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AI-Powered Search Could Yield Staggering Productivity Returns

Nearly all (99%) global IT decision makers, regardless of region or industry, recognize generative AI's (GenAI) transformative potential to influence change within their organizations, according to The Elastic Generative AI Report.

However, early adoption continues to be slowed by chaotic data estates, search challenges, and fears around privacy and security, regulation, and internal skills gaps.

Despite these headwinds, the report found most IT decision makers (88%) are eyeing increased investments in GenAI in 2024 and beyond. This points to optimism that these technologies are poised to drive operational efficiencies and productivity, accelerate decision-making, improve customer engagement, and bolster security postures.

"In a little more than 12 months, the disruptive potential of GenAI has shifted from reverie to reality, capturing the imaginations and budgets of IT and data leaders," said Matt Riley, GVP & GM of Search at Elastic. "While data may fuel this technology, search is the engine that powers its effectiveness. Unsurprisingly, businesses that adopt search-powered GenAI quickest — grounded by business context — will lead and uncover the insights needed to securely innovate, build more efficient businesses, and pioneer new customer experiences."

"Generative AI is still an emerging technology, but it already promises to disrupt how organizations operate and engage with customers and employees," said Jason Bloomberg, Managing Director of analyst firm Intellyx. "In particular, GenAI is transforming the data management, security, and search challenges many businesses continue to face. Thoughtful application of this new technology will help them better anticipate customer needs and differentiate themselves from their competition."

Key Concerns

There's high enthusiasm for GenAI across regions and industries, but key concerns stifle operationalization strategies.

Across verticals, GenAI is poised to deliver tangible benefits for organizations. More than half of respondents (57%) anticipate it will improve resource (i.e., staff time, better costs) and operational efficiency and productivity, improve customer experience (50%), and lead to more accurate decision-making (48%).

However, despite early interest and planned investment, internal and external roadblocks threaten to slow GenAI adoption. Nearly 9 of 10 organizations anticipate budgets for GenAI will increase over the next three years, despite nearly all respondents reporting adoption is being slowed, primarily by fears around the security and privacy of the technologies (40%), regulation issues (37%), and the skills gap to implement the technologies in house (36%)

While a skills gap may continue to slow GenAI adoption, 4 of 10 respondents believe it will eventually help upskill and educate employees

Siloed Data Ecosystems

Siloed data ecosystems continue to expand in size and complexity, complicating security, visibility, and real-time analysi.

Most organizations continue to struggle to get relevant insights from their data, which leads to sluggish decision-making. Unsurprisingly, 3 of 4 (75%) respondents report the ability to view data across their entire environment is a key challenge for their organizations, while 7 of 10 (68%) report critical decisions are stalled by slow analysis of their organization's data.

While search powered GenAI is recognized as a key to unlocking transformative insights, effective search capabilities remain stunted. Nearly all respondents (97%) believe a conversational search experience would make their organizations more productive, and nearly half (44%) believe their organizations could save an average of two days or more per week per employee.

The majority of organizations (94%) face data search challenges driven by a struggle to use search results effectively (45%), the inability to cover multiple sources of information (44%), and the inability to obtain responses quickly enough (42%).

Security and Observability

Despite evolving IT infrastructures and data estates, there's optimism GenAI will illuminate security and observability blindspots.

IT security environments and threat landscapes have never been more complex, but there's confidence GenAI will alleviate many challenges. Nearly all organizations (97%) face IT security challenges, from the ability to detect and respond to threats quickly to maintaining current and relevant security practices, but nearly all respondents anticipate GenAI will bolster security postures, such as improving automated threat detection systems (53%), generating training exercises (50%), and automating responses to common security problems (49%).

Holistic observability remains a significant operational pain point, but GenAI offers hope. Nearly all organizations (95%) report observability challenges, from data silos to more complex applications, but nearly all respondents are confident GenAI would benefit their observability postures, specifically to enhance missing data handling (48%), improve data masking and privacy (43%), and to conduct rigorous and regular data quality assessments with synthetic data (43%).

Methodology: The report was produced in conjunction with independent market research specialist Vanson Bourne, and solicited 3,200 IT decision makers and decision influencers across the US, Europe, and Asia-Pacific from more than a dozen sectors, including telecommunications, public service, retail, and financial services.

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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 ...

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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 ...

