By Adi Kuruganti, Chief AI and Development Officer, Automation Anywhere
Over the past couple of years, enterprise AI has become a part of everyday business conversations. Organisations have experimented with copilots, automated repetitive work and helped employees complete tasks faster. These early wins have demonstrated AI’s potential, but they have also exposed a limitation. Improving individual productivity doesn’t automatically change how work moves through an organisation.
The next phase of enterprise AI is unfolding at a different level. Instead of focusing on isolated tasks, organisations are beginning to rethink the workflows, decisions and governance that sit behind them. AI is now moving beyond assisting employees to participating in how work gets executed across the enterprise. This shift is what defines an AI-native operating model.
The execution gap
Recent McKinsey research illustrates why this matters. Organisations generating the greatest value from AI are nearly three times more likely than their peers to redesign workflows as part of AI deployment, 55% compared with just 20% of others.
This finding reflects what many enterprises are experiencing today. As organisations move beyond pilots, they are discovering that faster task completion doesn’t necessarily improve business outcomes. Work still moves through the same approvals, disconnected systems and manual handoffs that existed before AI.
Many AI initiatives are now running into this execution gap. The technology has advanced rapidly, but the operating model around it has changed far more slowly. AI is expected to work across processes that were designed long before intelligent systems became part of the enterprise.
The shift to AI-native workflows
The organisations making good progress are redesigning workflows so that intelligent systems become part of how work gets done across the enterprise. That changes the role AI plays inside the enterprise. Business rules, governance and decision-making are built into the workflow itself. AI agents, people and automations operate together across end-to-end processes, each contributing where they create the most value.
In practice, this means AI is no longer confined to assisting a single employee or department. It can coordinate actions across applications, trigger automations, surface relevant business context and involve people whenever judgement or approval is required. The workflow itself becomes more adaptive because intelligence is embedded throughout the process, allowing AI to coordinate decisions and actions across the workflow.
The impact is already visible across customer service, finance, IT operations and supply chain functions. Organisations are embedding intelligent systems into the processes that drive everyday operations, improving consistency, reducing delays and enabling better decisions across the workflow.
Governance enables scale
As AI becomes responsible for increasingly important decisions, governance moves from being a compliance exercise to an operational requirement.
Organisations need confidence that intelligent systems operate within defined business policies, that decisions can be understood and that human oversight is available whenever necessary. Those capabilities allow enterprises to expand AI adoption without compromising accountability.
Enterprise work rarely happens inside a single application. A customer request, a finance approval or an employee onboarding process typically moves across multiple teams, systems and policies before it reaches completion. AI may improve individual steps, but consistent outcomes depend on how those steps work together.
This is why orchestration has become such an important capability. It provides the framework that enables AI agents, enterprise applications, automations and people to operate as part of a coordinated workflow instead of a collection of disconnected tasks.
The next operating model
Leading organisations are now building enterprise AI on foundations that combine orchestration, governance and structured execution. These capabilities make it possible to scale multiple AI initiatives while maintaining consistency across the business.
The next phase of enterprise AI will be defined by how effectively organisations redesign work around intelligent systems. AI-native operating models will help enterprises respond faster, adapt more easily and execute with greater consistency because intelligence becomes embedded in the way the organisation operates.