For the past few years, enterprise AI has been polite. It answered when asked. Suggested when prompted. Waited on the sidelines.
That phase is ending.
In 2026, AI will stop behaving like an assistant and start showing up as a team member — embedded in operations, collaborating with humans and other systems, and increasingly measured on outcomes rather than clever responses. This is not another technology upgrade. It is a shift in how work itself gets done.
From Individual Productivity To Team Performance
Most early enterprise AI focused on individual productivity. “Draft this. Summarise that. Answer faster.” It was useful, but the scope was limited. Enterprise value has never come from individuals working faster in isolation. It comes from teams coordinating better across functions, systems, and time zones to deliver business results. That is where AI is now heading.
The next generation of enterprise AI systems are being built with context, intent, and boundaries. These systems monitor processes, coordinate tasks, escalate exceptions, and act within guardrails without waiting to be prompted.
In practice, they behave less like tools and more like digital colleagues — aware of what needs to happen next.
Consider operations teams where AI monitors incidents, classifies severity, proposes remediation, and keeps stakeholders informed — while humans step in for judgment and accountability. Or finance teams where digital workers handle reconciliations and compliance checks, allowing leaders to focus on insight and decisions.
The unit of value is no longer the AI model. It is the end-to-end workflow, delivering a measurable outcome.
Three Shifts Shaping Agentic AI In 2026
As enterprises move from experimentation to scale, three shifts stand out.
From Copilots To Colleagues: Interaction models will move from prompting to delegating. Leaders will delegate goals — resolve an issue, onboard a vendor, close a case — and expect AI systems to coordinate the steps across systems and teams.
Agent-To-Agent Interaction: AI agents will increasingly interact directly with other enterprise agents. In areas such as procurement, service delivery, and supply chains, digital agents will negotiate terms, manage service levels, and coordinate actions using shared protocols. This will compress cycle times in ways manual coordination cannot.
Agentic Governance And Decision Support: Some organisations are beginning to use AI agents to simulate the downstream impact of major decisions before they are finalised. These systems will not replace executives or boards, but they will help surface risks and test assumptions at speed and scale.
The India Question: ‘But Labour Is Cheaper Here’
In India, this evolution often meets a familiar objection: If labour costs are lower, why invest in digital workers at all?
It sounds practical. It is also incomplete. AI is not competing with wages. It is competing with constraints. Even in low-cost labour markets, enterprises pay a hidden tax on manual execution — delays, rework, handoffs, attrition, and compliance risk. Cheap labour can still produce expensive friction.
More importantly, the real pressure on enterprises today is not cost-per-employee. It is speed, consistency, and the ability to operate at scale without failure.
How fast can you respond to a customer? How quickly can you resolve an outage?
How reliably can you comply, every time, at volume? These challenges are not solved by adding more people — even affordable ones. They are solved by redesigning work so humans and machines operate together.
Waiting for labour cost pressure before investing in capability is a risky strategy. Competitive advantage today comes from latency reduction, resilience, and outcome certainty, not wage arbitrage.
Why India Is Well Positioned
There is an irony here. India’s strength in process-driven work, shared services, and large-scale operations makes it particularly well suited to AI-augmented teams.
Banks managing millions of interactions can use digital workers for first-level triage and routine checks, while human teams focus on complex cases and relationships. Manufacturers can deploy AI agents to monitor supply chain disruptions and escalate risks before humans would notice. Global capability centres can move from execution-heavy roles to outcome ownership.
This is not about replacing people. It is about using skilled talent where it matters most, instead of consuming it on repetitive, low-judgment work. The better question for leaders is no longer whether AI is cheaper than labour, but rather whether any organisation can afford to scale without AI.
Autonomy Requires Trust
As AI becomes more capable, the real bottleneck is no longer intelligence. It is trust.
Enterprises that succeed will embed AI into systems that clearly define roles, policies, approvals, and accountability. People must know when AI can act autonomously and when it must escalate.
This is not about removing humans from the loop. It is about moving humans onto the team — leading, supervising, and owning outcomes. As Indian enterprises and global capability centres take on greater responsibility for global operations, success will depend not on how quickly AI is deployed, but on how reliably it scales without eroding trust — across regulators, customers, partners, and investors.
The Leadership Shift Ahead
What lies ahead is not primarily a technology challenge. It is a leadership one. The organisations most at risk will be those that move slowly, optimise only for labour cost, or remain dependent on vendors for strategic capability. Leaders must stop asking where AI can automate tasks and start asking how work itself should be redesigned when non-human team members are available.
That requires new operating models, new metrics, and new comfort with delegation — even when the delegate is digital.
The Bottom Line
In 2026, enterprises will not succeed because they adopted more AI. They will succeed because they integrated AI into how work actually happens. India built its global advantage on talent at scale. The next advantage will come from embedding digital colleagues into enterprise workflows — enabling human judgment and autonomous systems to collaborate at industrial scale.
AI, in short, needs to trade its tiara for a hard hat — less theatre, more utility. That is the real beginning of the Age of Actionable AI.
Sumeet Mathur is Senior Vice President & Managing Director, ServiceNow India Technology & Business Centre.
Disclaimer: The views expressed in this article are solely those of the author and do not necessarily reflect the opinion of NDTV Profit or its affiliates. Readers are advised to conduct their own research or consult a qualified professional before making any investment or business decisions. NDTV Profit does not guarantee the accuracy, completeness, or reliability of the information presented in this article.
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