How to Launch AI Business Process Automation: A Breakdown for IT Directors | Tantal

Tantal Blog: AI Automation for Business Processes

In Thursday at 11:47 IT director of a major distributor closed the task, on which the team had been working for three quarters: AI agent processed 2,300 incoming applications without a single operator on the line. By Friday, the pilot showed an ROI of 5.8x – a figure he presented to the board of directors on Monday.

Below is what distinguishes AI automation from a regular bot, where it pays off in a quarter, how much a pilot costs, and which errors bury a project before the first result.

What's hidden behind the term "AI Automation"

Under one term, three different technologies are hidden, and the confusion between them is worth budgets. Before going to the CEO with a proposal, it's essential to understand what exactly you're talking about.

RPA (Robotic Process Automation). The robot repeats human actions in an interface: opens an email, copies the order number, inserts it into 1C. It doesn't think – clicks on a pre-written script. Good where the process is rigid and doesn't change.

ML Models. They predict based on data: which customer will leave, which payment will be fraudulent, how much product to order for stock. The model gives a probability, and a human or the next system decides.

AI Agents. They read incoming requests in natural language, understand context, choose an action, and perform it: respond to a customer, create a CRM entry, escalate complex cases to an operator. These are what close the scenario with 2,300 applications. Real-world implementations – in the AI Assistants section.

39% of Russian companies already use AI for business process automation (ComNews, January 2026). Your direct competitor has a high probability of having completed their first pilot – and counting the money you're spending on manual operations.

Five processes where AI gives quick results

Not every process is worth automating first. Below are five scenarios where output is measured quickly, and the figure speaks for itself. According to Unicoconnect and Aristral (2025-2026), the average return on investment is $3.70 per dollar invested, with productivity growth ranging from 26% to 55%.

1. Handling incoming applications. The AI assistant responds to typical questions, creates entries, and passes complex cases to humans. Before: an operator closes 40-60 applications per shift. After: up to 95% of typical applications go to automation, with people handling exceptions.

2. Document flow and OCR. The system recognizes invoices, bills, contracts, extracts requisites, and distributes them into accounting fields. Before: an accountant manually enters data, makes mistakes on the fly. After: minutes instead of hours, error field shrinks significantly. How it works with videos and documents – in the Computer Vision section.

3. Analysis and predictions. The model calculates demand, outflow, loading, and suggests actions. Before: reports are prepared weekly and are already outdated. After: daily updates.

4. HR screening. AI filters resumes based on job requirements and assigns primary interviews. Before: a recruiter manually reviews hundreds of responses. After: only relevant candidates reach the final interview.

5. Lead qualification. The assistant asks clarifying questions to an incoming lead, filters out non-targets, and passes them to a manager. Before: sales department wastes time on "just curious." After: managers receive a warm list.

How much does a pilot cost, and when does it pay off

A pilot is one process, one measurable result, lasting 6-8 weeks. The task of the pilot is to test the hypothesis on real data and get a figure that can be taken to finance.

The pilot budget usually consists of four parts: process audit and description, model development and training on your data, integration with current systems (CRM, 1C, phone), and support during testing. Industrial implementation – scaling up a proven solution for the entire volume – is the next step. Mixing them in the budget is not allowed: finance will see the factory price where you're asking for a prototype.

The payback formula: take current costs on the segment (operator FOT, error cost, lost leads), subtract costs after automation, divide pilot cost by monthly savings – and get months.

Companies successfully applying AI automation record a return of 5.8x in 14 months (Aristral, 2026). The Russian segment of generative AI grew fivefold to 58 billion rubles by 2024 (Bothub / Habr, 2025). Demand for contractors is growing with it, and the window "do it before a competitor cheaper" is gradually closing.

Four errors that bury AI projects at the start

78% of major global companies implemented AI in 2025 (Unicoconnect). Most went through a failed pilot first. Here are four traps that can be seen ahead.

Automating chaos. If the process is inherently flawed, AI will make it fast and flawed. Symptom: the process has no description, each employee does it their way. Straighten out the process first, then automate.

Taking on too complex a first case. The more complex the case, the higher the chance that the pilot will get stuck. Symptom: task description has over ten exceptions and "what ifs." Start with simple and repeatable ones.

Not appointing an internal owner. Without someone responsible for the project inside the company, any implementation gets bogged down in approvals. Symptom: when asked "who's handling this," there's no single name. Even non-typical tasks – like AI Video Farm – without an owner first go into the long box.

Waiting for results without data. AI learns from your data. If there's none or it's in chaos, the model has nothing to learn from. Symptom: "the data is somewhere, but nobody can collect it." Assess data before starting, not after.

How to choose the first process: a 15-minute checklist

A good first case passes three criteria. Run the candidate through them on an internal meeting.

• Repeatability. The process happens dozens and hundreds of times per month in a similar scenario. One-time tasks are not cost-effective to automate.
• Data availability. There's history: applications, documents, deals that can be trained on and tested.
• Measurable output. You name the metric before and after – processing time, error count, operation cost.

| Scenario | Suitable for start | |—|—| | Handling typical support applications | Yes – repeatable, historical data available | | Invoice recognition and extraction | Yes – high volume, clear output | | Strategic decision on entering a new market | No – one-time, no repeatability | | Lead qualification | Yes – flow, measurable conversion | | Creative task without success criteria | No – output cannot be measured |

According to "Yakov and Partners" (2025), AI can bring up to 13 trillion rubles to the Russian economy by 2030 – but only where the first case is chosen thoughtfully.

When to go to a contractor – and what to take to the meeting

Three signs that a process is better left to an external team.

Lack of expertise inside. Your developers are strong in web integrations but haven't worked with ML models or AI agents. Hiring a team for one pilot is more expensive than taking a ready-made one.

Tight deadlines. A result is needed by the tender, season, or board report. An external team with a proven process starts faster than an internal one from scratch.

Need for independent evaluation before budgeting. You're not prepared to defend the figure until you see: does the process fit, what's the volume, where are the risks. This also includes if you plan to collect data through an app: it's easier to combine this into a single pipeline upfront than to attach mobile development later.

To get a meaningful assessment rather than "fingers crossed," prepare beforehand:

• Process scheme – how an application, document, or lead passes from entry to result now.
• Operation volume – how many applications, documents, or transactions per month.
• Current stack – CRM, 1C, phone, messengers.
• Costs on the segment – FOT and error cost on this process.
• Desired KPI – which metric and how much you want to shift.
• Examples of incoming data – a few real applications or documents.

Tantal – Skolkovo resident with 281+ implemented projects. We implement AI assistants and computer vision under specific business tasks, not selling a boxed product that needs to be broken in afterwards. Leave an application for pilot assessment on the services page.

Next step

You have a process candidate but are unsure if it's suitable for automation? Describe one task – and our team at Tantal will prepare a primary assessment within two working days: is the process suitable, what's an approximate pilot volume, and expected payback period. Request a diagnosis on tantal.ai/services.