I overlooked it — it's my fault; I missed the defective product — it's my fault again. They like to attribute production errors to the quality control department. Having learned from experience, JCC employees try to monitor more strictly: if you do it imperfectly, finalize it, if you don't design it properly, redo it. So it turns out: each side wants the best, but they get nit-picking and shifting responsibility.
Why is this so? Every day, OCC specialists have to fill out and process a lot of documents and keep hundreds of standards in their memory. Often this whole system is manually operated. That's why you have to double-check, write it down on a piece of paper, and then transfer it to the system and check it out. This job is big and invisible, so related departments may feel that the JCC is mired in bureaucracy and is deliberately slowing down their processes.
This article will explain how artificial intelligence will make the work of the quality control department more productive, increase the transparency of processes and help you achieve the desired results faster.
AI against anxiety, or how to cancel the fire mode
OKK is engaged in quality control in dozens of processes and hundreds of documents. A huge amount of routine work. The most stressful thing in this situation is that the tasks do not end. They all look important and urgent. It's not clear what to do faster. Anxiety sets in and fear of missing important things.
In a state of anxiety, a person focuses on little things. He double-checks the same things several times, compares the data endlessly, and sometimes he mentally turns off the process and does things automatically. Nervousness is growing, and so is pressure from colleagues, because they think that requests are deliberately ignored.
This is what OKC employees look like in the eyes of colleagues
Stress leads to mistakes at work, burnout and a tense team atmosphere. To avoid this, management sees two ways out:
- The first is to hire new employees to relieve the old ones. But this is a new danger: it is impossible to constantly increase staff. Payroll budgets are not rubber, so are the company's resources, and qualified personnel are becoming rare in the labor market. Plus, in fact, everything will remain at the same level: people will continue to manually process data, routine and obsession will not disappear, and the problem of burnout will remain.
- The second is to automate processesto reduce manual operations. Technology makes it possible to do this. For example, AI reduces information processing time from 3 hours to 15 minutes, completing routine tasks ten times faster.
That is, with the introduction of AI, the team will remain the same, and the fire mode will go away. Processes will stop freezing due to the human factor. The number of errors will be reduced. The team will be able to focus on preventing problems and improving productivity. The company will come out of the operational crisis.
The tasks that AI can help with today:
- listen to calls from sales managers and check them for compliance with scripts;
- process negative customer reviews and draw conclusions about product quality or supplier integrity;
- check the correctness of the documents;
- find errors in large data sets;
- advise employees on internal regulations.
The only challenge in this transition is finding a contractor with a relevant background. It is important that the company not only has experience in implementing AI, but also understands the processes of integration into business processes. Then the transition will be smooth, and employees will quickly start using the new tools.
Spoiler: KT.team has just such experience.
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AI will analyze calls in minutes that employees would spend several weeks on
Imagine: a company sells rubble. Managers work using scripts, but the conversion of calls to leads remains low. There are two ways to solve the issue ↓
The first one: give employee Marina the task of listening to calls and writing a report on each one. For reliable analytics, Marina will evaluate 20 calls, each lasting 10-15 minutes. The audition will take three hours. Marina will then write a report. It will take a whole day to complete the task.
Marina listened to 20 calls, but didn't understand half of them, and the saddest thing was that they were not show calls. Because the manager raided his subordinates the day before, and they did better than usual that day. The analytics picture turned out to be blurred
The second one: connect AI. Now there's no need to check how much Marina's ears and willpower can bear. In AI, you can upload all call recordings → leave → get transcription and analytics. At the summer, the manager will see which calls match the scripts, which ones don't, and what the problem was.
It will take two hours to solve the problem, and Marina will spend another hour reading the report, coffee and handing the letter to the manager. The longest part of this process is deciding the fate of poorly performing employees. But this has nothing to do with Marina or the AI tool anymore.
An example of recommendations from an AI assistant after analyzing a call to a client
AI will check if there is a “broken phone” in communication with customers, contractors and colleagues
Oleg and Kirill, two printing house managers, became the heroes of the story. Oleg has everything clear: projects are flying, production is not in progress, and customers are happy. Kirill, on the contrary, has surprises and problems every now and then. Both are trying, the experience is approximately equal, and it is not very clear what the problem is.
The manager began to figure it out. The answer was impossibly simple: both employees read the regulations, but only Oleg uses them at work, and Kirill acts spontaneously. In practice it looks like this ↓
The client asked how much it costs to produce and deliver advertising brochures from City A to City B. According to the regulations, the manager must specify the number of brochures, the client's city of residence, the preferred delivery method, and the timing. Technical aspects are also needed: type of paper, cutting and varnishing. After the information is collected, an application is made. Further down the chain: production → warehouse → logistics.
Kirill's problem is that he is used to working the old fashioned way: he writes information in a notebook, keeps something in his head, and applies for production orally. The printing workers, of course, immediately forget everything or understand them in their own way. So it turns out that Kirill's diligence does not lead to anything.
