Why Every Business Website Needs an AI Bot

Artificial intelligence has moved past the hype and into practical, everyday business applications. One of the most effective — and accessible — ways businesses are using AI is through website bots. These bots improve customer experience, plug gaps in lead follow-up, and even provide valuable insights into what prospects and customers care about most.

In this discussion, the team at Simple Machines explores why AI bots are becoming a must-have for business websites, how companies are seeing value, and what steps to take if you’re considering deploying one.

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Transcript

Hi everyone. Thanks for joining us for today’s discussion, Why Every Business Needs an AI Bot on Your Website, where we’ll cover real wins, smart strategies, and tips for how to get started. I’m Charlie Nadler, Chief of Strategy at Simple Machines, and I’m here with Jill Golden, President of Simple Machines, and Tim Stall, Principal Engineer and technical partner who’s been on the front lines of building and deploying AI bots to set the stage.

AI has obviously been in a hype cycle for a while—there’s been a lot of hot air—but what we’re starting to see is a real shift from buzz into practical, real business use cases. Today, we’re going to dig into one of the most effective use cases we’re seeing: AI-powered website bots.

So, Tim, I’d like to start with you, because your background includes the gamut of tech. Why focus on AI chatbots?

Sure. It’s a great question. First, there’s a very easy barrier to entry—you’ve got to start someplace—and it’s very visible to your customers. The first thing your customers see is your website. It’s the front door to your company. They see, “Oh, here’s practical AI that’s actually helping me.” It’s a service to your customers, and the sky’s the limit. It’s easy to get your foot in the door, see what kinds of questions your customers are asking, and then expand accordingly. It’s the best of all worlds.

And from your perspective, why is this an obvious place to start? Obviously there are lots of different types of AI businesses are thinking about—why AI chatbots?

As far as tales from the front lines go with our clients and prospective clients, small business owners and the types of businesses we work with—many executives, founders, owners, and sales resources—are stretched pretty thin. They’re often juggling sales, account management, and customer service. This leads to lagging response times and diminished customer experience. Many of these businesses compete on customer service and experience, so they quickly see this as a competitive edge. As Tim mentioned, it’s very easy to pilot, and as they dip their toe into the AI waters, it’s something they can operate and analyze quickly, with very tangible goals around response times, lead coverage, and so on.

What we’re seeing is it’s an easy way to take a very straightforward AI use case and deploy it within their organization. Let’s dig into some specifics—objectives and value. It will depend on the business, but Tim, what are common areas where businesses are seeing ROI? What tangible gains do these bots tend to deliver?

If you start with the website chatbot, the first is helping with customer conversion. Instead of someone navigating your whole website trying to guess what they want, they can just talk to a bot in a normal, conversational tone. It’s easier for them to find what they’re looking for. You can take that a level further and put the bot over your search catalog so it’s easier to find the product they want to buy.

It’s also easier in terms of customer retention. If you ask an owner, “What are the top five concerns your website visitors have?” they often won’t know offhand—how would they? The barrier to having someone submit a form is high. But with a bot, people start talking right away. The customer intelligence the owner gets is hard data. Every owner wants to make decisions based on actual customer feedback. That’s just on the web chatbot side.

Then you can go down a big pipeline: if it works on the web, you can share it with your back-office employees. Say there’s HR policy or finance policy lookup—before, an employee might not want to ask a question. Now they can ask the bot and get answers. It can even help avoid safety violations because the guidance is embedded. No one wants to look silly asking a “dumb” question; now they can ask, “What’s the safety protocol for X?” without fear of judgment. There are so many angles where it helps—and measurable outcomes, for sure.

Yeah. And I’m curious what you think there too—you’re talking to prospects and clients every day who are sharing challenges. How does this line up with what you’re seeing?

A big one I come across a lot is plugging a leaky funnel with lead response times and follow-ups. We spoke with a life sciences company that was missing over 200 leads per month. We showed how the bot can respond and push information directly into their CRM and marketing software. The revenue potential, once the conversion math was applied—just like Tim alluded to—was huge.

