Lead (Intro) 2:14 AM. Chicago. The smartphone of Mike, owner of the “Auto-Drive” auto parts online store, vibrates on the bedside table. Another notification. The screen illuminates the bedroom with cold light for a second: > Telegram: “Hello, how much is shipping for a rear bumper for a Camry to Austin?”

Mike turns over to the other side. He won’t answer. His managers — Sarah, Andrew, and intern Max — are also asleep. They have a legal break until 9:00 AM. And the customer, who is currently holding a card in his hands and is ready to part with $250, waits exactly 3 minutes. Not receiving an answer, he closes the dialogue and goes to competitors — to a large marketplace or to someone whose “green light” is on around the clock.

In the morning, Mike will see this message. But it will be too late. — “We lost him, boss,” Andrew will say, sipping instant coffee. — “He bought it somewhere else.”

This scene is repeated a thousand times every night in small and medium-sized businesses across the US. We are used to considering sales as some kind of mystery tied to charisma and “chemistry” between people. We pay managers salaries, commissions, and KPIs so that they talk to people qualitatively.

But let’s be honest: 80% of their work is not high-level negotiations. It is the work of a help desk. — “How much does it cost?” — “Is it in stock?” — “Are there any discounts?”

We overpay living people for working as biorobots. And at the same time, they get tired, get sick, burn out, and (oh, horror!) sleep at night. In 2025, this is an impermissible luxury. Because this work has already been learned to be done better. And we are not talking about those stupid bots from 2018 that forced you to press the number “1” and then froze.

We are talking about “iron employees” who never sleep.

Chapter I. Evolution: From “Dumb Buttons” to AI Agents

To understand why now is the time to fire the “help desk” in the sales department, we need to look back. Why does your eye likely twitch at the word “chatbot”?

The Era of Buttons (2016–2022)

Mike has already tried automation. In 2020, in the wake of the pandemic, he ordered a bot from a studio for $500. He was promised an “auto-funnel” and an “explosion of sales”.

In reality, it looked like this: Client: “Hi, do you have a radiator for a 2015 Ford Focus?” Bot: “I don’t understand you. Choose a menu item: 1. Catalog 2. About us 3. Contacts”

This is called Linear Logic. The bot was like an old push-button telephone: a step to the left, a step to the right — an error. This is not a dialogue, this is an interrogation. The client felt as if he were filling out a tax return, not buying a spare part. The result of Mike’s experiment: the bot was turned off after a month. Clients simply yelled into the chat: “CALL A HUMAN!!!”, because breaking through the menu was harder than dealing with the DMV.

The Era of Intelligence (2024–…)

What changed? Two things.

Telegram became a platform. It is no longer just a messenger. It is the WeChat of the Western world. With 950 million users (according to mid-2024 data), it has overtaken almost all competitors.
LLMs (Large Language Models) appeared.

A modern bot (let’s call him “Mike-2.0”) does not work according to scripts. Under the hood, it has a neural network (GPT-4o, Claude, or fine-tuned Llama models) that understands the meaning, not keywords.

What it looks like in reality:

Client: “Listen, I need a bumper for a ‘17 Camry, black, do you have any?”

Old bot: Error. Enter the part number.

New AI Agent:

Understands context: “‘17 Camry” — this is a specific model year. “Black” is the body color.
Goes to the database (API request): Checks Mike’s CRM for availability of items.
Formulates an answer: “Yes, there are two options in stock. OEM (slightly used, but in perfect condition) for $250 and a new aftermarket one for $120. Both are black, no need to paint. Which one should I send a photo of?”

This is not magic. This is RAG (Retrieval-Augmented Generation) — a technology where a neural network first searches for facts in your database, and then “humanizes” them for the client. The bot behaves like an experienced warehouse manager Joe, who remembers everything. Only Joe sleeps, but the bot doesn’t.

And most importantly: “Mike-2.0” is not annoying. He knows how to joke (if allowed), handle negativity (“Sorry for the delay in delivery, here is a coupon for $10”), and close the deal.

Chapter II. What the “Iron Employee” Can Do (Besides Not Sleeping)

When entrepreneurs hear the word “bot”, 9 out of 10 imagine stupid spam mailing or an answering machine “We will call you back”. But in 2025, an AI agent is a full-fledged “digital employee”. He doesn’t smoke, doesn’t ask for leave in July, and doesn’t forget to call back.

