Skip to main content

IT Leaders Are Leveraging AI Agents to Unlock Autonomous Transformation in 2025

Shayde Christian
Cloudera

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale.

Data backs this perception — according to recent insights from Cloudera, 96% of IT leaders plan to expand their use of AI agents over the next year, with half anticipating significant, organization-wide deployment. This signals a major inflection point: enterprises are no longer asking if they should use AI agents — but how fast they can scale them.

What Sets Agentic AI Apart?

AI agents represent a step-change from the chatbots of the past. Unlike traditional bots, which rely on scripted workflows and fixed inputs, agentic AI systems are empowered to act autonomously. These agents can reason, plan, and act on behalf of users — adapting dynamically to real-world scenarios within the guardrails set by humans in the loop.

Whether model-based, goal-driven, or built across multi-agent ecosystems, agentic AI is built to handle complexity. Unlike higher-code robotic process solutions of the past, it evaluates inputs in real-time, identifies optimal strategies, and executes decisions with minimal human oversight when appropriate. The result: enhanced operational efficiency, lower costs, superior customer experiences, and smarter decision-making at scale.

Real-World Applications Across Industries

Initial implementations of agentic AI are concentrated in IT operations and customer-facing functions — but adoption is rapidly expanding. Enterprises are integrating AI agents into customer support, marketing, and predictive analytics to streamline operations and unlock new value.

However, not all industries utilize agentic AI the exact same way given each sector's unique challenges and AI needs. In the financial services sector, for example, agentic AI is deployed to bolster cybersecurity, enable intelligent advisory services, and ensure data access remains compliant with strict authorization protocols. Meanwhile, manufacturers are turning to AI agents to optimize supply chains, automate complex processes, and enhance quality control. In fact, nearly 50% of manufacturing organizations are actively exploring these applications, according to Cloudera.

Other industries are following suit:

  • Retail is leveraging agentic AI for hyper-personalized shopping experiences.
  • Healthcare is improving patient outcomes and reducing administrative burdens.
  • Telecommunications is harnessing AI agents to deliver smarter, data-driven customer support.

Across sectors, the potential is clear: agentic AI is becoming a cornerstone of intelligent enterprise operations.

Navigating the Roadblocks: Trust, Integration, and Ethics

Despite the momentum, challenges remain. Enterprise leaders cite data privacy, integration complexity, and high implementation costs as top concerns. Integrating AI agents into legacy systems is particularly difficult for large organizations with deeply embedded IT infrastructure. This isn't a plug-and-play technology — it requires thoughtful planning and cross-functional alignment.

Equally important is addressing the ethical dimension of AI. When trained on historical datasets, AI agents can unintentionally replicate — and even amplify — societal biases. The consequences are real: a Yale study recently highlighted how bias in medical AI systems can manifest across every phase of the development lifecycle, from data curation to post-deployment use.

This is a wake-up call for enterprises. Ensuring fair, transparent, and accountable AI systems means prioritizing data diversity, implementing continuous auditing, and embedding ethical governance into every layer of the AI pipeline.

Building the Foundation: Why Agentic AI Demands a Modern Data Architecture

To successfully adopt agentic AI, organizations must first assess their data infrastructure. This means ensuring their architecture supports secure, compliant, and scalable data management. A modern data stack — combined with robust governance protocols — is essential to unleashing the full potential of AI agents. Without reliable infrastructure, AI agents can't access or process the information they need to make accurate decisions. Robust governance ensures data is secure, compliant, and free from bias — building the trust and accountability necessary for responsible AI deployment at scale.

Equally critical is workforce readiness. Organizations must invest in upskilling technical teams to manage, monitor, and optimize AI agents; however, knowledge of the business is still fundamental to implementation success. Starting with small-scale pilots allows businesses to measure performance, understand operational impact, and refine strategies before scaling enterprise-wide.

The Bottom Line

Agentic AI is not a trend — it's the next evolution in enterprise intelligence. AI agents will play an increasingly strategic role as businesses strive to become more agile, customer-centric, and data-driven. The leaders in this next chapter of digital transformation will be those who not only embrace agentic AI — but do so with purpose, precision, and a strong foundation of trust.

Shayde Christian is Chief Data and Analytics Officer at Cloudera

Hot Topics

The Latest

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

Image
Broadcom

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...

