Using AI Agents to Increase Your Productivity and Drive Sales [All the Pros and Cons]

For businesses, AI agents can sound like a dream come true: increased productivity, reduced administration on internal teams, and humans outsourcing repetitive tasks in favor of the ones they love.

As a consumer, I personally love it when I can self-serve efficiently through an AI agent. It saves time, bypasses the need to listen to tinny hold music on the phone, and I don’t disrupt a hard-working human’s day with my easy-to-solve query. Win-Win.

So, where’s the rub?

Well, as a consumer, you probably already know. AI agents aren’t always as helpful as we want them to be. They can feel impersonal and waste time when you just want human engagement. Not to mention, it’s reported that Americans are concerned about the future of AI. Bentley University and Gallup found that 75% of respondents said that AI would reduce the number of jobs, and 77% of respondents don’t trust businesses that use AI.

That said, I’m an optimist in every sense of the word and don’t think we need to worry about our jobs or AI agents. This article is all about AI and AI agent usage. I’ll explain what an AI agent is, how it works, the benefits, drawbacks, and types, and how to use AI agents in a way that actually works. This article leans into AI agents positively, finding solutions to potential drawbacks. It helped me see how many AI agents are already in my life, with no disastrous consequences.

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Table of Contents

• What is an AI agent?
• How do AI agents work?
• Benefits of AI Agents
• Drawbacks of AI Agents
• Types of AI Agents
• How to Use AI Agents That Work
• Trial an AI agent. It might surprise you!

What is an AI agent?

An AI agent is software that can interact with its environment or the data input. The AI agent can process information and then provide solutions.

To help understand what an AI agent is, see the screenshot below. It demonstrates an AI agent, in the form of a chatbot, at work.

The chatbot has processed its environment and the data input (the question I asked), then responded with a relevant and helpful reply.

Later, I’ll break down the types of AI agents, how they work, and what they can be used for.

How do AI agents work?

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As demonstrated in the helpful infographic above, there are four main components in the AI agent:

• The agent itself and its environment.
• Perception covers the AI agent’s ability to interpret inputs.
• The brain is where decision-making, planning, and reasoning take place.
• Action means the output or literal action.

The steps are:

• Receiving the input.
• Perceiving the information.
• Processing the information.
• Providing the output.

AI agents are not something to be daunted by. In fact, many AI tools are used by both small businesses and large, not to mention your personal life (I’ll show you how later).

Let’s understand how AI agents work using my HubSpot Chatbot example above.

I gave the chatbot an input. I said: “Hello, where can I find pricing information?”

The chatbot perceived and processed the information I had provided, even contextualizing the query and “understanding” this isn’t a simple answer.

The output was a message and a question. The chatbot said, “Hi there 👋 Welcome to HubSpot Sales! You can find pricing information in the chatbot or on our website. To point you in the best direction, what specific package or product are you looking to get pricing for?”

I liked the question back. It shows just how well the chatbot can perceive and process information; it knows it needs more information.

From this response, I knew how to get the pricing information I wanted, but the chatbot also encouraged further interaction so it could be more helpful. An example of an AI agent done well.

Benefits of AI Agents

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According to my research, the chatbot is one of the most familiar types of AI agents. When I reached out to people asking about their uses of AI agents, 51% of responses were on chatbots and customer service support. Naturally, I’ve included the benefits and drawbacks of those, but I wanted to dig deep and find other not-so-well-known benefits.

I’ve pulled in insights from people using AI agents, and I was impressed with the openness of those who contributed and their willingness to acknowledge the pros and cons of AI agents. I’ll share what I learned below.

Reduce customer service burnout and increase customer satisfaction.

Christian Hed, CMO of Dstny, has a lot of hands-on experience with AI agents. He was responsible for launching an AI chatbot agent at Dstny and deployed it with thousands of companies internationally.

AI agents can help customers handle thousands of customer support queries through simple interactions. We’ve already reviewed an exchange with a chatbot above, which was fit for this purpose.

Hed explains, “Many companies receive more than ten thousand customer support queries per day. Some receive direct calls that ping directly into an individual or team phone line, which can be a nightmare.

“AI agents are perfect for handling those initial interactions, like ‘Who do you want to talk to?’ or ‘What is this about?’ which significantly reduces the number of unnecessary interactions between human agents and customers.

“Many calls with easy responses can also get answered immediately by the AI agent — saving everyone time.

