According to The Harris Poll, commissioned by Google, 88% of the young leaders surveyed had expressed their desire to use AI for a task that feels overwhelming. Having AI agents at your disposal makes a big difference when you already know that enterprise workflows are unmanageable. 

One big reason for enterprise tasks being overwhelming is that enterprise information lies in silos. When your people want critical information to do their work, it remains inaccessible and makes them struggle to accomplish even a small task, let alone a complex one.

One study, ‘Survey of Knowledge Worker Communication Needs, Google Inc. (2024),’ reveals that enterprise workers toggle four to six tools to ask and answer questions. If this is an everyday scenario, imagine the high operational costs for time and productivity losses. 

AI agents are a massive game changer for refining work processes to increase productivity and efficiency. In most scenarios, advanced Generative Artificial Intelligence agents help achieve advanced reasoning, enterprise search capabilities, and task accomplishment abilities without human intervention. 

Here, we will unravel a complete guide to AI agents in the enterprise, as well as benefits, use cases, implementation, and more.

AI agents are autonomous and advanced Generative Artificial Intelligence systems or applications trained to achieve specific goals independently of human intervention. Unlike Generative AI, which works with prior knowledge, AI agents connect with multiple database retrieval tools to supplement their prior knowledge and boost reasoning. Thus, AI agents can plan, make decisions, and execute actions. 

These agents use reinforced learning capabilities to self-learn by observing and adapting to changing environments. They make human-like decisions about nuanced actions and perform tasks to achieve goals. AI agents can independently handle simple to complex, multi-step workflows, using evolving knowledge and experience from past and ongoing user interactions across the systems. 

For example, a customer would like to apply for a home loan, but the manager finds that he has very few documents to prove his credit health, which a system can easily ignore and reject. But soon, he recognizes a way for him to apply with documents detailing immovable property. Like humans, who exhibit fluidity in decision-making in changing conditions, AI agents can also learn to recognize patterns, reason, plan, make decisions, and execute tasks.  

AI agents can continuously self-learn to improve performance, whereas generic GenAI systems need human inputs to perform and execute tasks. 

For example, a user asks to transfer $100 from account A to account B. GenAI systems suggest several steps for him and execute an action. However, AI agents do it instantly without instructing multi-step actions for the user. AI agents also showcase the history of transactions and balances— an additional advantage for the user.

AI agents can perform various tasks based on their programmed capabilities. AI agents can combine data perception, planning, reasoning, and action capabilities from simple to multi-step workflows. Not all enterprise AI agents possess these traits. However, most advanced AI agents exhibit data parsing, reasoning, planning, and action capabilities. 

• Data perception and collection: To solve a problem independently, AI agents must be able to collect and parse real-time data quickly. They are trained to gather data from various sources, such as social media, company systems, customer or employee interactions, etc., to understand intent and context and respond accordingly. For example, if a customer ticket is raised, the AI agent system would fetch data from CRM, chat interface, collaborative channels, ERP, and social media to gather data and handle queries efficiently. 

• Decision-making: Enterprise AI agents use machine learning models to identify data algorithms and patterns and make decisions. For example, if a user requests a new headphone, AI agents would refer to the order history, past interactions, company policies, and current context to reach an appropriate conclusion and make decisions. Learning from past interactions and refining their response processes helps improve decision-making. 

• Knowledge management: AI agents already have trained data and some operational rules. Their reinforcement learning abilities allow them to capture company data and update their responses accordingly. AI agents do not just rely on trained data when a specific question arises. Using RAGs, they can retrieve and match data with their trained data to form a perfect answer. When AI agents provide relevant responses, they can update their resources or knowledge bases through constant learning and evolution.

• Action execution: When a decision is made, AI agents understand the problem resolution goal to execute an action and provide a solution. For example, if an employee requests PTO, AI agents know the user needs time off or leaves. So, they review his existing PTO details and execute the task for him without asking him to go through multi-step workflows. 

• Learning and adaptation: AI agents are autonomous. They learn over time by observing ongoing and evolving interactions through a feedback loop. This helps them improve their decision-making capabilities and refine their answers for seamless support. The continuous learning abilities ensure AI agents provide appropriate answers and scale effectively. 

Assuming enterprise workflows are challenging and overwhelming is never wrong. They are, indeed. AI agents can help reduce your team's workload and optimize it efficiently. They can autonomously recommend a good choice, solve routine problems, and engage in follow-up interactions to boost productivity and efficiency.

