What are AI Agents and how they can automate your business
AI agents are at the center of business automation. Discover how this technology streamlines operations, cuts costs, and boosts customer experience.
Key Takeaways
AI agents automate complex tasks across various business systems, freeing up human workers for higher-value activities.
By reducing manual labor and errors, AI agents can significantly lower operational costs and improve efficiency.
AI agents offer unparalleled accuracy and consistency in data processing and decision-making, minimizing risks and ensuring reliable outcomes.
The scalability of AI agents allows businesses to handle growing workloads and adapt to changing market demands without increasing headcount.
AI agents deliver personalized and efficient customer service, improving satisfaction and loyalty.
Last year, I started Multimodal, a Generative AI company that helps organizations automate complex, knowledge-based workflows using AI Agents. Check it out here.
AI Agents are intelligent software programs that can independently navigate various business systems, gather relevant information, and make decisions to accomplish specific tasks without human oversight.
These agents use integrations to access different platforms, analyze data, and execute actions across multiple applications, streamlining processes and optimizing workflow efficiency based on predefined rules and objectives.
I’ve been working with AI Agents at Multimodal for over a year now. While this technology is not brand new, it’s only starting to gain momentum now when it comes to real-world business applications. Here’s the key: AI is not just getting smarter, it's becoming more autonomous, and AI Agents are leading this change.
The technical background to AI Agents
AI Agents are made of multiple components, and each component plays a crucial role in how the whole system functions:
1. Perception: This is how an AI agent sees the world around it. It's the equivalent of our senses. In the digital realm, this means gathering information from various sources like APIs (Application Programming Interfaces), documents, databases, emails, or even the web.
2. Reasoning: Once the AI agent has gathered information, it needs to make sense of it. This is where reasoning comes in. It's the decision-making engine that analyzes the data and figures out the best course of action. There are different ways an AI agent can reason:
Rule-based reasoning: This is like following a strict set of instructions. The agent has predefined rules and conditions, and it acts accordingly. This works well for highly structured tasks with predictable outcomes.
Machine learning-based reasoning: This is where the AI agent learns from experience. It analyzes data patterns to make predictions and decisions. This is more flexible and can handle complex scenarios where the rules are not always clear-cut.
3. Action: Once the AI agent has decided on a course of action, it needs to carry it out. This is where the "actuators" come in. These are the mechanisms that allow the agent to interact with its environment and execute tasks. This could involve anything from updating a database record to sending an email or triggering a process in another application.
4. Learning: While not all AI agents have this capability, some are designed to learn and improve over time. They do this by analyzing feedback and data from their actions, refining their decision-making processes and becoming more effective over time.
The tech stack that makes it possible
1. Natural Language Processing (NLP): In the context of AI agents, NLP enables them to read and interpret documents, emails, and other text-based data. This unlocks a wealth of information that was previously inaccessible to traditional automation tools.
2. Machine Learning (ML): ML algorithms can analyze vast amounts of data, identify hidden correlations, and make predictions based on learned patterns. This allows agent function to become more intelligent and adaptable over time, making better decisions and achieving better outcomes.
3. Integrations (APIs): These are the connectors that allow AI agents to communicate and interact with different systems and applications. This is essential for AI agents that need to access and update data in multiple systems to complete a task.
How AI Agents evolved
Large language models (LLMs) like ChatGPT, which once seemed revolutionary, are now just the foundation for an intelligent agent. These agents don't just respond to prompts, they act on them, often with minimal human intervention.
From where I stand, several startups are using AI Agents to harness the true capabilities of large language models for businesses. Even giants like OpenAI are already collaborating with startups to develop and deploy agents for commercial applications, and the possibilities are truly exciting.
At Multimodal too, we’re leading how this technology shapes the future of work. We're building AI agents that tackle complex workflows in banking, insurance, and healthcare.
Are AI Agents like chatbots?
One question I often hear from both clients and businesspeople is, “Are AI Agents basically super capable ChatGPT alternatives?” Let's cut through the noise: AI agents are not just glorified chatbots.
Chatbots, while useful for answering basic questions or automating simple interactions, are limited in their capabilities. They operate on predefined scripts and struggle to handle complex tasks that require multiple steps or decision points.
AI agents, on the other hand, are a different breed altogether. They're autonomous entities capable of understanding tasks, planning actions, and executing those actions across a wide range of systems and applications. This is the key differentiator: AI agents are built to take action, not just conversation.
Think of it this way: a chatbot is like a customer service representative who can answer basic questions about your account. An AI agent is like a personal assistant who can book your travel, manage your calendar, and even negotiate deals on your behalf.
How AI Agents will impact business
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The potential impact of AI agents is immense. McKinsey predicts that automation technologies, including AI agents, could automate activities that currently consume 60 to 70 percent of employees' time. This could free up humans for more strategic, creative, and fulfilling work.
If you’re a business leader, you need to stay ahead of this curve. AI agents are not just a technological innovation, they're a business imperative. They have the potential to streamline operations, reduce costs, and unlock new growth opportunities.
AI Agents in banking: augmenting banking teams
Loan processing: Wading through loan applications is a tedious, time-consuming process. AI agents can shoulder this burden by pre-screening applicants, extracting key data points, and flagging potential red flags like inconsistencies or high-risk factors. This allows loan officers to focus their expertise on complex decision-making and building relationships with clients.
