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GenAI Delivering Strong ROI

Google Cloud says Among those with GenAI in production, 86% of those report an increase in revenue, estimating growth of 6%+

The majority of executives (61%) are harnessing the power of generative AI, with at least one application in production, and among these early adopters, 86% of those reported an increase in revenue, estimated at more than 6%, according to new  global research from Google Cloud.

The survey found that GenAI initiatives that are in production are driving benefits in four primary areas:

Productivity: Almost half (45%) of executives who reported improved productivity indicated that employee productivity has at least doubled as a result of GenAI rollouts at their organizations.

Security: 56% of executives reported that GenAI has bolstered their organization's security posture, with 82% of those execs citing improved ability to identify threats and 71% reporting a reduction in time to resolve a security issue.

Business growth: 77% of execs reporting business growth said they have improved leads and customer acquisition as a result of GenAI solutions.

User experience: 85% of executives reporting an improved user experience indicated specifically that user engagement has increased from GenAI, and nearly the same number reported improved user satisfaction (80%).

"Generative AI is not just a technological innovation; it's a strategic differentiator," said Oliver Parker, VP, Global Generative AI Go-To-Market, Google Cloud. "Our research shows that early adopters of GenAI are reaping significant rewards, from increased revenue, to better customer service, to improved productivity. Organizations investing in GenAI today are the ones that will be best positioned to succeed in the coming decade."

C-Suite Champions Key to Closing GenAI Adoption Gap

The speed by which organizations move from piloting GenAI to full-scale production is a strong indicator of its success, according to the research. Of the executives surveyed that are currently leveraging GenAI in production, 84% say their organizations were able to move from pilot to production in under six months.

However, a significant adoption gap remains: 39% of enterprises overall have still yet to implement the technology in production, with 21% actively testing, 12% evaluating use-cases, and 5% not started. This lag is particularly pronounced in regulated industries like financial services and manufacturing. In the EMEA region, organizations are significantly less likely to have been leveraging GenAI in production for more than one year.

C-Suite champions are key to GenAI success as 91% of respondents with robust C-level support of GenAI at their organization also report increased revenue estimates of 6% or more.

Additionally, "GenAI Leaders" — organizations that extensively utilize and invest in generative AI — exhibit key characteristics that set them apart:

Strategic alignment: 76% of leaders effectively aligned their AI strategies with broader business goals, compared to the global average of 69%.

Dedicated teams: 54% of leaders invested in dedicated generative AI teams, 13% more than their counterparts.

Significant investment: A staggering 86% of leaders plan to allocate at least half of their future AI budgets to generative AI, a stark contrast to the 67% average.

"Our data underscores the importance of executive-level support and strategic alignment for maximizing the potential of generative AI," said Carrie Tharp, VP, Strategic Industries, Google Cloud. "By connecting financial business drivers with technology drivers, organizations can ensure that AI strategies are not just innovative but also tightly intertwined with core business goals. This strategic alignment is the key to escaping the dreaded 'pilot purgatory,' and accelerating towards tangible business impact, leveraging AI to transform operations, enhance customer experiences, and unlock new avenues for growth."

An Emerging GenAI Reinvestment Cycle: Technology, Talent and Data

The early success of GenAI is sparking a reinvestment cycle that's driving further innovation and growth. Nearly half of respondents surveyed (49%) plan to reinvest the gains from GenAI to further improve operating profit margins. Specifically, the topic three areas for investment are:

Technology: 47% plan to invest in aligning business and technology to support change management for user adoption of AI.

Talent: 46% plan to invest in upskilling their workforce and attracting new talent with AI expertise.

Data quality: 43% said they plan to invest in data quality and knowledge management to ensure their GenAI applications are built on a solid foundation of accurate and reliable data

"The most successful organizations aren't just implementing GenAI. They're fostering a culture of innovation through experimentation," Parker added. "By reinvesting early gains in technology, talent, and data, these companies are building a sustainable AI ecosystem, creating a flywheel of innovation that will continue to drive growth and competitive advantage in the years to come."

Methodology: The report is based on a survey of 2,508 senior leaders of global enterprises ($10M+ revenue), conducted by Google Cloud and National Research Group from February 23-April 5. The survey respondents represent organizations from North America, Latin America, EMEA, and APAC, and across key industries including Financial Services, Manufacturing & Automotive, Retail & Consumer Packaged Goods, Telecommunications, Healthcare & Life Sciences, and Media & Entertainment.

