The future of IT automation is here, and it’s powered by AI.
In 2025, artificial intelligence (AI) will be the driving force behind the next generation of IT automation. What once seemed like a distant possibility is now becoming a reality as AI enables systems to learn, analyze, and act autonomously. This shift allows organizations to automate processes more deeply, enabling real-time decision-making and enhanced adaptability.
As AI continues to progress this year, its integration into IT automation will reshape how organizations operate, opening up new opportunities for growth and innovation.
Gartner's Top Technology Trend for 2025: Agentic AI
Gartner identified Agentic AI as the leading technology trend for 2025, highlighting its transformative potential to provide systems with greater autonomy and decision-making capabilities. This breakthrough trend, along with nine other innovative technologies, is set to redefine traditional business models by driving new efficiencies, empowering smarter decision-making, and opening the door to new ways of working. These trends collectively reflect the growing convergence of advanced AI, automation, and human-machine collaboration, reshaping industries worldwide.
Figure 1: 2025 Top 10 Strategic Technology Trends by Gartner
What is Agentic AI?
Agentic AI is a type of artificial intelligence that can act autonomously and make decisions based on its environment, goals, and learned experiences without constant human interaction. Unlike traditional AI, which typically follows predefined rules or requires human input for decision-making, Agentic AI can take independent actions, adapt to changing circumstances, and pursue its objectives with minimal guidance. This capability allows it to perform complex tasks and solve problems in dynamic environments, making it highly valuable for applications in robotics, automation, and decision support systems.
What is the difference between Agentic AI and Workflow Automation?
Agentic AI is pushing the boundaries of automation by introducing systems capable of autonomous decision-making and adaptability. As organizations seek more advanced solutions to optimize operations, this shift naturally influences the broader landscape of IT automation, where routine tasks and complex processes are being automated to drive greater efficiency and smarter outcomes. In this context, understanding the distinctions between AI agents and traditional workflow automation becomes crucial for organizations aiming to harness the full potential of these emerging technologies.
Figure 2: Agentic AI vs Workflow Automation
Workflow automation operates based on predefined rules and conditions, automating repetitive tasks with minimal need for decision-making. For example, consider resource provisioning using workflow automation. The process usually begins with a user submission, moves to an approval phase, and ends with the deployment of resources. Once the user requirements are defined, the workflow operates autonomously, executing each step without human intervention beyond the initial submission and approval, delivering a streamlined and efficient process.
However, workflow automation is limited when handling situations outside the established rules. So, when unexpected variables or complex scenarios arise—such as conflicting resource requirements, incomplete submissions, or unforeseen errors—the system may stall or require manual intervention to resolve the issue. This reliance on predefined rules means workflow automation lacks the flexibility to adapt or make decisions in real time, which can hinder efficiency in dynamic or unpredictable environments.
Agentic AI, on the other hand, can make independent, intelligent decisions based on data, context, and past experiences. They are more flexible and able to adapt to complex and dynamic situations, addressing challenges that workflow automation cannot handle.
In the previous example, where there are conflicting resource requirements, an AI agent can analyze the situation in real time, prioritize tasks based on urgency or organizational goals, and resolve the conflict autonomously. Similarly, if a user submission is incomplete, an AI agent can identify the missing information, request clarification from the user, or make educated assumptions based on historical data to keep the process moving. This ability to think and act beyond rigid rules makes AI agents invaluable for managing unpredictable scenarios while maintaining efficiency and accuracy.
Other Technology Trends from Gartner
Aside from Agentic AI, other technology trends in 2025 demonstrate the expanding role of AI across various domains. AI Governance Platforms are becoming crucial to ensure ethical AI practices, offering tools to manage bias, compliance, and accountability. At the same time, disinformation security is emerging to combat the spread of AI-generated misinformation by verifying content authenticity and protecting against impersonation.