But the main thing is that it is not even Kirill's problem, but his leader's problem. Because his KPIs and his reputation suffer. If you don't know how to explain it clearly, it means you're a bad leader.
TOh, what is obvious to one person may be decidedly incomprehensible to another. Compliance with regulations helps you avoid falling into the network of broken phones
One of the tasks of OKC is to help businesses avoid disruption of communications and ensure that employees comply with standards. To do this, you need to control a lot of work correspondence and calls. This is where an AI assistant comes in handy.
AI can:
- analyze calls: decrypt voice recordings and messages and highlight the main thing in them;
- understand the essence of the conversation: whether the employee was having a productive dialogue using a script or saying something for himself and was unable to get all the necessary data;
- determine the presence of negativity in communication: whether a conflict is brewing in the team.
The manager receives a report on each call: transcript and summary. This makes it clear what, how and how correctly the employee is communicating. It can be seen whether a person works according to the regulations, how upgraded their soft skills are, and whether they need to improve their product competencies.
An example of analyzing a call from an AI assistant
AI is a vaccine against forgetfulness: it will remind you how, when and how
“Kira, how do I fill in the table? Peter Ivanovich, where are the data for 2024? Semyon, I'm going on vacation tomorrow, who's going to replace me?”
Most companies have regulations for all business processes, from concluding an agreement with a supplier to going on vacation. Of course, no one is urging them to teach them, and why. Therefore, regulations are quietly in the knowledge base on demand. But there's a catch. Searching is too lazy. You also need to go somewhere, remember where the necessary information is, read it and highlight the main thing. It's easier to ask the quality control department: they know everything.
The circle closes. The JCC is focused on order, so they answer everyone. They waste time, but the team is not becoming more aware. And it will never increase as long as good JCC employees respond to their colleagues at the expense of their tasks.
To deal with this situation, you can connect an AI assistant. He will always be available to answer:
- how to apply for a vacation;
- what to do if you get sick;
- where are business letter templates;
- how the premium is calculated.
The system works as easily as any search engine. Employees won't have to wait for one of their colleagues to free themselves and listen to them, or search the knowledge base for the right regulations for a long time. And OCC will have less work to do to fix the same errors.
This is how an AI bot answers an employee's question about vacation
AI will help identify problematic goods or unscrupulous suppliers
It happens that there are many complaints about a product. And the OCC needs to find out what the reason is. It takes a long time to sort out claims manually: you need to see whether they are really relevant or whether customers simply did not like the color, style, and material. You also need to sort requests, rank them by frequency and formulate a problem for the procurement department.
For example, a buyer complained about an incompleteness: the paper towel stand came without a screw connecting the pin and the base. He refused the goods and issued a refund. The control department must enter the case into the database and check whether it is an isolated or systemic error. If systemic, then conduct an inspection at the warehouse: why employees systematically forget to put parts.
Speed is important in processing negative messages, because customers will continue to write angry reviews until you understand the reason. Artificial intelligence will help here as well.
It will help you:
- understand which product has the most problems;
- specify the percentage of returns and recurring complaints;
- determine which batch received the largest number of returns;
- sort products by groups and popular issues.
The AI will analyze the data and make initial recommendations. This squeeze will make it easier and faster for purchasing and quality control teams to solve problems.
An example of data analysis on refusals to purchase and recommendations from an AI assistant
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AI will check that documents are filled out correctly so as not to slow down the work of related departments
Contracts, acts, supporting papers, commercial offers, reports — every day employees are faced with filling out dozens of documents. Some people like to meticulously proofread every letter, but most people do it mechanically. They will fill out the counterparty agreement incorrectly, and then the company pays storage fees in excess of the norm. They will not fully issue the waybill, and the car with the cargo ends up at customs, and the expenses are again borne by the enterprise.
Not everyone finds it easy to use bureaucratic language and compliance with rules. Errors occur due to the human factor. It is unlikely that it will be possible to change this, but it is easy to make a mistake in a way that is impossible.
AI guarding document flow:
- notices anomalies in contracts — it is much easier to double-check individual fragments of the text than to proofread it in full;
- works in a few minutes — the work of related departments does not freeze due to the fact that the employee has no time for papers yet;
- eliminates typos and flaws — AI's eyes do not blur and will definitely not miss a thing.
It is enough to give the system rules for filling out documents, and it will quickly do the job.
This is how AI checks the tender application with the regulations
People or machines: how much time and money does the company spend on each
Let's compare the process and costs in both cases. For the analysis to be correct, let's imagine a situation: the control department is overloaded, there are more and more tasks, and the team burns out. The manager is faced with a choice: hire a couple more employees or take the risk and implement AI. So, what will happen in terms of costs — in rubles and in days ↓
By outsourcing at least some of the routine tasks to an AI assistant, you can reduce inspection time by 50% and give feedback in 2 hours instead of 24 hours. The recommendations will be implemented more quickly, and the company will develop.
After the company begins to scale and the volume of documents and data increases, you will not have to urgently look for new OKK employees. The AI assistant will successfully cope with any amount of work and avoid mistakes due to the human factor.
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