Agentic AI moves enterprises from assistance to autonomous decision-making across workflows

From assistance to autonomy: How agentic AI is redefining enterprises

Artificial intelligence (AI) has long promised to change the way enterprises operate. For years, the focus was on assistants, systems that could surface information, summarize documents, or streamline repetitive tasks. While valuable, these technological assistants were reactive: they waited for human prompts and provided limited support within narrow boundaries. Today, a new chapter is unfolding. Agentic AI, whose systems are capable of autonomous decision-making and multi-step orchestration, represents a significant evolution. These systems don't just assist, they act. They evaluate context, weigh outcomes and autonomously initiate actions, orchestrating complex workflows across functions. They adapt dynamically and collaborate with other agents in ways that are beginning to reshape enterprise operations at large. For leaders, this shift carries both opportunity and responsibility. The potential is immense, but so are the governance, trust and design challenges that come with giving AI systems greater autonomy. Enterprises must be able to monitor and override any actions taken by the agentic AI systems. Shift from assistance to autonomy Traditional AI assistants primarily respond to queries and perform isolated tasks. They are helpful but constrained. Agentic AI pushes further: multiple agents can collaborate, exchange context and manage workflows end-to-end. Imagine a procurement workflow. An assistant can pull vendor data or draft a purchase order. An agentic system, however, can review demand forecasts, evaluate vendor risk, check compliance policies, negotiate terms and finalize transactions. It does this all while coordinating across global business departments, including finance, operations and compliance. This shift from narrow support to autonomous orchestration is the defining leap of the next era of enterprise AI. It is not about replacing humans but about embedding intelligence into the very fabric of organizational workflows. Rethink enterprise workflows The goal of every enterprise department is focused on efficiency, scale and standardization. But agentic AI challenges enterprises to think differently. Instead of designing workflows step by step and inserting automation, organizations now need to completely reimagine and architect intelligent ecosystems for orchestrating processes, adapting to evolving business needs, and enabling seamless collaboration between humans and agents. That requires new thinking. Which decisions should remain human-led, and which can be delegated? How do you ensure agents access the correct data without overstepping boundaries? What happens when agents from finance, HR and supply chain must coordinate autonomously? The design of workflows is no longer about linear handoffs; it is about orchestrated ecosystems. Enterprises that get this right can achieve speed and agility that traditional automation cannot match. Accelerate agentic AI-led transformation with a unified platform In this environment, unified platforms become critical. Without them, enterprises risk a proliferation of disconnected agents working at cross-purposes. A unified approach provides the guardrails with shared knowledge graphs, consistent policy frameworks and a single orchestration layer that ensures interoperability across business functions. This platform-based approach not only reduces complexity but also enables scale. Enterprises don't want dozens of fragmented AI projects that stall in the pilot stages. They want enterprise-grade systems where agents can collaborate securely and consistently across the enterprise. Unified platforms simplify outcome monitoring and strengthen governance -- both critical as systems become increasingly autonomous. Build trust and accountability As AI systems act with greater independence, the stakes rise. An agent who makes flawed decisions in customer service may frustrate a client. An agent that mishandles a compliance process could expose the enterprise to regulatory risk. That's why trust and accountability must be designed into agentic AI from the start. Governance is not an afterthought; it is a foundation. Leaders need clear policies defining the scope of agentic autonomy, transparent logging of decisions, evaluating and monitoring agents and escalation mechanisms when human oversight is required. Equally important is cultural trust. Employees must believe these systems are partners, not threats. This calls for change management, training, and communication that positions agentic AI as augmenting human capability rather than replacing it. Measure business value early One of the most common pitfalls in enterprise AI adoption is the gap between promising pilots and at-scale results. Studies show that a significant percentage of AI projects never make it past experimentation. Agentic AI cannot afford to fall into this trap. Enterprises must measure business value early and continuously. This includes efficiency gains, cost reductions, error avoidance and even intangible benefits like faster decision-making or improved compliance. Success will be defined by automation coverage across processes, reductions in manual intervention and the ability to deliver new services at speed and scale. When designed responsibly, agentic AI can deliver exponential improvements. A procurement cycle reduced from weeks to hours, or a compliance review automated at scale, can fundamentally alter enterprise performance. Preparing for the future The rise of agentic AI does not mean handing over control to machines or codes. Instead, it marks the next phase of enterprise transformation, where humans and agents operate side by side in orchestrated systems. Leaders should start by piloting agentic systems in well-defined domains with clear governance models. From there, scaling across the enterprise requires investment in unified platforms, robust policy frameworks, and a culture that embraces intelligent automation as a partner in value creation. The enterprises that succeed will be those that approach agentic AI not as another tool, but as a strategic shift. Just as ERP and cloud once redefined operations, agentic AI is poised to do the same, reshaping workflows, governance, and the very way decisions are made. Agentic AI is moving the enterprise conversation from assistance to autonomy. That change comes with objective complexity, but also with extraordinary promise. The foundation for success lies in unified platforms that enable enterprises to orchestrate with intelligence, govern with trust, and scale with confidence. The journey is just beginning. And for enterprise leaders, now is the time to lead with vision, responsibility, and ambition. N Shashidhar is VP and Global Platform Head of EdgeVerve AI Next. Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they're always clearly marked. For more information, contact [email protected].

