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Machine Learning: The Top Benefits and Barriers

Most organizations are adopting or considering adopting machine learning due to its benefits, rather than with the intention to cut people’s jobs, according to the Voice of the Enterprise (VoTE): AI & Machine Learning – Adoption, Drivers and Stakeholders 2018 survey conducted by 451 Research. Despite being new, there is a healthy amount of adoption of these technologies already. Almost 50% of survey respondents have deployed or plan to deploy machine learning in their organizations within the next 12 months. According to Nick Patience, founder and research vice president for software at 451 Research, this paints a more realistic picture of machine learning adoption than is often portrayed. Out of many possible benefits presented to survey respondents, almost half (49%) cited gaining competitive advantage as the most significant benefit they have received from the technology. Improving the customer experience came a close second, cited by 44% of respondents. Despite all the hype around mass job losses, lowering costs was cited by only a quarter of our survey respondents. According to 451 Research, this demonstrates that AI and machine learning is an omni-purpose technology that can bring numerous benefits to organizations, beyond just lowering costs through increased automation.
Alternatively, respondents say the most significant benefit they realized or expect to realize are competitive advantages (49%) and an improved user experience for their customers (44%). This seems to indicate that decision-makers care more about the long-term impact that comes from gaining and retaining customers rather than a short-term fix that comes from cutting costs.
There are some barriers, however. When asked "what is your organization’s most significant barrier to using machine learning?" most cited a shortage of skilled resources as the top barrier (36%). Skilled resources in the context of machine learning usually means data science skills. And a lack of those skills is reinforced further by the finding that data access and preparation is the second biggest barrier cited by survey respondents. 451 Research expects the lack of skills to gradually decline as a barrier as tools become easier to use and the population of users who can leverage machine learning expands. When all is said and done, organizations large and small will need more personnel to ensure their machine learning deployment brings the business benefits that matter most. About Voice of the Enterprise (VoTE): AI and Machine Learning: This newest VoTE survey research report provides actionable data and insight and a broad, integrated view of enterprise AI/machine learning strategies and initiatives, their underlying business and technology driver, and the nature, pace and direction of AI/machine learning adoption. Data was collected via roughly 550 web-based surveys conducted with IT end-user decision-makers around the world from small, midsize and large enterprises in both private and public sectors.

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Machine Learning: The Top Benefits and Barriers

Most organizations are adopting or considering adopting machine learning due to its benefits, rather than with the intention to cut people’s jobs, according to the Voice of the Enterprise (VoTE): AI & Machine Learning – Adoption, Drivers and Stakeholders 2018 survey conducted by 451 Research. Despite being new, there is a healthy amount of adoption of these technologies already. Almost 50% of survey respondents have deployed or plan to deploy machine learning in their organizations within the next 12 months. According to Nick Patience, founder and research vice president for software at 451 Research, this paints a more realistic picture of machine learning adoption than is often portrayed. Out of many possible benefits presented to survey respondents, almost half (49%) cited gaining competitive advantage as the most significant benefit they have received from the technology. Improving the customer experience came a close second, cited by 44% of respondents. Despite all the hype around mass job losses, lowering costs was cited by only a quarter of our survey respondents. According to 451 Research, this demonstrates that AI and machine learning is an omni-purpose technology that can bring numerous benefits to organizations, beyond just lowering costs through increased automation.
Alternatively, respondents say the most significant benefit they realized or expect to realize are competitive advantages (49%) and an improved user experience for their customers (44%). This seems to indicate that decision-makers care more about the long-term impact that comes from gaining and retaining customers rather than a short-term fix that comes from cutting costs.
There are some barriers, however. When asked "what is your organization’s most significant barrier to using machine learning?" most cited a shortage of skilled resources as the top barrier (36%). Skilled resources in the context of machine learning usually means data science skills. And a lack of those skills is reinforced further by the finding that data access and preparation is the second biggest barrier cited by survey respondents. 451 Research expects the lack of skills to gradually decline as a barrier as tools become easier to use and the population of users who can leverage machine learning expands. When all is said and done, organizations large and small will need more personnel to ensure their machine learning deployment brings the business benefits that matter most. About Voice of the Enterprise (VoTE): AI and Machine Learning: This newest VoTE survey research report provides actionable data and insight and a broad, integrated view of enterprise AI/machine learning strategies and initiatives, their underlying business and technology driver, and the nature, pace and direction of AI/machine learning adoption. Data was collected via roughly 550 web-based surveys conducted with IT end-user decision-makers around the world from small, midsize and large enterprises in both private and public sectors.

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...