Enterprise AI agents are designed with advanced large language models to execute specific tasks and achieve goals. For example, AI agents execute customer onboarding to make the customer feel comfortable with the product and turn him into a long-term customer. So, how do AI agents achieve this capability and automate tasks for employees, customers, and various stakeholders? 

AI agents combine the abilities to integrate with data, fetch real-time information, and automate tasks. Here is how these capabilities can make a difference. 

A new way to interact and simplify complex enterprise data 

Enterprise AI agents are designed to make API calls to enterprise data systems or retrieve information from database systems to simplify knowledge discovery. Unlike traditional AI, AI agents can refer to a user's interaction history in the company-wide systems and generate tailored or personalized recommendations to help him find a solution.

For example, if the user previously had an issue with a computer display, AI agents can check the history to learn how many times he had a similar problem. Now, it can recommend a new monitor for him for a more satisfying experience. 

Conversely, users can use self-service interfaces with AI agent systems to synthesize complex information, summarize emails and notes, or uncover insights to be more productive. 

Real-time information discovery across the enterprise 

AI agents combine enterprise data systems into one integrated system and turn it into a multi-modal search agent. By making API calls, AI agents help bring real-time company data to the fingertips of employees or customers, giving them a central source of enterprise truth.

Layered with LLMs, AI agent-based search systems can unlock varied potentials that help simplify critical data, summarize emails, and translate different languages for employees to take ownership and perform tasks. Simultaneously, these AI agents unleash conversational assistance to answer complex questions, make proactive suggestions, and perform actions based on the company’s unique information. 

Enterprise workflow automation 

AI agents for enterprise seamlessly automate single-step to multi-step workflows. For every specific task, multi-agent systems come together to drive towards a particular goal and complete the task. For example, when an employee raises a request through an integrated search or collaborative system of a service desk system, AI agents identify their specific role and accomplish the task.

If a request about a new laptop, one agent would detect the intent, the other would send the ticket to the right team, or another would solve it with the available company data. 

For your employees or customers, enterprise AI agent-built systems provide a single location to find and access agents who can automate repetitive tasks and streamline operations. 

The enterprise has complex needs for maintaining workflows, which are difficult for enterprise leaders to manage and streamline. At the same time, simple workflows also require support teams to spend lots of effort and time. To alleviate the load on your teams, you can leverage AI agents. There are a variety of AI agents— custom AI agents and simple intelligent agents. And, of course, not all are the same. 

Simple reflex agents

These AI agents possess a fundamental understanding of logic, primarily based on the if-then or condition-action principle. Other than perception worldwide, they only work around the current scenario. For example, email auto-responders can send automated emails when some users register for a service. 

Model-based reflex agents 

These intelligent agents are much better than simple reflex agents. They perceive the world using internal models and make autonomous decisions despite missing critical information. For example, model-based reflex agents can work without sensors and help identify traffic patterns. 

Goal-based agents 

These AI agents are programmed to achieve some goals. As a result, they examine their actions to identify their impacts on the goals. This means goal-based agents can understand complex scenarios and make autonomous decisions to achieve a goal. For example, inventory reorder schedules use AI agents to maintain stock levels automatically. 

Utility-based agents 

These utility-based agents determine the best approach among many solutions. As the name suggests, they use utility functions to make decisions. Resource-allocating systems, for example, use utility-based agents to optimize energy use, balancing machine use and production goals.

Learning agents 

AI learning agents differ from preprogrammed agents that work only using predefined knowledge. Learning agents can learn from past experiences, adapt seamlessly to their environment, and learn quickly. They can also learn from the feedback loop and optimize their performance. For example, customer support chatbots continue to improve their responses based on interaction outcomes. 

Hierarchical agents 

These hierarchical agents are structured in a tiered system where higher- and lower-level agents work toward a common goal. The architecture system breaks down complex tasks into manageable tasks with better control and decision-making. For example, hierarchical agents in manufacturing control systems help coordinate various stages of production processes. 

Multi-agent system 

As the name suggests, a multi-agent system is built by multiple combinations of autonomous agents. These agents work in a shared environment to achieve independent or collective goals. For example, multi-agent systems for employee support can share tasks between themselves for service desks and quickly solve problems. 