“If you have ten thousand inbound customer support tickets a day, this can save hundreds of hours of time over a week. This last year of AI innovation has cut costs dramatically for customer support teams.”

What I like: I have to admit, I am a big fan of AI agents used in this way, as long as the process is efficient. I don’t mind answering questions like ‘What is this about?’ or ‘Who do you want to talk to?’ if it speeds up my chances of reaching the correct department or person.

Analyze large data sets to improve products based on customer feedback.

Aljay Ambos, head of marketing and AI expert at Twixify, has an excellent use case and benefits from an AI agent. He uses AI agents as strategic collaborators during campaign ideation. When Twixify is ready to release new features, the team analyzes different types of information, such as customer feedback, market trends, and what competitors are doing.

But, they’re not doing this manually. Ambos uses Twixify’s AI agent to speed up the process.

Ambos said, “Our AI agent scanned thousands of customer support conversations. It presented a common problem: Many users needed assistance adjusting AI-made legal documents to comply with local rules. Such understanding, which a human team might not have seen because of the quantity of work, became the foundation idea of our campaign.”

Based on the AI agent’s findings, Twixify launched its campaign. I asked Ambos if there was a measurable success, and there was. Ambos said, “Our strategy boosted user engagement by 36%. It also enabled more people to use the new feature we introduced.”

What I like: Aside from the measurable success, I love this use of an AI agent — and I think it makes a lot of sense. AI agents can process data more efficiently than any human. I can see how the Twixify team would feed in large amounts of data from several sources to map out features they need to roll out to be competitive and meet customer needs.

Inventory Management

Iryna Balaban is the CEO and co-founder of Elite Maids NY, a cleaning service in New York City.

Before Balaban introduced an AI agent for inventory management, keeping track of cleaning products for thousands of cleaning jobs was a logistical disaster. Now, Balaban uses an AI agent and describes its mission to improve inventory management as a “tremendous success.”

Balaban describes how the AI agent tracks inventory: “The AI keeps track of cleaning supplies such as disinfectants, general-purpose cleaners, and microfiber cloths. [It] calculates usage history and anticipates upcoming cleaning. The AI knows when items are running out of stock and automatically reorders them. This way, our cleaning crews are never without supplies, which means there is never any delay or disruption in our service.”

Impressive.

The quantitative measures are easy to spot: There are no delays for customers, and frustrations to staff are diminished as they turn up to work with all the tools they need to do their best job.

Curious, I asked Balaban if there was a measurable impact.

There was. “This AI integration has had profound effects. Out-of-stock cases have decreased by 90% since implementing the system. This not only increases our client satisfaction but saves us money. Automating reorders and avoiding emergency purchases at the last minute has saved us some money on cleaning products. Also, AI allows us to detect patterns in our usage data so that we can get bulk purchase discounts from vendors and further grow our profit margins.”

What I like: This might be one of my favorite uses of an AI agent. It is an excellent example of AI automation understanding its environment and the information supplied, and how an action (reordering stock) can take the mental load off people. I like how impactful this is. It is also an excellent example of how AI agents benefit people: repetitive, boring tasks, like reordering stock and managing inventory, are given to the AI so humans can do what they love.

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Drawbacks of AI Agents

If you want to integrate AI agents into your business, it helps to know the drawbacks and common pitfalls so you can avoid them. Below, I’ve listed drawbacks and some solutions to avoid them when you integrate your own AI agent.

Since chatbots were the most common use of AI agents in my research, let’s start there.

Complex queries need a human.

Although Christian Hed has an innovative AI agent solution that reduces customer service burnout and increases the speed at which a customer can get an answer, he recognizes that some queries are beyond the AI agent. In these scenarios, the AI agent can cause frustration.

I appreciated Hed’s openness to acknowledge the pros and cons of AI agents, particularly with this matter.

Hed said, “Some queries are more complex and need a human to answer. An AI agent just causes frustration, as the customer knows they need support and wants to bypass it.”

What I like: I agree entirely with this. In fact, it was the first sticking point that came to my mind when I considered the drawbacks of AI agents. This is a disadvantage that I’m sure many of us can relate to. A personal recommendation for eliminating this issue is to use an AI agent that can quickly escalate a query to a human rep when the time calls for it. I feel most satisfied with AI agents that respond quickly to the request to speak with a human.

Lack of Empathy

I spoke to 55 experts about AI agents, and eight people mentioned the idea of “empathy.” It was a fairly common theme amongst AI experts when it came to drawbacks.

