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Video Consumption Growing While User Experience Lags Behind

More than half of consumers watch videos on mobile devices – expected to grow 45 percent in three years

Digital video consumption is viral and, according to a new study released by IBM and International Broadcasting Convention (IBC), more than half of the 21,000 consumers surveyed are using mobiles every day to watch streaming videos, and that number is expected to grow 45 percent in the next three years.

Today, the explosive growth of new digital content available via online video distribution networks such as YouTube competes directly with traditional broadcasting creating a new connected landscape with data at the center. With this shift in industry competition, media and entertainment companies aim to maximize content investment and return while providing a differentiated and exceptional customer experience. Ninety-two percent of surveyed media and entertainment executives say cognitive technologies will play an important role in the future of their business.

The Creating a living media partner for your consumers: A cognitive future for media and entertainment study, conducted by the IBM Institute for Business Value (IBV), is based on findings from two studies. The first is a survey of nearly 21,000 consumers in 42 countries about their video consumption habits, and the second offers insights from 500 global media and entertainment executives about the impact of cognitive computing on their industry.

Globally, the study found 51 percent of surveyed consumers -- and 67 percent in emerging markets — access free, over-the-Internet video from providers such as YouTube, Facebook and Snapchat, whereas 48 percent access video through regular subscription services from traditional pay-TV providers. Among the 55 percent of surveyed respondents who watch video regularly on mobile devices, about a quarter spend one to two hours using mobile broadband instead of WiFi.

Despite consumers’ drive to go mobile, many respondents say the experience leaves much to be desired. For example, 65 percent of surveyed consumers very often or regularly experience buffering problems and 62 percent have long waiting times to start a video.


Although media companies have advanced in recent years, most lag digital disruptors in the application of data, machine learning and advanced automation to deliver next-generation experiences at scale. Cognitive capabilities can play a critical role in this transformation by unlocking and interpreting previously inaccessible data, yielding audience, content and contextual insights that can help media companies reach viewers with compelling, personalized experiences.

With the rapid evolution of customer preferences and demands, media companies face immense pressure in a hyper-competitive market. The IBV and IBC recommend organizations embrace the opportunities that the marketplace is currently presenting by:

Applying cognitive technology to achieve personalization

Delighting and engaging each individual consumer by understanding the personalized, in-the-moment experiences each customer craves is critical. Cognitive applications in media and entertainment can help do just that, by delivering audience insights and content enrichment, as well as content prediction to create a compelling customer experience based on audience preferences, affinities and tastes.

Revamping infrastructure to meet the coming demands

Moving from several hundred channels to several million “cable channels for one” that predict and serve individual needs in real time will require much more flexible and scalable processes. Companies will need to implement hyperscalable systems to manage the ever-expanding data processing necessary to analyze, personalize, and distribute video content. Such a platform must be scalable to accommodate growth, resilient to support uninterrupted service and secure to manage identities and protect valuable assets.

Content value chains — from acquisition through production to distribution — need to be unified, requiring workflow automation, which must consider the media content, associated rights and technical and descriptive metadata.

Media workflow systems must monitor system infrastructure, the location of content and distribution channel characteristics. By applying cognitive methods to both audience insights and content distribution, media companies can create an architecture that scales automatically based on predictions of audience demands and peak loads, helping to match costs and resources dynamically to changing market conditions and business or operational needs.

Reengineering business models to profit from in the new media landscape

Media companies will need to make backend systems and processes more intelligent to fully monetize the new opportunities while cutting costs and refocusing investment on content and customer experience.

As media companies look to the future, those that apply data to optimize revenues and costs and strip out non-core activities will free up funds to reinvest in content and enabling technologies, driving further growth and success. Emerging technologies like cognitive solutions and blockchain may play a key role in that future. Industry leaders will be those who can institutionalize such capabilities as part of their Digital Reinvention efforts and focus their companies on investing in great content and delivering superior customer experiences.

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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 ...

