The Future of AI in Business: How to Prepare Your Organization for the Upcoming Change

In today’s episode of the “Business Tech Talks powered by BlueSoft” podcast, we discuss key insights from a conversation about real-world implementations of artificial intelligence (AI) in Polish organizations, focusing on adoption barriers, building solid business cases, and the practical experiences of BlueSoft and Digital University in working with companies across various sectors. Below you will find a summary of the episode transcript.

The State of AI Adoption in the Polish Market

Experts divide Polish companies into four categories based on their approach to AI:

• Large enterprises: They have data and structured processes, but struggle with clearly calculating tangible business benefits.
• Mid-sized companies: Often managed by a single decision-maker, they face competency gaps and limited capacity to adopt digital transformation projects.
• Small businesses: Primarily benefit from ready-made, subscription-based tools and licensed solutions.
• “AI-native” startups: Companies built from the ground up around algorithms, addressing niche market needs and leveraging venture capital funding.

Jowita Michalska points out that many organizations are “AI fashion followers”—adopting AI because of hype, without proper preparation in terms of education or internal usage guidelines.

Hype vs. Real Needs: Why Companies Implement AI

AI adoption is crucial for maintaining competitive advantage. Organizations that ignore this shift risk sharing the fate of brands like Sears, Blackberry, or Kodak, which failed to survive previous technological transformations.

Key business drivers include:

• Cost pressure: Rising labor costs in Poland force automation in customer service, finance, accounting, and legal operations.
• Error reduction: Every mistake translates into a measurable cost; AI significantly improves decision quality (e.g., in complaint-handling processes).
• Personalization (Customer Experience): Customers expect a level of personalization shaped by global digital leaders, pushing companies to use AI for upselling and customer retention.

Why Poland Ranks Low in AI Adoption (Eurostat)

With only around 5% of companies implementing AI, Poland ranks near the bottom of European statistics. The reasons are multidimensional:

• Low level of social trust: One of the lowest in Europe, leading to prolonged decision-making and negotiations.
• Technological debt: Many organizations rely on outdated internal systems (“legacy cores”) with only superficial upgrades.
• Traditional management mindsets: IT is still often viewed as a back-office function, rather than a driver of innovation.
• Mental barriers: Limited openness to global knowledge exchange and a tendency to operate in silos.

AI Implementation Strategies and Paths

Arkadiusz Wójcik outlines three primary approaches to AI implementation:

1. Specialist support: Using off-the-shelf licensed tools (e.g., Copilot, Perplexity, Salesforce) to improve efficiency in specific roles.
2. Partial process automation: Deploying robots and agents to independently handle selected process segments.
3. Custom projects: Building tailored solutions from scratch (Blueprint approach), delivering the greatest transformation but requiring time and patience.

Education and the Concept of Lifelong Learning

A major barrier to AI adoption is the lack of competencies. Experts emphasize that:

• Boards must understand how to buy technology: Leaders often don’t know what questions to ask vendors or how to assess security implications.
• Lack of time for learning: Current work models leave little room for upskilling, requiring a systemic approach to lifelong learning.
• The role of HR: Instead of layoffs, organizations should invest in reskilling, which is more cost-effective and sustainable than replacing employees.

Data Security and Technological Barriers

Many organizations—especially in healthcare and public sectors—delay AI adoption due to concerns about sensitive data. Experts stress that data separation in AI systems is possible, enabling secure, closed environments where confidential data is not used to train public models. The real challenge is often limited technical awareness at the executive level.

Measuring ROI: Where AI Truly Delivers Savings

Today, AI excels primarily at cost optimization and efficiency gains. Companies that successfully combine business expertise with algorithmization achieve fast returns on investment. Using AI for direct revenue generation (e.g., large-scale customer acquisition) remains far more complex and requires cautious decision-making.

The Future: Autonomous Agents

The market is moving from simple chatbots toward fully autonomous agents capable of making independent business decisions. Within the next 2–3 years, automation and robotization will become unavoidable, and the greatest success will belong to organizations that adopt these solutions early.

The Employee Perspective: Fear of AI

Fear of job loss dominates many organizations, often fueled by sensational media narratives.

• Demystification: AI should be explained as a tool—just as computers replaced typewriters, AI changes how work is done, not the need for human work itself.
• Job evolution: Self-checkout systems did not eliminate cashiers but transformed their roles; similarly, automated announcements changed—not removed—railway staff responsibilities.
• New skills: Entirely new, often more engaging professions will emerge, replacing repetitive tasks.

Implementing AI can be compared to moving from manual soldering to automated soldering machines. While the goal remains the same—creating a connection—the new method enables vastly greater scale, precision, and repeatability, shifting the employee’s role from executor to operator of an advanced process.