And the fringe benefit Tim mentioned—the insight loop, seeing what visitors and prospective clients are asking—is a great revelation of what people want and expect on the site. We have a manufacturing company with a bot trained on all their product data and SKUs and how various products can be used in applications and design. The bot fields product questions all day. It’s trained to recommend products based on application, explain what a product can and can’t do, and it’s continuously tuned so answers get better over time. It doesn’t just give a correct answer—it can give a correct answer that’s more advantageous to the company (e.g., recommending a higher-margin product when appropriate).

Seeing what people are asking is great for creating a feedback engine for sales scripts, FAQs we can anticipate, and marketing content or product decisions based on what people are most curious about—or asking for that we don’t yet have. Every interaction will teach the bot something and teach you something.

Totally. I have to admit, that fringe benefit of insights didn’t occur to me until we started deploying these. It’s a goldmine: what we thought people would ask about might not be what they’re actually looking for. Now we know sales messaging, campaign ideas, and content we should push—super valuable.

And as it fields those interactions, it can start to qualify—collect name, email, contact info. Taking it a step beyond Q&A, it can capture that info and push it into your CRM—like HubSpot. That’s key and technically straightforward to deploy.

Cool. One thing we should clear up: not everyone is familiar with the different types of bots. When we talk about AI bots—Tim, can you explain the difference? Some folks have had frustrating decision-tree experiences.

Sure. You can have a “dumb” bot—poorly trained with bad documents, bad tuning. The canonical example: if it’s trained on the entire web instead of your specific data, it starts giving your competitors’ products as referrals. You ask, “What’s a good product for X?” and it replies with competitors’ info. With friends like that, who needs enemies?

Or it mishandles PII, or doesn’t give practical answers—just long-winded responses to questions you didn’t ask. The canonical example is hallucination. Brand damage becomes a real concern. A troll asks, “Why do you hate purple clothes?” and the bot obliges with nonsense. Or you ask it to set an appointment or get an account balance—it says “Yes, I’ve received your resume and forwarded it along,” but there’s no back-office hookup. Now a customer says, “I gave my info to the bot,” and you’re stuck.

When the tech first came out, it was cool, and a lot of “ambulance chasers” rushed out bots that weren’t applied in a practical way—garbage in, garbage out. Long answer short: there are many ways to poorly train a bot.

That ties into another common question: we see lots of AI agents and bots out there—some are very cheap. “Can’t I just buy one, throw it on, and say good to go?” Why is a managed service better than set-it-and-forget-it?

Great point. Think of it like this: you can get a WordPress subscription for $9/month—but that’s a universe away from “here’s our entire website; it’s the front door to everything.” With a managed service, trained professionals walk you through end to end, and there’s also the platform behind it. Sure, you can pull models from GitHub and go nuts. But think about staff time. If your team is truly brilliant enough to reverse engineer an entire platform in an afternoon, wouldn’t they be better used on your proprietary roadmap—features only you can build?

People compare apples and oranges. “I can get free or cheap bots.” They ignore staff training, the customizations, and the first time you hit a platform roadblock. It’s like buying a cheap bike at a garage sale when your goal is to go to Hawaii. The bike won’t get you there—no matter how cheap it was. The “tyranny of the urgent” leads to picking the cheapest transportation, but it can be far more expensive when you realize it didn’t work and you have to restart from scratch—now you’re further behind.

Yeah, for sure. And we’ve been deploying these bots for a while—we’re seeing transcripts and summaries come through. Part of this is watching how the bot evolves with training and service. Jill, you alluded to this—what’s struck you as you’ve watched deployments and feedback?

What’s been interesting—back to Tim’s point—you can deploy one of these with a cheap overseas service, but this is an ambassador for your brand, speaking on your behalf and answering questions. I wouldn’t trust my business to a $5/month overseas solution. People are starting to see the differences Tim described versus the cheap alternatives.

They’re also understanding the bot is only as good as the data—product data, internal knowledge base—whatever you feed it. Just like any employee. A bot may give a correct answer, but it might not be the best answer you’d give in that scenario. They appreciate the iterative aspect—oversight and continuous improvement using real data the bot sees on the front lines.

