Few places in Poland symbolise strategic thinking and the future of investment quite like the Warsaw Stock Exchange (GPW). I had the honour of meeting top leaders of the financial market there to grapple with a fundamental question: What is Artificial Intelligence to an organisation built on data and trust?
The strategic answer is clear: AI is not a fad. It’s a technological, economic, and social revolution—and in financial services, it is existential. For boards and decision-makers facing regulatory risk and investor expectations, one distinction is crucial: Is your current AI implementation a chaotic sprint, or a planned marathon towards durable advantage?
The “Quick Implementation” Trap: Cost of Diffusion, Not Growth
In finance—where speed and precision seem paramount—it’s easy to fall for the illusion of an AI sprint: the temptation to “bolt on” generative tools without a fundamental shift in strategy.
This is the essence of AI Trap #5: deploying AI without a clear strategic “why.” The result? Scattered resources, expensive pilots that never scale, and, ultimately, disappointment that erodes the Board’s trust in the technology.
In an era of exponential AI—where algorithms can run millions of simulations faster than any analyst—the “more and faster” formula stops working. Leaders must unlearn old reflexes and learn to act differently.
Poland at a Crossroads: From Low-Cost Economy to Innovation
The market context is unequivocal: for Poland, AI transformation is a strategic necessity.
Eurostat data is unforgiving: Poland still competes primarily on low wages, with low labour productivity. Continuing to rely on a cost advantage is a dead end. The new AI economy moves us beyond the knowledge economy, as experience itself can be rapidly acquired by technology.
Paradoxically, while 87% of Polish firms think of AI chiefly as cost reduction, it is revenue growth (the focus of only 13% of firms) that powers long-term advantage. AI opens new possibilities. We need a mindset reset.
The ability to create innovation has never been more accessible. This is the moment to democratise innovation.
Three Pillars of an AI Leader: Strategy, Capability, Imagination
Leaders in the AI era must pivot away from technocentrism and focus on three essentials:
- Strategy & Game Plan: Where is AI essential—and where is it harmful?
- AI deployment must be targeted and purposeful (ANI – Artificial Narrow Intelligence). Instead of copying others (Trap #4), build your own AI Task Map:
- Must: Where AI is already market standard (e.g., spam filtering, credit scoring).
- Want: What will lift the quality of work and make it distinctive and competitive.
- Won’t: Which areas must remain uniquely human (ethics, values, relationships).
- A Culture of AI Proficiency: Leave no one behind
AI work is a multi-dimensional, iterative endeavour requiring an entire ecosystem of roles, not only AI engineers (Trap #7). Establish continuous learning at multiple levels:
- Fluency for All Employees: Everyday tool use (ChatGPT, Gemini, Midjourney).
- Power Users: Deepen prompting, critical thinking, and output verification.
- AI Guardians: Control and high-risk functions (Legal, Compliance, AI Ethics).
- Leaders must be risk-aware—research shows that in Poland, women are over-represented in AI-exposed roles (28% of women vs 17% of men in the highest-risk group). AI integration must be inclusive.
Imagination Over Routine: Don’t stop at automating the obvious
We must ditch routine thinking and short-termism that block innovation. In the generative era, human work falls into three buckets:
- Automate: Routine tasks.
- Assist: Tasks where AI is a helper (e.g., analysis, translation).
- Augment: Tasks where AI expands human expertise (Sparring Partner, Knowledge Mentor, Strategist, Innovation Catalyst).
- AI needs curious, creative minds to discover and delegate work in buckets 2 and 3 systematically.
Investing for Advantage: Shift from Cost Cutting to Revenue Creation
For globally competitive financial institutions, AI thinking must go beyond simple cost maths. Yes, 87% of firms in Poland start with cost reduction—but actual strategic value lies in revenue growth (as seen by only 13%).
AI is a new competitiveness catalyst:
- Democratised Innovation:
Agile fintechs can use AI to build products and customer experiences once reserved for giants.
- Data to Decisions:
AI converts massive financial datasets—from algorithmic trading to credit scoring—into immediate, strategic decisions.
It’s time to change the lens. It’s time for an AI Strategy.
Imagination as the New Currency: A Shortage of “What If”
As I underscored on stage: “In the AI era, the biggest bottleneck is a shortage of imagination.”
AI excels at automating what we already do. A leader’s role is to ask: What value can we create with AI that we have never made before? We must use AI to augment our expertise and seek novel applications that build a one-of-a-kind edge.
The Long Game with AI
The AI revolution is a long-distance race. Failure doesn’t lie in pilots that don’t pan out—it lies in the absence of strategic vision. Financial leaders must be both optimists (spotting potential) and pessimists (assessing risk and regulation).
Ride the AI wave—strategically. Build a game plan instead of dashing off in a chaotic sprint. That is the only way to turn transformation into trust and an enduring competitive advantage.
Ready to plan an AI strategy and proficiency at scale in your organisation?
At Booster of Innovation, we specialise in AI Implementation Strategy for Leaders—helping boards move from chaos to an actionable AI strategy and measurable results.
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