Enterprise AI agents: Beyond productivity

AI agents, capable of orchestrating complex workflows to support human workers, help enterprises meet escalating business demands. They combine large language models (LLMs), machine learning, reasoning capabilities and external tool integration to handle complex and nuanced work. They parse context and adapt to changing circumstances, allowing them to help users streamline processes and improve decision-making across an organization.

Traditional automation long handled repetitive, rule-based tasks. And in recent years, generative AI (gen AI) transformed the business ecosystem, delivering AI assistants and other technologies designed to reduce workload and deliver delightful user experiences. AI agents represent a new paradigm—intelligent systems that choose strategies, learn from outcomes and act autonomously with minimal human supervision to achieve specific goals.

Agentic AI stands to fundamentally redefine how businesses operate. Traditionally, enterprise software helped workers organize data or complete tasks. But today, artificial intelligence has the potential to operate autonomously alongside human employees, ushering in a new era of human-machine partnerships. 

This evolution matters because enterprises face increasingly complex challenges: Handling customer inquiries requires understanding intent and emotion—as well as meeting consumer expectations of continuous support. Supply chain decisions demand real-time analysis of variables too complex for a single human to understand. Strategic planning involves synthesizing information from a vast number of disparate sources.

Increasingly, enterprises use AI in HR, procurement, sales, finance and IT departments to increase efficiency and reduce routine manual work. In recent years, IBM deployed agentic AI internally across a wide variety of workflows and functions to 270,000 employees. The initiative resulted in an estimated USD 4.5 billion productivity impact.

The shift toward agentic AI is industry-wide: Gartner predicts 60% of IT operations will incorporate AI agents by 2028. Forrester forecasts the world’s top five human resources management platforms will offer digital employee management capabilities in the coming year.

Beyond simple productivity gains, agentic AI transforms enterprise operating models. When agentic AI is embedded across departments and functions, its ability to make accurate, real-time decisions increases dramatically. With manual work performed by digital workers, employees necessarily shift focus to more value-driven work.

Today, enterprise data originates from a wide range of sources, spanning financial records, digital collaboration software and customer relationship management (CRM) platforms. Unifying this data reduces redundancies across platforms and allows for more accurate business-level forecasting.

In contrast to earlier AI capabilities, modern intelligent agents are capable of autonomy and draw on external tools or APIs to orchestrate workflows from end to end. Multi-agent systems collaborate across systems to complete a goal.

Often confused with AI assistants, AI agents draw on similar technologies but are proactive, rather than reactive, in nature. Where an AI assistant can complete a task in response to a user command or prompt, AI agents plan and execute multi-step processes based on an initial prompt. Some key features of enterprise AI agents include: 

Rather than implementing a fixed sequence of steps, AI agents work toward objectives. For example, telling an agent to resolve a billing discrepancy would result in the agent determining the necessary steps—checking account history, identifying errors and communicating with users. This approach happens without requiring explicit instruction for each action.

Contextual understanding allows agents to interpret information within broader business contexts. This level of comprehension allows agents to work with more ambiguous instructions and incomplete information, much as human employees do. 

Depending on the number of tools and APIs AI agents are granted permissions for, they’re able to not just suggest solutions but execute them. This capability might mean querying a database, generating a report, sending a message or triggering a workflow. This ability makes them genuine productivity multipliers rather than sophisticated chatbots. 

Learning and adaptation allow agents to improve over time. They observe which approaches succeed, refining their strategies in the process. 

Multi-step reasoning allows agents to break complex tasks into manageable components. This approach allows them to develop strategies and execute multi-phase plans. When asked to analyze market opportunities, for instance, an agent might identify relevant data sources, gather information, analyze findings and present recommendations. 

As organizations overcome the early hurdles to implementing enterprise AI agents, they’re seeing the potential for agentic workflows. Some key benefits include: 

Dramatic productivity gains emerge as agents handle work that previously required substantial time and reduced the amount of energy that human workers were able to devote to high-value tasks. Manual processes that once took weeks compress into days or hours. And employees who once spent days on data complication and analysis redirect that time toward strategic thinking and relationship building. 