In computing, quantum systems are pushing boundaries, solving complex problems previously thought unsolvable. Hybrid computing bridges classical and quantum technologies, providing businesses with scalable, future-ready solutions. Sustainability is also in focus, with energy-efficient computing driving innovations to balance performance with environmental responsibility.
Additional breakthroughs include spatial computing, which merges physical and digital environments through AI and augmented reality, enabling immersive applications. Polyfunctional robots are transforming industries with multitasking capabilities, while brain-machine interfaces offer new opportunities for personalized human-computer interactions. These technologies collectively reflect the dynamic, AI-powered future of innovation.
Forrester's Prediction for 2025: The Reality of GenAI Adoption
Despite the excitement surrounding groundbreaking technologies, Forrester predicts that less than 1% of core business processes will be orchestrated by generative AI (GenAI) in 2025. While enthusiasm for GenAI remains high, the reality of implementation presents significant hurdles. Organizations continue to rely heavily on robot process automation (RPA) and other traditional automation tools to orchestrate critical processes, as the operational maturity of GenAI is still in its infancy.
AI’s potential is undeniable, but its journey to widespread adoption is hindered by persistent challenges. Questions about autonomy, risk, and efficiency dominate the conversation: What level of AI-driven decision-making is safe without compromising outcomes? When should humans step in to oversee and guide AI systems? Addressing these concerns will be key to balancing leveraging AI’s capabilities and ensuring its responsible use in the years ahead.
IT-Conductor’s Predictions for IT Automation in 2025 and Beyond
At IT-Conductor, our predictions come not just from industry reports but from the real-world challenges and successes we encounter while working with our customers. We’ve seen trends take shape in practice—trends that could redefine the way organizations adopt and use IT automation in 2025 and the coming years. While we may not produce research in the traditional sense, the insights gained from these day-to-day experiences help shape our outlook for the future of IT automation.
Figure 3: IT-Conductor's Predictions for IT Automation in 2025 and Beyond
Here’s what we foresee for 2025 based on what we’ve seen with our customers:
AI and ML Integration
Integrating AI and machine learning (ML) into IT automation revolutionizes how organizations handle data, make decisions, and improve operational efficiency. Organizations can achieve more intelligent, data-driven workflows by combining AI and ML with existing automation tools. AI and ML algorithms enable systems to analyze vast data, predict outcomes, and adapt to evolving scenarios. This integration helps automate complex decision-making, allowing organizations to streamline tasks traditionally requiring human intervention, such as anomaly detection, predictive maintenance, and resource optimization.
As AI advances, the synergy between AI and ML with IT automation tools will foster smarter, self-optimizing systems that learn from past performance and dynamically adjust operations. This evolution will unlock new levels of efficiency, allowing businesses to react to changes faster and more precisely, driving cost savings and innovation across industries.
Hyperautomation
Hyperautomation refers to using advanced technologies—such as AI, RPA, and ML—to automate as many business processes as possible. Beyond simple task automation, hyperautomation focuses on the end-to-end automation of workflows involving sophisticated technologies that work together to deliver highly autonomous systems. Workflow orchestration is a key driver in hyperautomation, facilitating the seamless automation of siloed systems and tools that require complex decision-making processes.
In 2025, we expect to adopt hyperautomation to drive greater operational efficiency and enhance customer experiences. With AI, hyperautomation will reach a level where entire business operations can run with minimal human input, improving the speed and accuracy of both IT and business processes. This widespread automation will allow organizations to free up valuable resources and focus on higher-value tasks, all while maintaining flexibility in adapting to new market conditions.
Cloud-native Automation
As more and more organizations migrate to cloud environments, cloud-native automation is becoming a critical element for effectively managing cloud infrastructure. Cloud-native applications demand a level of adaptability that traditional tools can’t match. Automation tools designed for these environments allow dynamic scaling, faster deployments, and self-healing systems that adjust to real-time conditions.