Empathy is a drawback of the AI agent; it simply can’t do it. If you have someone using an AI agent who really wants human connection, AI isn’t going to cut it.

There are solutions to avoid this, though.

Take a hybrid approach.

John Russo is VP of healthcare technology solutions at OSP Labs. Russo says, “To combat [lack of human empathy], businesses can implement AI agents in tandem with human agents. This hybrid approach ensures that more complex or emotionally sensitive queries are handled by real people, while routine tasks are automated. Additionally, constant fine-tuning and regular updates of the AI models can help reduce errors.”

In the world of healthcare, emotionally sensitive queries are going to be common for Russo and OSP Labs.

Russo explains how OSP balances chatbots and humans, “Chatbots handle FAQs on our website, improving response times and allowing human agents to focus on more complicated issues.”

What I like: As I said in my intro, I like being able to serve myself when it’s efficient. Chatbots handle FAQs, response times, etc., while human agents handle complicated or sensitive queries. Perfection.

Avoid AI agents entirely when human connection is needed.

There is always an option not to use AI agents for particular purposes.

Parker Gilbert, co-founder at Numeric, recommends “[using] AI agents strategically, focusing on tasks where human interaction isn’t essential or incorporating human oversight where needed.”

What I like: A strategic analysis of where an AI agent best works is beneficial (or where it isn’t) seems like the minimum a company can do for its customers. Considering where AI agents are most valuable will give you all the benefits of AI while avoiding the drawbacks.

Managing Data Accuracy

I think there’s a temptation to be too confident in AI agents to do their thing. But as Kevin Shahnazari, founder and CEO of FinlyWealth, learned, it doesn’t always provide accurate and helpful actions.

FinlyWealth uses AI agents across its credit card recommendation platform.

Shahnazari explains how the AI agent works, “The AI agent analyzes credit card application patterns. Our agents process thousands of data points to predict approval odds, saving users from damaging their credit scores with failed applications.”

Shahnazari credits the system with “[preventing] over 2,000 likely rejections while identifying better-matched card options for users.”

The challenge that Shahnazari found was that their AI agents started making overly conservative recommendations.

When asked how to solve this, Shahnazari said, “We solved this by implementing a hybrid approach where agents flag potential matches for human review. This combination improved recommendation accuracy by 35% while maintaining the efficiency of automated screening.”

What I like: While reliable, unless maintained and trained properly on good data sources, the AI agent’s quality may be low, or it could deteriorate. The value in what Shahnazari said is in the AI agent hybrid approach, adding that layer of human review. It’s a nice crossover with the hybrid approach mentioned earlier regarding AI and empathy.

I think the nice thing about a hybrid approach and making hybridity known to your workforce is that you will give them assurance that AI is not going to take their job anytime soon. Ultimately, AI agents and humans are better at working together. I also like that the AI agents can flag humans when action is needed, saving humans from having to constantly watch the AI.

Types of AI Agents

I think it helps to break down the types of AI agents to increase understanding and, therefore, remove the fear. Knowledge is power, and I think there’s a good chance you’ll be surprised by the types of AI agents that you are using nonchalantly and without threat in your day-to-day life. Even with an optimistic viewpoint, this was a pleasant surprise for me.

1. Learning Agents

Learning agents are the most familiar type of AI agent. You will have experienced learning agents if you shop online, stream TV, or listen to music online.

Learning AI agents work by improving their performance over time by learning from experience. For example, if you watched three movies over three days with the same actor and watched those movies to the end credits, then a learning agent might suggest another movie with the same actor.

Best for: AI-powered personal assistants, fraud detection systems, and self-learning robots.

2. Simple Reflex Agents

You’ll be familiar with this type of AI agent if you’re into smart homes. You might find this use case particularly non-threatening.

Simple reflex agents can be used in things like thermostats. These agents don’t have any context (unlike model-based, coming next). Simple reflex agents act based on the current situation without considering past experiences. They follow condition–action rules (if X happens, do Y). So, sticking with thermostats: If the temperature drops below a certain temperature, the heating kicks on. If the desired temperature is met, it turns off.

Best for: Basic automated systems like thermostat controls, spam filters, or rule-based chatbots.

3. Model-Based Reflex Agents

Model-based reflex agents are a type of AI agent that maintains an internal model of the world. They make decisions based on both current input and, importantly, stored knowledge. This helps the AI make informed decisions that are aligned with your brand.

