UiPath Unveils Key Trends Transforming AI and Automation in India for 2025
UiPath, a leading enterprise
automation and AI software company, 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.
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