How to implement AI in a company: the order of steps

Why most AI implementations fail

A company buys a ready-made solution, connects it to the messengers and waits for a result. The robot answers off the point, customers get annoyed, reps go back to doing things by hand. A month later the implementation is written off as a failure.

The reason is not the technology. AI works when it is clear which task it solves and how it fits into the process. If the sales team has no rules, the pipeline in CRM does not reflect reality and reps work on instinct, the robot will only amplify the chaos.

Implementation starts with a sales review. You have to find where the company loses money: enquiries left unanswered, customers who wait three days for a price, deals stuck at one stage for months. Those are the points to automate first.

Where to start: a review of the sales team

The first step is to look at how the team works now. Where enquiries come from, who handles them, how long it takes to get the first answer, at which stage customers drop off most often. It often turns out that a third of leads gets no answer at all, because the enquiry arrived in the evening or landed in the wrong channel.

In the live GetGate project it turned out that 27% of enquiries arrive in the evening and at weekends. They used to wait until Monday morning, and by then some customers had already found another supplier. The robot began answering at once, asking clarifying questions and writing the data into CRM. By morning the rep had a record with the fields filled in and could see how hot the customer was.

Without a review it is not clear what to automate. If the problem is that reps cannot close deals, an answering robot will not help. You need scripts, work on objections and quality control of the dialogues. Technology solves a specific pain, not sales in general.

Putting the CRM in order

AI relies on data. If the CRM is a mess — fields unfilled, deals hanging for years, statuses named differently by everyone — the robot will not be able to work with it. Before automation the CRM has to be put in order.

We build the pipeline around the customer's real process. Every stage is a specific action: the quotation was sent, the meeting was agreed, the invoice was issued. Tasks are attached to each stage and set automatically. The rep does not have to remember to call three days after the meeting: the system reminds them.

Then we connect the channels. Telegram, WhatsApp, Instagram, the form on the website, Avito — everything must reach the CRM automatically. The enquiry is created at once, the rep sees the history of the correspondence, the robot knows the context. Without that link the implementation stalls: data is lost, customers get duplicated, reps work out of several interfaces.

In that same GetGate project five product groups are counted automatically. The rep used to look by hand at what the customer had ordered before and suggest related items. Now the system itself shows what to upsell and sets a task for the call. The routine is gone, the rep is selling.

Training the robot on real dialogues

A sales robot is not a chatbot with canned answers. It learns from the company's correspondence, understands the context and answers to the situation. But for it to work well it needs a base: scripts, frequent questions, ways of answering objections.

We take reps' real dialogues from the last months, pick out the typical scenarios and train the robot on them. It learns to answer questions about price, deadlines and terms of work, to clarify the details of an order, to explain how one service differs from another. It recognises voice messages and photos and remembers the history of correspondence with the customer.

In our tests on real correspondence the robot's answer was no worse than a human's in 80% of cases in a blind comparison. It means the customer does not feel the difference. They get a quick answer to the point and move on down the pipeline.

The robot does not replace the rep. It qualifies the enquiry, records the data, answers standard questions. As soon as the customer is ready for a meeting or asks for an individual offer, the deal goes to a human. The rep gets a warm customer with a filled-in record, not a cold enquiry with one phone number.

Quality control and analytics

Automation without control is a lottery. The robot may start answering off the point, a rep may lose hot customers, the pipeline may fill with junk leads. You need a system that sees this and signals.

AI listens to calls and reads chats, scoring them against a checklist: did the rep greet the customer, did they clarify the need, did they propose a next step, did they handle the objection. It scores each rep and finds the losses. The manager sees who works well, who needs a review, where the team sags.

Analytics shows plan against actual in real time. How many enquiries arrived, how many were handled, how many turned into meetings, what the conversion is at each stage. A breakdown by channel: where the hottest customers come from, where the lead is expensive but converts poorly. A breakdown by service and by rep. A revenue forecast from the pipeline. Alerts when something sags.

Without analytics it is not clear whether the implementation works. The robot answers — but are sales growing? The CRM is configured — but do reps use it? Data is needed to refine the system and scale what gives a result.

Building the processes and training the team

Technology does not work without people. Reps have to understand how to use the CRM, when to enter the dialogue after the robot, how to push warm customers along. If this is not explained, they will ignore the system and work the old way.

We write the rules: what the rep does at each stage of the pipeline, how to answer typical objections, what the next step is for every lead. We review the deals that closed and those where the customer said no. We look for patterns: why some reps sell more, where customers are lost, what can be improved.

Onboarding new reps becomes faster. It used to take a person a month to get into the product, learn to answer questions and make their mistakes. Now they open the CRM and see the scripts, the templates, the history of successful deals. The robot takes the initial qualification, and the rep works with warm leads from day one.

In the live GetGate project about an hour of working time is freed up for a rep every day. They used to answer the same questions, clarify the delivery address, ask again what exactly the customer needed. Now the robot does that work and the rep is selling.

Scaling and growing the system

The first implementation is a test of a hypothesis. You connect the robot to one channel, set up a simple pipeline, launch it on part of the enquiries. You watch how it works, collect feedback from reps and customers, fix the mistakes.

When the system has shown a result, you scale. You connect the remaining channels, add scenarios for other services, bring in quality control, deepen the analytics. The robot learns new answers, the CRM grows automations, the processes get more precise.

It is important to move gradually. If you automate everything at once, it is unclear what worked and what did not. Better to start with one pain — handling evening enquiries, for example — solve it and move on. Each step brings a measurable result, and you can show it to the team.

AI in sales is not a one-off implementation. It is a system that grows with the company. New channels appear — we connect them. The product range changes — we train the robot. Reps are hired — we adapt the rules. Technology works when it is alive and adjusts to the business.

What to avoid when implementing AI

The main mistake is starting with the technology rather than the task. A company hears about AI, wants to be modern and buys a solution. But if it is unclear what exactly needs automating, the money goes nowhere. Review first, tool second.

The second mistake is implementing without the team. The manager decides for everyone, configures the system, launches the robot. Reps do not understand why it is needed, fear being replaced and sabotage the rollout. People have to take part from the very start: explain what slows them down, test the solution, give feedback.

The third mistake is expecting an instant result. AI is not a magic button. The first month goes on setup, training the robot and running the processes in. The result comes when the system is working, the reps have got used to it and the data has accumulated. Dropping it after a week because «it did not take off» gets you nowhere.

The fourth mistake is automating a bad process. If reps work without scripts, the CRM is not kept up and customers are lost for no reason, the robot will only speed up the disorder. First put things in order, then automate.

In short: where to start right now

If you are thinking about implementing AI, start with a review. Look at where the sales team loses money: enquiries without an answer, slow reaction, low conversion at certain stages, no data to decide on. Write down the three main pains.

Then check the CRM. If there is none or it is not used, start with it. Build the pipeline around the real process, connect the channels, automate the tasks. Without that foundation the robot will not work.

When the CRM is in order, train the robot on real dialogues and launch it on one channel. Give it the simplest but most frequent questions. See how it copes. Collect feedback from reps and customers. Refine and scale.

We help companies walk this path from the review to a working system. We take the processes apart, bring in the CRM, train about the sales robot, set up analytics and quality control. More about how this works and what a company gets is on the page about implementing AI in business.