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2016 APM Prediction: Application Performance and Delivery Remain Top-of-Mind

Todd DeCapua

It's a new year and the fight for market share amongst all enterprises has never been fiercer. Organizations compete for customers on multiple fronts — in person, on the web, and via mobile applications. Today, it's not enough to churn out applications, products, and services faster than the competition. Anything that fails to meet a high performance standard could cause a serious loss of brand value, revenue, and the ability to attract and maintain customers and competitive advantage.

Below are three predictions for what we can expect in 2016:

1. Load Testing will move to the cloud

In 2016, we'll see more enterprises moving load testing to the cloud. Companies are under constant pressure to deliver products and services to the market faster than ever before. Meanwhile, business owners are exhausted by in-house IT departments with slow delivery cycles. In this dichotomy, performance must not be sacrificed. By using load testing from the cloud capabilities, the business and technology teams both get what they need: Technology gets an affordable, secure way to test system and application performance before it reaches production, and the business doesn't have to wait so long that it loses competitive advantage.

2. Enterprises will prioritize lifecycle virtualization

Organizations today are expected to deliver quality products and services to market on time, and therefore, they must have the ability to virtualize production conditions and ensure their solutions are ready for prime time. For instance, releasing a buggy video-streaming application can have major consequences, but imagine the ramifications of releasing autonomous vehicle software that causes a self-driving car to blow through red lights — or a home security system that can be overridden by a simple hack. In 2015, less than half of performance and development professionals used Release Quality as a baseline metric to judge application performance, according to a report sponsored by HPE and blind survey executed by YouGov. This number is expected to rise in 2016 as organizations fully appreciate that there are no second chances with respect to making a strong first impression based on performance.

3. Organizations will adopt a "Continuous Evolution" model

Continuous Evolution will leverage new channels of delivery for end-users and businesses. The way of doing business is changing, and a need to evolve to the next level is more evident than ever before. Being challenged by mobile is the story of the past, and now is the time for businesses to hit the multiplier by leveraging their existing mobile capabilities. Companies must learn to take the best of their mobile investments, then accelerate them through their backlog with DevOps practices, and deliver game changing results via IoT. Effectively, Continuous Evolution enables a business to ensure the delivery of quality, scale, and performance throughout its products and services; so businesses impress their customers with amazing experiences.

Though the year ahead brings many unknowns, users will continue to judge businesses by their performance. Those who embrace the changes ahead will rise to the top, while the others will simply be left behind.

Todd DeCapua is Chief Technology Evangelist at Hewlett Packard Enterprise.

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

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AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

2016 APM Prediction: Application Performance and Delivery Remain Top-of-Mind

Todd DeCapua

It's a new year and the fight for market share amongst all enterprises has never been fiercer. Organizations compete for customers on multiple fronts — in person, on the web, and via mobile applications. Today, it's not enough to churn out applications, products, and services faster than the competition. Anything that fails to meet a high performance standard could cause a serious loss of brand value, revenue, and the ability to attract and maintain customers and competitive advantage.

Below are three predictions for what we can expect in 2016:

1. Load Testing will move to the cloud

In 2016, we'll see more enterprises moving load testing to the cloud. Companies are under constant pressure to deliver products and services to the market faster than ever before. Meanwhile, business owners are exhausted by in-house IT departments with slow delivery cycles. In this dichotomy, performance must not be sacrificed. By using load testing from the cloud capabilities, the business and technology teams both get what they need: Technology gets an affordable, secure way to test system and application performance before it reaches production, and the business doesn't have to wait so long that it loses competitive advantage.

2. Enterprises will prioritize lifecycle virtualization

Organizations today are expected to deliver quality products and services to market on time, and therefore, they must have the ability to virtualize production conditions and ensure their solutions are ready for prime time. For instance, releasing a buggy video-streaming application can have major consequences, but imagine the ramifications of releasing autonomous vehicle software that causes a self-driving car to blow through red lights — or a home security system that can be overridden by a simple hack. In 2015, less than half of performance and development professionals used Release Quality as a baseline metric to judge application performance, according to a report sponsored by HPE and blind survey executed by YouGov. This number is expected to rise in 2016 as organizations fully appreciate that there are no second chances with respect to making a strong first impression based on performance.

3. Organizations will adopt a "Continuous Evolution" model

Continuous Evolution will leverage new channels of delivery for end-users and businesses. The way of doing business is changing, and a need to evolve to the next level is more evident than ever before. Being challenged by mobile is the story of the past, and now is the time for businesses to hit the multiplier by leveraging their existing mobile capabilities. Companies must learn to take the best of their mobile investments, then accelerate them through their backlog with DevOps practices, and deliver game changing results via IoT. Effectively, Continuous Evolution enables a business to ensure the delivery of quality, scale, and performance throughout its products and services; so businesses impress their customers with amazing experiences.

Though the year ahead brings many unknowns, users will continue to judge businesses by their performance. Those who embrace the changes ahead will rise to the top, while the others will simply be left behind.

Todd DeCapua is Chief Technology Evangelist at Hewlett Packard Enterprise.

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...