The Rise of AI Agents: Transforming Enterprises and Shaping the Future

The Rise of AI Agents

* AI Generated Content

The future of AI lies in its ability to learn, adapt, and make decisions in real-time, enabling systems to perform tasks once thought impossible

– Yann LeCun,
“Godfather of AI”, VP & Chief AI Scientist – Meta

What are AI Agents and Why They Matter?

Generative AI has entered a transformative phase, marked by the emergence of AI agents. AI agents are LLM-powered computer programs that can perceive their environment, make decisions, and take actions to achieve specific goals. They are designed to operate autonomously, meaning they can function without constant human intervention. This advancement marks a crucial shift from basic automation to comprehensive problem-solving capabilities. AI agents now function as collaborative partners, working alongside humans to solve complex problems.

The evolution from conventional AI assistants to autonomous agents represents a fundamental reimagining of human-machine interaction. While early chatbots could only respond to queries, today’s agentic systems can understand context, maintain ongoing dialogues, and take action across multiple steps to achieve specific goals. Essentially, this integration of reasoning capabilities, logical processing, and the ability to access external information – all connected to a Generative AI model- represents the concept of an agent, or a program that goes beyond the standalone capabilities of a Generative AI model. This leap forward has opened new possibilities for how organizations can leverage AI technology.

The transformation is already reshaping the business landscape– real-world adoption rates tell a compelling story: approximately 51% of companies have already deployed AI agents in production environments, with mid-sized organizations (100-2,000 employees) leading the charge at 63%[1]. The momentum is building rapidly, as 78% of organizations are actively developing agents for future deployment[1].

Characteristics of AI Agents

Behind the widespread hype around AI agents lie distinct characteristics that make them particularly powerful for enterprise applications. AI agents have characteristics that define their ability to operate intelligently and autonomously within an environment.

Understanding the AI Agent Framework

These characteristics are implemented through a structured framework that enables AI agents to function cohesively. The below image represents a potential structure of an LLM-based agent (just one of many possible configurations).

Image Source: The Rise and Potential of Large Language Model Based Agents: A Survey

It consists of three core components: the brain, perception, and action.

In essence, an AI agent extends beyond the basic question-answering capabilities of a traditional LLM. It processes feedback, retains memory, plans future actions, and collaborates with various tools to make informed decisions.

Benefits of AI Agents

With these foundational capabilities in mind, agentic AI offers a range of transformative benefits that can drive substantial improvements across various business functions:

AI Agents at Work: Industry Applications

Let’s explore some scenarios that illustrate how AI agents could enhance business operations across different industries.

Retail Industry Use Case

Another use case involves a B2B logistics scenario where goods need to be transported between locations. Currently, customers contact logistics providers, and these providers need to carry out an extensive verification process. This includes checking equipment availability, vehicle requirements, expertise needed at both loading and unloading points, etc. The process involves numerous back-and-forth communications with customer service for clarifications and corrections. AI agents can streamline this entire process. When a sufficient amount of good quality internal data is readily available, these agents can automatically perform necessary checks and route decisions to appropriate stakeholders in alignment with shipment requirements. This significantly reduces the time and complexity involved in logistics planning.

Beyond these examples, AI agents can be applied across any business offering, providing the ability to scale operations without additional human resources. Human employees remain the most valuable assets in an organization, particularly considering their cost and domain expertise. By automating routine tasks and supporting decision-making, AI agents help businesses optimize their existing resources while driving growth and innovation.

Challenges

AI agents offer significant benefits, but organizations must address key challenges when deploying them. Data privacy concerns require ensuring robust security measures due to the large volumes of data involved. Ethical challenges, such as potential bias or inaccuracy in AI models, necessitate guardrails. Additionally, the technical complexities of implementing AI agents demand specialized expertise, and the intensive computational requirements for training and deployment can lead to high infrastructure costs. These factors must be carefully considered to ensure successful AI agent integration.