AI enhances cloud-native automation by providing intelligence for load balancing, resource allocation, and system optimization. This ensures that cloud infrastructure performs at its peak with minimal human intervention. AI-driven cloud-native automation will enable organizations to proactively manage resources, predict failures, and reduce operational overhead while maintaining high reliability and efficiency.
Security Automation
Security automation leverages AI and machine learning to streamline and enhance cybersecurity efforts. With the growing complexity of cyber threats, traditional manual approaches to security are no longer sufficient. AI-powered security automation tools can identify potential threats, respond to incidents in real time, and continuously learn from emerging attack patterns to improve defense mechanisms. From intrusion detection systems to automated incident response protocols, AI enables faster, more accurate security measures that protect sensitive data and infrastructure without requiring human intervention at every stage.
In 2025, security automation, driven by AI and ML, will be a critical element in safeguarding businesses against evolving cyber threats. These tools will provide more proactive defense mechanisms, continuously analyzing network traffic, identifying vulnerabilities, and predicting future attacks. With AI handling much of the security workload, businesses can reduce human error, enhance compliance, and ensure a more resilient cybersecurity posture across their systems and applications.
DevOps and Automation Synergy
DevOps, which focuses on collaboration between development and IT operations teams, is increasingly integrating automation tools to enhance the software development lifecycle (SDLC). Automation in DevOps allows for continuous integration and continuous delivery (CI/CD), streamlining workflows and speeding up the development and deployment processes. AI is key in DevOps automation, providing insights through data analysis and predictions, improving testing, code quality, and monitoring. Machine learning models can identify potential issues early in development, preventing bottlenecks and enabling proactive resolution.
The synergy between DevOps and automation, driven by AI, will continue to evolve in 2025, allowing organizations to deliver software products faster and more agilely. AI-enabled tools will provide deep insights into operational performance, automate testing and monitoring, and accelerate the feedback loop between developers and operations. This collaboration will lead to more frequent and reliable releases, enhancing overall software quality and organizational productivity.
Data Center Operations Automation
With the increased demand for AI computing resources, hyperscalers are building massive data centers to accommodate these workloads. Managing such large-scale infrastructures requires robust automation solutions to handle power/energy management, cooling, capacity planning, and predictive maintenance. This growth drives the next generation of “autonomous data centers,” where AI-powered automation will continually optimize environmental conditions, resource allocation, and uptime.
By 2025, data center operations automation will be essential for ensuring operational efficiency, reducing energy consumption, and maintaining service-level agreements (SLAs). AI and ML-driven monitoring systems will process data from thousands of sensors, identifying trends and anomalies in real time. As a result, organizations can proactively address potential problems before they escalate, minimizing downtime and operating costs. Ultimately, these autonomous data centers will pave the way for more sustainable and resilient IT operations, better supporting the growing need for AI and data-driven services worldwide.
Final Thoughts
2025 is a pivotal year for IT automation, with AI playing a central role in transforming IT and business operations. Gartner's prediction of Agentic AI as a leading trend reflects its potential to revolutionize decision-making and drive greater autonomy across business processes. While Agentic AI offers more dynamic capabilities than traditional workflow automation, limited by predefined rules, the journey toward full-scale adoption remains challenging. Forrester’s forecast highlights the slower-than-expected uptake of GenAI, with less than 1% of core business processes being orchestrated by it, showing that while enthusiasm is high, practical implementation is still in its early stages.
At IT-Conductor, we predict that other critical trends, including hyperautomation, cloud-native automation, AI and ML integration, and security automation, will shape the future of IT operations. As organizations continue to prioritize IT automation in their budgets, these emerging technologies will streamline operations and foster greater flexibility and scalability. By integrating AI, machine learning, and automation tools, organizations can optimize workflows, reduce manual interventions, and improve decision-making processes. This strategic focus on automation will empower organizations to outpace competitors, quickly adjust to market shifts, and maintain operational resilience.