These are seven types of AI agents for enterprise workflows. It depends on what business functions you want to streamline and optimize performance for better production gains and speedier customer service delivery. Besides core business operations in the manufacturing, goods, and financial industries, enterprise AI agents can unlock immense potential for service desks—no matter what they type your business functions.

Among many applications, enterprise artificial intelligence agents are better designed to be used as chatbot support for service desks or, more familiarly, as AI personal assistants— and vice versa. So, when your enterprise workflows are overwhelming, AI agents kick in to help you tackle problems autonomously before they become a significant challenge and eliminate them before any impact can be felt. Here are some familiar use cases for enterprise leaders to control and reap benefits. 

For IT support 

AI agents are game changers for streamlining IT support requests, especially when you know they take a lot of effort from your service desk team, even for routine and minimal tasks. 

Supplementing employees with adequate knowledge 

As discussed earlier, AI agents can access your enterprise data, such as Confluence, Dropbox, Google Drive, Notion, and many other systems, to retrieve knowledge and help accomplish tasks. AI agents enable you to bring company knowledge to one place within Slack or MS Teams, allowing your employees to find what they need to complete a task.

For example, your development teams are working on a project for the client implementing supply chain management analytics for procurement. Anyone who wants to know the progress can easily ask the system and get the latest info instantly. This quick help reduces workloads on the project team while also keeping the team updated about the policies, changes, etc. 

For instance, Workativ’s Knowledge AI uses RAG agents to connect with various enterprise systems and bring real-time data to your employees' fingertips through chatbots inside MS Teams or Slack.

Helping with a password reset 

Struggling with forgotten passwords for multiple apps your employees use is a common productivity challenge for your business. AI agents can eliminate the routine tasks of your service desk for password reset issues by autonomously helping employees fix the problem without raising a ticket to the service desk team.

By integrating with HR tools, such as Microsoft Azure AD or Okta, AI agents can learn and verify employee identity when a request for password reset comes. Then, they suggest the best plan to resolve the issue without much effort. 

For HR support 

Enterprises with global employees have challenging HR needs. Each office in different regions or zones follows individual policies. Enterprise leaders need one integrated system that can provide relevant information specific to employee needs in different time zones. AI agent-built collaborative system can help remove challenges for a unified employee experience.   

Employee onboarding 

Enterprise employees have strenuous documentation for security. But, onboarding them seamlessly is challenging. AI agents can ease the tasks for every stakeholder, such as HR, IT, managers, and team members, by removing repetitive tasks such as adding members to the group, company systems, inviting employees for an introduction call, asking for documentation, etc.

AI agents remove all unnecessary manual steps and automate and streamline processes to speed up employee onboarding and improve experience. 

Employee leave management 

Your employees can feel disappointed when they receive late responses regarding time-off requests. When combined with internal interfaces for Slack or MS Teams, Enterprise AI agents act as a single contact point for employees to seamlessly communicate about their time-off requests and manage the lifecycle from checking balances to submitting requests and seeking approvals. 

For Sales and marketing teams 

Your marketing teams unleash efforts to drive leads, which your sales teams try to convert. AI agents help automate multi-step workflows, saving time and improving work processes for faster outcomes. 

Customer interaction optimization 

AI agents interact with your CRM systems, observe activities, and recommend the best action to drive sales for your teams. When your customers ask specific product questions or the project's status, AI agents can engage in an engaging conversation to provide factual answers using your company data and existing interaction data.

Optimized marketing campaigns 

Your marketing teams can speed up marketing campaign processes effortlessly. All AI agents help simplify the categorization of campaigns and build materials per the target audience segment. It is not just that AI agents generate relevant content materials for marketing drives but also track KPIs to recommend improvements.

For customer support 

Your broad customer base always seeks instant answers to their queries as they want a quick solution to their problems. A moment of delay could mean losing them out to your competitors. AI agents take charge on your behalf and find solutions when needed. 

Subscription upgrade request 

Your customers want to upgrade their services. They often want to connect with human agents to ensure the accuracy and relevance of their responses and execution. However, AI agents can ease the process by efficiently managing the flow with minimal supervision. 

For finance support 

Loads of requests for disbursal, approval, and many other things would bombard your finance teams. Enterprise AI agents easily take care of financial requests and help build healthy financial operations. 

Payroll information delivery 

It is no more hard work for your finance teams to monitor payroll details for every employee and then update them on requests. AI agents can securely provide payroll-related information to employees when they need it through a self-service interface. By integrating with payroll software, AI agents can provide full payroll details, personalizing experiences for every employee without delay.

