c-suite

AI Agents: The next frontier for enterprise decision-makers

insight
May 12, 2025
9 min read

Author

anurag

 
Anurag Sahay

Managing Director & Global Business Unit Head - AI and Data at Nagarro. He is strategically and technically leading the Generative AI segment, minimizing the risk and maximizing its impact for the clients.

Business leaders are juggling market shifts, operational challenges, and competition—all while drowning in data and racing against time. This pressure leads to decision fatigue, reactive leadership, and missed opportunities—a rut that is hard to break. Today’s business landscape requires smarter, fluid problem-solving that goes beyond traditional methods to keep up with the pace of change. 

AI agents are doing exactly that for us by answering it with technology. 

What are AI Agents?
Sense-Think-Respond cycle   

 “AI agents function in a continuous Sense-Think-Respond cycle, constantly gathering data, evaluating their next steps, and executing actions autonomously.” 

 

One of the most anticipated shifts in AI is that AI Agents are slated to become mainstream by 2025. These agents can operate autonomously and make decisions quickly with minimal human intervention. While AI models are known to be dependent on user prompts and commands, these AI agents learn, change, and act autonomously. 

AI agents are taking on more human-centric roles by providing guidance, mentorship, and even therapeutic support, working on less direct input. This increasing sophistication enables a deeper, more intuitive interaction in both personal and professional contexts.

They also integrate vast amounts of user data and continuously refine it to understand preferences and anticipate needs. This advancement, led by AI agents in the enterprise, represents a significant breakthrough for them, boosting productivity, redefining decision-making, and transforming how organizations and individuals interact with technology. 

Why it matters now


Today’s business environment is defined by volatility, operational inefficiencies, and information overload. AI agents offer a way out, cutting through complexity with speed and scale. Early adopters report 10x productivity, near-zero error rates, and new revenue streams. AI agents become essential.

Just like machines revolutionized manual labor, AI agents are set to transform knowledge work.

In this race, hesitation is the biggest risk. The most agile companies are already deploying AI agents to scale operations, make sharper decisions and unlock new value. Deloitte predicts that by 2027, 50% of companies using AI will be supported by autonomous agents.   

 


Three types of AI Agents — and where to start 

 
AI agents are swiftly moving out of research labs and into real-world business operations, changing how the industry works. Leaders are fast waking up to the game-changing potential of AI agents- Gartner, for example, has declared “agentic AI” (autonomous AI agents) to be the top strategic tech trend for 2025, describing them as a “goal-driven digital workforce that autonomously makes plans and takes actions.” McKinsey also estimates that harnessing such AI could unlock trillions of dollars in value; their research pegs the long-term AI opportunity at $4.4 trillion in annual productivity gains for businesses. 

How are AI agents changing industries today? AI agents can be divided into three categories: LLM chain agents, LLM workflow agents, and autonomous agents, each with its own real-world applications.   

 

 

Types of AI Agents

LLM Chain-Based Agents

Streamlining knowledge tasks

LLM chain-based agents are task specialists, LLMs that act like smart assistants, following a clear, step-by-step playbook to get things done. They use large language models to go through a specific sequence or a chain of actions that help them understand context, pull the right information, and deliver useful results, all in a repeatable way. Give them a goal, and they methodically work through the steps to achieve it for you.

More and more businesses are bringing these agents on board to speed up tasks like answering customer questions, summarizing reports, or even creating content to increase productivity without overcomplicating the process. 

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A breakup of potentials:

customer-support
Customer support and FAQ automation
AI agents like Airi at Jaja Finance handle routine customer queries by simply retrieving answers from internal databases. The result is a 90% reduction in response times and 30–40% of the support team’s time available for complex questions. By answering repetitive questions in seconds, these agents increase service quality and allow human teams to focus on important things.
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Internal knowledge assistants
At Morgan Stanley, an AI-powered assistant gives advisors instant access to about 100,000 research reports. What once took hours of search now takes minutes, helping advisors respond to their clients faster and with sharper insights. It’s a research analyst at their fingertips. 
Note-icon
Document analysis and reporting
Chain-based agents speed up contract review and reporting in legal and finance teams. For example, an e-commerce company’s legal team reduced contract review time by 20–30% using AI tools that flag key clauses and risks. Similar agents help audit teams summarize reports, saving hours of manual work and speeding up decision-making. 

What is the next step for the leaders? 

Start small, act fast. Chain-based agents are an easy way to get started with AI, quick to deploy and with immediate impact. Focus on high-volume, recurring tasks such as customer support, reporting, or internal research. Achieve initial success, build trust and then scale to broader use cases. The advantage lies with the businesses that act early and learn from practice. Use these agents to integrate AI into your processes today. 

LLM Workflow-Based Agents

Complex processes

Workflow-based agents are smart coordinators who ensure that the entire operation runs smoothly. These agents don’t just answer a question and then stop; they connect the dots across multiple steps in a business process. They integrate seamlessly with enterprise systems like CRM, ERP, or ticketing platforms, triggering actions and pulling humans into the loop for approvals when it counts. These systems are digital conductors that orchestrate everything from customer support escalations to financial operations and supply chain tasks.

The payoff is shorter cycle times, fewer handoffs, and far more consistent results—all leading to greater efficiency across the board.

