OpenAI agents launching in 2025. Salesforce CEO announces AI agents as third wave of AI. Microsoft adds 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 integrate 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: AI is developing at a dizzying pace, and most leaders are struggling to keep up. “Innovation is happening faster than you can imagine or adapt to it, and large organizations are racing against time to move from data to value to insights to action,” notes Abhas Ricky, chief strategy officer at Cloudera, a hybrid data platform.

Read on for a crash course on AI agents, including a definition of this new technology and answers to questions about security, team impact, and the investment required for leaders to catch up with their organization.

What are AI agents?

AI agents are advanced AI systems that can perform complex tasks and make decisions on their own. They can analyze data, make predictions, offer insights, converse, solve problems, create strategies and much more. They learn over time and adapt to real-time data, providing a high level of accuracy, efficiency and agility.

How are AI agents different from ChatGPT and other LLMs? AI agents work independently, following instructions to use various tools to complete tasks. ChatGPT doesn’t do anything on its own: humans must enter a question or prompt to get a response.

Like any tool, AI agents will not magically solve every business problem. But they are extremely powerful, especially when you combine agents to create agent workflows, allowing them to accomplish complex tasks.

Answers to 7 burning questions about AI agents

With any new technology comes a wave of worry, fear, and excitement. AI agents are, of course, no different. Here are some answers to executives’ top questions about technology:

Are AI agents just fancy chatbots? No. This is a common misperception. During a recent webinar on AI agents hosted by my company, Centric Consulting, we asked attendees what they thought AI agents were. Nearly 20% responded with “chatbots”. Chatbots rely on user input, while agents use AI and natural language processing. AI agents can have a conversational interface, just like a chatbot, but it doesn’t have to.

How can I minimize security risks when entrusting entire processes to AI? Officers must have clear rules about what they are allowed to do. Agents who try to please everyone tend to fail spectacularly. Once the agent is operational, actively monitor inputs and outputs during the initial usage phase. This helps ensure 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 report anomalies.

“In the first phase of agent deployment, you need to constantly update humans,” says Daniel Dines, CEO of UiPath.

Is there already data on the impact of AI agents on organizations? Study predicts that agentic A.I. achieve 60% productivity gains for organizations. AI agents are most powerful when combined to create agent workflows. Compared to single, one-off AI agents, agent workflows can tackle more complex tasks, solve more complex problems, and further improve efficiency and productivity.

What do agent workflows look like? Ricky gives an example of agents working together to complete tax invoice reconciliation and loan underwriting independently. “Imagine Agent 1 reads tax documents, Agent 2 pulls additional sources, Agent 3 evaluates the tax data, Agent 4 writes the memo for you, Agent 5 checks the facts and Agent 6 formats the memo. » Ricky said. “There is potential for a 99% productivity improvement, as well as significant improvements in consistency.”

OK, but how do companies use AI agents in real life? Some forward-thinking organizations have already successfully deployed AI agents. Technology is making its way across many industries, including insurance, marketing, manufacturing, customer service, financial services, supply chain, and healthcare.

For example, my company recently helped a health technology organization create an AI agent to analyze and extract demographic data from disparate sources (patient records, pharmacy orders, hospital discharge notes, etc.). ) to facilitate prescription management. The tool reduced manual work by 82% and increased accuracy to almost 100%.

To share a few more quick examples:

• Hippocratic AI, a generative AI-based healthcare company, designed an AI agent for “low-risk, diagnostic-free, patient-oriented healthcare tasks,” reducing the burden on overworked nurses, social workers and nutritionists.
• A&B Valve uses AI agents to extract specifications for each valve part from technical specification documents and sales brochures. Along with inspection forms, these specifications are used in a machine learning model to determine if an inspection has any potential anomalies.

What impact will AI agents have on employment? AI will make some jobs obsolete. But it will also create new opportunities, although these new jobs will take some time to emerge. Leaders must figure out how to train tomorrow’s workers who can use AI to solve problems and innovate.

There is also a cultural component to the impact of AI on employment. Yes, some leaders will choose to downsize. But leaders can instead choose to position technology as a tool to accelerate market growth or dramatically increase your most valuable asset: your people.

“It’s more about job transformation than job elimination,” says Dines. “Jobs will evolve as robots and agents take over certain tasks. But with today’s technology, it’s actually very difficult to replace a job. Agents can perform very specific tasks, while most tasks are broader.

How can my team follow the AI? It is possible for organizations to keep pace with the rapid advancement of AI, but it requires investment and an agile strategy. Ricky suggests that leaders need a change in the processes around innovation. Agile is no longer enough, he says. “The challenge of applying the agile mindset to the world of AI is that there is a new model every Sunday, a new agent every Monday, and a new framework every Tuesday. By the time your agile team adapts, you’re already behind. You need to integrate the core set of AI skills and processes into your development lifecycle.

How do I get started with AI agents? Unfortunately, there are no shortcuts here. This is a heavy strategic effort. But to paint a general picture of what you need to do to get started with AI agents:

First, determine your priority use cases with AI vision workshops. Repeat this exercise every six months at least. If you review your use cases and priorities only once a year, you’ll be woefully behind: the landscape is changing rapidly and what’s possible today will have changed by next month.

“It’s probably the fastest growing technology out there,” Ricky says. “Agent workflows and regenerative agents are being developed by large organizations and multiple vendors for a wide variety of use cases at breakneck speed. Designing agentic systems to enable agents using basic models to execute complex, multi-step workflows in a digital world will help move from thinking to action.

Next, create an AI roadmap and define agent goals and KPIs. Implement data, security and compliance governance. Like any other AI tool, data is the foundation of your success. “You need to be able to trust the data that you’re going to use to train your model,” Ricky explains. “Only then will you get the information and actions you can trust.”

Finally, build an intelligent team that understands the purpose and role of AI in your organization; this team should support continuous learning and adaptation as AI advances.

Ricky warns executives that AI and agent systems, done well, are a capital-intensive game. “We are now looking for a relatively larger than usual capital investment in AI technologies, in the hope that it will produce results many times greater than what you are investing in,” he says.

“You can’t wait until ROI is achieved on a test use case and then deploy a larger budget. It’s one of those technologies that requires a leap of faith. By the end of the year, companies that invest in AI applications and agent workflows will surpass those that do not. They will leverage agentic systems that manage multiplicity, respond to natural language, and work seamlessly with existing software tools and platforms, thereby accelerating their benefits and beating the competition in a shorter time frame. »

While integrating AI agents into your organization can be challenging (there are many strategies to consider, significant governance to put in place, and team members to involve), the potential benefits are huge. Leaders must act now to begin strategizing how to use this powerful technology to transform their organizations and capture ROI of AI.