The "APTEKA ANTs" brand is the undisputed leader of pharmacy retail in Ukraine. The company's strategy is based on the constant introduction of innovations and optimization of business processes. However, rapid scaling and the growth in the number of complex projects revealed a bottleneck: most managers managed changes intuitively.
Most companies don't fail because they lack ideas or resources. They stagnate because they lack clarity. Too many initiatives, parallel directions. Too little understanding,Where are we really going, and why?
The company's IT department needed to implement a set of works aimed at developing a talent management system and overall improving the efficiency of the IT function. There was a need to implement a systemic approach to competency management that would deeply take into account the specifics of the work of technological departments.
“Marino, I want teams to improve performance by 13 times”. Although this emotional request from a customer was made about a decade ago, team performance remains a major challenge for leaders today.
This year, SkillsUP celebrates its 15th birthday! Over the past decade and a half, we have graduated thousands of talented students, helped them find their way in the world of technology, acquire a new profession, and radically change their lives.
Construction projects are complex systems with a large number of participants, dependencies, and risks.
The company operates with a large volume of operational tasks, including communication, reporting, data handling, and internal processes.
Data analytics has been on the list of the most in-demand digital professions for several years now. Companies are accumulating more and more data and need specialists who can turn it into understandable business insights. But along with the development of technology, the role of the Data Analyst is also changing.
A few years ago, creating a website almost always meant working with code. You needed to know HTML, CSS, sometimes JavaScript, and understand hosting and content management systems. For most people, this looked complicated and highly technical.
Recently, the term AI agents has been increasingly used in the context of business, automation, and personal productivity. But what does it actually mean in practice? And how does an agent differ from the AI chats based on large language models (LLMs) that many people are familiar with?
Imagine this: you've just successfully delivered a project, closed a complex task, or finally found that one bug that had been tormenting you for two days. Your manager praises you. What do you feel? Joy and pride? Or the intrusive thought: "I just got lucky. Next time they will definitely realize that I actually don't know how to do anything"?
Not long ago, the phrase “experience with AI” in job descriptions sounded like a nice bonus. Today, the situation is changing: more and more companies directly expect employees to be able to use AI in everyday tasks – regardless of their position.
When teams talk about “product quality,” everyone puts their own meaning into this term: for some, speed is crucial; for others, design; and for some – that it “just works.” That is why the same product can simultaneously receive feedback like “well done” and “everything needs to be redone.”