LLM Workflow-Based Agents

A breakup of potentials

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time machine
Adaptive customer operations
United Airlines uses an AI agent to manage real-time communications during flight disruptions. The agent analyzes weather, crew, and aircraft data to create personalized messages for travelers instantly. What used to require customer teams to dig through systems now happens in seconds, improving service for passengers and freeing up staff to focus on important tasks.
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Back-office process automation
Workflow agents automate complex internal tasks such as onboarding, invoice processing, and IT support. They reduce errors and save time by monitoring themselves and only escalating exceptions. One company even uses an AI “orchestrator” to assign coding tasks to other AI helpers and manage variable workflows with dynamic decisions. The result? Fewer manual errors, faster processes, and teams that can focus on strategic tasks.
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Multi-system tool integration
These agents are the digital glue connecting isolated systems to streamline tasks. Imagine a sales agent who updates the CRM, drafts a quote, books a meeting, and notifies the sales manager—all automatically. Companies like IBM are seeing great success here: their HR onboarding agent reduces time to productivity by completing tasks in parallel, even outside of business hours, so humans only need to handle the exceptions. 

What’s the next step for leaders?

Think bigger and connect the dots. Workflow-based agents go beyond automating tasks — they streamline entire processes in your business. Start by targeting workflows with too many manual steps or system silos like onboarding or customer escalations. Use these agents to eliminate delays, reduce errors, and keep your teams focused on high-value tasks. The future of efficiency lies not in isolated automation but in orchestration, and leaders who build these intelligent workflows now will outperform the competition tomorrow.

Autonomous Agents

Toward a goal-driven digital workforce

Autonomous agents work with minimal supervision, continuously planning, learning and adapting to achieve high-level goals. They combine advanced thinking, tools, and memory to accomplish open-ended tasks without a fixed set of rules — they act with true "agency" in how they get the job done. These are virtual team members working alongside humans on complex, high-impact initiatives. Although still nascent, they are proving their worth in areas such as strategic analysis and creative problem solving, paving the way for a truly purpose-driven digital workforce.

autonomous agents

A breakup of potentials

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applications
Data-driven decision support
Autonomous agents transform analytics-heavy decisions. Slalom's "fleet of analysts" enables real-time pricing in more than 80 countries by processing market data, inventory levels, and competitor movements. The Agents also streamline enterprise architecture reviews and analyze policies and risks to reduce manual workload. The result: faster, data-driven decisions at scale, freeing up experts for higher-value tasks. 
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Adaptive problem-solving
In manufacturing and finance, autonomous agents act as real-time problem solvers. They track data streams such as factory sensors and market signals to identify problems and suggest immediate solutions. From rerouting late deliveries to adjusting investment portfolios, these agents respond dynamically, reduce downtime and risk, and support teams with continuous, data-driven recommendations. 
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Towards fully automated assistants
The next frontier is end-to-end autonomy. Aspiring agents can research, create content, manage campaigns, and optimize tasks with minimal control. Imagine digital employees pursuing goals like audience growth or seamless onboarding. Deloitte predicts that by 2027, half of companies using AI will be using such agents, boosting productivity and transforming knowledge across all industries.

What is the next step for leaders?

Prepare to scale and set your sights higher. Autonomous agents are not just assistants, they become strategic partners capable of achieving complex goals on their own. Start testing these agents in areas that benefit from continuous decision-making, such as dynamic pricing or supply chain management. The more they learn and improve, the more they can be extended to more ambitious, cross-functional goals. The organizations that embrace autonomous agents early will gain a key advantage: a self-improving digital workforce that works around the clock, unleashing agility and growth at scale.

AI agents scale work, reduce costs, and promote growth. Those who come first have an advantage — those who come too late risk being left behind. The future is AI-powered teams working 24/7 to accelerate business outcomes.

AI Agent effectiveness: from experimentation to enterprise value

As AI continues to transform IT operations, organizations need to move from experimentation to accountability and ensure that AI delivers real, scalable value, not just automation. AI agents are indispensable for businesses, but their value must be measured periodically and not assumed. Move beyond theoretical efficiency gains by setting a clear benchmarking framework that evaluates AI performance in real-world scenarios.

IBM Research's IT Bench is one such initiative. It provides structured benchmarks for AI in key areas such as Site Reliability Engineering (SRE), FinOps cost management, and compliance assessment. These benchmarks help organizations understand how well AI automates, optimizes, and improves IT operations.

Take compliance as an example. For an AI agent to be effective, it must:

  • Interpret complex regulations and assess their impact on the business.
  • Translate compliance requirements into automated checks.
  • Monitor systems for risks and compliance with standards.

 

AI can be the game changer for you. Its performance is measured, optimized, and aligned with business objectives. Businesses can measure AI's accuracy and reliability in these critical tasks by applying rigorous performance metrics. "AI-driven automation is projected to contribute an additional $15.7 trillion to the global GDP by 2030, underscoring AI's role in economic growth and industry transformation."- Source: converzation.com

 

Shift from experiments to enterprise-grade performance.

Build accountability into your AI agenda with clear, measurable goals.

Treat AI as a core asset — and watch it unlock real business value. 

 


Explore Ginger AI:
Intelligence that empowers 

Ginger AI’s Agentic AI capabilities bring intelligent support to every team, amplifying productivity, accelerating decisions, and unlocking true enterprise efficiency. 

 


Explore NIA:
Bridging the digital abyss

NIA is your enterprise’s AI control center, centralizing LLM apps and data for smarter, faster knowledge retrieval. 
 

 

AI agents don’t replace your people—they elevate them.

By offloading the repetitive and accelerating the strategic, they free your teams to think bigger, move faster, and create more. The human potential behind AI is what truly drives transformation.

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