Executive Crash Course on AI Agents

OpenAI agents will launch in 2025. Salesforce CEO declares AI agents to be the third wave of AI. Microsoft is adding agent capabilities to Copilot.

The message here is clear: AI agents are going to be big, and leaders need to start strategizing now on how to implement this powerful technology into their organizations.

If you’re not sure what AI agents are, you’re already behind the AI ​​curve. You’re not alone either: artificial intelligence is advancing at a breakneck pace, and most leaders are finding it difficult to keep up. “Innovation is happening faster than you can imagine or adapt to, and large organizations are rushing to move from data to value to insight to action,” says Abhas Ricky, chief strategy officer at Cloudera, a hybrid data platform.

Read on for a crash course for AI agents, including defining this new technology and answering questions about security, team impact, and the investments leaders need to make sure their organization catches up.

What are AI agents?

AI agents are advanced AI systems that can independently perform complex tasks and make decisions. They can analyze data, make predictions, come up with ideas, communicate, solve problems, create strategies, and much more. They learn and adapt to real-time data over time, providing high levels of accuracy, efficiency and flexibility.

How are AI agents different from ChatGPT and other LLMs? AI agents work independently, following instructions and using different tools to complete tasks. ChatGPT doesn’t do anything on its own—people must enter a question or prompt to get an answer.

Like any other tool, AI agents cannot magically solve all business problems. But they are extremely powerful, especially when you bring agents together to create agent workflows that allow them to perform complex tasks.

Answers to 7 burning questions about AI agents

Any new technology brings with it a wave of apprehension, fear and excitement. AI agents are, of course, no different. Here are some answers to popular questions from leaders about this technology:

Are AI agents just fancy chatbots? Nope. This is a common misconception. During recent webinar on AI agents At my company Centric Consulting, we asked participants what they thought AI agents were. Almost 20% responded using “chatbots.” Chatbots rely on user input, while agents use artificial intelligence and natural language processing. AI agents can have a conversational interface like a chatbot, but this is not a requirement.

How to minimize security risks when outsourcing entire processes to AI? Agents should have clear rules about what they are allowed to do. Agents who try to be everything to everyone tend to fail. Once the agent is live, actively monitor inputs and outputs during the initial usage phase. This helps provide transparency and explainability, creating an audit trail so you can have confidence in the technology. As you scale, you can move to passive monitoring to identify anomalies.

“In the first phase of agent deployment, you need to keep people in the loop at all times,” says UiPath CEO Daniel Dines.

Is there already any data on how AI agents will impact organizations? One study predicts agent AI will achieve a 60% increase in productivity for organizations. AI agents are most effective when they are combined to create agent-based workflows. Compared to individual AI agents, agent-based workflows can solve more complex problems, solve more complex problems, and achieve greater efficiency and productivity gains.

What do agency workflows look like? Ricky gives an example of agents working together to autonomously reconcile tax accounts and process loans. “Imagine the first agent reads tax documents, the second agent retrieves additional sources, the third agent collates tax data, the fourth agent writes a memo for you, the fifth agent checks the facts, and the sixth agent formats the memo.” Ricky says. “There is the potential for a 99% increase in performance, as well as a significant improvement in stability.”

Okay, but how do companies use AI agents in real life? Some forward-thinking organizations have already successfully deployed AI agents. This technology is permeating many industries, including insurance, marketing, manufacturing, customer service, financial services, supply chain, and healthcare.

For example, my company recently helped a health technology organization build an artificial intelligence agent to analyze and extract demographic data from disparate sources (patient records, pharmacy orders, hospital notes, etc.) to help manage prescriptions. The tool reduced manual labor by 82% and increased accuracy to almost 100%.

Here are some more quick examples:

• Hippocratic AI, a healthcare company focused on generative artificial intelligence, developed an AI agent for “low-risk, non-diagnostic, patient-centered healthcare tasks,” reducing the burden on overworked nurses, social workers and dietitians.
• A&B Valve uses artificial intelligence agents to extract the characteristics of each valve part from technical specifications and sales brochures. Along with the validation forms, these specifications are used in the machine learning model to determine if there are potential anomalies in the validation.

How will AI agents impact jobs? AI will make some jobs obsolete. But it will also create new opportunities, although those new jobs will take some time to emerge. Leaders must figure out how to create the workforce of the future who can use AI to solve problems and innovate.

There is also a cultural component to how AI impacts the workplace. Yes, some managers will choose to downsize. But instead, leaders can position technology as a tool to accelerate market growth or grow your most valuable asset—your people.

“It’s more about transforming jobs than eliminating them,” Dines says. “Work will evolve as robots and agents take over some tasks. But in reality, with current technology, it is very difficult to replace work. Agents can perform very specific tasks, although most jobs are broader than that.”

How can my team keep up with AI? Organizations can keep up with the rapid development of AI, but it requires investment and an agile strategy. Ricky suggests that leaders need to change processes around innovation. According to him, Agile is no longer suitable. “The problem with applying an agile mentality in the world of artificial intelligence is that every Sunday there is a new model, every Monday there is a new agent and every Tuesday there is a new structure. By the time your agile team adapts, you are already behind. You need to embed core AI skillsets and processes into your development cycle.”

How do I get started with AI agents? Unfortunately, there is no shortcut here. This is a tough strategic climb. But to paint the big picture of what you need to do to get started with AI agents:

First, identify your highest priority use cases through AI Vision Workshops. Repeat this exercise at least every six months. If you reassess your use cases and priorities even once a year, you’ll be far behind – things change quickly, and what’s possible today will change by next month.

“This is probably the fastest growing technology,” says Ricky. “Agent-based workflows and regenerative agents are being developed by large organizations and multiple vendors for a variety of use cases at breakneck speed. Developing agent systems that enable agents using basic models to perform complex, multi-step workflows in the digital world will help move from thought to action.”

Next, create an AI roadmap and define goals and agent KPIs. Set up data, security, and compliance management. Like any other artificial intelligence tool, data is the foundation of your success. “You need to be able to trust the data you’re going to use to train your model,” says Ricky. “Only then will you get ideas and actions you can trust.”

Finally, build a smart team that understands the purpose and role of AI in your organization; this team must take responsibility for continuously learning and adapting as AI evolves.

Ricky warns leaders that artificial intelligence and agent systems, when done right, are a capital-intensive game. “Today we expect relatively larger than usual investments in artificial intelligence technologies, expecting them to deliver results many times greater than what you invest in,” he says.

“You can’t wait for a test use case investment to pay off and then roll out a larger budget. This is one of those technologies that requires determination. By the end of the year, companies that invest in artificial intelligence applications and agent-based workflows will outperform those that do not. They will use agent systems that manage multiplicity, respond to natural language, and work seamlessly with existing software tools and platforms, increasing their advantage and beating competitors in less time.”

While integrating AI agents into your organization can be challenging (there are many strategies to consider, important controls to implement, and team members to buy into), the potential benefits are enormous. Leaders need to act now to begin strategizing how to use this powerful technology to transform their organizations and capture Return on investment in artificial intelligence.