Aleksandr Sheremeta, Co-Founder and Managing Partner at DATAFOREST, led a pivotal session titled "The Future is Here: Essential IT Instruments for Business Growth."
As a recognized digital transformation expert, Aleksandr brings a unique blend of technical and corporate expertise to the modern tech landscape:
- Data Science Leadership: 18 years of deep, hands-on experience in the data science sector.
- DATAFOREST Vision: For the past nine years, he has spearheaded the firm's mission to help organizations leverage AI to automate business operations and launch cutting-edge technological products.
- Corporate Foundation: 13 years in risk management and IT, featuring senior leadership roles at the National Bank of Ukraine, UniCredit, Erste, and Sberbank.
In this article, we distill the core takeaways from his 2026 presentation, exploring exactly how artificial intelligence is fundamentally reshaping today's business processes.
Top 15 Tech Trends
McKinsey highlighted the top trends in tech and conducted an analysis to understand whether global companies are currently investing in these technologies, doing certain pilots, or already implementing these solutions in their companies.
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These trends can be grouped into 5 main categories: AI revolution, digital future, communication and computing, advanced engineering, and sustainable world.
AI starts with data. But why are we seeing such rapid changes now? How did we get to this point? Looking back, everything that is happening today is the result of two key factors: big data and the development of computing power. The progress in these areas has opened up opportunities for businesses.
We have officially entered the era of the automation of thinking. Standard LLM models were merely the first step in changing how we interact with information; today, the landscape has completely shifted toward native reasoning and computational models. We no longer have to wait years for systems that can think through complex problems logically and execute advanced mathematical calculations. Instead of relying strictly on external plugins or coded functions to crunch numbers, modern 2026 AI architectures possess built-in reasoning chains. They don't just predict the next word—they pause, validate their logic, process intricate data sets, and self-correct. For businesses, this means moving away from simple chatbots and transitioning directly into deploying highly autonomous AI agents capable of complex decision-making.
This year, Deloitte did research among large companies and identified that 80% believe that AI can improve efficiency. Also, 55% analyze and test GenAI solutions, while 52% believe that this is an opportunity for a new revenue stream for their company, but only 37% have implemented and integrated it into their business processes.
The big difference between what we have today in terms of GenAI and traditional artificial intelligence is data analysis. Today's models understand and interpret the data that we provide to them and, based on this data, they make assumptions and then generate texts, pictures, or code. This inability to create something new, although they are called generative, is the difference between what we have today and what we had in the past. GenAI is a new paradigm of the world, and it is changing the customer experience, how we work, our processes, value proposition, and more.
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How is Gen AI changing the customer experience?
Increased productivity
Productivity increases through reducing the routine work that people do. We optimize the backlog of tasks to help our employees do them more easily. At the same time, we change the process of performing the work itself when the employees reduce the administrative and operational burdens. Automation with AI takes employees to a higher level and allows them to create. This way, their knowledge and time are focused on understanding what is happening and how to improve this process or create something new.
For managers, this means less micromanagement. Instead of 10 employees doing some manual work, managers can hire only one person who will control the process of automating tasks and improve it.
And it is not only at the employee level that the change is taking place, but also at the management level. That is, we no longer need to administer our employees, we do not need to spend our precious time organizing the process for many employees, because we can optimize. We do not need 10 employees to perform some monotonous work. We can leave only one person who will monitor this process and improve it.
Creating innovation
By automating tasks, we free up time to focus on creating a better customer experience and improving the way we interact with our customers. When moving from increasing productivity to creating innovations, the paradigm of our interaction changes in general.
Changing behavioral patterns
New interaction paradigms emerge as customers, employees, and managers become proficient in AI communication.
Our clients are becoming part of this process; they understand what is being done, who is on the other side, whether it is AI, and what processes are automated. Most clients are okay with it. Of course, at first, there is a certain challenge, but after AI is integrated, we see a positive reaction from the clients. At DATAFOREST, a company that is involved in AI development, we often receive requests for development that were prepared by a person using artificial intelligence. There is a pattern of how AI works, and you can tell that this was prepared using AI. From what we see, these requests are getting better.
How business integrates AI into real processes
Today, we can see the active implementation of artificial intelligence into business processes: from customer interaction analytics to the automation of routine work. And although the idea of creating a “bot that will do everything” sounds great, the reality is much more complicated and requires a gradual process of AI integration. Let’s figure out why.
Many companies are already using AI to:
- analyze communication with customers;
- automatically form and update a knowledge base;
- create AI copilots that help employees in the work process;
- Launch AI bots that can directly interact with customers.
It may seem that instead of going through all these steps, you can immediately create a powerful customer bot based on the LLM model and simply transfer all communication to it. However, this approach carries a number of risks.
Models like ChatGPT can confuse facts, inaccurately convey information, or respond differently to the same requests. Then, a completely logical question arises: Are we ready to trust such a tool to communicate with customers?
Often, companies that have gone through the first experiments change their approach: they use AI not directly for customers but as an assistant for employees.
AI needs to be trained. If we want the model to work effectively, it needs to be trained on high-quality content. Here, a new challenge arises: Where can we get this data?
Most companies do have internal knowledge bases: documents, files, videos, and presentations on Google Drive. But how relevant are these materials? When was the last time you updated them? This is what usually remains behind the scenes. It turns out that simply transferring this data to the model is not enough–it either works incorrectly or does not work at all.