IT Leaders Are Leveraging AI Agents to Unlock Autonomous Transformation in 2025

Shayde Christian
Cloudera

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale.

Data backs this perception — according to recent insights from Cloudera, 96% of IT leaders plan to expand their use of AI agents over the next year, with half anticipating significant, organization-wide deployment. This signals a major inflection point: enterprises are no longer asking if they should use AI agents — but how fast they can scale them.

What Sets Agentic AI Apart?

AI agents represent a step-change from the chatbots of the past. Unlike traditional bots, which rely on scripted workflows and fixed inputs, agentic AI systems are empowered to act autonomously. These agents can reason, plan, and act on behalf of users — adapting dynamically to real-world scenarios within the guardrails set by humans in the loop.

Whether model-based, goal-driven, or built across multi-agent ecosystems, agentic AI is built to handle complexity. Unlike higher-code robotic process solutions of the past, it evaluates inputs in real-time, identifies optimal strategies, and executes decisions with minimal human oversight when appropriate. The result: enhanced operational efficiency, lower costs, superior customer experiences, and smarter decision-making at scale.

Real-World Applications Across Industries

Initial implementations of agentic AI are concentrated in IT operations and customer-facing functions — but adoption is rapidly expanding. Enterprises are integrating AI agents into customer support, marketing, and predictive analytics to streamline operations and unlock new value.

However, not all industries utilize agentic AI the exact same way given each sector's unique challenges and AI needs. In the financial services sector, for example, agentic AI is deployed to bolster cybersecurity, enable intelligent advisory services, and ensure data access remains compliant with strict authorization protocols. Meanwhile, manufacturers are turning to AI agents to optimize supply chains, automate complex processes, and enhance quality control. In fact, nearly 50% of manufacturing organizations are actively exploring these applications, according to Cloudera.

Other industries are following suit:

  • Retail is leveraging agentic AI for hyper-personalized shopping experiences.
  • Healthcare is improving patient outcomes and reducing administrative burdens.
  • Telecommunications is harnessing AI agents to deliver smarter, data-driven customer support.

Across sectors, the potential is clear: agentic AI is becoming a cornerstone of intelligent enterprise operations.

Navigating the Roadblocks: Trust, Integration, and Ethics

Despite the momentum, challenges remain. Enterprise leaders cite data privacy, integration complexity, and high implementation costs as top concerns. Integrating AI agents into legacy systems is particularly difficult for large organizations with deeply embedded IT infrastructure. This isn't a plug-and-play technology — it requires thoughtful planning and cross-functional alignment.

Equally important is addressing the ethical dimension of AI. When trained on historical datasets, AI agents can unintentionally replicate — and even amplify — societal biases. The consequences are real: a Yale study recently highlighted how bias in medical AI systems can manifest across every phase of the development lifecycle, from data curation to post-deployment use.

This is a wake-up call for enterprises. Ensuring fair, transparent, and accountable AI systems means prioritizing data diversity, implementing continuous auditing, and embedding ethical governance into every layer of the AI pipeline.

Building the Foundation: Why Agentic AI Demands a Modern Data Architecture

To successfully adopt agentic AI, organizations must first assess their data infrastructure. This means ensuring their architecture supports secure, compliant, and scalable data management. A modern data stack — combined with robust governance protocols — is essential to unleashing the full potential of AI agents. Without reliable infrastructure, AI agents can't access or process the information they need to make accurate decisions. Robust governance ensures data is secure, compliant, and free from bias — building the trust and accountability necessary for responsible AI deployment at scale.

Equally critical is workforce readiness. Organizations must invest in upskilling technical teams to manage, monitor, and optimize AI agents; however, knowledge of the business is still fundamental to implementation success. Starting with small-scale pilots allows businesses to measure performance, understand operational impact, and refine strategies before scaling enterprise-wide.

The Bottom Line

Agentic AI is not a trend — it's the next evolution in enterprise intelligence. AI agents will play an increasingly strategic role as businesses strive to become more agile, customer-centric, and data-driven. The leaders in this next chapter of digital transformation will be those who not only embrace agentic AI — but do so with purpose, precision, and a strong foundation of trust.

Shayde Christian is Chief Data and Analytics Officer at Cloudera

Hot Topics

The Latest

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

Image
Broadcom

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...