Implementing AI in business: 3 practical tips from a software house on how to scale up without losing your human touch

2026-01-30 | Joanna Iwanków Maison

In recent years, IT development has been dominated by AI, and there are no signs of this trend slowing down. What is more, investment in this technology stack will continue to grow.

 

But let’s be honest: technology itself is just a tool. The real art lies in knowing how to integrate it into company processes so that employees don’t waste time on tedious, repetitive tasks, allowing the company to operate more efficiently and… earn more. Implementation AI in business should not be an end in itself, but a way to reclaim time for what truly matters.

 

Below is a visualization of AI-powered IT service layers prepared by Gartner. With this post, we are launching a series of articles in which we will explore the various layers of such services.

 

We will discuss specific implementations and their results. However, we won’t be uncritical. We will also address the risks that can arise from using AI (especially in the context of law and cybersecurity).

 

AI in business - layers of AI-driven services

 

#1 Why we believe it is worth focusing on synergy rather than replacing people

Most companies make the mistake of trying to replace humans with algorithms. We recommend a different approach: AI should take over what is boring, repetitive, and arduous. This allows our engineers to focus on what provides higher added value: complex problem-solving, negotiations, dispute resolution, and building business relationships.

 

We believe in the human2human model. Even if an algorithm prepares a data analysis for us in a second, the final decision is made by a human who understands the context and the client’s emotions.

 

That is why our teams are eager to experiment with new solutions within R&D projects. We do not impose tools from the top down. Instead, we provide the opportunity to test and develop one’s skills (while merely maintaining legal and security frameworks) without unnecessarily stifling creativity.

 

Thanks to this approach, we have completed several AI-related projects this year—both internal ones for our company’s needs and commercial ones for our clients. And this is exactly what we would like to share. Perhaps it will serve as an inspiration for you on how to scale your business using AI.

 

 

 

#2 How AI actually accelerated our sales

 

In sales, we have shortened the lead qualification process through the use of AI. In the past, analyzing tender documentation or RFIs took us many hours. Today, thanks to our own AI solutions, we do it in a few minutes.

 

The algorithm instantly identifies key requirements and risks. As a result, we know almost immediately whether a given project is a “go” or a “no-go” for us. This is a huge time saver, allowing us to focus on refining the offer for the client we actually want to work with.

 

 

 

#3 Hyper-personalization instead of dumb chatbots

Many associate AI in customer service with annoying chat bots. We are going a step further. We use machine learning to analyze massive datasets: from website behavior and purchase history to social media mentions.

 

What does this provide? Instead of sending generic offers, we provide the sales team with insights into the client’s actual, often unnamed needs. This allows for “tailor-made” solutions that hit the mark.

 

But please note: at Devqube, the client always speaks with a human. Technology is meant to support this relationship, not act as a barrier to it. We approach every client and project individually, and the human2human element in the business relationship is crucial to us.

 

A certain revision of the use of agents/chatbots etc. can be observed in the customer care market. Meanwhile, businesses are realizing that they cannot exclude clients who want to be served in a traditional model. We respect that.

 

 

 

Summary: AI is an opportunity that should be used wisely

 

The potential of AI lies in assisting humans in solving complex problems and increasing process efficiency. The mass adoption of AI is leading to dynamic changes in the labor market, as Kamila Adaszyńska wrote in this article. The key is to continuously develop competencies and learn in order to keep up with the technological transformation and build synergy between man and machine. We also have 2 articles discussing the use of AI in software development.

 

Our clients use AI to gain a competitive advantage, automate production, optimize supply chains, personalize offers, and make more accurate business decisions through the analysis of large datasets. Soon, we will discuss an implementation in a company that has a massive, dispersed knowledge base. This involves decades of projects, contracts, completions, subcontractors, machine parameters, etc. Due to the nature of the industry, the model must be closed and not connected to the Internet.

 

Our role is the effective realization of these goals without overlooking risks related to security, data privacy, the risk of discrimination by algorithms, and the necessity of maintaining a critical analysis of information. Because, quite frankly… are you 100% sure right now what your employees are sending to LLMs and where it ends up?

 

And if you are wondering how AI could help in your specific case, just write to us. We don’t promise the moon; only solid engineering knowledge and a human approach. Let’s check together what we can build for you.

 

AI in business - contact devqube

 

FAQ: Frequently asked questions about implementing AI in business

💡 Will AI replace my employees?

No, if it is implemented wisely. AI should be an assistant that takes the burden of repetitive tasks off people, allowing them to focus on creativity and strategy.

 

💡 Is implementing AI safe for my company data?

Yes, provided that the appropriate architecture is chosen. At Devqube, we design systems that can operate in closed environments, guaranteeing full data privacy.

 

💡 Where to start with AI implementation in a small company?

Start by finding the biggest “bottleneck” – the process that takes the most time and is repetitive. This could be inquiry analysis, handling simple tickets, or generating reports.

 

 

Joanna Iwanków Maison
Ostatnio opublikowane przez Joanna Iwanków Maison