For example, at Multimodal, we worked with Direct Mortgage to automate loan applications with explainable AI agents. The result? 20x faster application approval process and 80% cost reduction per document.
Fraud detection: Traditional fraud detection systems often rely on rule-based approaches that can be easily circumvented. Fraud detection agents, with their ability to learn and adapt, can detect subtle patterns and anomalies that might signal fraudulent activity. This proactive approach can save banks millions of dollars in losses.
Feedzai, a leading financial risk management platform, uses AI agents to analyze real-time transaction data and detect fraud with a high degree of accuracy.
AI Agents in insurance: streamlining operations and reducing costs
Claims processing: Insurance claims processing is often bogged down by manual tasks like data entry, document verification, and initial assessment. AI agents can automate these processes, freeing up claims adjusters to focus on complex cases and customer interactions.
An example is Lemonade, a digital insurance company that uses AI agents to handle claims in a matter of minutes.
Underwriting: Assessing risk and determining policy terms can be a time-consuming and complex process. AI agents can analyze vast amounts of data to identify risk factors, predict potential losses, and suggest appropriate policy terms. This can streamline the underwriting process, reduce errors, and improve profitability.
As an example, we recently worked with an insurance company to automate a part of the underwriting process using an AI Agent capable of data extraction. Through this Agent, we achieved 95% accuracy and reduced the processing time to under 15 seconds.
Compliance: Staying compliant with ever-changing regulations is a major challenge for insurance companies. AI agents can continuously monitor regulatory updates, flag potential compliance issues, and even suggest corrective actions.
I was at an Insurtech conference in New York recently where I saw a lot of startups working to streamline compliance with agentic workflows. Building explainable AI agents can be a challenge. But if done right, they can significantly ease the compliance burden in regulated industries like insurance.
AI Agents in healthcare: improving patient care and operational efficiency
Patient onboarding: The patient onboarding process can be a frustrating experience for both patients and healthcare staff. AI agents can automate appointment scheduling, insurance verification, and medical history collection, allowing staff to focus on providing personalized care.
Data entry and analysis: AI agents can be trained to read and interpret medical records, summarizing key findings for clinicians, researchers, and administrators. This can save valuable time, improve accuracy, and unlock new insights that can lead to better patient care.
Another example that I personally worked on a couple months ago was document analysis for a healthcare organization. Using AI Agents, we increased workflow efficiency and reduced data labelling errors.
These are just a few examples of how AI agents are transforming the way businesses operate in banking, insurance, and healthcare.
Working with this technology so intimately for so long now, here’s the one thing I understand: any industry where there’s immense knowledge work that typically eats up human hours can benefit from autonomous agents. In fact, these regulated and document-heavy industries is where the most potential for AI Agents lies.
Benefits and ROI of AI Agents
Like with any AI technology, ROI is a complex measure for AI Agents too. I dive deep into the specifics in one of my previous articles here. Meanwhile, here are some of the most tangible benefits of AI Agents:
Operational efficiency
Picture this: your employees no longer bogged down by tedious, repetitive tasks, but instead focusing their energy on strategic thinking, creative problem-solving, and building relationships. This is the promise of AI agents.
They handle the grunt work, allowing your team to focus on high-value activities that drive innovation and growth.
Cost reduction
The financial benefits of AI agents are undeniable. By automating tasks that were once done manually, you can significantly reduce labor costs.
But it's not just about cutting costs. AI agents can also help you avoid costly errors that can arise from manual data entry or processing. This can translate into significant savings in areas like fraud detection, claims processing, and regulatory compliance.
Improved accuracy
Humans make mistakes, especially when faced with repetitive or complex tasks. AI agents, on the other hand, are relentless in their accuracy.
They can process vast amounts of data without tiring, ensuring that every detail is captured and analyzed correctly. This can lead to significant improvements in data quality, decision-making, and overall operational performance.
Scalability
As your business grows, so does your workload. AI agents provide a scalable solution that can handle increasing volumes of data and transactions without the need to hire additional staff.
This not only reduces costs but also allows you to respond to market fluctuations and seize opportunities more quickly.
I also host an AI podcast and content series called “Pioneers.” This series takes you on an enthralling journey into the minds of AI visionaries, founders, and CEOs who are at the forefront of innovation through AI in their organizations.
To learn more, please visit Pioneers on Beehiiv.
Implementing AI Agents into your business
Any time you consider adopting AI systems for your business, you have to be calculated and careful. As someone who has built AI software for enterprises for over a decade, here’s my advice:
Think about strategic alignment. Take a step back and ask yourself: What are the biggest challenges facing my organization? Where do my employees spend the most time and is it really automatable? Do I need end-to-end agentic support or just partial automation?
Privacy is super important. A lot of companies building AI Agents and automation solutions today promise data privacy. But ask if your data leaves your walls while they build and deploy these agents. Also, take a look at certifications and compliance of any partner you choose to work with.
Make sure AI Agents integrate well. The last thing you want is to rip and replace existing systems and frustrate your employees even more. Remember, technology should always work for you, not the other way around.
I’ll come back in 2 weeks sharing more of my experience on using AI to grow and optimize your business.
See you then,
Ankur.