UiPath today announced the top five trends in Artificial Intelligence (AI) and automation poised to transform the work landscape in 2025. In 2024, the seamless integration of AI, automation, and data analytics took center stage, with Generative AI leading the charge. As these technologies continue to converge, 2025 promises to usher in a new era defined by heightened productivity, innovation, and deeper collaboration between humans and machines.
Arun Balasubramanian, Vice President and Managing Director, India and South Asia, UiPath, said, “In 2025, the rise of agentic AI, combined with advanced orchestration, is set to transform the technological landscape. This evolution isn’t about machines taking over tasks; it’s about fostering a collaborative ecosystem where AI amplifies human ingenuity. At UiPath, we envision a future where agentic AI and agentic automation drive the next wave of innovation. To stay competitive and future-ready, organizations must harness this power, underpinned by strong data governance and security frameworks.”
Trend One: The dawn of agentic AI – AI moves from thought to action
2025 will see significant advancements in agentic AI—intelligent, autonomous agents capable of understanding, planning, and executing decisions independently. According to Gartner, agents will be making 15% of all decisions autonomously by 2028. As they operate with advanced reasoning, they will be able to tackle complex, dynamic tasks that were once beyond the reach of software robots. A key priority will be building an orchestrated agentic ecosystem that – integrates agents, humans, and robots into cohesive workflows while ensuring measurable outcomes, control, visibility, and active governance. Orchestration capabilities will become increasingly critical for coordinating tasks, managing workflows, and optimizing operations across diverse enterprise technologies and systems. For instance, UiPath is focused on empowering agents to orchestrate processes across the entire enterprise application ecosystem, instead of targeting singular platforms, to automate at scale.
Orchestration will become key to agentic AI adoption, with increasing expectations for supporting multiple agents—independent or collaborative—and integrating their actions into seamless, well-orchestrated workflows. Agentic automation will also gain momentum in 2025, unlocking new opportunities for end-to-end automation.
Trend Two: The great work reallocation – Humans and machines share the load
As agentic AI transforms the workplace, businesses will begin reimagining roles, workflows, and operational models. This will lead to a dynamic redistribution of tasks between human and virtual workers. The shift will require significant investments in upskilling, retraining, and reassigning employees. Organizations must seek talent with the right combination of strong technological expertise and essential skills such as critical thinking, problem-solving, and creativity. They will lean on process and task mining to identify jobs with the highest portion of AI-doable tasks, while human resources teams will drive workforce transformation efforts.
IT departments will play a pivotal role in building an AI and automation-infused workplace ecosystem that facilitates seamless collaboration between humans and machines. The current automation center of excellence (CoE) will emerge as the new center of agentic automation, skilled at building and managing agents.
Trend Three: Built-in AI soars in 2025, leading to scalable gains
Scalability challenges are fueling interest in ‘built-in GenAI’ as organizations seek to overcome the trough of disillusionment. The demand for built-in AI will reach new heights in 2025 as enterprises prioritize technologies that deliver tangible business value. Leading enterprise software providers are embedding AI across their platforms, turning once-complex tasks into seamless processes.
As AI becomes integrated in more solutions, enterprises reap AI gains without the pain. By 2025, AI adoption will continue to be fueled by agentic AI developments. For example, UiPath Autopilot for Developers has cut automation development time by 75%, while Autopilot for Testers has reduced manual testing by 50%. Such innovations highlight the potential of integrated AI tools to simplify workflows, enhance productivity, and make AI adoption easier for businesses of all sizes. As AI becomes ubiquitous, businesses must ensure they’re fully leveraging built-in tools and train employees to maximize their potential.
Trend Four: New tools like RAG and internal LLMs will help tame the data deluge
The massive influx of data that once posed productivity challenges is now being addressed with innovative solutions like knowledge graphs, retrieval-augmented generation (RAG), and internal LLMs.
These tools are transforming how organizations access, organize, and leverage their data. Knowledge graphs connect fragmented information across diverse data sources to generate actionable insights, while RAG enhances GenAI outputs with real-world data. Additionally, organizations are developing private LLMs to securely harness proprietary data, transforming enterprise data into a strategic advantage within their own firewalls.
Global spending on LLMs is projected to exceed $22 billion in 2030, growing at a CAGR of 48.8% from 2024 onward. These innovations will reshape how businesses interact with data in 2025, empowering teams to “ask the machine” to retrieve and synthesize data —turning data into a strategic advantage.
Trend Five: AI regulation escalation – Organizations race to get AI-regulation ready
The need for robust regulatory frameworks is intensifying amid accelerated AI adoption. Some key obstacles hindering organizations from developing and deploying GenAI tools and applications are concerns around risk, regulation and governance. In 2025, organizations will face heightened scrutiny around AI usage, data origin, and governance practices. As governments and industry bodies around the world ramp up efforts to ensure responsible AI deployment, organizations will need to implement robust data governance and security measures, prioritize transparency in AI algorithms, and establish clear accountability for AI-driven decisions.