Agents, Robots, and Humans: Building the Future of Work Together

By DebDeep Sengupta The future of work is undergoing a seismic shift, driven by agentic AI. Businesses are now facing the challenge and opportunity of redesigning jobs and workflows to maximize the strengths of both human employees and AI-driven agents. According to LinkedIn data, job skill sets have evolved by 25% since 2015 and are expected to shift by 65% by 2030 globally. Similar trends are emerging across regions including India, where employees and executives see GenAI as a tool for enhancing efficiency, not a job threat. However, this transformation demands a strategic reallocation of responsibilities, prompting organizations to rethink workforce structure and operational models. The changing landscape of work Starting 2025 and beyond, companies must fundamentally restructure how they operate. This means reshaping roles, training employees, and distributing tasks between humans and AI agents. This transition will be led by the C-suite leaders, backed by consultants and operations experts specializing in AI-driven business models and large-scale change management. For enterprises to successfully integrate AI, they must carefully orchestrate how work is divided between humans and machines. Without robust infrastructure, governance, and processes, AI adoption risks becoming fragmented and unsustainable. Likewise, a workplace without clear human roles and structured systems can lead to inefficiency and confusion. The key to a seamless transition to agentic automation lies in building a collaborative AI ecosystem, one where AI agents, RPA robots, and employees work together efficiently while maintaining control, visibility, and accountability. Seamless AI-human collaboration through Orchestration In an enterprise setting, orchestration functions as the central nervous system for automation, ensuring the right AI agents receive the right data, at the right time, to make informed decisions and take meaningful actions. It ensures smooth coordination across business processes, minimizes bottlenecks, and enables automation to scale effectively. By integrating AI agents, human employees, and robots into a unified workflow, orchestration creates a seamless ecosystem where AI handles complex decision-making, robots execute tasks, and humans provide critical oversight. The growing role of AI agents in key industries Agentic AI is poised to reshape industries by automating complex, time-sensitive, and data-driven tasks. Some key areas where this transition is already underway include: Healthcare: AI agents enhance patient care by monitoring information flow and supporting employees in making probabilistic, non-rules-based decisions. Banking: AI agents streamline core banking operations by automating customer data reconciliation, accelerating account opening, and ensuring compliance through real-time monitoring. They analyze transactions for fraud detection, generate regulatory reports, and handle high-volume back-office tasks, improving accuracy, reducing turnaround time, and enabling faster, error-free service delivery. IT and Technology: AI agents streamline development and IT operations by auto-generating code, triaging bugs, and managing CI/CD pipelines. Internally, agents monitor system performance, synthesize user feedback, and assist in product decisions, driving faster innovation and cross-team productivity. Manufacturing: AI agents optimize shop floor operations by monitoring machine performance, predicting maintenance needs, and minimizing downtime through real-time alerts. In supply chain and procurement, agents analyze demand patterns, automate inventory management, and trigger replenishment workflows -- improving efficiency, reducing waste, and ensuring production continuity. Real Estate: AI agents automate property operations by validating invoices, updating SAP systems, and handling tenant communications. They enhance customer experience with personalized updates, predictive query handling, and app-based engagement, resulting in faster response time and more reliable service delivery. These applications are just the beginning. Any industry that relies on complex, labor-intensive and data-driven processes stands to benefit from agentic automation. Leading the transition: What enterprises should do now? Identify the key workforce of the future: Start by identifying roles that will change with automation. Process mining and task mining technologies play a key role in analyzing workflow, identifying inefficiencies, and determining tasks suitable for AI. Go with the best-suited technology: Selecting the right technologies depends on a strong partnership between agents, robots, and humans. In an autonomous business, structural tasks like data entry and transfer are managed by robots, while agents focus on more complex tasks like forecasting, comparison, innovation, and problem solving. Keeping humans in the loop is crucial to ensuring accountability, applying judgment, and aligning automation with business goals. Upskill and reskill your workforce: Businesses need to invest in targeted training programs to help employees manage AI tools and interpret data. Equally important are human capabilities such as creativity, critical thinking, and problem solving. Above all, employees must understand how these changes will boost their productivity and contribute to business goals. Optimize workflow for human-machine collaboration: Businesses need to redefine their processes to maximize both AI and human strengths. This requires synergy across departments, seamless integration, and clearly defined goals. The path forward The role of AI in the workplace is rapidly expanding, and AI agents will play a pivotal role in shaping the future of work. These agents will not only assist employees but also take on decision-making, predictive analysis, and real-time execution of complex tasks. To stay competitive, business leaders must act now. Companies that successfully redesign jobs, upskill employees, and create a well-structured AI-human collaboration -- where agents, robots, and humans work in harmony, will define the next decade of innovation. (The author is Area Vice President for South Asia, and the views expressed in this article are his own)