"Customers have bought AI, in the last one-and-a-half years, the way they bought SaaS in silos — from one co-pilot in this department to one agent there. And they realise that's not how work is happening. It cannot happen in silos," the company's co-founder and chief operating officer, Ankur Kothari, told Business Standard.
Most customers, in a rush to demonstrate that they are AI-native, deployed agents in some workflows while using Co-pilot in others, only to realise that this was not delivering the desired results while incurring costs and generating minimal returns on investment.
"So understanding the value of orchestration, that work happens across systems, is critical. It's not about showing something cool, some chatbots here and there and showing it to people. You have to truly change your business model and it is about orchestrating work," he added.
The company, backed by Goldman Sachs and SoftBank Vision Fund, said the focus is more on end-to-end automation — back end to front end — instead of automating certain mission-critical processes, as enterprises seek greater returns on investment from their AI initiatives.
Enterprise adoption of AI has lagged general adoption globally due to a host of reasons, including scattered data, orchestration problems, change management issues, reskilling and even token-maxxing, which has led to inflated AI budgets in a short span of time with minimal returns.
Meta Chief Executive Officer Mark Zuckerberg recently admitted AI agents had not progressed as quickly as he had expected, even as the company laid off thousands of employees in its push to improve efficiency, productivity and reduce costs. Meta is projected to spend as much as $145 billion on AI infrastructure this year, a significant portion of Big Tech's more than $700 billion outlay on the technology.
Automation Anywhere's platform, Kothari said, can auto-resolve 80 per cent of IT service management tickets, which can bring down costs by 50 per cent. That is why the conversation has shifted towards tangible outcomes from a board perspective. And for that, contextual data, or enterprise-specific data, becomes critical rather than general data.
"A year ago, there were a lot of discussions about chatbots and small use cases here and there. If you spend one entire year using AI and you have 10 good use cases to show and yet it did not impact your P&L, it is a long time where there are companies who have moved faster," he added.
Automation Anywhere, whose customers include KPMG, Cargill and Petrobras, also said usage of its agentic platform has been doubling sequentially and many enterprises are now looking to create an autonomous enterprise, especially in areas such as FinOps, NetOps, security, finance, HR and customer support.
"The thought process is how do I make a few departments autonomous where 40-60 per cent of processes are automated. That is a big mindset shift. For the first time, so many customers are looking to change the way businesses operate. So suddenly, from an IT conversation, this is now a boardroom conversation, which changes the dynamics of our business," said Kothari.
 "Automation Anywhere | Image: Company Website")