5 processes you can automate in the quality control department to stop firefighting

Imagine: a company sells crushed stone.

Managers follow scripts, but the conversion of calls into leads remains low.

There are two ways to solve this ↓ First: to give the employee

Marina is tasked with re-listening to the calls and writing a report on each one.

Marina will take 20 calls for evaluation, each lasting 10-15 minutes.

Because the day before, the manager had chewed out the subordinates, and that day they worked better than usual.

The analytics picture came out blurred_ Second: connect AI.

There is no longer any need to test how much Marina's ears and willpower can take. You can upload all call recordings to AI → let it process them → get transcripts and analytics. In the summary, the manager will see which calls matched the scripts, which did not, and what the issue was.

Solving the task will take two hours, plus another hour

Marina spends time reading the report, having coffee and forwarding the letter to her manager.

The longest part of this process is deciding what to do with underperforming employees.

But that no longer has anything to do with either Marina or the AI tool. _Example of recommendations from an AI assistant after analyzing a customer call_

Kirill - two print shop managers.

For Oleg, everything runs smoothly: projects fly along, production does not screw up, clients are happy. For Kirill, on the contrary, there are constant surprises and slip-ups.

Both try hard, their experience is roughly equal, and it is not entirely clear where the catch is.

The answer turned out to be incredibly simple: both employees read the regulations, but only

A client asked how much it costs to produce and deliver promotional brochures from city A to city B.

According to the regulations, the manager must confirm the number of brochures, the client's city of residence, the preferred delivery method, and the deadlines.

Technical details are also needed: paper type, die-cutting and varnish coating.

Once the information is collected, an order is created.

Then down the chain: production → warehouse → logistics.

Kirill, the problem is that he is used to working the old-fashioned way: he writes information in a notebook, keeps some things in his head and passes the order to production verbally.

The print shop workers, naturally, immediately forget it all or interpret it their own way.

But the main problem is not even Kirill's, but his manager's.

Because his KPIs and reputation take the hit.

If you cannot explain clearly, you are a bad manager. _What is obvious to one person may be completely unclear to another.

Following procedures helps avoid the broken telephone effect_

One of the quality control team's tasks is to help the business avoid communication breakdowns and ensure employees follow standards.

This requires monitoring many work conversations and calls.

The manager receives a report on every call: a transcript and a summary.

This makes it clear what the employee is talking about, how they communicate, and how accurately. You can see whether the person follows procedures, how strong their soft skills are, and whether their product knowledge needs improvement. _Example of a call analysis by an AI assistant_

Pyotr Ivanovich, where is the data for 2024? Semyon, I’m going on vacation tomorrow, who will cover for me?"

Most companies have regulations for every business process, from signing a contract with a supplier to going on leave. Of course, no one is urging you to memorize them — and why would you.

So the regulations sit quietly in the knowledge base until someone needs them.

You have to go somewhere, remember where the right information is, read it, and pick out the key points.

It's easier to ask the quality control department: they know everything, after all.

The circle closes. The QC department aims for order, so they answer everyone.

To handle this situation, you can bring in an AI assistant.

It will always be ready to answer: - how to apply for leave; - what to do if you get sick; - where the business letter templates are stored; - how the bonus is calculated.

The system works as simply as any search engine.

Employees will not have to wait until a colleague is free to listen to them, or spend a long time searching the knowledge base for the right procedure. And the quality control team will have less work correcting the same mistakes. _This is how the AI bot answers an employee's question about vacation_

Contracts, acts, supporting documents, proposals, reports - every day employees deal with filling out dozens of documents. Some people enjoy carefully proofreading every line, but most do it mechanically.

Another time a counterparty's contract is filled out incorrectly, and then the company pays penalties for warehouse storage beyond the limit.

One time the waybill isn't fully completed, the truck with the cargo gets held up at customs, and the costs once again fall on the company.

Bureaucratic language and following the rules do not come easily to everyone.

Mistakes appear because of the human factor.

Changing that is unlikely, but making mistakes practically impossible is easy. AI guarding document workflows: - spots anomalies in contracts - checking individual text fragments is much easier than proofing the whole document; - handles the job in minutes - related teams do not get blocked because the employee is not available to handle paperwork; - eliminates typos and flaws - AI does not get tired or overlook anything.

All you need to do is give the system the rules for filling out documents, and it will do the job quickly. _This is how AI checks a tender application against the policy_