data — either on-prem or cloud — on a case-by-case basis. This approach is efficient and flexible but poses a challenge when it comes to automating and orchestrating these environments.


Source: Stonebranch

Other key automation trends from the report include:

Automation is Evolving into Orchestration

Driven by a desire to orchestrate across complex hybrid IT environments, 82% of respondents plan to replace legacy IT automation tools or add new automation tools to the mix.

Democratization of Automation Continues to Grow

88% of respondents enable end-users across the business with self-service access to IT automation. Those who plan to add or replace IT automation tools with a more modern solution cited a desire to add self-service as the leading reason for change.

Machine Learning Pipelines are Evolving and Growing in Importance

A significant 74% of respondents have already incorporated data and ML pipelines to operationalize their AI-driven initiatives. This is an indicator of many companies' strong commitment to the pivotal role of AI in shaping their future success.

Peter Baljet, Stonebranch CTO, concludes, "Our findings show that companies are adapting their IT automation and orchestration strategies to meet the specific challenges that come with cloud technologies. In the complex world of today's hybrid IT environments, automation is crucial to enhance performance, secure systems, and control costs."

Methodology: The report is based on a survey of IT automation professionals and executives, conducted by Censuswide between January 24 – February 5, 2024.

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Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

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Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

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Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

The Incident Load Is Rising, but Recovery Times Aren't Moving

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

Your AI Pipeline May Have a Document Observability Problem

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

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If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

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Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...