Published
Where AI ambition meets operating reality
AI agents are in production. But are they delivering the business results enterprises expected?
Our research with 2,005 enterprise executives shows AI progress isn't yet delivering the expected business outcomes. While enterprises put agents to work, operating model transformation and financial accountability aren't keeping pace.
Agentic operations can change that by redesigning work around people and agents, connecting data and technology, governing autonomy, and creating accountability for value.
Four stats tell the story
Agents are scaling. Value isn't. It's an operating model problem.
79%
report AI agents live in production
49%
of AI initiatives deliver expected value, on average
15%
of AI initiatives fully connect AI value to P&L lines
9%
completed an operating model transformation
Unlock value with agentic operations
Read the reportAI maturity is rising
The share of enterprises rated Advanced or Leaders on Genpact’s AI Maturity Index has risen from 27% to 52%. But Leaders have moved only slightly, from 12% to 17%.
Moving to the next step requires more than deploying AI. Enterprises need to redesign how work gets done and connect AI investment to measurable returns.
People and AI agents are scaling together
Among respondents with agents in live production, 69% report increasing their agent populations over the past year, while 92% have maintained or increased human headcount.
This creates the potential for an autonomy dividend: the enterprise value captured when AI-enabled gains translate into better business performance, not simply greater productivity.
Capturing that value requires leaders to redesign roles for people and agents and put freed capacity to work where it matters most.
What Leaders do differently
Leaders are the top 17% of enterprises in Genpact’s AI Maturity Index. Backed by a CEO-level mandate, they connect AI adoption with how work, technology, governance, and financial accountability operate together, building the foundations for Agentic Operations.
51% of Leaders pursue proactive, enterprise-wide operating model transformation for agentic AI, compared with 17% of other enterprises
51% create new roles specifically for human-agent operations, compared with 22% of other enterprises
87% provide agents with timely read and write access across the systems their workflows require, compared with 54% of other enterprises
47% have governance embedded across the full agent lifecycle, compared with 11% of other enterprises
49% fully integrate AI value metrics with specific P&L lines, compared with 7% of other enterprises
“I am far more involved in AI-driven operating model transformation than I’ve been in any kind of other program. This is one of those transformations that requires leadership to not only set the stage and define an end state of vision, but to go through it themselves and be open about their own learning experiences, their own trials and tribulations. It’s required because of the amount of change and the speed of the change that you’re asking the whole organization to go through.”
Caitlyn Carr
President and CEO, Coca-Cola Bottlers’ Sales and Services
What agentic operations means in practice
Agentic operations brings people, AI agents, processes, data and technology, governance, and value capture together in one operating model. It moves enterprises beyond deploying AI for individual tasks and toward redesigning how work gets done across the enterprise.
See agentic operations at work
Where should enterprises start with agentic AI?
There is no single path to maturity. Our research points to a practical progression, based on where enterprises are today and what Leaders do differently.
- Prove it
Start with one process with significant manual work and clear potential for value. Establish the baseline, define human and agent roles, and assign executive and financial ownership before agents go live. Define the business outcome and address data and integration gaps that could limit results.
- Scale it
Expand across processes. Build reusable data and technology foundations, develop human-agent roles, and evolve governance into controls that work while agents run. Define value metrics and decide where freed capacity will go.
- Run it
Move from function-by-function deployment to enterprise-wide transformation. Align business and technology around a CEO-level mandate, redesign work across functions, govern agents throughout their lifecycle, and connect AI-enabled gains to P&L outcomes and business planning.
There is no single path to maturity. Our research points to a practical progression, based on where enterprises are today and what Leaders do differently.
- Prove it
Start with one process with significant manual work and clear potential for value. Establish the baseline, define human and agent roles, and assign executive and financial ownership before agents go live. Define the business outcome and address data and integration gaps that could limit results.
- Scale it
Expand across processes. Build reusable data and technology foundations, develop human-agent roles, and evolve governance into controls that work while agents run. Define value metrics and decide where freed capacity will go.
- Run it
Move from function-by-function deployment to enterprise-wide transformation. Align business and technology around a CEO-level mandate, redesign work across functions, govern agents throughout their lifecycle, and connect AI-enabled gains to P&L outcomes and business planning.
Put autonomy into practice
Genpact (NYSE: G) is the Agentic Operations company, where frontier AI meets context-rich process intelligence. For decades, we have run mission-critical operations for the world’s leading enterprises across finance, risk, supply chain, banking, insurance, and procurement. Today, with Agentic Operations, agents run the work, and our experts own the outcomes, so clients grow faster, operate leaner, and convert more to cash.
Unlock value with agentic operations
Read the reportFrequently asked questions (FAQ)
Agentic Operations is an operating model that brings together process intelligence, people and AI agents, data and AI, governance and security, and value capture to help turn AI progress into measurable business value.
Autonomy in practice means deliberately deciding where AI agents act, where people decide or validate, and how people and agents work together to produce measurable business outcomes. It does not mean maximizing AI autonomy everywhere.
The autonomy dividend is the enterprise value captured when AI-enabled gains translate into better business performance, not simply greater productivity.
AI deployment is advancing faster than enterprises' ability to capture its value. Genpact's research finds that 79% of respondents report AI agents live in production, yet respondents say that, on average, only 49% of AI initiatives funded in the past 24 months have delivered the business value originally expected, and just 15% fully connect AI value metrics to specific P&L lines. The challenge is increasingly operational: redesigning work, redeploying capacity, establishing ownership, and connecting AI-enabled gains to measurable business outcomes.
Enterprises can capture more value from agentic AI by moving from deploying individual agents to redesigning how work operates across the enterprise. Genpact's research identifies five areas to address together: redesign processes end to end, build roles for human-agent work, create a scalable data and AI foundation, govern agents throughout their lifecycle, and connect AI-enabled gains to accountable P&L outcomes.
Not necessarily. Among respondents with AI agents in live production, 69% report increasing their agent population over the past year, while 92% have maintained or increased human headcount. The bigger opportunity is to redesign the work people do alongside agents and deliberately redeploy AI-enabled capacity toward higher-value work and business outcomes.
Genpact surveyed 2,005 senior enterprise decision makers across 17 industries and 14 primary business functions at companies with annual revenues from $1 billion to $50 billion+. The quantitative research was supplemented by in-depth interviews with enterprise and technology leaders.