AI Agents for Business: Full Guide

Intro

Generative AI (Gen AI) is our past, present, and future. The rise of Gen AI has unlocked significant opportunities for multiple organizations, especially with the significant boost of AI agents for business and AI agents enterprise. These AI-powered software tools can plan and execute tasks or assist humans by providing specific services in absolutely different areas.

Just imagine that according to the recent McKinsey research, applying generative AI to customer care functions could increase productivity by between 30 to 45 percent. Jorge Amar, McKinsey’s Senior Partner, says: “My belief is that gen AI agents can and will transform various corporate services and workflows. It will help us automate a lot of tasks that were not adding value while creating a better experience for both employees and customers.” 

Their other partner Lari predicts that 50 percent of today’s work activities could be automated to occur around 2055.

Business process automation is one of the biggest fields where building AI agents for enterprise can be applied. AI agents for businesses have the powerful potential to accelerate the automation of numerous workflows that would otherwise demand significant resources. The impact of AI agents for enterprise applications is significant, with studies suggesting that 60 to 70 percent of work hours in the global economy could theoretically be automated using current technologies, including generative AI. These advancements highlight the powerful potential of AI agents business applications, but reaching full-scale adoption will require both thoughtful solution development and a strong push toward enterprise integration.

What is AI Agent and How it Works.

Let’s go back to our agent expert Lari Hämäläinen for a while. He defined AI Agents really well: “When we talk about gen AI agents, we mean software entities that can orchestrate complex workflows, coordinate activities among multiple agents, apply logic, and evaluate answers. These agents can help automate processes in organizations or augment workers and customers as they perform processes. This is valuable because it will not only help humans do their jobs better but also fully digitalize underlying processes and services.”

Open-source AI Agents as well as closed-source ones are usually used in the areas of a company’s workflow automation (human resources, finance, customer service, administration, marketing, sales, logistics, hiring, and many other departments). Their flexibility and ability to be combined in a multiagent system, allow them to be used in absolutely different businesses, starting with the cases when you need to build an MVP of AI Agent, and finishing with the complex enterprise custom development.

For a better understanding of how AI Agent works, let’s have a look at the schema below. Additionally, feel free to read our previous article about the origin and structure of AI agents. 

Overall, the workflow of AI Agent looks like this:

1. Businesses usually provide the data they need to use to get the desired outcome.
2. Agent receives the data and using an AI engine or additionally, such tools as Large Language Models, or Large Vision Models, processes this data. This is actually the process of business task automation itself. 
3. Optionally, all these processes may be managed by the user in software solutions, customized with multiple integrations or add-ons. 
4. Finally, the processed data is converted into the needed result (output). It can be a generated answer to the customer, a custom image, a PDF report, or anything else you need for your business.

Finally, if you would like to learn more about how AI Agents for enterprise could improve your workflow, don’t hesitate to contact us. Our AI experts will help you to understand how these agents work, and how exactly they may benefit your business.

AI Agent Use Cases For Business

AI agents are actively transforming business operations across industries. From boosting legal reviews to enhancing customer experiences, leading companies are deploying AI agents to drive efficiency and innovation. 

These intelligent agents can handle a complex variety of tasks, ranging from automating processes like compliance checks to supporting customer service teams and even assisting with marketing strategies. 

Let’s have a look at some notable examples.

Visa: AI-Powered Shopping Assistants

Visa has partnered with tech giants like OpenAI, Microsoft, and IBM to develop AI agents capable of making purchases on behalf of users. Through the Visa Intelligent Commerce platform, these agents can handle tasks such as searching for products, booking vacations, and ordering groceries, all within user-defined spending limits. 

This initiative aims to streamline online shopping and reduce transaction abandonment by allowing AI to manage routine tasks while users retain final decision-making authority. 

JPMorgan Chase: Automating Legal Document Analysis with COiN

JPMorgan Chase has implemented an AI-powered platform called Contract Intelligence (COiN) to automate the review of legal documents. COiN utilizes machine learning to analyze complex contracts, extracting key terms and identifying potential risks. 

This system has significantly reduced the time required for contract analysis, saving approximately 360,000 hours annually and allowing legal teams to focus on more strategic tasks. 

