How AI assistants are changing data management and routine work in business

10.10.2024 In this article we discuss how AI takes over routine tasks, boosts your employees' efficiency and helps the company thrive.

Let's look at examples from sales, HR, software development and other fields.

Reading time: 9 min. In the comments on one of our previous VC articles, someone asked, "How do you calculate the financial benefit of implementing AI? It is not free, and the scale of the savings is not obvious." Indeed, the cost of implementing AI may seem too high precisely because the return on investment is unclear.

Yet AI in business already delivers real benefits to many companies: it cuts costs, improves operational accuracy and helps save resources, freeing up employees' time for higher-margin and creative work. In this article we discuss how AI takes over routine tasks, boosts your employees' productivity and helps the company thrive.

Putin will use examples and case studies to explain why adopting AI is not just a trend but a practical tool for business growth and optimization. And to start with — a bit of our favorite statistics.

HR specialists mostly interact with people, but they also do a lot of routine work: tracking metrics, preparing assessments and giving feedback. AI assistants streamline these processes, freeing them to focus on more important things.

He works as one of three HR specialists at a company of 150 people.

In the morning he meets with new employees who have come for a trial week and introduces them to the team, or interviews candidates. During the day

Oleg is constantly busy: answering questions from new hires, meeting one-on-one with employees, evaluating their performance, and running pulse surveys.

Data on all these tasks needs to be entered into systems and analyzed on a daily basis: what is employee satisfaction?

What are the most common questions and problems they face?

What warning signs come from employees?

Oleg felt like a hamster on a wheel: routine tasks took up almost all his time, and there was hardly any energy left for strategic tasks.

Oleg stayed late at work to prepare a report for his manager or finish feedback after interviews. The AI assistant took over data collection, questionnaire analysis, performance evaluation, and report preparation. And

Oleg focused on what cannot be delegated: communication.

Metaphorically, you could say that instead of pedaling,

He was able to meet with candidates and new hires more often, and gained a better understanding of employees' joys and challenges.

After adopting AI, the eNPS — a measure of employee loyalty — increased.

On the other hand, there are professions where communication doesn't seem to be the main part of the job. Developers, for example, spend most of their time writing code.

According to research, some of them are so absorbed in working with the computer that they lose the skill of empathy.

However, to achieve an excellent result, a developer needs not to write more code, but to better understand the client's goals.

Andrey was used to receiving decomposed tasks and clear instructions from the project manager.

He did not try to dive into the details of the feature: why was it needed, which goals did the client want to achieve with it? Because of this

Andrey worked mechanically, writing code for separate tasks in the task tracker, without seeing the product roadmap as a whole or understanding how a specific feature was supposed to strengthen it.

But often the result did not fully meet the client's expectations, and

Andrey had to redo the same thing several times.

The manager may have decomposed the task incorrectly.

Or break it down so finely that the meaning was lost.

Or some requirements important to the client were lost during decomposition.

Andrey started using AI to write code. After all, Git contains millions of lines of code showing how to solve tasks in common languages. And code is just another dataset, that is, a source of knowledge for AI.

Andrey spends less time on typing work.

Now he has freed up time to dive into project details and understand the client's needs.

Now most of the working day

He meets with the client to better understand the goals and value of each new feature in the application.

He knows which metrics the feature should improve, how it should integrate into the current system, and how it will evolve in the future.

He thinks through the feature's logic more deeply and carefully. …Then he writes a detailed prompt, from which the AI generates almost production-ready code.

All Andrey has to do is test and refactor it.

As a result, the client accepts 90% of features on the first try because they fully meet the project's requirements and goals, while the rest are accepted with minor changes.

Thanks to this, the client's company brings the product to market faster and saves resources. The IT team also gets higher client satisfaction ratings and has become more profitable, because it hardly has to work under warranty, meaning it does not waste time rewriting or reworking things.

But at KT.Team, we have already started implementing this approach. And experience has shown that it works.

What is the meeting lifecycle in your sales department?

It gets scheduled, held, the results are recorded and… what next?

If a sale falls through, how do you analyze the reasons?

If a client wants to return to the matter in six months, how do you prepare for the new meeting? Imagine being able to talk to your meeting transcripts as if to a real person. Another tool helps with this — an AI meeting secretary for sales teams, project teams, technical support and onboarding of new employees.

It stores video and audio calls and can analyze them against any query.

It can find the needed information even when it is stored in different files. For example, AI will recall the history of meetings on a specific project or tell you which topics were discussed last month.

You can ask the question in free form, as if you were talking to a colleague.

The search takes only a few seconds, and the answers are detailed and structured. The AI secretary transcribes recordings while accounting for professional jargon and terminology, can produce a full or brief meeting minutes and send it to the participants.

Another example: a new employee joins the company and needs to learn all the regulations and follow them.

If a newcomer does not complete training, their actions can lead to the loss of money and the company's reputation. For example, by forgetting to mention an important deal condition, a sales manager risks losing a key client.

Usually department heads train new employees themselves or assign this to key, most skilled and often most expensive employees.

They monitor compliance with guidelines: they review meeting recordings, analyze and assess managers' behavior, give feedback, check ad creatives and so on.

This takes a lot of time that could be spent onboarding a new hire or on high-margin sales tasks. The AI secretary will take over the standardized, regulated part of this work.

It will analyze a recording of a new employee's conversation with a client, compare it against the guidelines and give detailed feedback with recommendations.

The AI secretary can also generate a report on any meeting metrics adopted by the department, such as employee friendliness, adherence to the stages and recording of agreements.

Saves time. The AI takes on the task of processing all the information accumulated within the company: it quickly analyzes the data and delivers concise summaries.

This makes it possible to recall important details within minutes and avoid the risk of looking unprepared.

The choice here is not between a manual approach and AI, but between work not getting done and work being handled effectively by an assistant.

Out of an eight-hour workday, an employee is truly productive for only four hours, and that is when they need to complete as many tasks as possible.

Handing routine work to AI assistants lets people focus on what matters and work better: experts estimate the efficiency gain from adopting AI at 20-25%.

Integrating AI is like switching from a manual transmission to an automatic one: it has its drawbacks, but the savings on manual effort are enormous.