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GenAI Inspiring Greater Enterprise Adoption of Other AI Types

The rapid rise of creative "right-brain" generative AI (GenAI) has opened the door to greater adoption of the more analytical "left-brain" AI decisioning solutions by global businesses, according to new research from Pegasystems. 

The study, conducted by research firm Savanta, found 95% of respondents felt the increased prevalence of GenAI was directly responsible for their adoption of other types of AI tools, with one third saying it played a major role in their decision. It also showed generative AI has become the predominant way of deploying artificial intelligence (AI) within global enterprises, highlighting the extent to which it has been embraced as both a productivity enhancer and a creative partner for innovation. 

The study explored how business decision makers are implementing AI through the lens of the two sides of the human brain: the more rational, analytical AI decisioning side (left-brain), and the more creative, generative side (right-brain). It found that right-brain generative AI is the most used AI within enterprises today, with two in five respondents (44%) saying they use it mostly for creative or productivity-enhancing tasks such as content creation (61%), curating large stores of information (54%), or in conversational chatbots (51%). Conversely, less than a third of all respondents (30%) predominantly use rational left-brain AI decisioning solutions, such as predictive analytics (57%), or decision management tools (42%). Only 25% of respondents use an equal number of both left and right brain AI tools. Other findings from the research include:

AI spend is on the rise…but so are transformational expectations

92% of respondents say it's likely they will increase their use of AI in the next five years, with 74% saying they are either extremely or very confident AI can add transformational business value to their organization over the next five to 10 years. In the short-term, the vast majority (82%) also expect to be able to directly attribute up to half of their increased profits over the next three years to their use of AI. However, 85% say they spend up to half of their annual IT budget on AI solutions. With 77% admitting to at least some level of waste in their budget spend due to a lack of a proper strategy, it's clear that more care is required around how and why these investments are made.

But … businesses overestimate their AI understanding

The vast majority of respondents (93%) say they have a good understanding of AI and the way it works. Despite this, 80% think AI has been in general business use for less than five years — with just 7% saying it has been in use for 10 years or more, despite mainstream usage dating back to the 1980s. Meanwhile, nearly two-thirds (65%) could not correctly identify an accurate definition of generative AI — despite only 3% admitting they don't know what the technology is. These numbers could explain why nearly two-thirds (61%) say they have had a failed AI implementation.

AI trust and the enterprise — it's complicated

: Half of respondents (47%) are concerned with resting the success of their brand on AI, while 51% also admit they have concerns over AI transparency and bias. 42% are also worried about AI taking their jobs, while 40% are concerned about the potential enslavement of humanity by AI-powered robots. Despite these concerns, a majority (62%) have some level of trust in AI's ability to completely run a department if they felt it would improve overall results. Meanwhile, 41% of respondents prefer to trust a human to build customer relationships, provided they had assistance from AI — compared to just 15% who trust a human more without AI intervention.

Demand for AI skills is growing

Two in ten (20%) think their organization has weak AI skills and experience, while more than one quarter (28%) say this presents a barrier to further AI use within their business. However, 98% find prior AI skills and experience valuable when considering new applicants to join their team, suggesting a growing importance of fostering an AI-literate workforce. Those with hands-on AI experience such as prompt engineering are most in demand (64%), followed by experts in AI theory and academics on the subject (46%). Just 5% are not proactively looking to hire anyone based on their AI skills or experience. 

"Generative AI is the flag-bearer of a new wave of AI enthusiasm, so it's no surprise that so many businesses are using it as a catalyst to not only explore other types of AI but also to drive more creativity and innovation," said Don Schuerman, CTO, Pega. "The next few years are going to see continued growth, not only in the acceleration of artificial intelligence in all its various forms, but also in terms of its adoption. To make the most of this, organizations must ensure they have the requisite skills, expertise, and understanding to make their AI projects a success. In the coming years, we expect to see more and more businesses not just adopting AI productivity tools, but partnering with AI to drive innovations that produce the best possible outcomes for themselves and their customers." 

Methodology: Pega surveyed more than 500 business decision makers worldwide on their views, understanding, and plans for implementing AI solutions, as well as the challenges and opportunities they see in the technology. The results included responses from North America, the United Kingdom, France, Australia, and Germany.

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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 ...

