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

10/10/2024 In this article, we will discuss how AI takes over routine tasks, improves employee efficiency, and helps the company thrive. We will look at examples from sales, HR, and development

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 excellent results, a developer does not need to write more code, but to better understand the customer's goals. The programmer

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. Why did that happen?

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 customer were lost during decomposition. There are many reasons. Not long ago

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 Andrey's work time is not spent coding.

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.

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 customer's company brings the product to market faster and saves resources. The IT team gets higher customer satisfaction scores and has become more profitable, since it hardly works on warranty fixes and therefore does not spend time rewriting or reworking. Sounds like a made-up story?

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

AI optimizes data analysis and information management.

You do not know the date and time the regulation was created, you do not know the file name, and finding it by an arbitrary wording is hard.

You would have to dig through dozens of files to find one needed line. AI makes it possible to find a specific document even by a small detail of its content - a last name, a date, a phrase from a task discussion, or the area of use.

It can analyze thousands of files in ten seconds, present the necessary information in a readable format, and provide a link to the source.

Another problem with large volumes of data in the repository is document obsolescence.

They become outdated, get duplicated or start contradicting one another. Sometimes it's easier to create a new file than to figure out which of the old ones to use.

This leads to confusion and inefficient work among the company's employees. AI can find outdated documents and determine which data needs updating.

Management decisions vary widely - from hiring a new employee to building a three-year financial strategy.

Here we will look at a specific (but common) case - participating in a tender: whether it makes sense, the speed and quality of preparing tender documentation, and the number of approvals. Companies often have only one tender specialist who evaluates how promising the bids are.

He reviews dozens of tenders every week and decides which ones are worth joining and which ones to decline. Because of the large workload, he is not always able to process the requests in time: the documentation may contain hundreds of pages, and only on page 75 does it become clear that the tender is not a fit.

The company will not take part in it, and the specialist simply did not have time to get to more suitable options. In the end, the specialist's time was spent, and there was not enough left for a more promising tender.

The company lost potential revenue.

Even selecting tenders for evaluation becomes automated and personalized - AI picks only those relevant to the company. The AI assistant breaks the tender tasks into stages and estimates them in hours and money, then provides a detailed scope of work and estimate that the expert only needs to verify.

The whole process takes just a few minutes and frees up about 30% of the expert's time for Like4Like alone, meaning only for the number of tenders they could handle manually. As a result, the same employee evaluates several times more tenders than before.

To compile the list of documents needed to participate, the AI tender assistant finds keywords in the documentation and the platform's request.

It files bids according to each platform's rules, and if your database happens to be missing a required certificate, it notifies a person about it.

After implementing the AI assistant, the conversion from applications to wins increases by 10-30%, and rejections due to tender rule violations drop to almost zero.

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 revisit the questions in six months, how do you prepare for the next meeting? Imagine being able to talk to meeting transcripts like to a real person. Another tool can help with this - an AI meeting secretary for sales teams, project teams, support, and onboarding new employees.

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

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.

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 an assistant handling it efficiently. It saves money.

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.