Yulia Boltutskaya

Expert in business AI transformation

Yulia Boltutskaya is the Executive Director of WMT AI. She leads the operational development of the AI practice, the introduction of generative AI into business processes, and the assessment of technology risks. Her work connects technology projects, team preparation, and changes to daily operations.

Background and education

From 2018 to 2021, Yulia worked as a delivery manager at an international software-engineering company, responsible for schedules, delivery quality, and communication with international customers.

She joined the wider WMT team in 2021, working on recruitment and specialist development before leading the HR function. As HR Director from 2023 to 2025, she developed teams and processes for technology specialists. Since 2025, she has served as Executive Director of WMT AI.

Her education includes a master’s degree in business management and a specialist degree in social communications. She has also completed professional training in business AI transformation.

Core expertise

Yulia specialises in introducing language models into workflows, designing AI-first and AI-native operating models, prompt engineering, and change management. Her work also includes technology-risk assessment, team development, and corporate learning for leaders and specialists.

Software-delivery experience helps her see the full project, from defining the task and distributing responsibilities to quality control. HR experience helps her account for skills, motivation, and team dynamics when introducing new tools.

Approach to AI transformation

Implementation begins with a specific process, its owner, available data, and a measurable business task. The team identifies which operation needs to move faster, what knowledge the system needs, where errors occur, and which metrics can establish an effect.

A practical pilot follows. Its outcome is tested in a working scenario and, when validated, integrated into the corporate process. Employee learning, solution development, and data rules become parts of one implementation plan.

Prompt engineering and an AI-native model

Yulia treats working with AI as a professional skill. Prompt engineering turns a work task into a clear sequence: provide context, prepare a system instruction, and verify the output.

An AI-native company designs processes with AI as a regular participant. Employees, digital assistants, data, and management rules have defined roles. People remain accountable for the final result and quality control.

Role in WMT AI projects

Yulia leads operational development and helps prepare corporate programmes, intensive courses, and webinars for executives and specialists. Her topics include language models, digital assistants, AI culture, and change support after training.

Participants learn through their own tasks. Yulia helps connect those learning outcomes with the team’s continuing work and the development of WMT AI products.

Speaking and media

Yulia works as a speaker and mentor on preparing teams for AI-assisted work. She takes part in WMT AI learning programmes and delivers intensive sessions, webinars, and corporate classes.

Her topics include generative AI, prompt engineering, team development, and operating-model change. In professional commentary, she recommends starting with a business question: where are money, customers, or time being lost?

A connected operating perspective

Yulia combines technology-delivery management, HR, operations, and AI strategy. She examines a project at several levels: the business task, technology, process change, and team preparation.

In this model, the value of technology is tested in daily work. Each process has an owner, the team has rules for using the tool, and the result is assessed against agreed measures.