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Apica: Trends and Predictions for 2017

Sven Hammar

The following are five forward-looking trends highlighted by Apica:

API Economy – The New Business Engine

As more and more applications are created to help communicate, work, purchase and play more efficiently, developers and application providers leveraging application programming interface (API) will become the norm. Many of these tools are large and tie to other parts of an organization like transactions, shipping and warehousing. According to Kristin R. Moyer, VP and distinguished analyst at Gartner, "The API Economy is an enabler for turning business or organizations into a platform."

Prediction: Due to this trend and drive many parts of the business we will aggregate a number of these functions on to API’s driving the economy of things. To ensure these combined API’s deploy and function properly application creators will lean more heavily on visibility and testing solutions.

Speed of Application Development

In order to stay competitive organizations have speed up their application development to light speed by moving away from the traditional three layers of testing to an automated model. Unfortunately, some organizations hesitate to automate and continue this methodical approach to application development resulting in competitors eating up any market share available. Some of today’s most advanced applications are the ones that are integrated and automated within the test automation phase.

Prediction: We predict that more and more automation will require new levels of testing to speed up the development process. Testing and analytics tools today can provide a holistic view of application development to where you can now test new features that weren’t available months ago.

DDoS Attacks Continue in a Social Way?

Distributed denial-of-service (DDoS) attacks continue to flood the enterprise with disruptions and targeted miss conduct. But not like they did a few years ago. Today’s attacks are being rolled out through social media channels that weren’t even a consideration just a few years ago. The reality of these types of DDoS attacks are that they are being delivered via real people versus bots or computers now. More and more of these attacks are tied to specific actions of companies that the consumer doesn’t agree with. What was old is new again, as DDoS attacks move from the traditional attack vectors to social channels.

Prediction: We expect to see more attacks on the horizon as companies continue to take advantage of social media to build customers and brand confidence. Organizations will look to traffic visibility tools to ensure these attacks, just like the traditional attacks of the past, don’t create downtime and disrupt sales and a key communication channels to customers.

B2B Shopping Experience Becoming More like B2C

As e-Commerce will dominate the news headlines over the rest of the year, it is B2B e-Commerce that is becoming the bigger revenue generator in the U.S. and around the world. Forrester Research reports that B2B e-Commerce sales in the U.S. reached nearly $800 billion in 2015, and predicts that it will grow to $1.13 trillion in 2020. Clearly there is an increasing demand among business owners to have a similar experience when shopping for business solutions as they do when buying a cell phone or other personal items online.

Prediction: B2B organizations will take advantage of all of the B2C industry advancements to improve their model and shopping experience. Despite having low volume, B2B tends to have higher value for each sale. It will be important to ensure online assets are available and meet e-Commerce purchase models.

Analytics for Both Sides of the Business

Comparing sales data with performance data creates a strong bond between the two. A platform that performs faster will lead to higher sales. On the flip side, the loss can be significant when performance takes a hit. For instance, Amazon found that a 100ms increase in page load latency translates to a 1 percent drop in sales. Performance is an often overlooked KPI.

Prediction: Performance and sales will align more in 2017 as organizations establish KPIs to increase profits. Maximizing profitability will take presence in order to capitalize on conversions without overspending on unnecessary infrastructure. Advanced load testing platforms provide the means to test web applications performance under real-life end-user demands without doing it on a live, unsuspecting audience.

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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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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.

Apica: Trends and Predictions for 2017

Sven Hammar

The following are five forward-looking trends highlighted by Apica:

API Economy – The New Business Engine

As more and more applications are created to help communicate, work, purchase and play more efficiently, developers and application providers leveraging application programming interface (API) will become the norm. Many of these tools are large and tie to other parts of an organization like transactions, shipping and warehousing. According to Kristin R. Moyer, VP and distinguished analyst at Gartner, "The API Economy is an enabler for turning business or organizations into a platform."

Prediction: Due to this trend and drive many parts of the business we will aggregate a number of these functions on to API’s driving the economy of things. To ensure these combined API’s deploy and function properly application creators will lean more heavily on visibility and testing solutions.

Speed of Application Development

In order to stay competitive organizations have speed up their application development to light speed by moving away from the traditional three layers of testing to an automated model. Unfortunately, some organizations hesitate to automate and continue this methodical approach to application development resulting in competitors eating up any market share available. Some of today’s most advanced applications are the ones that are integrated and automated within the test automation phase.

Prediction: We predict that more and more automation will require new levels of testing to speed up the development process. Testing and analytics tools today can provide a holistic view of application development to where you can now test new features that weren’t available months ago.

DDoS Attacks Continue in a Social Way?

Distributed denial-of-service (DDoS) attacks continue to flood the enterprise with disruptions and targeted miss conduct. But not like they did a few years ago. Today’s attacks are being rolled out through social media channels that weren’t even a consideration just a few years ago. The reality of these types of DDoS attacks are that they are being delivered via real people versus bots or computers now. More and more of these attacks are tied to specific actions of companies that the consumer doesn’t agree with. What was old is new again, as DDoS attacks move from the traditional attack vectors to social channels.

Prediction: We expect to see more attacks on the horizon as companies continue to take advantage of social media to build customers and brand confidence. Organizations will look to traffic visibility tools to ensure these attacks, just like the traditional attacks of the past, don’t create downtime and disrupt sales and a key communication channels to customers.

B2B Shopping Experience Becoming More like B2C

As e-Commerce will dominate the news headlines over the rest of the year, it is B2B e-Commerce that is becoming the bigger revenue generator in the U.S. and around the world. Forrester Research reports that B2B e-Commerce sales in the U.S. reached nearly $800 billion in 2015, and predicts that it will grow to $1.13 trillion in 2020. Clearly there is an increasing demand among business owners to have a similar experience when shopping for business solutions as they do when buying a cell phone or other personal items online.

Prediction: B2B organizations will take advantage of all of the B2C industry advancements to improve their model and shopping experience. Despite having low volume, B2B tends to have higher value for each sale. It will be important to ensure online assets are available and meet e-Commerce purchase models.

Analytics for Both Sides of the Business

Comparing sales data with performance data creates a strong bond between the two. A platform that performs faster will lead to higher sales. On the flip side, the loss can be significant when performance takes a hit. For instance, Amazon found that a 100ms increase in page load latency translates to a 1 percent drop in sales. Performance is an often overlooked KPI.

Prediction: Performance and sales will align more in 2017 as organizations establish KPIs to increase profits. Maximizing profitability will take presence in order to capitalize on conversions without overspending on unnecessary infrastructure. Advanced load testing platforms provide the means to test web applications performance under real-life end-user demands without doing it on a live, unsuspecting audience.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.