Conclusion

As AI agents continue to evolve, they’re reshaping how businesses operate, make decisions, and interact with customers. While challenges around data privacy and ethical considerations remain, the potential benefits of increased efficiency, improved decision-making, and scalable operations make AI agents a compelling solution for forward-thinking organizations.

Industry leaders echo this sentiment:

We are at a pivotal moment where the true potential of AI is reaching an inflection point. This is the turning point where AI will finally deliver on the promises we’ve anticipated for years. In the future, our interactions will predominantly be with these AI agent

– Sathish Kumar EV,
Director – Product Management, EdgeVerve

Want to learn more about implementing AI agents in your organization?

References

AI-Powered Ecosystem Integrations, Your Path to Next Generation Supply Chain

As companies navigate the complexities of B2B integrations and the unprecedented strain on supply chains due to global conflicts, environmental crises, and other unforeseen circumstances, the need for a resilient, collaborative B2B ecosystem has never been stronger.

This is where GenAI stands out as a transformative force, redefining how supply chains operate. Over the next decade, a key transformation in supply chains will be the integration of GenAI across functions as a dynamic tool that adapts in real-time to B2B market disruptions. This paves the way for a more resilient and responsive supply chain.

How can enterprises make the shift to an autonomous supply chain? How can they achieve supply chain resilience and agility? Deep dive into the insights shared by industry leaders at EdgeVerve and Microsoft on the future of supply chain integration.

The current state of supply chain integration: Challenges and opportunities

According to a report by Deloitte, 44% of all supply chain executives expect to experience a supply chain shock in the next 24 months due to various external challenges, including inflation, price volatility, and geopolitical instability.

Organizations today thus want to gain a connected and customer-centric view, but many struggle with siloed systems which prevent a unified view that’s critical for decision-making; data fragmentation and integration from diverse sources/formats remains a monumental task, and the adoption of AI and new technologies to transition toward an autonomous supply chain.

So, how can organizations simplify and optimize B2B operations across the supply chain?

The path to a more integrated supply chain with AI

According to a KPMG report, “6 in 10 global organizations plan to invest in digital technology to bolster their supply chain processes, data synthesis, and analysis capabilities.”

Gartner states that the key factors motivating supply chain investments in emerging technologies over the next 5 years include improving supply chain resiliency (35%), enhancing decision-making (35%), and improving process efficiency (31%).

That said, consumer goods companies are adopting digital disruptors to optimize supply chains, shifting from reactive to proactive models to better anticipate consumer demand, improve forecasting accuracy, and overcome challenges like siloed data and disconnected systems.

With the advancements in generative AI, leaders are looking for smart solutions to create a more connected supply chain ecosystem, transforming the supply chain from a cost center to a value creator. AI-powered supply chains are reimagining business operations, enabling enterprises to unlock greater growth, efficiency, and visibility.

Strategies for harnessing AI to build a resilient, collaborative B2B supply chain

With generative AI, we’re redefining how the world works at one of the most significant technological inflection points of our time. GenAI has the ability to empower organizations to adapt to their unique contexts and adopt a goal-based approach. This involves leveraging optimization strategies in highly complex business environments and fostering collaboration within connected ecosystems. By integrating diverse signals from trading partners across networks, businesses can drive greater operational awareness and unlock data for near real-time insights.

– Felice Miller,
Business Strategy Leader for Supply Chain & Operations at Microsoft

Embracing GenAI to unlock the power of autonomous supply chains

Organizations today are looking to shift toward an autonomous supply chain. EY 2024 Supply Chain Survey indicates that 27% of C-suite leaders expect to achieve a mostly autonomous supply chain by 2040, while 39% of supply chain executives believe this could happen by 2030.

An autonomous supply chain harnesses data and analytics to optimize processes and continuously learn, adapt, and proactively respond to disruptions. Embracing GenAI to unlock the power of autonomous supply chains is imperative for organizations as they learn to adapt to changes and orchestrate everything seamlessly. AI in supply chains is thus key for enterprises to gain a competitive edge and explore unlimited possibilities in the future.

