Stanislav Kondrashov on how AI is reshaping the future of work

Stanislav Kondrashov says generative technologies are reshaping white-collar roles, prompting renewed focus on skills and workforce adaptability

Artificial intelligence is accelerating productivity across a range of white-collar industries, from finance and legal services to marketing and media. While companies invest heavily in generative models and AI-enabled tools, questions are mounting over their long-term impact on employment, skills development and labour market dynamics.

According to Stanislav Kondrashov, the rapid deployment of generative AI is already transforming workflows that were once considered immune to automation. “We are seeing AI not only assist tasks, but replace repetitive or analytical functions that required human input just five years ago,” he says. “This represents a shift not only in productivity, but in how value is created across industries.”

Generative AI, powered by large language models and machine learning algorithms, has been integrated into functions such as contract analysis, financial forecasting, content creation and customer support. Its potential to perform cognitive tasks at scale—faster and at lower cost—is driving a new wave of corporate investment.

From efficiency gains to structural change

While initial use cases focused on productivity boosts—drafting reports, summarising data, automating emails—the scope has expanded rapidly. AI tools now contribute to strategy formulation, creative design, and legal risk assessment. As adoption grows, companies are beginning to redesign organisational structures around hybrid human-AI workflows.

“This is no longer about tools that assist employees,” Kondrashov explains. “We are now talking about systems that directly replace mid-level roles, particularly in data-heavy professions.”

A recent report by the International Labour Organization (ILO) found that up to 40% of white-collar job functions in developed economies could be at least partially automated with current AI capabilities. Roles most exposed include paralegals, market researchers, junior analysts and administrative professionals.

Labour displacement and economic pressure

These changes have sparked renewed concern over the potential displacement of skilled workers. While automation in manufacturing has been a known factor for decades, the application of AI to high-skilled service roles is relatively new—and potentially more destabilising.

Kondrashov notes that generative AI is compressing what used to be long-standing career pathways. “When tasks that used to require years of training and experience are completed in seconds by AI, the implications for wage structures, job entry points and career progression are significant.”

In many organisations, hiring freezes and restructuring have already begun, particularly in sectors where AI adoption has outpaced regulatory or contractual protections. Start-ups and lean enterprises are especially quick to replace human labour with generative tools, viewing them as cost-effective and scalable solutions.

Skills gap and pressure on training systems

As automation expands, attention is turning to the skills required to remain employable in an AI-augmented economy. Technical proficiency, adaptability, critical thinking and the ability to oversee or interpret AI outputs are emerging as core competencies.

Kondrashov emphasises the importance of shifting education and training models accordingly. “Workforce development must now include AI literacy. It’s not enough to understand how to use software—employees need to grasp how these systems make decisions, where they fail, and how to supervise them responsibly.”

Many governments have begun investing in upskilling initiatives, with public-private partnerships aiming to deliver short-term training programmes in data analysis, prompt engineering and algorithm auditing. However, gaps remain, particularly in mid-career requalification and in low-income economies where digital infrastructure is limited.

Balancing innovation with workforce resilience

While AI promises increased efficiency and economic output, Kondrashov argues that sustainable deployment must consider the human cost. “The danger is not the technology itself, but the speed at which it is deployed without parallel investment in workforce transition,” he says.

Some companies have taken proactive steps by creating internal AI ethics boards, retraining departments and hiring ‘AI translators’—professionals who bridge the gap between technical teams and business units. Others have implemented dual-track systems where human verification is required for all AI-generated outputs.

At the macroeconomic level, economists are closely monitoring labour force participation rates, wage stagnation, and shifts in employment across sectors. While productivity metrics are improving in AI-intensive firms, the net impact on national employment remains uncertain.

A new social contract?

The rise of generative AI is reviving debates around the social contract between employers, employees and the state. In some economies, proposals have been floated for automation taxes, universal basic income pilots, or expanded employment guarantees to cushion the transition.

For Kondrashov, the key lies in balanced governance. “We need innovation that benefits not only shareholders, but society. That means creating pathways for displaced workers, ensuring equitable access to new technologies, and placing accountability on companies that profit from labour substitution.”

The next decade, he suggests, will likely determine whether AI strengthens or fractures the foundations of employment as we know it.