Think of your model-based reflex agent as an AI with a boundary. It requires context assistance so that it understands and responds in accordance with your brand values, for example.

With these agents, you can input data that helps keep the AI on track, ensuring it makes decisions within a pre-defined framework.

Simply, these could be generative AI models that take into account your tone of voice.

Best for: AI assistants, like HubSpot’s Breeze Copilot.

Check out HubSpot’s AI Breeze Copilot.

4. Goal-Based Agents

Goal-based agents are a type of AI agent that you might be already using. Is your sales team using a CRM with built-in AI? You’re likely already moving and grooving with goal-based agents.

Goal-based agents make decisions based on pre-defined goals. The agent can evaluate the environment and select an action that brings a pre-defined goal closer.

Sticking with the CRM example, goal-based agents can assist sales teams in knowing what actions to take for which prospects. Instead of sales personnel following arbitrary actions for every prospect, a goal-based agent might spot commonalities between prospects that close and identify the information or action they want from your sales teams.

The goal-based agent, built within a CRM, can suggest sales strategies based on data. It might tell you to send follow-up emails because a follow-up email after xx days has xx% success rate. It takes the overwhelm out of what to do next and provides data-driven, goal-orientated action. Not bad, eh?

Best for: CRMs

Get started with HubSpot CRM.

How to Use AI Agents That Work

AI agents generally don’t work perfectly immediately, but they are pretty good. If you’re taking AI agents seriously, then you may benefit from these tips that will help you use AI agents that actually work.

Train them.

I introduced Aljay Ambos earlier; he used the AI agent to come up with new features. Since his use case was so impactful, with a 36% boost in user engagement, I asked him for tips on how he got there.

Ambos said, “We trained it. We made special datasets that focus on the specific language, worries, and goals of our audience.

“If you are considering using AI agents, focus on making them personal. A standard AI model can only help you to a certain point. When you customize it to understand your niche and integrate seamlessly with your team, you can go beyond simple automation and build a real partnership that produces significant results.”

Be strategic.

This point of using AI strategically has cropped up earlier in this article, but I wanted to keep it here as I think it is essential if you want to use AI agents in a way that works.

The world is abuzz with AI, its benefits, and all the use cases. It is very easy to get distracted by the promise of an AI agent that will change your life or business, but if you’re not integrating AI strategically, it will fail.

Matthew Franzyshen, business development manager at Ascendant Technologies Inc., warns that businesses must integrate AI agents into their workflows. He says, “Make sure that you actually know what you need them for. Don’t add AI agents just because that’s what everyone else is doing. Audit and understand your business needs so you can choose one that is aligned with your unique business goals.”

I like Franzyshen’s tip. If you audit your business and understand why you’re integrating an AI agent, you’ll be more likely to use it (especially if it’s integrated into workflows) and assure staff who feel AI is going to replace them.

Refine chatbot scripts.

There are ways in which you can manage chatbot outputs and customer satisfaction using AI agents.

Nikita Sherbina, co-founder and CEO at AIScreen, provides an example of how managing chatbots contributes to success. He says, “A specific example of our success is the implementation of AI chatbots, which handle 80% of routine customer queries, reducing response times by 60%. To maximize effectiveness, we continuously refine chatbot scripts based on user feedback and ensure seamless escalation to human agents for complex issues.”

I like this idea. No matter how much information you give an AI agent, you don’t know how effective it will be until you try it. No doubt, once you put a chatbot in front of your customers, you’ll get feedback, good and bad, about how it works. It’s important to consider all feedback and work on improving the chatbot bit by bit.

The good news is an AI agent can help you analyze all the feedback, just like Ambos from Twixify taught us earlier.

Trial an AI agent. It might surprise you!

When I’m on the internet, particularly social media, there seem to be two camps: one for AI, another against it, and there’s rarely any common ground. Putting this article together made me see AI agents differently.

As I said, I was pleased with the openness of people using AI and their acknowledgment of the good and the not-so-good. In writing this article, I got to dig into the types of AI agents, and even I (an optimist) became more optimistic about AI. I was introduced to AI uses that I hadn’t considered for an AI agent and these uses were in my everyday life already.

The more hype I hear around the cons of AI agents, the more I believe that well-intentioned professionals need to get behind AI to drive this incredible tool into ethical usage that feels good for all.

AI is in more places than we realize, and it’s not going anywhere, but at least we can see that AI can be helpful, especially if used with some of the tips provided in this article.