DHL: Enhancing Logistics with AI Agents

DHL integrates AI agents across various aspects of its logistics operations. For instance, AI-powered sorting robots, known as DHLBots, have increased parcel sorting capacity by over 40%, achieving 99% accuracy. Additionally, DHL employs AI-driven customer service chatbots to provide real-time shipment tracking and assist with inquiries, enhancing the overall customer experience. 

Virtual Stylist Chatbot on Kik

H&M launched an AI-powered agent on the Kik messaging platform, serving as a personal digital stylist. This AI agent interacts with users by presenting style quizzes, showcasing outfit options, and gathering preferences to offer tailored clothing recommendations. 

By remembering user choices, the chatbot enables personalized retargeting in future interactions. This approach simplifies the shopping experience and has led to increased customer engagement and conversion rates. 

In a bold move, H&M announced plans to create AI-generated digital twins of 30 real-life models for use in marketing campaigns and social media content. These digital replicas aim to enhance creative processes while maintaining a human-centric approach. Models retain ownership rights over their digital counterparts and are compensated similarly to traditional campaigns.

Real Examples of Using AI Agents in Business

We have already discovered that AI agents can be applied in different business fields. Now, let’s look at real examples of AI Agent use cases. 

IONI

IONI is an AI-driven compliance platform designed to simplify regulatory management by automating risk identification, document analysis, and compliance monitoring. Using the latest AI models and real-time data processing, IONI ensures that businesses stay ahead of regulatory changes and minimize compliance risks.

Key Features:

• Gap Analysis: Automatically compares internal compliance policies, contracts, and regulatory requirements to identify discrepancies, highlighting areas where organizations fall short of compliance and suggesting corrective actions. 
• Document Drafting: Facilitates the creation and updating of compliance-related documents and policies, providing customizable templates and source-cited suggestions to ensure consistency and alignment with current regulations. 
• Real-Time Insights: Offers real-time updates on regulations, amendments, and potential risks, enabling informed decision-making and timely adjustments to compliance strategies. 
• Document AI Research: Simplifies legal analysis by instantly retrieving relevant information from extensive regulatory databases, providing precise, well-researched insights with citations in seconds. 

IONI also offers customizable compliance agents tailored to specific organizational needs, integrating seamlessly with existing workflows and supporting multiple languages for international compliance requirements. 

Sierra

Sierra is a conversational AI platform that enhances customer service by delivering empathetic, real-time support across multiple channels. Founded by former Salesforce executive Bret Taylor and ex-Google executive Clay Bavor, Sierra focuses on creating AI agents that can handle tasks traditionally performed by humans. Sierra's clients include well-known brands like WeightWatchers and Sonos.

Key Features:

• Contextual Understanding: Sierra's AI agents maintain context throughout conversations, remembering previous interactions to provide personalized and effective support. 
• Voice Interaction: Introduced in October 2024, this feature allows Sierra agents to engage with customers over the phone, handling complex tasks like processing service tickets and recognizing emotional cues to adjust their tone in real-time. 
• System Integration: The agents connect with existing systems like CRM and billing platforms to provide contextual assistance, resolve issues efficiently, and manage tasks such as account updates or order processing.
• Supervisor Agents: To manage the autonomy of generative AI tools and mitigate risks, Sierra has developed "supervisor agents" that oversee the primary AI agent's information, ensuring accuracy and reliability in customer interactions. 

Relevance

Relevance is a modern AI platform designed to help businesses feel the power of artificial intelligence to gain deeper insights from their data. It specializes in understanding and analyzing unstructured data, such as text, images, and audio, which traditionally pose challenges for businesses to interpret and utilize effectively. 

Using machine learning and natural language processing (NLP)  in their AI agents, Relevance AI allows startups, SMBs, and enterprises to enjoy more informed decision-making and strategic planning. What sets Relevance AI apart is its user-friendly interface and the flexibility it offers to businesses across various industries. Whether you’re in marketing or customer service, the platform provides AI Agents that can be integrated seamlessly into existing workflows. 

Finally, this platform can build and deploy AI models without needing extensive coding knowledge, making it accessible even to non-technical users. By simplifying the process of data analysis and making AI more accessible, Relevance AI helps businesses stay competitive in an increasingly data-driven world.

Beam

Beam is a custom AI agent platform that enables business owners to create highly personalized, context-aware customer experiences. The platform is developed to integrate seamlessly with existing systems and 3rd party APIs, allowing businesses to automate routine tasks, and provide instant responses to customer inquiries. This helps companies and enterprises improve customer satisfaction, reduce operational costs, and increase CSAT.

