Before you start Googling: No, AI agents aren’t secret spies fueled by mysterious government schemes. The AI agents I’m talking about actually serve an entirely different purpose.
Despite their misleading name, AI agents were designed to assist, enhance, and optimize the operations and workflows of various businesses, especially enterprise-level ones. Although they’re a relatively new technology, AI agents are already changing how companies troubleshoot issues, simplify processes, and increase employee productivity.
In this post, I’ll talk about what AI agents are, how they serve different business needs, and (if you’re considering using them) what to expect from their capabilities. Plus, you’ll get some real, unfiltered, industry-informed advice from two very well-versed AI agent experts.
Let’s get into it.
Table of Contents:
- Statistics You Should Know About AI Usage in Sales
- What do AI agents do?
- How To Build an AI Agent, According to Wesley Baum, Bluleadz’s AI Specialist
- AI Agents Use Cases
- AI Agents for the Win?
What are AI agents?
AI agents are intelligent systems that autonomously perceive, analyze, and act to achieve specific goals based on data and algorithms.
As I mentioned earlier, AI agents are relatively new to enterprise-level companies. However, their adoption is growing rapidly as enterprise-level businesses recognize their ability to do the things that their human reps either 1) don’t have the time to do or 2) no longer can do (not because they’re not equipped to, but because they’re focusing on more high-impact, strategic tasks and requests).
At the core of all AI agents is the capability to streamline decision-making and problem-solving efforts, making operations — no matter what department they’re happening in — easier, faster, and less time-consuming.
Thus, mostly every AI agent can:
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- Analyze data
- Automate tasks
- Make intelligent decisions (based on predefined rules or machine learning models)
- Answer common queries
- Detect anomalies (like fraud or security threats)
Beyond these functions, AI agents can also learn and adapt over time. Through machine learning, they refine their responses, optimize processes, and improve efficiency based on patterns, feedback, and data. This makes them an ideal investment for businesses looking to enhance productivity, reduce manual work, and make data-informed choices faster.
There’s still much to learn about AI agents, from how they serve businesses and employees to the long-term impact of their integration across industries. But before I dive into the nitty-gritty details, I think it’s important to cover some need-to-know statistics about overall AI usage in sales.
All of this said, visit the following section to explore key insights on how AI is shaping the sales landscape.
Statistics You Should Know About AI Usage in Sales
When it comes to using AI in the enterprise biz world, salesfolks are the most keen on leveraging it to get things done.
Here’s a closer look at salespeoples’ overall sentiments around AI adoption and effectiveness, according to HubSpot’s 2024 Sales Trends Report:
- 81% of sales pros say AI tools help them in their work
- 78% of sales pros say AI tools help them be more efficient
- 66% of sales pros agree that AI tools help them provide better personalization
- 41% of sales pros use AI to recognize and respond to buyer emotions or sentiment (83% of them say it’s effective)
Additionally, HubSpot’s 2024 AI Trends for Sales Report revealed the following:
- 43% of salespeople say they’ve been using AI at work regularly (this is a 19% uptick from 2023)
- 87% of salespeople report increased usage of their CRM tools thanks to AI integrations
- 42% of salespeople say task automation tools are one of the top ways they use AI at work
- 69% of surveyed salespeople with an AI-powered CRM say that AI integrations make them more likely to use their CRM
So, what does this all mean? Well, a few things. Here’s my short n’ sweet summary:
- This data represents the dawn of a new day in how businesses do sales, customer engagement, and operational efficiency
- The impact of AI agents is no longer a “what if” scenario — they’re actively transforming how salesfolks, marketers, customer service representatives, C-suite execs, etc., work, interact with clients and personalize their strategies
- AI agents are becoming essential teammates — not replacements — that empower employees to focus on more meaningful, strategic tasks while automating the repetitive ones
With adoption rates rising and AI-driven tools proving their ROI, it’s clear that AI agents are becoming essential assets for modern enterprise businesses. But beyond just the numbers, what exactly are these AI agents doing to drive such impact? And what should enterprise-level businesses know before investing in this new tech?
I tapped Jeannie Jaworski, Senior Customer Success Manager at HubSpot, and Wesley Baum, AI Specialist at Bluleadz, for answers to your (likely) questions, comments, and concerns. Scroll through the next few sections to read what they said.
What do AI agents do?
AI agents can do many things, but their effectiveness in delivering results all depends on how they’re programmed. Still, regardless of their assigned function or industry application, AI agents, ultimately, have been designed to work collaboratively — both with each other and with employees.
I chatted with Jeannie Jaworski, Senior Customer Success Manager (North America) at HubSpot, to get her honest perspective on:
- How her clients have been adapting to the emergence of AI agents
- What primary concerns she’s come across with her own customers
- What she’s been recommending to ensure the businesses she serves engage both AI agents (and human ones) with balance
Check out what she had to say below:
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1. AI agents are here to improve how your reps work, not replace them.