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AI-Powered Search Could Yield Staggering Productivity Returns

Nearly all (99%) global IT decision makers, regardless of region or industry, recognize generative AI's (GenAI) transformative potential to influence change within their organizations, according to The Elastic Generative AI Report.

However, early adoption continues to be slowed by chaotic data estates, search challenges, and fears around privacy and security, regulation, and internal skills gaps.

Despite these headwinds, the report found most IT decision makers (88%) are eyeing increased investments in GenAI in 2024 and beyond. This points to optimism that these technologies are poised to drive operational efficiencies and productivity, accelerate decision-making, improve customer engagement, and bolster security postures.

"In a little more than 12 months, the disruptive potential of GenAI has shifted from reverie to reality, capturing the imaginations and budgets of IT and data leaders," said Matt Riley, GVP & GM of Search at Elastic. "While data may fuel this technology, search is the engine that powers its effectiveness. Unsurprisingly, businesses that adopt search-powered GenAI quickest — grounded by business context — will lead and uncover the insights needed to securely innovate, build more efficient businesses, and pioneer new customer experiences."

"Generative AI is still an emerging technology, but it already promises to disrupt how organizations operate and engage with customers and employees," said Jason Bloomberg, Managing Director of analyst firm Intellyx. "In particular, GenAI is transforming the data management, security, and search challenges many businesses continue to face. Thoughtful application of this new technology will help them better anticipate customer needs and differentiate themselves from their competition."

Key Concerns

There's high enthusiasm for GenAI across regions and industries, but key concerns stifle operationalization strategies.

Across verticals, GenAI is poised to deliver tangible benefits for organizations. More than half of respondents (57%) anticipate it will improve resource (i.e., staff time, better costs) and operational efficiency and productivity, improve customer experience (50%), and lead to more accurate decision-making (48%).

However, despite early interest and planned investment, internal and external roadblocks threaten to slow GenAI adoption. Nearly 9 of 10 organizations anticipate budgets for GenAI will increase over the next three years, despite nearly all respondents reporting adoption is being slowed, primarily by fears around the security and privacy of the technologies (40%), regulation issues (37%), and the skills gap to implement the technologies in house (36%)

While a skills gap may continue to slow GenAI adoption, 4 of 10 respondents believe it will eventually help upskill and educate employees

Siloed Data Ecosystems

Siloed data ecosystems continue to expand in size and complexity, complicating security, visibility, and real-time analysi.

Most organizations continue to struggle to get relevant insights from their data, which leads to sluggish decision-making. Unsurprisingly, 3 of 4 (75%) respondents report the ability to view data across their entire environment is a key challenge for their organizations, while 7 of 10 (68%) report critical decisions are stalled by slow analysis of their organization's data.

While search powered GenAI is recognized as a key to unlocking transformative insights, effective search capabilities remain stunted. Nearly all respondents (97%) believe a conversational search experience would make their organizations more productive, and nearly half (44%) believe their organizations could save an average of two days or more per week per employee.

The majority of organizations (94%) face data search challenges driven by a struggle to use search results effectively (45%), the inability to cover multiple sources of information (44%), and the inability to obtain responses quickly enough (42%).

Security and Observability

Despite evolving IT infrastructures and data estates, there's optimism GenAI will illuminate security and observability blindspots.

IT security environments and threat landscapes have never been more complex, but there's confidence GenAI will alleviate many challenges. Nearly all organizations (97%) face IT security challenges, from the ability to detect and respond to threats quickly to maintaining current and relevant security practices, but nearly all respondents anticipate GenAI will bolster security postures, such as improving automated threat detection systems (53%), generating training exercises (50%), and automating responses to common security problems (49%).

Holistic observability remains a significant operational pain point, but GenAI offers hope. Nearly all organizations (95%) report observability challenges, from data silos to more complex applications, but nearly all respondents are confident GenAI would benefit their observability postures, specifically to enhance missing data handling (48%), improve data masking and privacy (43%), and to conduct rigorous and regular data quality assessments with synthetic data (43%).

Methodology: The report was produced in conjunction with independent market research specialist Vanson Bourne, and solicited 3,200 IT decision makers and decision influencers across the US, Europe, and Asia-Pacific from more than a dozen sectors, including telecommunications, public service, retail, and financial services.

Hot Topics

The Latest

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 ...