Not long ago, the focus was on what AI agents could do. Today, enterprises are asking a different question: Can AI agents deliver reliable, measurable outcomes once they become part of everyday business operations?
Across India and APJ, organisations are moving beyond proof of concepts and pilots to focus on operationalising AI at scale. The challenge is no longer adoption, but embedding AI into business workflows in a way that consistently delivers business value.
That shift is exposing a new challenge — organisations have shown that AI agents can reason, retrieve information and complete increasingly sophisticated tasks. But moving those capabilities from controlled pilots into live enterprise environments means coping with changing policies, multiple business systems, incomplete information and countless operational exceptions. We’ve proven AI agents can complete work, now we need to ensure they can do so reliably within the enterprise complexity.
These customer conversations mirror a broader shift across the industry. McKinsey’s latest State of AI research found that organisations generating the greatest value from AI are nearly three times more likely to redesign workflows as part of AI deployment than their peers. Specifically, 55% have redesigned workflows, compared with just 20% of other organisations.
The message is clear. Beyond just deploying better AI models, the competitive advantage now lies in redesigning how work gets done across the enterprise.
This shift is also changing how organisations think about AI adoption.
Across APJ, one of the biggest misconceptions is that AI agents are simply smarter chatbots. When in in reality, AI agents are autonomous workers that deliver tangible business outcomes.
Successful organisations are now moving beyond conversational AI and embedding agents into enterprise processes, systems and governance.
Today, orchestration, business rules, and governance are becoming increasingly important as AI systems take on more responsibility.
This reflects how AI is changing the nature of enterprise work. Instead of simply automating repetitive tasks, organisations are now looking to intelligent systems to make decisions, interpret unstructured information, and coordinate activities across multiple applications. As AI takes on greater responsibility, organisations need a framework that ensures these decisions are grounded in enterprise processes, policies and governance rather than operating in isolation.
Enterprise work is rarely linear. A routine employee onboarding request can move through HR, identity management, IT, finance and security before it is complete. Every step depends on another application, approval or policy. AI agents can contribute to many of these individual tasks, but delivering a consistent outcome depends on how all those moving parts work together.
In order to progress, organisations need to understand that orchestration is not separate from AI. It is what enables intelligent systems, people and enterprise applications to work together in a coordinated and predictable way.
KPMG offers a useful illustration of this journey. The Big Four organisation began by automating a recruitment process that relied heavily on manual effort. Then, once KPMG was successful with that, it expanded to more than 150 automations across HR, finance and other business operations. Only then were AI agents introduced into those workflows, building on existing governance, orchestration and business rules rather than replacing them. This progression is important.
AI was not expected to replace operational discipline. It was introduced into workflows that already had clearly defined business rules, governance controls, and orchestration in place. The agents enhanced those processes, helping teams make faster decisions while maintaining consistency, oversight and compliance. It is a reminder that enterprise AI creates the greatest value when intelligence is built on a strong operational foundation rather than deployed in isolation.
As AI systems become more autonomous, trust becomes just as important as capability. The priorities for enterprise AI are also evolving. Organisations are no longer asking only about AI capabilities. They want tangible ROI, end-to-end orchestration, contextual intelligence, governance, compliance and observability before they scale deployments. Enterprise leaders need to understand how agents reach decisions, which systems they access and when human intervention is required. Without that visibility, organisations are unlikely to scale AI beyond isolated use cases.
Governance, therefore, is what gives organisations the confidence to expand AI adoption across increasingly complex business operations, without slowing innovation down.
The next phase of AI adoption across the enterprise is about ensuring AI agents can operate reliably inside complex business environments, where consistency matters as much as intelligence.
The organisations that realise the greatest value from AI over the coming years will be those that combine intelligent reasoning with orchestration, governance and operational resilience to deliver consistent business outcomes. For CIOs and technology leaders, that means looking beyond individual use cases and adopting a platform approach that is simple, secure, scalable and sustainable. Such a foundation can support multiple AI initiatives while ensuring consistency, governance and long-term value across the enterprise.
The industry has spent the last several years proving that AI agents can think.
The next challenge is ensuring they can execute.
(The views expressed are personal)
This article is authored by Sanjay Rohatgi, SVP and GM, APJ, Automation Anywhere.