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GenAI Delivering Strong ROI

Google Cloud says Among those with GenAI in production, 86% of those report an increase in revenue, estimating growth of 6%+

The majority of executives (61%) are harnessing the power of generative AI, with at least one application in production, and among these early adopters, 86% of those reported an increase in revenue, estimated at more than 6%, according to new  global research from Google Cloud.

The survey found that GenAI initiatives that are in production are driving benefits in four primary areas:

Productivity: Almost half (45%) of executives who reported improved productivity indicated that employee productivity has at least doubled as a result of GenAI rollouts at their organizations.

Security: 56% of executives reported that GenAI has bolstered their organization's security posture, with 82% of those execs citing improved ability to identify threats and 71% reporting a reduction in time to resolve a security issue.

Business growth: 77% of execs reporting business growth said they have improved leads and customer acquisition as a result of GenAI solutions.

User experience: 85% of executives reporting an improved user experience indicated specifically that user engagement has increased from GenAI, and nearly the same number reported improved user satisfaction (80%).

"Generative AI is not just a technological innovation; it's a strategic differentiator," said Oliver Parker, VP, Global Generative AI Go-To-Market, Google Cloud. "Our research shows that early adopters of GenAI are reaping significant rewards, from increased revenue, to better customer service, to improved productivity. Organizations investing in GenAI today are the ones that will be best positioned to succeed in the coming decade."

C-Suite Champions Key to Closing GenAI Adoption Gap

The speed by which organizations move from piloting GenAI to full-scale production is a strong indicator of its success, according to the research. Of the executives surveyed that are currently leveraging GenAI in production, 84% say their organizations were able to move from pilot to production in under six months.

However, a significant adoption gap remains: 39% of enterprises overall have still yet to implement the technology in production, with 21% actively testing, 12% evaluating use-cases, and 5% not started. This lag is particularly pronounced in regulated industries like financial services and manufacturing. In the EMEA region, organizations are significantly less likely to have been leveraging GenAI in production for more than one year.

C-Suite champions are key to GenAI success as 91% of respondents with robust C-level support of GenAI at their organization also report increased revenue estimates of 6% or more.

Additionally, "GenAI Leaders" — organizations that extensively utilize and invest in generative AI — exhibit key characteristics that set them apart:

Strategic alignment: 76% of leaders effectively aligned their AI strategies with broader business goals, compared to the global average of 69%.

Dedicated teams: 54% of leaders invested in dedicated generative AI teams, 13% more than their counterparts.

Significant investment: A staggering 86% of leaders plan to allocate at least half of their future AI budgets to generative AI, a stark contrast to the 67% average.

"Our data underscores the importance of executive-level support and strategic alignment for maximizing the potential of generative AI," said Carrie Tharp, VP, Strategic Industries, Google Cloud. "By connecting financial business drivers with technology drivers, organizations can ensure that AI strategies are not just innovative but also tightly intertwined with core business goals. This strategic alignment is the key to escaping the dreaded 'pilot purgatory,' and accelerating towards tangible business impact, leveraging AI to transform operations, enhance customer experiences, and unlock new avenues for growth."

An Emerging GenAI Reinvestment Cycle: Technology, Talent and Data

The early success of GenAI is sparking a reinvestment cycle that's driving further innovation and growth. Nearly half of respondents surveyed (49%) plan to reinvest the gains from GenAI to further improve operating profit margins. Specifically, the topic three areas for investment are:

Technology: 47% plan to invest in aligning business and technology to support change management for user adoption of AI.

Talent: 46% plan to invest in upskilling their workforce and attracting new talent with AI expertise.

Data quality: 43% said they plan to invest in data quality and knowledge management to ensure their GenAI applications are built on a solid foundation of accurate and reliable data

"The most successful organizations aren't just implementing GenAI. They're fostering a culture of innovation through experimentation," Parker added. "By reinvesting early gains in technology, talent, and data, these companies are building a sustainable AI ecosystem, creating a flywheel of innovation that will continue to drive growth and competitive advantage in the years to come."

Methodology: The report is based on a survey of 2,508 senior leaders of global enterprises ($10M+ revenue), conducted by Google Cloud and National Research Group from February 23-April 5. The survey respondents represent organizations from North America, Latin America, EMEA, and APAC, and across key industries including Financial Services, Manufacturing & Automotive, Retail & Consumer Packaged Goods, Telecommunications, Healthcare & Life Sciences, and Media & Entertainment.

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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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...