When Jeannie and I got to chit-chatting, one of the first things I probed her about was the challenges she’s seen her clients face while onboarding AI agents into their business. She told me (candidly, of course) that the thing folks worry about most is likely the same fear you’ve already seen splashed across tons of news headlines: that AI is going to replace human reps.
“I think there is still a level of concern about people wanting to make sure this doesn’t replace their job,” she told me. And although she acknowledged this widespread concern, Jeannie also made it very clear that she knows there’s a different, more empowering way to look at the rise of AI.
“I think that [AI agents] are really valuable if you can see, like, your employees are already doing things that they don’t like to do because of the repetition involved, so those are the things you can outsource to the agent. So it’s definitely not replacing an employee, it’s just allowing employees to focus on things that do require the human touch, instead of the things that can be automated.”
2. If you collect data now, your AI agents (and your human ones) will reap the rewards later.
Finally, at the tail-end of our conversation, I asked Jeannie about what advice would she give to other enterprise-level businesses considering AI agents, and what factors should they evaluate before implementation.
Her response was indicative of two things: 1) data is everything and 2) you can’t measure AI effectiveness in a vacuum, then expect a clear path to meaningful improvement.
“It’s really important for you to be collecting data, not just about the AI agents’ performance, but the human agents’ performance,” Jeannie shared. She then further explained, “A lot of these tools — like HubSpot’s Breeze AI customer agent — will have built-in analytics to tell you about the quantity of the chats, the amount they were able to resolve on their own … all of that is built in. But if you haven’t built that out for your human agents, you won’t have anything to compare [that data] to.”
Jeannie went on to emphasize how getting granular with these data assessments is crucial to identifying areas for growth, pinpointing performance gaps, and, most importantly, enhancing the overall customer experience. “It’s important to [measure] things like CSAT to understand the satisfaction that your customers have when interacting with both your human and AI agents,” she said.
“If we’re not understanding how our human agents are able to respond and how customers feel about that, we’re missing the opportunity to see the success and the impact of responsiveness that AI agents have on a business’s customer experience.”
How To Build an AI Agent, According to Wesley Baum, Bluleadz’s AI Specialist
Wesley Baum, Bluleadz’s AI Specialist, knows everything there is to know about building and deploying enterprise AI agents. Heck, it’s quite literally what he does for a living.
So when we both had a free moment to connect, I asked him everything (yes, everything) I could about:
- Building effective AI agents
- What he’s learned about CRM integration and context awareness within AI models
- What future trends to expect from the evolution of AI agents
- How enterprise businesses should handle AI technology adoption
So, if you’re looking to give AI agents a try but …
- Don’t know what it takes to build and sustain them
- Have no clue what an AI-powered customer experience will look like for your company
- Want further clarity on how to actually ensure the long-term scalability and impact of AI tech overall
Then you’re in the right place. Here’s an overview of the most valuable insights that Wesley shared with me below:
1. The hardest part about building an AI agent? Knowing what’s needed to make it thrive.
During our conversation, I asked Wesley about the key steps involved in building an AI agent and, more importantly, what challenges he’s seen typically arise for businesses as they seek to customize an AI agent for their specific needs and workflows.
He shared that there are a couple of main components when building an agent. They’re as follows:
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- Picking the right model. According to Wesley, these decisions often come down to an “intelligence versus price ratio.” For example, depending on what you need your AI agent to do, you might not need to spend copious amounts of money on a high-end, more expensive model with extra reasoning capabilities right away.
- Prompting. Wesley says “prompting isn’t super important these days, but it still matters.” Prompting success primarily depends on providing context, however, the rules can change based on the model. Wesley recommends building out context through retrieval augmented generation, aka RAG.
- Providing a role/use case. As Wesley put it, “You have to give [your AI agent] as much information as you would provide to a human employee.”
Once you have all these pillars in place, Wesley says you can then start building out the AI agent of your dreams. He explained, “Once you have a model selected, you have your prompting and your roll down; you’ve identified a use case, of course, for this agent, you gave it their context using RAG systems, you actually have to build out the tools, right?”
Wesley then went on to share that this component of building an AI agent is more varied but, truthfully, not as complex.
“Really, it’s just certain APIs,” he added. Generative AI APIs are just application programming interfaces that allow developers to integrate AI models capable of creating content — such as text, images, code, or audio — into their own apps, tools, or platforms.
“Most of the agents that [businesses] are going to get value from, that enterprise solutions are going to build upon, will be through orchestrating APIs. Because it’s consistent,” he told me. “And so, those APIs are the tools in which an agent would interact with your text and the same way that you would interact with the UI.”
2. When it comes to building a proficient AI agent, it’s all about data.
When I asked Wesley about what challenges he’s seen his clients face the most when implementing an AI agent, he pointed out (similarly to Jeannie) one distinct thing: Data collection.
