Skip to main content

Innovation Explosion Sets Stage for New Wave of Hybrid Applications

Julie Craig

In the past, technology evolution was quantified as generational. So the first generation of computing was based on the vacuum tube, followed by the transistor, integrated circuits, and microprocessors. Even today, multi-generational software and hardware systems are very much alive in many modern data centers.

The interesting thing about the generational approach is that it has always been associated with linear progression and evolutionary growth. However the rapid technology evolution we are seeing today is more revolutionary than evolutionary, and IT is bearing the brunt of its impact.

Emerging from the financial doldrums of the 2008 – 2012 “downturn”, business and IT are confronted with an explosion of technology which has never been equaled in recorded history. Driverless cars, sensor-driven “swarms”, alternative energy sources, and “smart” electric grids are the tip of the iceberg. And in contrast to the generational, evolutionary innovation waves of the past, everything is happening at once.

Businesses are confronted with multiple concurrent streams of innovation encompassing cloud, sensor networks, Internets of Things (IoT), social media, and ubiquitous mobile. Each of these trends becomes, in turn, a foundation for the next wave of innovation. Each also provides business opportunities for creating the “next big idea” driving revenue from new products and services.

I have heavily covered this evolution with multiple research pieces over the past several years and am currently analyzing the results of my latest survey on the topic of the Extended Enterprise. This is an ecosystem-wide view of the trends currently impacting IT, summarized in a paper entitled Public Cloud Comes of Age: Application Performance Management (APM) Strategies & Products for a Production-Ready Cloud Ecosystem. With the paper scheduled to be published by December 15, I will be presenting a webinar on the topic on December 16. Click here to register .

The research focuses on the increasingly hybrid nature of today’s applications, particularly in view of the fact that this “explosion of innovation” is, essentially, being driven by software. Enterprise IT is already dealing with hybrid on-premise/public Cloud applications, and future hybrid deployments will encompass Internet of Things, mobile, and social media interactions and data as well.

A growing “API Economy” means that everything is connected. While interconnected apps take a toll on performance, particularly when they are connected via APIs, users expect the same levels of performance regardless of their device or location. And as data siphoned from sensor networks and related “Internet of Things” technologies increasingly becomes fodder for business-critical data stores and reporting, real time interoperability becomes increasingly important — as well as increasingly difficult to achieve.

In short, the challenges of monitoring and managing “hybrid” environments which I anticipated three years ago have now come to pass — in spades. Why is this important? Because all of these technologies are, or soon will be, leveraged by component-based applications and services. So in addition to “hybrid cloud”, we will also have “hybrid API-driven apps” (transactions executing via multiple API calls), “hybrid mobile” (mobile to mobile applications), and other combinations of “hybrids”.

Although it is an exciting and interesting time to be part of this evolution, one should also note the age-old curse that says: May you live in interesting times. IT professionals at all levels — including CIOs, Directors, and managers — are living in interesting times. With applications running “in the cloud”, accessing partner and supplier data centers, executing as mobile “apps”, driving down the road, and exchanging real-time data, the illusion of control has dissipated.

So the “big picture” question is: How can IT manage application performance and availability in an environment it can’t control? For enterprise IT, the question of the day then becomes, “Is YOUR organization ready to support these technologies”?

Hot Topics

The Latest

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 ...

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

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 ...

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 ...

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 ...

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 ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

Innovation Explosion Sets Stage for New Wave of Hybrid Applications

Julie Craig

In the past, technology evolution was quantified as generational. So the first generation of computing was based on the vacuum tube, followed by the transistor, integrated circuits, and microprocessors. Even today, multi-generational software and hardware systems are very much alive in many modern data centers.

The interesting thing about the generational approach is that it has always been associated with linear progression and evolutionary growth. However the rapid technology evolution we are seeing today is more revolutionary than evolutionary, and IT is bearing the brunt of its impact.

Emerging from the financial doldrums of the 2008 – 2012 “downturn”, business and IT are confronted with an explosion of technology which has never been equaled in recorded history. Driverless cars, sensor-driven “swarms”, alternative energy sources, and “smart” electric grids are the tip of the iceberg. And in contrast to the generational, evolutionary innovation waves of the past, everything is happening at once.

Businesses are confronted with multiple concurrent streams of innovation encompassing cloud, sensor networks, Internets of Things (IoT), social media, and ubiquitous mobile. Each of these trends becomes, in turn, a foundation for the next wave of innovation. Each also provides business opportunities for creating the “next big idea” driving revenue from new products and services.

I have heavily covered this evolution with multiple research pieces over the past several years and am currently analyzing the results of my latest survey on the topic of the Extended Enterprise. This is an ecosystem-wide view of the trends currently impacting IT, summarized in a paper entitled Public Cloud Comes of Age: Application Performance Management (APM) Strategies & Products for a Production-Ready Cloud Ecosystem. With the paper scheduled to be published by December 15, I will be presenting a webinar on the topic on December 16. Click here to register .

The research focuses on the increasingly hybrid nature of today’s applications, particularly in view of the fact that this “explosion of innovation” is, essentially, being driven by software. Enterprise IT is already dealing with hybrid on-premise/public Cloud applications, and future hybrid deployments will encompass Internet of Things, mobile, and social media interactions and data as well.

A growing “API Economy” means that everything is connected. While interconnected apps take a toll on performance, particularly when they are connected via APIs, users expect the same levels of performance regardless of their device or location. And as data siphoned from sensor networks and related “Internet of Things” technologies increasingly becomes fodder for business-critical data stores and reporting, real time interoperability becomes increasingly important — as well as increasingly difficult to achieve.

In short, the challenges of monitoring and managing “hybrid” environments which I anticipated three years ago have now come to pass — in spades. Why is this important? Because all of these technologies are, or soon will be, leveraged by component-based applications and services. So in addition to “hybrid cloud”, we will also have “hybrid API-driven apps” (transactions executing via multiple API calls), “hybrid mobile” (mobile to mobile applications), and other combinations of “hybrids”.

Although it is an exciting and interesting time to be part of this evolution, one should also note the age-old curse that says: May you live in interesting times. IT professionals at all levels — including CIOs, Directors, and managers — are living in interesting times. With applications running “in the cloud”, accessing partner and supplier data centers, executing as mobile “apps”, driving down the road, and exchanging real-time data, the illusion of control has dissipated.

So the “big picture” question is: How can IT manage application performance and availability in an environment it can’t control? For enterprise IT, the question of the day then becomes, “Is YOUR organization ready to support these technologies”?

Hot Topics

The Latest

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 ...

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

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 ...

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 ...

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 ...

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 ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...