Something else—harkening back to why managed service matters and why this is different: a lot of people have seen chatbots that are rigid, decision-tree based. For instance, you ask, “Do you do tours in Miami?” and it replies with “Here’s our book-a-tour page.” It’s not answering; it’s parsing keywords and handing you a pre-linked page. That’s not a real conversational, AI-enabled bot. Clients are seeing the caliber of conversations this can now have, versus what they were used to even a couple years ago.

That’s a good and important distinction, because many still think “chatbot = decision tree,” and those experiences rarely deliver what you need. I’m glad you called that out. Thinking about real-world examples: Tim, some interesting things I’ve seen—one consulting client has had chats in different languages that the bot handled, which I hadn’t thought about but is handy and saves time. The manufacturing client Jill mentioned—the bot answers tons of product questions, and the client said even when it tells a visitor, “No, we don’t offer that product,” they’re happy because a salesperson doesn’t have to field that call. It’s freeing up a ton of time. Any other trends jump out to you?

One thing is understanding the proportion of each type of comment. It’s one thing to say, “Someone asked this once,” but how big is it? Is 25% of visitors asking the same thing? Half? Quantifying with actual data is a breath of fresh air when you’re making decisions all day long. You might discover that half your audience keeps asking about “Made in the USA” products, or that you’re getting Polish and Spanish speakers. It opens your eyes.

You also see how visitors talk to you—their tone and keywords. People don’t want to be shoved into a box: “Pick your keyword.” The old flow makes people think, “None of these apply; I’m different,” and they get frustrated. A conversational bot takes the opposite approach: “Whatever’s on your mind, I’ll listen.” And doing that 24/7 matters. People are busy—by the time they get to your site and see “Talk with a live agent—come back Monday,” it sends the wrong message. There are a lot of measurable insights there.

It’s been eye-opening. So, let’s say someone’s watching and thinking, “This sounds great—I’m interested.” Jill, what first steps help prepare for success?

Good question. Looking at sales or marketing use cases, start by defining a goal based on where friction occurs: conversion on the website, lead follow-ups, or ushering high-traffic visitors to specific info or pages. Think: where do we experience concerns or frustrations? What are we hearing from prospects or customers that this could alleviate?

Define the goal. Start simple. A straightforward Q&A bot is an easy first foray into AI. What’s exciting is that people then start asking, “Could it do this?” “Could I do that?” Next, leverage what you already have—many already use a CRM like HubSpot and have a website. Use that to pump the bot full of knowledge. And set expectations—like a new employee, it only knows what you give it. It won’t be perfect on day one, but it will get better as people use it. Commit to iteration, stay open-minded, solve a small, specific, low-risk use case first, and go from there.

I like what you said about “it only knows what you tell it.” Another fringe benefit: if the bot doesn’t know something, maybe it should—maybe it should be on the website. We’ve seen that cycle of optimization, which is a nice upshot. And Tim, to Jill’s point—people start thinking, “Can it do this? Can it do that?” Of course it can do a lot—calculations, catalog searches. What’s a good order of priority for new users?

With the disclaimer that the bot we offer at Simple Machines can do a lot—many cheap bots cap out. They can’t call APIs effectively or can’t be trained on the right docs. Once you can call an API, you can do almost anything. HubSpot, for example, has a wonderful API. Whether it’s leads, deals—anything HubSpot exposes via API—you can connect the bot so it can insert a lead or do a text-based lookup. You could say, “Hey, remind me who’s the contact at X?” and the bot connects to HubSpot.

On security, because the bot calls APIs out to your systems, it’s not slurping your secure information into the bot. The Simple Machines approach respects “security first.” Your secure source of truth—like HubSpot—stays secure, and the bot calls into it. Many cheap or bad bots say, “Give us all the keys to the kingdom, then our bot can function.” Not good.

Once the bot can call APIs, you’re home free. Connect it to Zapier, n8n, HubSpot—and run. One use case is connecting it to a Google Sheet as a calculator, where the client maintains the sheet and the bot calls it. In other cases, it looks up records in HubSpot. In others, it books meetings. Anything with an API gives you thousands of options.

Well, we covered a lot today. We talked about why businesses should adopt bots, the differences between smart and dumb bots, and how teams can get started. Jill and Tim, appreciate your time today. If anyone wants to learn more, we’ve got plenty of resources at simplemachinesmarketing.com. Thanks!