Here are 4 specific tasks that Mike (our hero from the first chapter) completely delegated to the neural network.

1. Lead Qualification (Filter for Onlookers)

Pain: Mike’s managers had a problem: they spent 4 hours a day corresponding with people who were “just asking”. — How much is the spoiler? — Will it fit a ‘05 Civic? — What if I have $5?

A senior salesperson spent time consulting a teenager who wouldn’t buy anything.

Bot Solution: Now the dialogue is started by AI. It asks 3 qualifying questions before connecting a human:

Car make and year?
What exactly are you looking for (part or assembly)?
Budget: OEM or aftermarket?

If the bot sees that the request is “junk” (non-profile or inadequate budget), it politely sends the user to read articles on the site. Only a SQL (Sales Qualified Lead) gets to a live manager in CRM — a person with money and a clear need.

2. “Smart” Warming Up (Retention)

Pain: “Bought and forgot”. The client bought oil, left, and the store lost him forever. Managers hate calling a “cold” base: “Hello, you bought from us a year ago…”

Bot Solution: The AI agent remembers everything. It knows that client Alex bought motor oil 5 months ago. So, by mileage (about 6-8k miles), it’s time for him to change it again.

The bot writes itself:

“Alex, hi! Judging by the time, your ride has driven about 6,000 miles since the last oil change. The engine won’t say thank you if you delay. I’ve already collected a cart for you: the same oil + filter. The price is the same. Shall we arrange delivery for Friday?”

This is not spam. This is care. The conversion of such messages is 15-20% (versus 1-2% for regular mailings).

3. Checkout Right in the Chat (Telegram Stars)

Pain: The biggest “churn” of customers occurs at the payment stage. The manager throws a link to the site -> The client goes -> Forgets the password for the personal account -> He is too lazy to enter the card -> “Ah, I’ll pay later” -> Closes the tab.

Bot Solution: In 2024, Telegram introduced Stars and native payments. Now money is debited directly in the dialog. The bot sends a product card. The “Pay” button (Apple Pay / card) appears right there. No transitions to external sites. Click. FaceID. Paid. The fewer actions, the more money in the cash register.

4. Iron Discipline 24/7/365

Sounds banal, but this is a killer feature. — The bot works at 3 am on January 1st. — The bot answers instantly (the average reaction time of a person is 15 minutes, of a bot is 2 seconds). — The bot never has a “bad mood”, PMS, or a hangover. It is always polite, even if the client is rude.

Chapter Summary: Mike didn’t fire people to replace them with a calculator. He relieved them of “routine” and allowed them to deal only with closing deals and complex clients. But about this — and about exactly how much money it saved — in the next chapter.

Chapter III. The Economics of the Question (Mike’s Calculator)

Let’s put aside emotions and get out a calculator. Entrepreneurs are often afraid to implement AI, thinking that these are “technologies for Google and Tesla” that cost millions. The paradox is that not implementing them costs much more.

Mike (our store owner) sat down and calculated how much one average sales manager costs him, and compared it with the cost of an “Iron Employee”.

The figures are relevant for the US market at the end of 2024.

Option A: Living Person (Andrew)

Andrew is a good guy. He sells spare parts, sometimes is late, sometimes gets sick, and he needs to feed his family.

Salary (Net): The average salary of a sales manager in the US is around $60,000 per year. In major cities, a good salesperson won’t work for less than $75,000 (base + commission). Let’s take a conservative $5,000 per month take-home.
Taxes and benefits: To pay $60k net, the company pays taxes, insurance, and benefits (~30-40% on top). That’s another $2,000+ per month.
Indirect costs:
- Office space rent.
- Hardware depreciation (laptop/phone).
- CRM license (Salesforce/HubSpot).
- Coffee, snacks, team events. Minimum: $500/month.

Total per month: ~$8,000. Total per year: $96,000. And this is one employee who works 40 hours a week.

Option B: “Iron Employee” (AI-Bot)

How much does it cost to hire a robot that will replace Andrew in routine tasks?

Development (Capex): We are not considering cheap builders for $50 (these are toys). We are talking about custom development of integration with a database on Python/Node.js. The average market price (agency or experienced freelancer): $3,000 – $10,000 one-time. Let’s take the upper bar: $10,000.
Maintenance (Opex): The bot “eats” tokens (payment for using the neural network API, for example, GPT-4o). For an average store (1000 dialogs per month), this is about $50-70. Server (VPS): $20. Total: $100/month.