“The biggest issue people don’t expect is data. It’s all about data,” Wesley said. He even admitted that when he’s onboarding customers onto HubSpot’s CRM, the first thing he asks about is data architecture.
He also emphasized that he isn’t afraid to ask super specific questions, such as:
- Is your data built to handle AI initiatives?
- Do you have duplicate properties?
- Is your architecture not set up properly?
- Do you have quality issues?
- Are you integrating all of your different data sources and tech stack software that a human would use into one source of truth?
“All of this is very useful because, without proper data, ChatGPT’s not gonna change your business unless it’s built specifically for that, which is reliant upon your data quality.”
3. There will be a rising demand for customized, business-specific AI agent ecosystems.
Lastly, toward the end of our one-on-one conversation, I asked Wesley about how he anticipates AI agents evolving in the next few years and what emerging trends enterprises should pay attention to when considering AI adoption.
Not only did he keep it real with me but, furthermore, he underscored how personalization and scalability, inevitably, go hand-in-hand.
“[AI agents] have to be customized specifically to how every business does business,” he stated. “One-size-fits-all agents … they’re not going to dramatically change your business with AI. It will have an impact, but it won’t be transformative in the way that an eco-system of multi-agent systems — all working well within your tech stack, for your clients, for your products, communicating with your teams — would. All of it has to be customized.”
AI Agents Use Cases
Just like Jeannie affirmed earlier, AI agents aren’t here to take over the world, steal jobs, or make things harder for sales and customer service folks. However, they are here to simplify experiences, both for customers and employees.
Below is a list of ways AI agents are making things — whether it be your workflow or a customer’s journey — a whole lot easier, whether it be through integrating with other platforms (like your choice CRM software), accessing pre-existing knowledge bases, or diagnosing common issues.
Read through my full breakdown of what AI agents can do — along with customer-facing and employee-facing examples — below:
1. AI agents can answer knowledge queries.
No more digging through old docs or waiting on hold. With AI agents, the info is accessible whenever its needed.
Whether it’s an employee trying to understand internal policy or a customer looking for product support, AI agents can pull from vast knowledge bases to serve answers in real time.
2. AI agents can manage calendars.
AI agents can automate scheduling tasks, reducing back-and-forth coordination and streamlining time management. Honestly, it’s like having a personal assistant who actually lives in your calendar.
They check availability, avoid conflicts, and send invites before you even remember the meeting exists.
For example, a booking bot schedules consultations or demos by syncing with your team’s real-time availability. It could also automatically schedule follow-up meetings after a sales call and blocks focus time for important tasks.
3. AI agents can reset account information (like usernames or passwords).
No more “forgot password” loops of doom. AI agents can securely verify identity and walk users through simple account recovery steps — without human intervention.
For example, a self-service assistant can help users (or employees) reset login credentials quickly, even after working hours.
4. AI agents can summarize meeting conversations.
Forget scribbling notes while someone’s talking — AI agents have perfect recall.
They can analyze meeting transcripts in real time and generate concise summaries, action items, and follow-ups.
For example, an AI assistant can capture internal meeting highlights and email a follow-up summary (with assigned tasks and deadlines ... how slay is that?) to all meeting attendees.
5. AI agents can identify high-quality leads.
With AI agents, sales reps can spend less time guessing and spend more start closing.
By analyzing behavioral data and engagement signals, AI agents can score and prioritize leads based on likelihood to convert.
For example, after a discovery call, an AI agent can send a neatly packaged summary to the client, including next steps and discussed solutions.
6. AI agents can track a customer’s lifecycle.
AI agents can follow a customer from their first interaction to post-purchase support, ensuring a personalized and consistent experience.They also remember the little things — like past purchases, preferences, and pain points.
For example, if an AI agent is embedded into a CRM, sales reps can get a ranked list of warm leads in the CRM, complete with insights on intent signals and recent interactions.
AI Agents for the Win?
So… are AI agents the magic wand for every enterprise problem ever? Not exactly. But are they a next-level tool that’s already reshaping how businesses operate, scale, and support both employees and customers? Yeah. 100%.
As I’ve already re-iterated, AI agents aren’t here to completely dismantle the workforce as we know it. However, they are here to:
- Take the ugh out of repetitive tasks (I’m looking at anyone and everyone who works in sales or service)
- Make workflows smarter (and way less manual)
- Give your team room to focus on the high-impact stuff
Regardless of how you like to do work, AI agents are showing up, clocking in, and doing what needs to be done. At request, of course.
So, the TL;DR on AI agents? They’re not robots coming for your cubicle. They’re just digital teammates whose sole purpose is to make the 9-to-5 feel a little less chaotic. And if you’re still wondering whether AI agents are really worth the hype — don’t worry, the best insights are yet to come.
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- How Sales Teams are Using AI
- Giving Time Back to Sales Reps
- Keeping Up with AI Trends
- And More!
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