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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 ...

GenAI Inspiring Greater Enterprise Adoption of Other AI Types

The rapid rise of creative "right-brain" generative AI (GenAI) has opened the door to greater adoption of the more analytical "left-brain" AI decisioning solutions by global businesses, according to new research from Pegasystems. 

The study, conducted by research firm Savanta, found 95% of respondents felt the increased prevalence of GenAI was directly responsible for their adoption of other types of AI tools, with one third saying it played a major role in their decision. It also showed generative AI has become the predominant way of deploying artificial intelligence (AI) within global enterprises, highlighting the extent to which it has been embraced as both a productivity enhancer and a creative partner for innovation. 

The study explored how business decision makers are implementing AI through the lens of the two sides of the human brain: the more rational, analytical AI decisioning side (left-brain), and the more creative, generative side (right-brain). It found that right-brain generative AI is the most used AI within enterprises today, with two in five respondents (44%) saying they use it mostly for creative or productivity-enhancing tasks such as content creation (61%), curating large stores of information (54%), or in conversational chatbots (51%). Conversely, less than a third of all respondents (30%) predominantly use rational left-brain AI decisioning solutions, such as predictive analytics (57%), or decision management tools (42%). Only 25% of respondents use an equal number of both left and right brain AI tools. Other findings from the research include:

AI spend is on the rise…but so are transformational expectations

92% of respondents say it's likely they will increase their use of AI in the next five years, with 74% saying they are either extremely or very confident AI can add transformational business value to their organization over the next five to 10 years. In the short-term, the vast majority (82%) also expect to be able to directly attribute up to half of their increased profits over the next three years to their use of AI. However, 85% say they spend up to half of their annual IT budget on AI solutions. With 77% admitting to at least some level of waste in their budget spend due to a lack of a proper strategy, it's clear that more care is required around how and why these investments are made.

But … businesses overestimate their AI understanding

The vast majority of respondents (93%) say they have a good understanding of AI and the way it works. Despite this, 80% think AI has been in general business use for less than five years — with just 7% saying it has been in use for 10 years or more, despite mainstream usage dating back to the 1980s. Meanwhile, nearly two-thirds (65%) could not correctly identify an accurate definition of generative AI — despite only 3% admitting they don't know what the technology is. These numbers could explain why nearly two-thirds (61%) say they have had a failed AI implementation.

AI trust and the enterprise — it's complicated

: Half of respondents (47%) are concerned with resting the success of their brand on AI, while 51% also admit they have concerns over AI transparency and bias. 42% are also worried about AI taking their jobs, while 40% are concerned about the potential enslavement of humanity by AI-powered robots. Despite these concerns, a majority (62%) have some level of trust in AI's ability to completely run a department if they felt it would improve overall results. Meanwhile, 41% of respondents prefer to trust a human to build customer relationships, provided they had assistance from AI — compared to just 15% who trust a human more without AI intervention.

Demand for AI skills is growing

Two in ten (20%) think their organization has weak AI skills and experience, while more than one quarter (28%) say this presents a barrier to further AI use within their business. However, 98% find prior AI skills and experience valuable when considering new applicants to join their team, suggesting a growing importance of fostering an AI-literate workforce. Those with hands-on AI experience such as prompt engineering are most in demand (64%), followed by experts in AI theory and academics on the subject (46%). Just 5% are not proactively looking to hire anyone based on their AI skills or experience. 

"Generative AI is the flag-bearer of a new wave of AI enthusiasm, so it's no surprise that so many businesses are using it as a catalyst to not only explore other types of AI but also to drive more creativity and innovation," said Don Schuerman, CTO, Pega. "The next few years are going to see continued growth, not only in the acceleration of artificial intelligence in all its various forms, but also in terms of its adoption. To make the most of this, organizations must ensure they have the requisite skills, expertise, and understanding to make their AI projects a success. In the coming years, we expect to see more and more businesses not just adopting AI productivity tools, but partnering with AI to drive innovations that produce the best possible outcomes for themselves and their customers." 

Methodology: Pega surveyed more than 500 business decision makers worldwide on their views, understanding, and plans for implementing AI solutions, as well as the challenges and opportunities they see in the technology. The results included responses from North America, the United Kingdom, France, Australia, and Germany.

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 ...