Beam AI can continuously learn and adapt from every interaction, making it more effective over time. This dynamic learning capability ensures that the AI agent becomes increasingly attuned to customer needs and preferences, enabling more accurate and relevant responses. By offering a scalable, intelligent, and user-friendly platform, Beam helps businesses to boost their profits and improve customer engagement.

5 Tips on ROIs After AI Agent Integration

There are many ways how AI Agents can benefit your business but not all of them can bring you ROIs. Let’s have a look at the real examples (tips) that will help you to understand how it works.

Tip 1. Cut Operational Costs by Automating Repetitive Tasks

Implement AI agents to your company to take over time-consuming, repetitive tasks like searching and data entry, appointment scheduling, or handling common customer queries. This automation can significantly reduce labor costs and minimize human error. AI agents business applications are no longer the future - it is our present.

Real Business Case. An eCommerce company automated order processing and inventory management with AI agents, reducing operational costs by 30% and allowing staff to focus on strategic growth initiatives.

Tip 2. Boost Revenue by Improving Customer Experience. 

You can integrate AI agents to provide 24/7 customer support, ensuring quick, accurate responses that improve customer satisfaction and loyalty. Satisfied customers are more likely to return and spend more, increasing your revenue over time.

Real Business Case. Online IT School used an AI-powered chatbot connected to Slack to resolve customer issues instantly, resulting in a 35% increase in customer retention and a 25% rise in average customer spend.

Tip 3. Raise Profit Margins by Optimizing Decision-Making

AI agents can be particularly used to analyze large datasets and generate insights that inform smarter business decisions. You can significantly boost your profit margins by reducing risk and identifying profitable opportunities.

Real Business Case. A financial services company integrated AI to analyze market data, leading to more accurate investment decisions and a 29% improvement in portfolio performance, driving higher client returns and firm profits.

Tip 4. Increase Marketing ROI with Personalized Campaigns.

You may use AI agents to segment your customer base and deliver highly targeted, personalized marketing & sales campaigns. By increasing the relevance of your lead generation campaigns, you’ll see higher conversion rates and a better return on your marketing costs.

Real Business Case. A major retail enterprise leveraged AI agents in business applications to craft personalized email campaigns. The result? A 41% increase in click-through rates and a 24% uptick in sales - significantly boosting both marketing performance and sales ROI.

Tip 5. Speed Up Your Operations to Reduce Costs

We recommend integrating AI agents into your business operational processes to identify inefficiencies and optimize workflows. This option can help you to reduce costs and improve overall business performance, leading to greater profitability.

Real Business Case. A manufacturing firm used AI agents to monitor production lines in real-time, reducing downtime by 35% and cutting operational costs by 20%, which directly enhanced the company’s profit margins.

Overall, these tips show practical steps that your company can take to integrate AI agents effectively, ensuring a strong ROI and financial outcomes.

Conclusion

The rapid growth of generative AI has transformed how businesses use AI agents and AI chatbots - especially those based on large language models (LLMs). Early on, challenges like hallucinations and high processing costs limited these tools to basic use cases, such as providing expertise or generating images. However, as technology matured, AI agents in enterprise applications began evolving rapidly. Initially too error-prone for complex workflows, LLMs with around 80% accuracy struggled with multi-step processes. Today, thanks to advancements in reliability and orchestration, AI agents are becoming core components of AI agent applications in business, streamlining operations and unlocking new AI agents business applications across industries.

However, the late advancements have significantly broadened the potential applications of LLMs. Innovations in the accuracy of LLMs, such as GPT-4 Omni or LLaMa 3 improved memory structures, enhanced logic frameworks, and better evaluation methods have opened the way for these models to handle more complex workflows. LLMs can now self-correct and provide higher accuracy, especially when paired with experienced humans to manage tricky cases. This top-notch collaboration of AI technologies and human skills results in high-quality outcomes.

To sum up, in recent years, the nature of many generative AI case studies has given way to a trend towards standardization and industrialization, resembling packaged software. This shift will streamline implementation and reduce costs, making it easier for businesses to deploy AI agents across a broader range of real-world applications, including niche use cases for multiple enterprises.