Implementing enterprise-wide AI systems removes data silos and reduces reliance on fragmented workflows. This approach reduces errors and provides a more holistic view of an organization’s operations, as well as saving time otherwise spent on replicating processes. 

Decision quality improves with agents’ ability to process vast information volumes and identify patterns a human might miss. For example, an AI agent-assessing supplier performance simultaneously considers market trends, quality metrics and contractual terms—synthesizing insights that might take a team of analysis days or weeks to compile manually. 

Agents’ always-on capacity provides capabilities impossible with human-only teams. Agents respond to customer or employee inquiries at any time of day, across time zone and apply policies uniformly. This means seamless global operations without expensive staffing redundancies. 

Scalability without proportional cost increase fundamentally changes a business’ bottom line. AI agents handle volume spikes without extra costs.

Reduced human error in routine operations improves quality and reduces costly mistakes. Agents don’t mis-transcribe numbers or forget steps. In compliance-sensitive industries, this reliability provides substantial risk mitigation.

Among the earliest adopters of agentic AI, the customer service industry has come to embrace the technology: According to the IBM Institute for Business Value, by 2027 executives forecast a shift fully toward autonomous automation, with 71% aiming for touchless customer support inquiries. Modern customer service agents handle inquiries across channels such as email, chat and voice, understanding context and sentiment.

These agents resolve common issues autonomously, from resetting passwords to order tracking and basic troubleshooting. Beyond such reactive support, AI agents proactively identify customers experiencing difficulties and, if empowered, reach out with assistance. They can also analyze support conversations to identify product issues and improvement opportunities, creating a continuous feedback loop between customers and product teams.

IBM integrates AI into both its client and support experiences. An AI-driven tool resolved 70% of inquiries and time to resolution improved by 26%.

AI agents orchestrate complex business processes that span multiple systems and require adaptive decision-making. Unlike traditional workflow automation that follows rigid logic, AI agents optimize processes in real-time.

In procurement, agents can manage entire purchase-to-pay cycles—identifying needs, researching suppliers, generating purchase orders and processing invoices. When the B2B firm Dun & Bradstreet implemented an AI tool to streamline risk evaluation and vendor selection, for instance, it found the time spent on procurement tasks reduced up to 20%.

And for project management, AI agents coordinate team activities and monitor progress. They schedule meetings, prepare agendas and generate process reports highlighting risks and opportunities. 

AI agents democratize sophisticated analytics, making granular data-driven insights accessible to employees without specialized trainings. They can translate natural language questions into more complex queries and analyze the results, presenting findings in intuitive formats.

These agents, given the right tools, also proactively highlight anomalies. They might monitor key performance indicators, alerting stakeholders to concerning trends before they become crisis.

AI agents transform IT operations from reactive troubleshooting to predictive and proactive systems. They monitor infrastructure health, detect anomalies, diagnose issues and often resolve problems.

For cybersecurity, AI agents provide continuous threat detection and response. They analyze network traffic and identify suspicious activities, implementing containment measures when necessary.

Agents also enhance software development operations by automating testing and monitoring application performance—even suggesting code optimizations. In collaboration with human DevOps teams, they handle routine tasks while flagging issues requiring human expertise. 

In manufacturing environments, AI agents optimize production planning, quality control and maintenance operations. They proactively analyze demand forecasts and inventory levels along with material availability to keep production lines efficient. Predictive maintenance agents analyze equipment performance data to forecast failures before they occur, scheduling maintenance during optimal windows to minimize production disruption. This shift from reactive to predictive maintenance dramatically reduces unplanned downtime and extends equipment lifespans.