We go back to where we started: the basis of any effective implementation of AI is high-quality, structured, and up-to-date data. Without this, the phrase "launching AI for customer service" can only be considered an experiment.
You should pay extra attention to constantly updating the knowledge base, analyzing live communication with customers, and forming an internal system to train AI.
What capabilities are already available for businesses?
Most companies already have AI tools that can be used to collect and analyze knowledge. For example, Zoom automatically generates meeting transcripts and even creates short summaries. These materials are a source of valuable information that can be analyzed both at the management level and at the level of individual employees.
We can determine which communication was effective and which was not. We know the results of the calls: where sales were made, who exactly led the conversation, and how. This data can be analyzed to extract real examples of behavior that led to a positive result.
Formation of a knowledge base and its use
If we build a knowledge base with constant analytics, it will allow us to create AI assistants for employees. Especially for newcomers, this assistant can speed up the onboarding process and help them adapt to corporate communication standards.
One practical way to understand when it’s time to move from a simple copilot solution to a full-fledged automated agent is to give employees the opportunity to rate the usefulness of the answers. A simple metric like “working/not working” allows you to see how ready the system is for launch.
Three approaches to implementing AI in business
Approach 1: AI for employees
This scenario involves using ready-made AI tools. ChatGPT, Midjourney, HeyGen—all of these can be used by employees to help them work more efficiently. This solution does not require complex development.
Approach 2: Quick automation through existing systems
Even a simple IT landscape (Google Sheets, CRM, email, websites) can be automated using an LLM and integration platforms like Zapier or Make.com. For example, let’s review a marketing case: a user left their email on the website. We can analyze which page they came from and what interests them, and create a customized email strategy using ChatGPT.
This does not require large investments, but it results in a better conversion than classic marketing touches.
We have a detailed article about the top AI tools for business that provides a detailed overview of HeyGen, Zapier, and many others.
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Approach 3: Full-fledged AI development
This includes developing complex AI agents. Such agents can integrate into various systems, perform specific actions, and build logical sequences.
For example, an AI agent in a real estate company can:
- Understand the user’s request
- Request additional information (if, for example, the city is missing)
- Analyze the database
- Provide relevant results
Each stage involves using a different agent, but they all interact in a logical chain. If you need to add extra information about an object, another agent is activated, which extracts data from structured or unstructured sources, summarizes it through an LLM, and returns it to the user.
How do AI agents work?
Imagine a call center employee. He works according to a script, acts within the framework of clearly defined rules, and does not deviate from the script. This is exactly how AI agents work: they perform the assigned role, but do not go beyond the given context. Without clear instructions, without understanding how to process information, and without a defined format of the result, even the best model will not show the expected effect.
This means that if we want the agent to work effectively, we must not simply give him a task, but provide the full context, the amount of information, the logic of analysis, and the desired result. Exactly how we do it with a new employee during the onboarding period.
Types of AI agents
We can break down AI agents into certain levels of complexity. There are simple ones that work with databases or separate functions. For example, an agent that searches for relevant articles or performs basic analysis based on a user's query.
There are more complex systems that work with terabytes of data. In such cases, it needs to process large amounts of information, find relevant fragments, assemble them into a logical answer, and do it quickly and automatically.
Computer Vision & Gen AI
Another area that has changed with generative AI is computer vision. Today, systems can analyze human behavior: build routes, measure time spent in rooms, and track staff actions.
While it used to take months or even years to create such a system, now it often takes a month or a month and a half to launch a prototype. This is a huge breakthrough for businesses because computer vision allows you to analyze staff workload, evaluate service speed, identify bottlenecks in the company’s work, and even build new approaches to monetization, such as introducing subscriptions.
How to start implementing AI: a simple step-by-step plan
Implementing artificial intelligence in your company doesn’t require an immediate, massive technological overhaul—in 2026, the most successful enterprise transformations begin with a focused, strategic idea.
1. Crowdsource Internal Ideas
Invite your managers and core team members to a targeted brainstorming session. Ask each participant to propose five areas where AI could optimize their department's workflow. You will likely find that two or three ideas overlap across teams—these consensus points are your ideal starting lines.
2. Launch Targeted Pilots
Don't attempt to scale these ideas company-wide on day one. Run small, contained pilot projects to test your hypotheses in real-world conditions. Build a PoC or MVP to validate the core concept and gather authentic user feedback. Treat these pilots like an investment portfolio: not every initiative will yield massive returns, but the successful ones will drive significant ROI.
3. Analyze and Evaluate Performance
After two to three months, conduct a rigorous review of the results. Identify which pilot outperformed expectations and where the greatest potential for long-term return on investment lies. Use this hard data to determine which initiatives deserve further enterprise rollout.
4. Architect for Scaling
Developing a pilot in today's tech landscape is rapid, often taking just a month or two. The true challenge lies in enterprise scaling: securing product investment, re-architecting processes, and ensuring seamless integration with your existing technological ecosystem.
5. Prioritize Change Management
Successful AI integration relies heavily on your people. Proactively prepare your team by positioning AI as an augmentation tool that enhances their capabilities, rather than a replacement. Demonstrate the new level of interaction they will have with products and services, and provide comprehensive training and support throughout the transition.
DATAFOREST offers specialized Gen AI consultations to help businesses pinpoint the most profitable use cases. If you need a proven tech vendor to guide your organization from the initial concept through development and enterprise scaling, book a call with Aleksandr to discuss your future project and maximize revenue growth.
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