Rapid Tech Expansion Creates Chaos for the Majority of Businesses

A vast majority (89%) of organizations have rapidly expanded their technology in the past few years and three quarters (76%) say it's brought with it increased "chaos" that they have to manage, according to Situation Report 2024: Managing Technology Chaos from Software AG.

This makes governance efforts more complex, organizations less agile and can harm core activities including service delivery and productivity.


Source: Software AG

Dr Stefan Sigg, Chief Product Officer at Software AG, said, "The complexity that organizations face in today's world of disruption, risk and rapid technology change is greater than ever. It's difficult to get a grip on all of this and be a successful organization. We see our customers overcoming these challenges by finding the right tools to manage this technology related disorder. What those tools are depends on how the challenges manifest — but there is an answer out there. And for those that find it, they can become more competitive, more efficient, and more resilient."

The three types of chaos identified as part of this research are:

■ Operational Chaos — where a maze of different processes and systems slow down, duplicate or disrupt day-to-day operations. Overcoming these operational barriers allows organizations to be more competitive, better controlled and more agile. Operational resilience is the prize for organizations that can manage operational chaos.

■ Chaos of Connectivity — where the expansion of systems is done without a plan to properly connect them together. Overcoming this lack of connectivity allows organizations to become more productive, agile, and better governed.

■ IT Chaos — where the multiplication of different systems is not done in a coordinated way and technology sprawls uncontrolled and unmanaged. Overcoming this IT threat enables organizations to control costs, plan future development and increase operational resilience.

Sigg continued, "Finding the right tools to manage the portfolio is key. But we should not be just talking about managing. These technology investments are being made as part of a transformation agenda. Organizations are aiming to differentiate themselves, be innovative and grow. Technology is a critical enabler for most of those plans. Greater transparency and control over the technology landscape will better align the tech and business agendas and set these companies up for success."

Impact of expansion

■ 69% of organizations have a higher number of disparate applications/systems compared to 2 years ago.

■ 71% say that number will be higher in two years' time.

■ 70% of companies have accrued more Technical Debt in the last year.

■ Managing legacy and new systems together is increasingly complex for 44%.

Agility issues

■ 80% say the size of technology infrastructure makes it harder to be agile and/or productive.

■ The same number (80%) feel complex tech makes them slow to launch new products/services, improve experiences for customers and employees and increase revenue/profitability.

Governance issues

■ 65% feel that tech complexity makes governance issues worse.

■ 46% say difficulty moving data out of legacy systems slows down decision making.

■ 81% say that a major pain point is not having a clear view/management of all systems.

Operational issues

■ 45% say duplicate process that cause internal conflict slows down action.

■ IT and LoB are in conflict about deploying new apps in 80% of organizations.

■ 82% of organizations say Shadow IT is a problem.

Methodology: The research was conducted in November 2023, across the USA, UK and Germany.

Hot Topics

The Latest

AI Is Hitting Operational Limits

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

Alert Fatigue Is No Longer a Morale Problem, It's a Reliability Risk and a System Failure

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

Most Enterprises Think They Can Switch AI Vendors in a Month ... Most Who've Tried Couldn't

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Your Observability Stack Has a Telemetry Pipeline Problem

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

Operator to Orchestrator: 80% of IT Pros See Shift in Role as AI Permeates Workflows

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

Gartner: 40% of Organizations Deploying AI Will Use AI Observability to Monitor Model Performance by 2028

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Almost Half of AI-Generated Code Fails in Production

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

AI Requires Design, Not Edicts

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

How Intelligent Orchestration Enables Enterprises to Move from AI POC to AI Production

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Reliability Is the New Bottleneck of Innovation

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Rapid Tech Expansion Creates Chaos for the Majority of Businesses

A vast majority (89%) of organizations have rapidly expanded their technology in the past few years and three quarters (76%) say it's brought with it increased "chaos" that they have to manage, according to Situation Report 2024: Managing Technology Chaos from Software AG.

This makes governance efforts more complex, organizations less agile and can harm core activities including service delivery and productivity.


Source: Software AG

Dr Stefan Sigg, Chief Product Officer at Software AG, said, "The complexity that organizations face in today's world of disruption, risk and rapid technology change is greater than ever. It's difficult to get a grip on all of this and be a successful organization. We see our customers overcoming these challenges by finding the right tools to manage this technology related disorder. What those tools are depends on how the challenges manifest — but there is an answer out there. And for those that find it, they can become more competitive, more efficient, and more resilient."

The three types of chaos identified as part of this research are:

■ Operational Chaos — where a maze of different processes and systems slow down, duplicate or disrupt day-to-day operations. Overcoming these operational barriers allows organizations to be more competitive, better controlled and more agile. Operational resilience is the prize for organizations that can manage operational chaos.

■ Chaos of Connectivity — where the expansion of systems is done without a plan to properly connect them together. Overcoming this lack of connectivity allows organizations to become more productive, agile, and better governed.

■ IT Chaos — where the multiplication of different systems is not done in a coordinated way and technology sprawls uncontrolled and unmanaged. Overcoming this IT threat enables organizations to control costs, plan future development and increase operational resilience.

Sigg continued, "Finding the right tools to manage the portfolio is key. But we should not be just talking about managing. These technology investments are being made as part of a transformation agenda. Organizations are aiming to differentiate themselves, be innovative and grow. Technology is a critical enabler for most of those plans. Greater transparency and control over the technology landscape will better align the tech and business agendas and set these companies up for success."

Impact of expansion

■ 69% of organizations have a higher number of disparate applications/systems compared to 2 years ago.

■ 71% say that number will be higher in two years' time.

■ 70% of companies have accrued more Technical Debt in the last year.

■ Managing legacy and new systems together is increasingly complex for 44%.

Agility issues

■ 80% say the size of technology infrastructure makes it harder to be agile and/or productive.

■ The same number (80%) feel complex tech makes them slow to launch new products/services, improve experiences for customers and employees and increase revenue/profitability.

Governance issues

■ 65% feel that tech complexity makes governance issues worse.

■ 46% say difficulty moving data out of legacy systems slows down decision making.

■ 81% say that a major pain point is not having a clear view/management of all systems.

Operational issues

■ 45% say duplicate process that cause internal conflict slows down action.

■ IT and LoB are in conflict about deploying new apps in 80% of organizations.

■ 82% of organizations say Shadow IT is a problem.

Methodology: The research was conducted in November 2023, across the USA, UK and Germany.

Hot Topics

The Latest

AI Is Hitting Operational Limits

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

Alert Fatigue Is No Longer a Morale Problem, It's a Reliability Risk and a System Failure

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

Most Enterprises Think They Can Switch AI Vendors in a Month ... Most Who've Tried Couldn't

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Your Observability Stack Has a Telemetry Pipeline Problem

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

Operator to Orchestrator: 80% of IT Pros See Shift in Role as AI Permeates Workflows

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

Gartner: 40% of Organizations Deploying AI Will Use AI Observability to Monitor Model Performance by 2028

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Almost Half of AI-Generated Code Fails in Production

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

AI Requires Design, Not Edicts

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

How Intelligent Orchestration Enables Enterprises to Move from AI POC to AI Production

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Reliability Is the New Bottleneck of Innovation

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...