Supply chain agents can also manage the intricacies of manufacturing, continuously adjusting procurement logistics based on production needs, transportation availability and other variables. These implementations can have impressive results:

In recent years, IBM Consulting® applied AI across its global supply chain, an initiative spanning over 2,000 suppliers and operating in more than 170 countries. These agents identified risk and validated supplier commitments, among other tasks, resulting in over USD 361 in supplier savings over three years. 

AI agents are reshaping talent management across the HR lifecycle, personalizing the employee experience and streamlining multiple administrative processes. Today, IBM’s AskHR tool automates more than 80 HR tasks and handles over 2.1 million employee conversations annually. And recently, SAP introduced agentic AI into its HCM platform, helping employees build career plans and manage payroll, among other functions.

In the recruitment process, agents source candidates and screen applications—reducing time-to-hire while improving candidate quality. For new hires, onboarding technology guides employees through paperwork and files necessary paperwork, personalizing and simplifying the process. And for ongoing employee support agentic HR can be a critical support for human employees, handling time-off requests and policy questions as well as necessary compliance paperwork.

Given AI agents’ ability to process large amounts of numerical data, financial services and operations stand to benefit enormously from the technology. Some agents might match invoices to purchase orders or receipts, while others flag discrepancies in ledgers. In expense management, agents process reimbursements or proactively detect potential fraud.

Financial planning and analysis AI solutions gather data from across an organization to generate forecasts or model various scenarios. For instance, IBM used AI to pinpoint the cost of its IT operations and drive USD 600 million in savings since 2022.

This helps translate complex financial information into accessible insights, supporting better decision-making throughout the organization. And in compliance, agents continuously monitor transactions against regulatory requirements, alerting teams to potential violations before they escalate.

Implementing AI agents successfully requires thoughtful planning and phased execution. Organizations that treat deployment as a technical project often struggle; those organizations that approach it as a business transformation initiative, enabled by technology, have a better chance to thrive. 

Rather than pursuing AI or its own sake, identify specific challenges where agents deliver tangible, real-world value. Prioritize high-volume, time-consuming tasks and identify what success looks like—whether that’s increased employee satisfaction, faster resolution rates or customer adoption. 

AI agent systems, as with other AI models, are only as effective as the data they consume. Evaluate whether the necessary, high-quality data exists and ensure it’s accessible. Prioritize data integrity: resolve quality issues and invest time in cleansing and standardizing datasets.

AI-powered solutions range from pre-built, enterprise-grade applications to customizable platforms and no-code tools. Some organizations build AI agents from scratch. Consider your technical capacity, customization needs and time-to-value requirements and choose a trusted partner. 

The most successful implementations don’t replace humans with agents but create effective partnerships. Define clear handoff protocols and establish feedback mechanisms so agents learn from human interventions. 

Establish guidelines for appropriate agent use. Implement monitoring and ensure that agents behave as intended. Create clear accountability frameworks defining responsibility when agents act autonomously. Prioritize observability and security for agentic AI over the long-term, and implement strict data retention and access control policies. 

Communicate clearly, early and often about how agents will improve the employee experience—as well as which workflows will change. Empower employees with robust training programs so they understand how AI-driven tools work, as well as develop new skills as their roles evolve. 

Establish processes for monitoring agent effectiveness from day one, gathering feedback and continuously refining performance. Measure agent performance against the KPIs established early in the planning process—and share results throughout the organization. 

According to the IBM Institute for Business Value, among organizations that have adopted generative AI, only one-third report implementing the technology into functional processes. To embrace the promise of agentic AI, organizations must integrate the technology into everyday workforce tools and think critically about how human‑machine partnerships can transform operating models.

Agentic AI represents more than an incremental improvement in enterprise productivity—it signals a large-scale transformation in how work gets done. Realizing this potential requires more than purchasing technology. It demands to reimagine processes and to redefine roles, as well as cultivating an organizational culture of innovation. When businesses unify data and deeply integrate agentic AI systems at the enterprise level, they stand to build more resilient and adaptive organizations.