There are few moments in time when new technology completely changes the way we live and work. Take the advent of the Internet, for example. For many of us, it’s hard to remember a time before we had access to so much knowledge at our fingertips.

What is harder to forget — especially for those of us working in the tech space — is the people and companies that capitalized on this early. They’re now titans of industry, like Apple, Microsoft, and Google.

We’re now experiencing a similar moment in time that’s already creating shockwaves in both our work and personal lives: the era of AI agents. The AI agent industry is showing a compound annual growth rate of 43% ; by 2032, it’s projected to grow to a staggering $110 billion dollars. To put this into perspective: in a few short years, the AI agent industry will be as valuable as cloud computing in North America and more valuable than the global music industry.

Companies are hiring AI Agents to do everything from code, sell, market, and provide customer service. The generative AI behind these agents is moving so fast that we expect leaders will employ AI agents to automate even more functions within the business.

Despite this undeniable impact, only 24% of customer service professionals currently use generative AI. Let’s get into why.

AI concerns: The fear of losing control

The number one reason businesses are holding back from optimizing their organizations with AI agents is the fear of losing control.

It’s understandable. To be frank, our previous experiences with AI for customer service haven’t been extraordinary. Chatbots, even ones powered by AI, are deterministic in nature, and they need humans to spoon feed them every word and tell them exactly what to do and when.

They excel in sourcing information and workflows from their training data and presenting it in a user-friendly way. But their capacity for reasoning is non-existent. Needless to say, chatbots fail to deliver on their potential.

AI agents are different. They’re powered by probabilistic technology — like the human brain. They’re designed to reason based on the knowledge provided to them and to learn from their experiences. For customer service, this means they can solve more complex problems and make decisions that lead to higher resolution rates.

The benefits of this are obvious, but it may still feel like you’re completely losing control when you're putting your trust in an AI agent to say and do the right things at the right time. Here’s how you should look at it: instead of thinking about an AI agent for customer service as “software,” you should consider the AI agent another employee on staff. And with their help, you can take your career to the next level.

With an AI agent, you’re simply going from doing a task yourself to delegating that task to someone else, giving you the opportunity to focus on more impactful work. It’s like going from an individual contributor to a manager.

And that’s where the key lies, in AI management.

The future of work: AI management

Let’s unpack AI management, starting with the shift from contributor to manager. As an individual contributor, you have your hands in the pie — you’re on the ground, doing the tasks, baking the pie yourself.

When you get promoted to manager, there’s now a level of hierarchy separating you from that task you used to do. Now, you have a team member who’s baking the pie, and your role is different in 2 keys ways:

1. Your scope includes more strategic tasks, like deciding on which pies to bake to reach the quarterly goals.
2. You are responsible for getting the best performance out of the team members you manage. This means coaching them and giving them feedback to continuously improve to ensure that they’re doing the best work possible, baking the most delicious pies.

Does this mean you’ve lost control? Absolutely not. In fact, if you insist on baking the pies yourself, you’re entering the lose-lose territory of micromanagement. To get the best performance out of AI agents, you simply must trade prescriptive control for something we’ve been doing for hundreds of years: management.

We know that organizations with great managers have better bottom line performance, so it’s safe to say that in the next 10 years the same will be true for organizations with great AI management.

At Ada, we have a front row seat into what being great at AI management means. Hundreds of AI Managers use Ada every day to track the performance of their digital customer service colleagues. After collecting their feedback, we’ve identified three core components to AI management: measurement, coaching, and performance.

Core component 1: Measurement

The most successful managers define what success looks like for the people they manage. They set goals that push their team’s performance, and they demonstrate ROI to their leadership.

For an AI Manager, their North Star is Automated Resolutions (AR). We define AR as a fully automated conversation between a customer and a company that is relevant, safe, and accurate, and doesn’t escalate to a human agent.

This metric provides the most accurate reflection of your AI agent’s success. It benchmarks success to being on-par or exceeding human expectations. Using AR, you can continuously evaluate your AI agent for opportunities to improve.

Core component 2: Coaching

We can confidently (and proudly) declare that Ada’s AI Agent automatically resolves over 65% of customer service inquiries within its first month of onboarding. This is also what you would expect from a human agent in their first 90 days at a new job.

But let’s be real, without coaching they’re unlikely to exceed that. They’ll stagnate, because coaching is how great managers help their employees improve.

AI Managers are given a similar role. They should check in on the AI agent regularly to evaluate performance, identify areas for improvement, and give them feedback. On average, Ada’s most successful AI managers spend 2-3 hours a week in Ada, coaching and managing their AI Agents.

Your AI agent won’t be perfect from the get-go, much like you don’t expect perfection from your human agents immediately. The more you coach them, the more they’ll improve.

Core component 3: Testing

There’s a reason most customer service calls start with a disclaimer that they’re being recorded. Customer service managers really do review those calls, and they give their direct reports feedback when they make a mistake and are given tips on areas they can improve on.

What if you could improve agent skills and capabilities without involving real customers? That’s exactly what testing your AI agent is for.

You’re not just sending the AI agent off and trusting them implicitly. AI managers can test them in a controlled environment to gain confidence first, running synthetic tests that simulate hundreds of customer conversations at once.

So before you lose a customer, test your AI agent to gauge its performance and make necessary adjustments, ensuring the AI agent is ready to handle more critical responsibilities.

Weighing fear vs opportunity: AI agents are the path forward

Soon, AI management is going to be a fundamental part of any job. Remember how people used to put “Microsoft Office Suite” on their resumes? Now, it’s just assumed that you know how to use this software. The same will be true for AI.

This makes it clear: AI agents will continue to grow and become more impactful in our organizations. The idea of an “individual contributor” will dissolve; everyone is going to be a manager of human employees or an AI employee. The best non-human employees are going to have the best human AI managers.

For businesses of the future, scaling AI management within an organization will translate directly to their rate of growth, so if you don’t embrace this shift, you’re gonna be left behind. And if you do take advantage of it, there’s a huge opportunity for you. It all comes down to this: will you let the fear of letting go hold you back?