In today’s episode of “Business Tech Talks powered by BlueSoft,” we welcome Bożena Leśniewska, Vice President of Orange Polska and a supervisory board member at BlueSoft, Integrated Solutions Polska, and BNP Paribas. We discuss leadership in the era of artificial intelligence and how to manage organizations where people and AI agents work side by side. We explore whether technological advancement will turn humans into “slaves to algorithms,” or, on the contrary, empower leaders to better leverage their teams’ potential and make more informed decisions. Our conversation covers the competencies of future leaders, implementing AI in large organizations, employee resistance to change, the importance of data, security and governance, and the responsibility that comes with deploying AI. Below is a detailed summary of the episode’s key points.

Leadership in the World of Artificial Intelligence

The discussion centers on how the role of leaders is evolving in organizations that are increasingly leveraging AI. Artificial intelligence is no longer just a tool for individual specialists—it is reshaping how work is organized, how decisions are made, how customers are served, and how processes are managed.

In this new reality, leaders cannot simply observe technological change from the sidelines. Their role is to understand how AI impacts the organization, its people, and its business model. Leaders must be able to assess where AI truly adds value and where its use may introduce excessive risk.

At the same time, leaders remain responsible for their people. They must not only manage results but also explain the rationale behind changes, build trust, and create an environment where employees can safely experiment with new technologies.

Will People Become Slaves to Algorithms?

A key topic in this episode is the concern that AI development could lead to a world where people are subordinate to algorithmic decisions. This risk arises when organizations implement AI without clear rules, accountability, or oversight.

However, technology itself does not determine whether AI will be a threat or an asset to an organization. The key lies in how it is used. Leaders decide which tasks are delegated to AI systems, how their actions are monitored, and when human intervention is required.

In this context, AI should be seen as part of a broader management system—not as an autonomous substitute for managerial responsibility. Algorithms can analyze data, propose solutions, and automate processes, but organizations must have clear guidelines for oversight and final decision-making.

How Is the Leader’s Role Changing in AI-Driven Organizations?

Implementing AI changes not only employees’ responsibilities but also the nature of leadership itself. Leaders increasingly need to combine business, technological, and interpersonal skills.

Their most important tasks include:

• Identifying processes that can be improved with AI;
• Assessing the quality and reliability of data;
• Understanding the limitations of AI models;
• Establishing guidelines for responsible technology use;
• Managing risk and ensuring security;
• Supporting employees in developing new skills;
• Fostering a culture open to change;
• Ensuring that AI-driven decisions align with organizational values.

The leader of the future does not need to be a programmer or a technology expert, but must have enough knowledge to ask the right questions and make informed decisions. Understanding both the capabilities and limitations of AI is essential.

Core Competencies for Future Leaders

The conversation highlights several competencies that will be especially important for managing organizations that leverage AI.

Technological Literacy

A foundational understanding of how AI works enables leaders to realistically assess the potential of proposed solutions. This helps distinguish genuine business applications from fleeting trends and marketing hype.

Technological literacy also includes knowledge of data management, cybersecurity, privacy, and the quality of information used by AI models.

Adaptability

The rapid pace of AI development means organizations must be ready for constant change. Leaders cannot assume that a single operating model will remain relevant for years to come.

Adaptability means being open to testing new solutions, learning from mistakes, and adjusting strategies as needed. It also involves guiding teams through each stage of transformation.

Empathy

Technology does not diminish the importance of human relationships. In fact, as more processes are automated, empathy and understanding employees’ needs become even more critical.

Leaders should recognize concerns about AI, including fears of job loss, uncertainty about future skills, and a lack of control over change. Empathetic leadership helps reduce resistance and build trust.

Critical Thinking

AI can generate convincing but inaccurate or incomplete answers. Leaders and employees must be able to verify information, analyze sources, and assess the consequences of proposed decisions.

Critical thinking is especially important when AI is involved in processes that impact customers, employees, or business outcomes.

AI Agents and Human-Technology Collaboration

The episode also addresses AI agents—systems capable of performing specific tasks, analyzing information, and executing process steps with limited human involvement.

Introducing such solutions can transform how work is organized. Employees will not only use AI tools but also collaborate with virtual “colleagues” responsible for certain tasks.

This model raises several key questions:

• Which tasks can be safely delegated to AI agents?
• When is human approval necessary?
• Who is accountable for errors made by the system?
• How should agent performance be monitored?
• How should effectiveness be evaluated?
• How should employees and customers be informed about AI use?

It is important to avoid uncritical personification of AI agents. While they may operate in ways that resemble human work, they do not bear responsibility in the same way as employees or managers. Responsibility always rests with the organization and those who design and implement these solutions.

Implementing AI in Large Organizations

The example of Orange Polska demonstrates that effective AI implementation is not just about purchasing tools or launching pilot projects. It is a long-term process that requires building the right organizational and technological foundations.

Companies often start with experimentation—individual teams test AI tools to see if they can accelerate daily work. Over time, however, it becomes necessary to move from isolated initiatives to a cohesive, strategic approach.

Key elements in this process include:

• Well-organized and accessible data;
• Appropriate technology infrastructure;
• Security protocols;
• Risk management procedures;
• Governance structures and oversight rules;
• Clear assignment of responsibilities;
• Employee skill development;
• Ongoing monitoring of implementation outcomes.

