With about 1,600 employees, the company develops AI-powered robotic process automation (RPA) solutions, and is currently valued at over $7.3 billion.
“The pressure and opportunity to capitalise AI to create a competitive advantage is very intense,” Ankur Kothari, the company’s co-founder and chief operating officer (COO), said in a recent interview. This adoption is picking up pace despite GenAI’s technical complexities, data security challenges, hallucinations, and regulatory, ethical concerns.
Given that enterprise workflows are rarely confined to a single application with employees, on average, spending only 15% of their time in their primary application, and 35-50% in eight to 18 other different applications they use to get work done, the “fragmented environment has created a demand for integrated AI solutions that can automate end-to-end workflows”. Kothari explained how “automating even 30% of incoming tickets (customer queries or complaints) can deliver immediate and transformative impact in customer service within weeks”.
Adoption of GenAI is picking up in India since large service providers such as TCS and Infosys are integrating AI into solutions for their global clients, and global capability centres (GCCs), serving as offshore hubs for international companies, “are mandated to create enterprise-wide AI impacts”, according to Kothari.
Organisations are using AI to enhance customer interactions, boost productivity, and reimagine workflows, creating value that extends across departments. They are focusing on specific, outcome-driven applications such as mortgage processing or supply chain optimisation. This shift from proofs-of-concept (POCs) to production underscores AI’s growing role in delivering tangible business outcomes, according to Kothari.
Adi Kuruganti, the company’s chief product officer, added that “customers are now being very specific like wanting to solve a warehousing scenario, or a mortgage loan application scenario”. He added that companies around the world and in India want AI agents to orchestrate workflows across platforms and processes. Autonomous AI agents, or the so-called ‘Agentic AI’ systems, refer to AI models capable of autonomous decision-making and action to achieve specific goals without human intervention.
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“Traditionally, automation has been more about FTE (Full Time Equivalent is the total work hours put in by all employees compared with that of a single full-time worker) and cost cutting, but AI agents and automation are helping enhance customer experience and generate more revenue,” Kuruganti explained.
He cited how Automation Anywhere helped an unnamed bank use AI agents to reduce the time for automotive loan pre-approvals from eight hours to just 45 minutes. This not only improved customer satisfaction, but also enabled a 40% increase in loan processing capacity, giving the bank a competitive edge. Similarly, an unnamed “large manufacturer that ships the labels for a big phone that is using AI agents to reduce the time it takes for a service representative to respond to tickets or claims from customers — from 15 minutes to just two minutes”.
But if RPA bots are getting replaced by AI agents or if AI agents are closing the next automation gap, “this will lead to an unmanageable number of AI agents with overlapping functionalities, poor governance, and high run and maintenance costs,” Bernhard Schaffrik, principal analyst at Forrester, cautioned in a recent blog.
Kothari and Kuruganti countered that RPA is evolving into agentic process automation (APA), which combines traditional RPA with advanced AI technologies including large language models (LLMs), enterprise copilots, and document processing.
For instance, automating tax processes now involves feeding tax codes into an AI model, enabling faster, more accurate computations without requiring extensive manual input from tax professionals. This evolution, explained both the executives, reflects a broader trend of integrating complementary technologies.
Ideally AI should be used by everyone. If you look at our enterprise co-pilot, it allows everyone to work with an AI agent, helping them become a 10x professional: Adi Kuruganti
Bots, application programming interfaces (APIs), document processors, and conversational AI work in tandem to handle complex processes, reducing automation timelines and unlocking new capabilities, they explained. These integrated systems can automate 40–60% of workflows, delivering up to 10x value compared to traditional methods.
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Automation Anywhere, which competes with companies including UiPath, Microsoft, Datamatics, SAP, SS&C Blue Prism, IBM, Salesforce, and Infosys (EdgeVerve), recently partnered with PwC India to deliver AI-powered automation solutions using GenAI. Focusing on sectors like finance, retail, and healthcare, the alliance aims to enhance efficiency, reduce costs, and improve customer experience, driving business growth and excellence.
That said, traditional AI and GenAI are “complementary”, and “AI’s effectiveness lies in its ability to combine traditional and generative capabilities”, both executives asserted. While traditional AI excels in tasks like invoice processing, and delivers reliable accuracy for structured processes, GenAI is ideal for handling complex, unstructured data such as contracts or logistics documents, according to Kuruganti. “Ideally AI should be used by everyone. If you look at our enterprise co-pilot, it allows everyone to work with an AI agent, helping them become a 10x professional,” he added.
Further, measuring the impact of AI, GenAI requires a focus on top-line growth, efficiency gains, governance standards, and experience transformation, according to Kuruganti. Kothari corroborated that as organisations scale their GenAI efforts, they need to establish “governance councils” to ensure data privacy, compliance, and ethical deployment, fostering trust and enabling faster adoption. The reason: effective governance includes defining clear protocols for data access, determining when human oversight is necessary, and aligning AI initiatives with overarching business objectives.
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Another critical aspect of AI adoption is the impact on the workforce. Historically, advancements like the internet and automation have reshaped jobs rather than eliminated them, and AI is following a similar trajectory. Employees are transitioning from manual roles to managing AI systems or becoming citizen developers—non-technical users who create AI-driven solutions, according to Kuruganti. For example, customer service agents now handle fewer but more complex cases, requiring advanced skills to deliver personalized experiences.
Hence, reskilling and upskilling are integral to this shift. AI tools, such as ChatGPT, have lowered the barrier for non-experts to engage with AI, enabling a broader range of employees to leverage these technologies. Organisations are investing in programs to help their workforce adapt, ensuring they can harness AI’s potential while focusing on higher-value tasks, Kothari said.
India, he added, stands out as a key player in this AI-driven transformation. Just as China became a leader in manufacturing during technological shifts, India has the opportunity to establish itself as a leader in AI and automation, he explained. “With its robust IT ecosystem and talent pool, the country is well-positioned to drive global innovation in this space,” he said.
Kothari concluded, “AI’s potential to drive competitive advantage is now at the forefront of CXO agendas. By integrating AI into end-to-end workflows, fostering responsible governance, and focusing on transformative outcomes, organisations are redefining what’s possible in today’s business landscape. The journey is still in its early stages, but the impact is already profound.”
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