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Gartner: Everyday AI and Digital Employee Experience Are 2 Years Away from Mainstream Adoption

Everyday AI and digital employee experience (DEX) are projected to reach mainstream adoption in less than two years according to the Gartner, Inc. Hype Cycle for Digital Workplace Applications, 2024.

"Everyday AI promises to remove digital friction, by helping employees write, research, collaborate and ideate," said Matt Cain, Distinguished VP Analyst at Gartner. "It is a core part of DEX, which is a concentrated effort to remove digital friction and improve workforce digital dexterity, which itself is one of the key factors that will drive organizational prosperity through 2030."

2024 has been a critical year for digital workplace application leaders, as the focus on hybrid and remote work dwindles and the need for a strategic concentration on everyday AI rises. Everyday AI is placed on the Peak of Inflated Expectations on the Gartner Hype Cycle for Digital Workplace Applications, 2024.

"Everyday AI technology aims to help employees deliver work with speed, comprehensiveness and confidence," said Adam Preset, VP Analyst at Gartner. "It supports a new way of working, where intelligent software is acting as more of a collaborator than a tool. The digital workplace is now entering the era of everyday AI."

As technology vendors seek ways to improve productivity among workers that go beyond traditional application and feature enhancements, they can look towards everyday AI. This technology not only delivers productivity benefits, but also provides new marketable offerings such as tools to help workers find and synthesize relevant information, answer questions more comprehensively and produce work artifacts more easily.

"Everyday AI will become more sophisticated, moving from services that, for example, can sort and summarize chats and email messages to services that can write a report with minimal guidance," said Preset. "In many ways, everyday AI is the future of workforce productivity."

Increased Emphasis on Organizations to Have a DEX Strategy

Nearly all employees are becoming digital employees as they spend more time working with technology than ever before. Because of this, organizations must have a strategy to measure and improve DEX to attract and retain talent to improve employee engagement and maximize discretionary effort and intent-to-stay.

Business leaders are looking for guidance on how technology can help boost productivity and organizational alignment. DEX emphasizes best practices that boost digital dexterity, attract and retain talent, and help employees deliver against business outcomes.

DEX is in the Trough of Disillusionment on the Hype Cycle, meaning that interest is waning as experiments and implementations fail to deliver. To increase the appeal and relevance around DEX, business leaders should take a holistic approach across IT and non-IT partners to build a meaningful environment that empowers employees to adopt new ways of working.

The Latest

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 ...

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 ...

Gartner: Everyday AI and Digital Employee Experience Are 2 Years Away from Mainstream Adoption

Everyday AI and digital employee experience (DEX) are projected to reach mainstream adoption in less than two years according to the Gartner, Inc. Hype Cycle for Digital Workplace Applications, 2024.

"Everyday AI promises to remove digital friction, by helping employees write, research, collaborate and ideate," said Matt Cain, Distinguished VP Analyst at Gartner. "It is a core part of DEX, which is a concentrated effort to remove digital friction and improve workforce digital dexterity, which itself is one of the key factors that will drive organizational prosperity through 2030."

2024 has been a critical year for digital workplace application leaders, as the focus on hybrid and remote work dwindles and the need for a strategic concentration on everyday AI rises. Everyday AI is placed on the Peak of Inflated Expectations on the Gartner Hype Cycle for Digital Workplace Applications, 2024.

"Everyday AI technology aims to help employees deliver work with speed, comprehensiveness and confidence," said Adam Preset, VP Analyst at Gartner. "It supports a new way of working, where intelligent software is acting as more of a collaborator than a tool. The digital workplace is now entering the era of everyday AI."

As technology vendors seek ways to improve productivity among workers that go beyond traditional application and feature enhancements, they can look towards everyday AI. This technology not only delivers productivity benefits, but also provides new marketable offerings such as tools to help workers find and synthesize relevant information, answer questions more comprehensively and produce work artifacts more easily.

"Everyday AI will become more sophisticated, moving from services that, for example, can sort and summarize chats and email messages to services that can write a report with minimal guidance," said Preset. "In many ways, everyday AI is the future of workforce productivity."

Increased Emphasis on Organizations to Have a DEX Strategy

Nearly all employees are becoming digital employees as they spend more time working with technology than ever before. Because of this, organizations must have a strategy to measure and improve DEX to attract and retain talent to improve employee engagement and maximize discretionary effort and intent-to-stay.

Business leaders are looking for guidance on how technology can help boost productivity and organizational alignment. DEX emphasizes best practices that boost digital dexterity, attract and retain talent, and help employees deliver against business outcomes.

DEX is in the Trough of Disillusionment on the Hype Cycle, meaning that interest is waning as experiments and implementations fail to deliver. To increase the appeal and relevance around DEX, business leaders should take a holistic approach across IT and non-IT partners to build a meaningful environment that empowers employees to adopt new ways of working.

The Latest

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 ...

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 ...