Expense management 

Gone are the days when your finance teams were bombarded by incessant requests to check expense invoices and release them. Enterprise AI agents can now seamlessly handle these strenuous tasks and automate the tedious reporting process for all employees. This, perhaps, frees finance teams from strenuous pressure and boosts employee experience.

For agent productivity 

AI agents are intelligent enough to raise a ticket to the service desk. To speed up agent response and quick resolution to user problems, AI agents are powerful enough to empower agents and help them solve problems with ease. 

Response generation for user questions 

Maintaining empathy is essential to keep users engaged and loyal to your business. AI agents help service desk agents create personalized responses while also addressing their problems. By retrieving quick AI suggestions, agents can provide the right solution and resolve their issues.

Quick and accurate summary 

AI agents can also quickly retrieve a summary of user request history so that service desk agents can relate to the request and address the problem instantly. While transferring a call to the service desk, AI agents generate an AI summary for quick reference and speedier resolutions.

Workativ’s Shared Live Inbox has integrated tools and features for agents to help them retrieve AI summaries and increase response for reduced MTTR. 

By leveraging these use cases with AI agents, enterprise leaders like you can realize great monetary value through productivity gains and employee and customer experience.

Adopting AI agents can unleash too many business benefits. From fetching data insights to improving service delivery, AI agents can do more for you and benefit your business. Here are some essential benefits you can derive from using AI agents. 

• Improved efficiency 

AI agents automate from response to repetitive tasks. This ability helps the service desk to handle multiple user requests and solve more problems than usual. For increased efficiency, enterprise businesses are able to transform customer experience.

• Enhanced personalization 

AI agents can help craft responses per user persona to personalize their experiences. Since AI agents can learn over time, users can expect relevant responses and help efficiently resolve their problems.  

• 24x7 availability 

With AI agents, enterprises can implement customer service or employee support around the clock without hampering the support. Because AI agents can handle queries autonomously, they ensure users get answers when needed. 

• Cost savings 

Substantial productivity gains and handling a massive volume of tickets in a stipulated time frame save enterprise businesses vast amounts of money. Agent time, including per-ticket cost, can be easily saved. Let alone the customer retention advantage.

• Data-driven insights 

AI agents can interact with massive volumes of data to generate valuable insights and help make performance improvements. Leveraging essential KPIs can provide great potential to correct existing methods and implement improved processes for improved efficiency. 

Artificial intelligence agents offer multiple benefits for enterprises. Many enterprises are showing interest in adopting generative AI. Since AI agents are the next frontier, leaders are assumed to be more keen to leverage them for essential benefits.

By now, you have learned that Generative AI agents can make a huge difference in your current processes. If you aim to implement GenAI agents into your enterprise workflows, here are the best practices for you. 

• Define clear objectives: Initially, you need to determine your aim behind AI agent implementation. If you want to improve employee support for productivity increase, minimize call volumes, or improve customer experience work on these areas. Having a plan ready will help you create a better implementation process and gain success. 

• Prepare your data: Having clean data is essential to create accurate responses for your objective. Ensure you collect and manage employee and customer interaction data and other interaction data. 

• Choose the right AI agent type: If you want to accomplish routine and general tasks, off-the-shelf reactive agents help. However, for more customized support needs, you need learning-based AI agents. For example, Workativ’s Knowledge AI is the right AI agent to help you meet custom employee support needs. 

• Integrate with existing enterprise systems: Ensure your AI agent systems seamlessly integrate with enterprise systems to sync with real-time data and improve response generation for unique and nuanced queries. Workativ’s Knowledge AI integrates with enterprise systems such as ServiceNow platforms, Workday, Freshdesk, etc, to fetch real-time responses and help improve user interaction. 

• Focus on user experience: Prioritize your users' ability to easily access AI agents to find what they need to accomplish a task. Integrating the ability of AI agents within MS Teams or Slack can simplify user experience and streamline enterprise workflows seamlessly. 

• Monitor and optimize performance: Regularly monitor AI agents' performance. You can also automate the scheduling of feedback forms to collect feedback and periodically improve their performance to maintain consistency and user experience. 

• Ensure human-in-the-loop: AI agents work autonomously. They can make mistakes or provide incorrect suggestions. Clear guidelines on how to intervene and control scenarios for complex and sensitive interactions must be established. 