For global companies, compliance is a big challenge, with ever-changing regulations across hundreds of countries. Automating the tracking and management of these regulations would remove a huge burden. These foundational improvements—enhancing accuracy, automating repetitive tasks, and enabling real-time decision-making—are the building blocks of a truly autonomous supply chain.

– Arvind Rao,
CTO at EdgeVerve

Read our complete interactive guide created in partnership with Microsoft and featuring insights from industry leaders Felice Miller, Business Strategy Leader for Supply Chain & Operations at Microsoft, and Arvind Rao, CTO at EdgeVerve, as they explore the below topics:

Unleashing the Power of Agentic AI in Operational Excellence

The PEX Report 2024/25, PEX Network’s flagship industry research, highlights that AI will be the leading investment area for enhancing operational excellence and driving business transformation in the next 12 months.

As explored in our previous blog, Transforming Operational Excellence: The GenAI Revolution , Generative AI (GenAI) is rapidly emerging as a key enabler for operational improvement across industries. From minimizing risks in compliance and reimagining customer interactions to optimizing processes and decision-making, GenAI empowers enterprises to streamline operations and provide actionable insights, freeing them to focus on strategic, innovative initiatives.

While GenAI leads the charge in transforming operations, experts now point to the rise of agentic AI as the next major evolution in the AI landscape.

The rise of agentic AI and its impact on OPEX

Agentic AI, which acts autonomously and proactively adapts to users’ needs, represents a monumental shift in AI’s role within organizations. Unlike traditional GenAI, which primarily responds to prompts, agentic AI anticipates needs, plans ahead, and becomes an active partner in decision-making. This shift allows AI agents to directly drive business outcomes by adding contextual understanding and memory to their responses.

Sathish EV, Director of Product Management at EdgeVerve, explains, “AI agents bring context to decision-making. Unlike isolated question-and-answer systems, human decision-making is always informed by context. Similarly, AI agents not only provide answers but also offer context and memory, allowing them to build on previous interactions. This memory enables AI to associate current decisions with past ones, making its responses more relevant and connected.”

Key advantages of agentic AI

How does agentic AI transform operations? Here are the key advantages:

The future of AI-first experiences

Looking ahead, AI agents and digital workers will drive business operations, transforming organizations into intelligent ecosystems. These systems will adapt in real-time to changing conditions, ensuring sustained operational excellence. Over the next five years, human involvement in execution will decrease, while their role in design, innovation, and strategic planning will grow exponentially.

As AI continues to evolve, businesses will shift to AI-first experiences, where AI drives entire processes, from customer interactions to decision-making. In these future scenarios, AI will not only enhance operational efficiency but also facilitate seamless, proactive interactions across organizations.

Looking ahead, we envision AI-first experiences, where AI drives entire processes. This could involve AI interacting directly with customers or internal stakeholders, acting as a gatekeeper between them and the systems or people that can solve their problems. In these scenarios, AI plays a central, proactive role, facilitating interactions across the organization. Both of these advancements – AI as a collaborator and AI-first experiences – are achievable only with AI agents.

– Sathish EV,
Director, Product Management, EdgeVerve

Download our latest report, created in collaboration with PEX Network, to discover:

Transforming Operational Excellence: The GenAI Revolution

The year 2022 marked the emergence of GenAI, and organizations across various industries and of all sizes explored its potential to streamline operations, improve efficiency, and reduce costs. GenAI is no doubt poised to revolutionize business operations with its transformative benefits.

According to the PEX Report 2024/25, AI is set to become the biggest area of investment for firms to achieve operational excellence (OPEX) over the next twelve months. More than half of surveyed professionals indicated that their organizations have already begun discussions around potential AI projects, particularly in operations. While GenAI has the power to enhance operations in finance, customer service, supply chain, and healthcare, it also comes with complex challenges, from cost and budget limitations to security and leakage risks associated with its use and implementation.