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 ...

Video Consumption Growing While User Experience Lags Behind

More than half of consumers watch videos on mobile devices – expected to grow 45 percent in three years

Digital video consumption is viral and, according to a new study released by IBM and International Broadcasting Convention (IBC), more than half of the 21,000 consumers surveyed are using mobiles every day to watch streaming videos, and that number is expected to grow 45 percent in the next three years.

Today, the explosive growth of new digital content available via online video distribution networks such as YouTube competes directly with traditional broadcasting creating a new connected landscape with data at the center. With this shift in industry competition, media and entertainment companies aim to maximize content investment and return while providing a differentiated and exceptional customer experience. Ninety-two percent of surveyed media and entertainment executives say cognitive technologies will play an important role in the future of their business.

The Creating a living media partner for your consumers: A cognitive future for media and entertainment study, conducted by the IBM Institute for Business Value (IBV), is based on findings from two studies. The first is a survey of nearly 21,000 consumers in 42 countries about their video consumption habits, and the second offers insights from 500 global media and entertainment executives about the impact of cognitive computing on their industry.

Globally, the study found 51 percent of surveyed consumers -- and 67 percent in emerging markets — access free, over-the-Internet video from providers such as YouTube, Facebook and Snapchat, whereas 48 percent access video through regular subscription services from traditional pay-TV providers. Among the 55 percent of surveyed respondents who watch video regularly on mobile devices, about a quarter spend one to two hours using mobile broadband instead of WiFi.

Despite consumers’ drive to go mobile, many respondents say the experience leaves much to be desired. For example, 65 percent of surveyed consumers very often or regularly experience buffering problems and 62 percent have long waiting times to start a video.


Although media companies have advanced in recent years, most lag digital disruptors in the application of data, machine learning and advanced automation to deliver next-generation experiences at scale. Cognitive capabilities can play a critical role in this transformation by unlocking and interpreting previously inaccessible data, yielding audience, content and contextual insights that can help media companies reach viewers with compelling, personalized experiences.

With the rapid evolution of customer preferences and demands, media companies face immense pressure in a hyper-competitive market. The IBV and IBC recommend organizations embrace the opportunities that the marketplace is currently presenting by:

Applying cognitive technology to achieve personalization

Delighting and engaging each individual consumer by understanding the personalized, in-the-moment experiences each customer craves is critical. Cognitive applications in media and entertainment can help do just that, by delivering audience insights and content enrichment, as well as content prediction to create a compelling customer experience based on audience preferences, affinities and tastes.

Revamping infrastructure to meet the coming demands

Moving from several hundred channels to several million “cable channels for one” that predict and serve individual needs in real time will require much more flexible and scalable processes. Companies will need to implement hyperscalable systems to manage the ever-expanding data processing necessary to analyze, personalize, and distribute video content. Such a platform must be scalable to accommodate growth, resilient to support uninterrupted service and secure to manage identities and protect valuable assets.

Content value chains — from acquisition through production to distribution — need to be unified, requiring workflow automation, which must consider the media content, associated rights and technical and descriptive metadata.

Media workflow systems must monitor system infrastructure, the location of content and distribution channel characteristics. By applying cognitive methods to both audience insights and content distribution, media companies can create an architecture that scales automatically based on predictions of audience demands and peak loads, helping to match costs and resources dynamically to changing market conditions and business or operational needs.

Reengineering business models to profit from in the new media landscape

Media companies will need to make backend systems and processes more intelligent to fully monetize the new opportunities while cutting costs and refocusing investment on content and customer experience.

As media companies look to the future, those that apply data to optimize revenues and costs and strip out non-core activities will free up funds to reinvest in content and enabling technologies, driving further growth and success. Emerging technologies like cognitive solutions and blockchain may play a key role in that future. Industry leaders will be those who can institutionalize such capabilities as part of their Digital Reinvention efforts and focus their companies on investing in great content and delivering superior customer experiences.

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 ...