Total for the first year: $10,000 (development) + $1,200 (expenses) = $11,200.

Final Score

Expense Item	Live Manager (1 year)	AI Bot (1 year)
Cost	~$96,000	~$11,200
Working hours	8 hours / 5 days	24 hours / 7 days
Scalability	1 person = 5-7 chats (limit)	Infinite. At least 10,000 chats.
Human factor	Gets sick, burns out, forgets	Absent

Mike’s Conclusion: The implementation of the bot pays off in the second month. In a year, the bot saves the company over $80,000 in pure cash. This is the cost of a brand new luxury SUV, which Mike can buy for himself simply by automating answers to the question “Is it in stock?”.

But does this mean that Andrew should be fired? No. About this — in the next chapter.

Chapter IV. Where a Person Is Still Indispensable (A Fly in the Ointment)

After the previous chapter, it might seem that I am calling for firing all people and replacing them with scripts. No. If you do this, your business will die in about a month.

The “Iron Employee” is ideal, but he is a sociopath. He has no soul, empathy, or intuition. Here are three zones where bots are not allowed (yet).

1. “I demand the manager!” (Empathy and conflicts)

Imagine a situation: a courier dropped a headlight, the client is furious, his repair is disrupted. He writes in the chat in caps, demanding blood. Bot: “We apologize for the inconvenience. Your feedback is very important to us.” This phrase is like a red rag. The client will explode.

A person is needed here. Only a live manager can read an emotion, sincerely (or professionally) sympathize, offer a personal discount, and “resolve” the conflict. The bot does not know how to apologize for real.

2. Big Deals (B2B and High Ticket)

Selling brake pads for $40 via chat is easy. Selling a contract for the supply of spare parts to a fleet for $200,000 a year is impossible.

Where checks are high, they buy not from a company, they buy from a person. Personal meetings, long negotiations, non-standard conditions, “having coffee” — this is the territory of people. The bot cannot play golf with a partner.

3. Strategy and “Intuition”

The bot works according to algorithms (even if they are AI). He cannot wake up in the morning with the thought: “Listen, let’s try selling maintenance kits to BMW owners at a discount, they are suffering without spare parts now!”. Creativity, hypotheses, non-standard moves — this is the prerogative of the human brain.

New Role: Neural Network Operator

So what happened to Mike’s managers? He didn’t fire everyone. He fired two “paper pushers” and left one — Andrew, the most experienced one.

But Andrew’s role has changed. He no longer sits on the phone answering “Yes, it’s in stock.” He sits in front of a monitor where the bot’s admin panel is open. He sees hundreds of dialogues conducted by AI. He is a pilot.

The bot handles the routine.
If the bot reaches a dead end or the client asks for a person, Andrew takes control.
Andrew deals with VIP clients.
Andrew comes up with new scripts for the bot.

The manager of 2025 is not a “dialer”. He is a supervisor and sales architect. And one such Andrew (with the help of a bot) makes the revenue that a department of five used to make.

Finale: Survival Instructions

We are on the verge of a paradigm shift. Those companies that in 2025 continue to force living people to answer questions “Until what time are you open?” simply will not survive in the efficiency race. They will be eaten by margin, payroll, and faster competitors.

What should an entrepreneur do right now?

Do not automate chaos. If you have a mess in CRM and managers forget to call back, the bot simply “scales” this mess. First, write down business processes on paper. The bot is an amplifier. Reinforce order, not entropy.
Start small (MVP). Do not try to build a “Death Star” for a million dollars right away. Start with a simple AI agent that can do two things: qualitatively qualify a lead and answer the top 10 questions from the FAQ. This will already remove 50% of the load from the department.
Get used to the new role of people. Your employees are no longer robots. Explain this to them. Tell Andrew: “Your task is not to answer calls, but to make sure that the bot sells, and connect where brains are needed.” This will increase their loyalty and your profit.

P.S. Mike, by the way, sleeps well since then. His store works around the clock. And Andrew recently closed a deal for the supply of spare parts to a taxi fleet while the bot processed 150 small orders for filters.

The future has already arrived. It’s just unevenly distributed. Which side are you on?

"I Fired the Sales Department and Hired a Bot": Why Managers Must Become Neural Network Operators in 2025