Without these elements, AI may remain a collection of uncoordinated experiments that fail to deliver lasting business value.

Data as the Foundation for Effective AI

One of the most important themes in the discussion is the critical role of data. AI can only be effective when it has access to relevant, up-to-date, and reliable information.

Poor data quality can lead to faulty analyses, misguided recommendations, and risky decisions. Organizations should treat building a solid data foundation as a core step in their AI transformation.

This includes:

• Organizing information sources;
• Ensuring data consistency;
• Defining access rules;
• Protecting sensitive data;
• Quality control;
• Assigning data ownership;
• Preparing data for use in AI models.

In practice, investing in AI must go hand in hand with investing in data architecture and the skills of those who manage it.

AI Security and Governance

Our guest emphasizes the need to build robust AI management systems. Organizations must know which solutions are in use, for what purposes, and what data they operate on.

AI governance should address both formal and practical issues. Clear policies are needed for:

• Information protection;
• Privacy;
• Cybersecurity;
• Regulatory compliance;
• System transparency;
• Risk assessment;
• Human oversight;
• Decision documentation;
• Responding to errors and incidents.

Responsible AI management should not be seen as a barrier to innovation. Well-designed policies enable safe scaling of solutions rather than hindering their development.

Employee Resistance to Artificial Intelligence

AI implementation is often met with resistance. This may stem from fear of job loss, lack of knowledge, anxiety about skill gaps, or a sense that changes are imposed without consultation.

Reluctance to adopt new technologies does not always mean a lack of openness. It is often a reaction to uncertainty and insufficient information. Employees want to know how AI will affect their roles, what skills will be needed, and whether they will receive support during the transition.

Organizations should therefore:

• Communicate AI implementation goals openly;
• Clarify which tasks will be automated;
• Highlight benefits for employees and customers;
• Provide training;
• Create space for questions;
• Enable safe experimentation with new tools;
• Involve employees in designing changes.

Leaders play a crucial role—not just as implementers of executive decisions, but as guides helping teams navigate the new reality.

Reskilling and the SkillsUp Program

One way to address concerns about AI is to invest in skill development. The conversation references the SkillsUp program, which highlights the importance of large-scale reskilling and preparing employees for a changing job market.

Reskilling should not be limited to learning how to use specific tools. It is about preparing employees to thrive in an environment where some tasks are performed by AI systems.

Key competencies include:

• Collaborating with AI tools;
• Data analysis and interpretation;
• Assessing the quality of AI-generated results;
• Understanding security issues;
• Critical thinking;
• Problem-solving;
• Communication;
• Creativity;
• Flexibility and a willingness to learn.

Developing these skills increases employees’ sense of agency and helps them see AI not just as a threat, but as a tool for professional growth.

Building a Culture of Responsible AI Use

Simply introducing AI usage policies is not enough to build a responsible technology culture. Regular training, clear communication, and leadership by example are essential.

Mandatory training in cybersecurity and compliance is a key component. Employees should know which data must not be entered into public AI tools, how to recognize risks, and how to respond to irregularities.

Responsible AI use also means understanding that every user impacts organizational security. Even the best procedures are ineffective if employees do not understand their importance or treat them as mere formalities.

The Leader as Guardian of Responsibility

In the AI era, leaders are not just accountable for results—they are guardians of responsibility. They must balance innovation with security.

A responsible leader:

• Does not implement AI just because it’s trendy;
• Assesses the business problem a tool is meant to solve;
• Evaluates the impact of technology on people;
• Demands transparency and oversight;
• Responds to irregularities;
• Ensures teams have access to knowledge and training;
• Clearly communicates the boundaries of AI use;
• Takes responsibility for technology-driven decisions.

This approach to leadership helps prevent responsibility from being shifted onto algorithms.

The Greatest Advantage of Future Leaders

The conversation concludes that the greatest advantage of future leaders will not be technological expertise alone. Equally important will be the ability to build trust, demonstrate empathy, and guide people through continuous change.

Effective leaders must combine several perspectives:

• Technological – to understand AI’s capabilities and limitations;
• Business – to assess the value of implemented solutions;
• Social – to understand the impact of change on employees;
• Ethical – to recognize the consequences of decisions;
• Strategic – to connect individual initiatives with the organization’s long-term development.

It is this combination of technological knowledge, adaptability, empathy, and responsibility that enables leaders to successfully manage organizations in a world of people and AI agents.

Summary: How to Manage AI in Business?

The episode “AI in Business Under Control” demonstrates that artificial intelligence is not just a technology project. It is an organizational, cultural, and managerial transformation that affects how work is done, what skills are needed, where responsibility lies, and how people and technology interact.

Organizations that want to leverage AI effectively should focus on:

1. A clear AI strategy;
2. High-quality data and appropriate infrastructure;
3. Security and governance;
4. Employee skill development;
5. Transparent communication;
6. Managing resistance and concerns;
7. Human oversight of key decisions;
8. A culture of responsible experimentation.

The key takeaway is that technology alone will not create a competitive advantage. Success will depend on leaders who can harness the potential of AI while safeguarding people, security, and responsibility.