• Ensure data privacy and security: Ensure you implement robust data and privacy measures. E2E encryption data and compliance with regulatory bodies are essential to guardrail customer or employee data and build customer trust.

By combining easy implementation with a no-code GenAI platform, seamless customization, and integration, Workativ helps you build your custom AI agents with the Knowledge AI platform.  Workativ’s Knowledge AI platform simplifies how you build and implement workflows with agentic AI abilities to automate and streamline manual workflows and elevate autonomous problem-solving capabilities. 

With Workativ, you can build a variety of AI agents to help your employees accomplish their tasks independently. Knowledge AI allows you to build,

RAG workflows: Knowledge AI enables you to refer to company data for accurate and contextual information for your employee questions. 

Agent hands-off: When your AI agents understand employees need immediate human assistance, they intelligently escalate a call with the entire context history.

Multi-agent systems: Knowledge AI helps build multi-agents that can take on various tasks based on their abilities and drive toward a common goal. For example, if a ticket seeks desktop assistance, Knowledge AI assigns various agents with their stipulated tasks, with some taking care of intent understanding. At the same time, some pass the ticket to the appropriate team for real-time problem-solving. 

Custom AI agents: Knowledge AI seamlessly integrates with your company data and public knowledge to help you build AI agents that meet your custom needs. 

Using Knowledge AI for enterprise AI agent development, you can elevate the scale of enterprise search, integration, and information discovery for MS Teams and Slack. This will give everyone in your company a central truth about enterprise search, data integration abilities, and customization. You can always expect to derive accurate answers for your employees.

Workativ has onboarded Fortune 100 companies and many small businesses with successful implementation strategies. Workativ’s Knowledge AI gives a better edge to GoTo, an industry leader in SaaS-based cloud communication and remote work assistance tools for their IT support needs.

Workativ helped GoTo realize the right value of Knowledge AI agents for their growing IT support needs with constant implementation support. As they scale, Workativ also helps them evolve and leverage the power of AI agents on top of their existing IT support platform.

For GoTo, it is easy to manage the IT support needs of 3500+ employees for routine tasks such as reset password, unlock account, install printer, disable MFA, update user phone number, etc. 

Contact our sales teams to learn how Workativ can power your AI agent journey for enterprise workflow needs.

Until now, only typical AI tools needed constant data upgrades to stay relevant and provide accurate and appropriate answers. However, this is quite labor-intensive for enterprise leaders. The adoption of GenAI exhibits a significant turning point, with instantaneous evolution on the AI side.

The gradual shift to agentic AI abilities holds promise for massive automation to transform enterprise workflows. The ability to learn, improve, and make decisions guides leaders in leveraging the benefits of AI to elevate productivity levels and build outstanding user experience. 

However, AI agents can throw challenges as you want to build them for your daunting enterprise workflows. Knowledge AI platforms from Workativ bring seamless iteration methods to build and deploy your agents to automate enterprise workflows — employee support, customer support, and anything you want without writing a single code. Just add your company data to the Knowledge AI platform and describe your workflows and how to execute a task—you can achieve your goal. 

Give Knowledge AI a try today to build AI agents for enterprise needs.

What are AI agents?

AI agents are autonomous artificial intelligence models that can learn, improve, and make decisions to perform a task independently to achieve a goal. 

What are the differences between AI agents and Generative AI? 

AI agents are independent models that can learn to adapt to changing scenarios and make decisions to perform tasks independently, like humans. On the other hand, GenAI is built for content generation, which requires prompts to perform an action. 

How do enterprise AI agents benefit businesses?

Enterprise AI agents can free service desk agents of repetitive and manual tasks. AI agents can learn about what people need and immediately offer help by making real-time decisions. Enterprise leaders can automate routine tasks such as employee onboarding, customer onboarding, password reset, account unlock, etc.  

What are the benefits of AI agents for enterprises?

Enterprise AI agents improve the efficiency and productivity of employees by allowing them to save time on repetitive tasks. Employees can use AI recommendations or AI answers to solve a problem. In many cases, AI agents can suggest straightforward solutions without asking users to go through multiple steps. Instead, they perform the task and save your organization many productive hours for cost efficiency. 

How can you implement AI agents?

Implementing AI agents can be complex, as it requires allocating a team of expert AI leaders, a substantial budget, and time—not to mention focusing on multiple bug fixes or changes. No code platforms like Workativ’s Knowledge AI can simplify how you want to implement and deploy your AI agents. Contact us today.