How can GenAI transform an enterprise’s journey toward operational excellence?

This blog spotlights the evolution of OPEX, its four key pillars, GenAI success factors and challenges, and the future of OPEX.

Download Our Latest Report

“Generative AI & the Transformation of Operational Excellence,” created in collaboration with PEX Network, to discover how GenAI can revolutionize your path toward operational excellence!

The OPEX landscape: Evolving needs and the role of emerging technologies

OPEX, or operational excellence, is where an enterprise continuously improves efficiency and effectiveness in business processes, streamlines operations, delivers value to customers, driving quality and profitability.

Operational excellence—a cornerstone of business success—has transitioned significantly over the years, evolving from traditional methodologies like Lean Six Sigma and robotic process automation (RPA) to today’s era marked by GenAI-powered copilots. Moreover, several factors have contributed to the evolution of OPEX, from businesses relying on new digital technologies, teamwork, sustainability, and customer satisfaction to meet the ever-changing demands of today’s markets.

As Priyank Mangal, Senior Director – Sales and Solutions – EMEA, EdgeVerve, points out, “There is a huge amount of pressure on operations to be more and more efficient.” What’s more—This amplifies the expectations of CIOs to lead innovation through the integration of emerging technologies like AI.

That said, what constitutes OPEX? And how can GenAI truly transform an enterprise’s OPEX? The answer lies in adopting a platform-based approach that unifies all four pillars of OPEX.

The four pillars of OPEX

According to N. Shashidhar, Vice-President and Global Product Head, EdgeVerve, OPEX essentially consists of four pillars – people, technology, data, and processes as defined below:

Each of the above elements is undergoing a significant transformation, and GenAI has only accelerated these transformations.

Challenges of implementing GenAI in OPEX

According to research from Microsoft and research/ analyst firm IDC, GenAI usage jumped from 55% in 2023 to 75% in 2024 among surveyed organizations. However, as per McKinsey, only 11% of organizations have adopted the technology at scale.

Though GenAI has enormous benefits, the reality of implementation is fraught with challenges. Some challenges include hesitation over the ROI of GenAI investments, the rapidly evolving legal and regulatory environment, bias and fairness implications, and security risks.

How can enterprises overcome these challenges and unlock real value from AI? Organizations thus must prioritize robust security measures, governance, and adaptability in their technology strategies to scale AI efforts beyond pilot projects. Here are a few critical success factors for enabling OPEX with generative AI.

What are the key success factors for enabling GenAI in OPEX?

Embracing the future: Emergence of agentic AI and AI agents

While GenAI has immense possibilities, experts highlight the rise of agentic AI as another seismic shift in the AI revolution, opening new doors for innovation across industries. Agentic AI acts autonomously, proactively adapting to users’ needs and planning. Agentic AI represents the next evolution in AI, going beyond traditional GenAI, which primarily responds to prompts. This capability allows them to be true partners in problem-solving and decision-making, driving significant business outcomes.

To learn more about the rise of agentic AI in operational excellence.

Read our blog

The future will thus see enterprises transcend AI for operational efficiency and ride the wave of change by harnessing the power of generative AI agents, unlocking new avenues for efficiency, productivity, and growth. Driving business impact at such speed and scale would require firms to move beyond experimental and point-based approaches to platform-based approaches.

Hence, enterprise AI platforms or platform-based approaches are likely to play a pivotal role in driving GenAI-powered OPEX transformations and shaping the next generation of AI-first enterprises.

I think we’re at a pivotal moment where the true potential of AI is at an inflection point. Instead of looking at AI as something in the distant future, we are now witnessing it reaching the level we’ve been anticipating for years.

– Sathish EV,
Director, Product Management, EdgeVerve

To dive deeper, download EdgeVerve’s latest report, “Generative AI & the Transformation of Operational Excellence,” created in collaboration with PEX Network, to discover: