Hi, this is a company KT.team. We are implementing IT solutions, including those based on artificial intelligence, in medium and large businesses.
AI-based products appear every day and promise companies a boost in efficiency and productivity. In practice, businesses are cautious about AI implementations, especially after a couple of failed attempts.
When talking to entrepreneurs, we often hear about mistrust in neural networks: the value of integrating them is not obvious, as is the impact on profits. And of course, business does not want to invest in an implementation that will not benefit from it. In this article, we'll look at the main concerns about AI, find out what is behind each of these fears, and explain how AI can be useful for your business.
Fear number 1. It is impossible to predict in advance how much it will cost to implement AI
This fear was born from previous experience with IT implementations. We (and you) have heard so many such stories: at first, the company expected to invest $2 million and launch an MVP in three months. But a year and a half later, the accounts already exceed 15 million — and the system has not been launched yet. Endless development is such a common problem that an IT project we launch on time often surprises customers.
With AI, too, there is a risk of being stuck in endless implementation with a bloating budget. If we also take into account the unpredictable benefits of integration, it becomes clear why businesses do not want to take risks.
It is not difficult to avoid this risk. Start implementing AI by simplifying small processes and routine tasks. For example, an AI assistant can be integrated to work with call information in a month and will take on the following tasks:
- It will make it easier to find agreements using a chatbot. You and your employees will no longer have to reread hundreds of emails and chats to find the information you need. Just ask an AI assistant a question and get an answer in 15 seconds.
- He will analyze the call according to important parameters: setting the agenda, compliance with the script, the manager's courtesy, willingness to solve the client's problems, fixing agreements, markers of mutual understanding, etc.
- It will automatically send a summary to you and the client, add to the CRM customer card and project documentation. To do this, the bot will decrypt the call and highlight key points.
What does it take to make the cost of AI deployment predictable?
1. Select one area where you work with a large amount of documentation and audio information. As a rule, it takes a lot of time for employees to save and find the data they need.
2. Together with the developer, determine what integrations are needed for the AI assistant to work effectively: conference calls, data warehouses, CRM, messengers, etc.
As a result, the cost of such an AI assistant can be predicted — it will consist of two components.
The cost of implementation. What you need to do to make the system work for a particular company.
The cost of using AI. It depends on the number of meetings that need to be processed and the language model chosen. For example, KT.team uses a TL; DV decryptor “under the hood” and pays 4000 rubles a month for it (approximately 3,000 calls). Of course, if your needs are greater, then subscribing to the service will be more expensive — but still, the amount will be incomparably lower than the business benefits.
Start with low-process solutions: implement them quickly and inexpensively. And you'll see how AI makes the job easier and decide which process to automate next.
Fear number 2. AI will make mistakes that will cause businesses to lose money and reputation
Imagine that you have an AI tender assistant. Based on the specified criteria, he assesses whether the company should participate in the selection. Are you sure that AI selects and applies for all relevant offers.
But one day, in line at a coffee shop, you meet a friend and find out that his company held a suitable tender for you — and you ignored it. This is an alarming signal: if the neural network made a mistake here, how many more mistakes did it make?
An AI assistant is like an employee who knows a lot but is also inexperienced. At the start of his work, he does not “understand” what is right and wrong. It takes time to train him.
This is a crucial and costly step that will determine whether AI will work flawlessly. A specialist who has always worked on tenders for you will have to validate every AI assistant decision and confirm or refute his decision. This approach will help artificial intelligence gain experience (dataset) and avoid mistakes in the future.
If your job requires responsible decisions or creativity and you can't do without a person, entrust only part of the tasks to a virtual assistant. For example, an AI assistant will be able to assess the terms of reference for a tender: he will break down large tasks into small ones according to an approved pattern and calculate each stage in hours and money. And if the assistant encounters a non-standard request, he will forward it to the person. This will save time for the tender specialist: he will only have to deal with tasks where it is impossible to make a decision without him. At the same time, the number of tender applications will definitely grow.
Fear #3. Previous AI experience will become irrelevant when business processes change
In business, processes are constantly changing. For example, today the company works with construction tenders and adjusts AI processes to perform these tasks. Tomorrow, the entrepreneur will also decide to bid for design. It seems that we will have to re-register all the algorithms and check whether everything works correctly. So we'll have to invest a couple more million in a new AI assistant?
No, you won't have to introduce a new AI assistant. Target orders and the method of assessing technical specifications have changed, but most of the automated stages have remained the same. For example, tenders should be collected from the same sites; they still need to be analyzed for relevance and the possibility of winning (are there any signs of a tender “for friends”). Regardless of the technical specification, it is necessary to collect information on the necessary documents. You won't have to retrain your assistant at these stages.
But refinement and further training will still be required: you will need to add new keywords to select interesting tenders. It will be necessary to develop a new methodology for assessing technical specifications, because the list of tasks in a design tender differs from a construction order.
But the process of further training is several times faster and cheaper than introducing a new assistant: just upload current regulations and files to the artificial intelligence system. These regulations will then be automatically uploaded to your assistant, then run the tests and you're done.
How an AI assistant improves the efficiency of different departments
Our company uses AI by HR specialists, project managers and developers. Here's what we saw after KT.team started using AI assistants.
HR. We have automated pulsations: an HR assistant processes data from surveys and highlights pains, growth points and work problems for each employee. HR specialists focused on working through these pains, and as a result, we were able to increase our loyalty index (eNPS).
Project manager. The AI assistant deciphers all the calls word for word and saves them. When a project misunderstandings arise or previous agreements need to be raised, the project manager can ask an AI assistant about them in a few minutes and receive a detailed answer, citing the source, date and time. A manager can focus on communicating with the team and clients rather than spending hours listening to old meetings.
The developer. The key thing in a developer's job is to understand the task correctly: what value a feature should bring and what result the customer wants to achieve. Using AI as a copailot helps with this. AI frees up a specialist's time from mechanically writing code and provides more opportunities to find out the customer's requirements, goals, and wishes; to think about the feature's logic, and to formulate restrictions.
Incorporating AI into your processes
Each company may have its own fears that prevent the introduction of artificial intelligence into work processes. For example, IT professionals often worry that when working with neural networks, confidential data will leak.
All problems can be solved so as not to deal with the implementation yourself, contact to us. We will discuss the tasks and offer a solution that will work specifically for your business. We will deploy the system on the cloud, your servers or offer a hybrid version, take into account security and workflow regulations and will be in touch throughout the entire implementation period.
And what tasks would you like to transfer to AI? Write to us.
