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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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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

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 ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...