AI in Business: Separating Facts from Myths

Bartosz Gonczarek Autor
Bartosz Gonczarek
Vice President, Co-founder
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    When you think about AI, do you see it as a powerful tool to embrace or a disrupting force? Many businesses struggle with this very question.

    To set the stage for our AI workshops, we often start with a thought-provoking comparison:

    How does Paul Atreides of Dune differ from Prometheus of Greek myth?

    • Dune envisions a future where AI resembling humans was outlawed, ultimately resulting in a rejection of AI technology due to its risks.
    • Mythical Prometheus did the opposite —  fearlessly embraced godly powers (symbolized by fire) defying all related risks for human empowerment.

    Where does your organization stand in comparison — cautious of new technologies to the point of refusal, or self-made defiant, Promethean-style?

    For each decisionmaker seeking the answer — we help to strike the right balance, avoiding blind adoption while unlocking AI’s full potential for growth.


    Case Study: Tauron Dystrybucja Workshop

    Ai in business

    Many large organizations, such as Tauron Dystrybucja, already use AI-based tools but are looking for a comprehensive environment tailored to their needs. Tauron Dystrybucja is a key player in the Polish energy sector, responsible for the distribution of electricity to millions of customers across southern Poland. As part of the Tauron Group, one of the largest energy companies in the country, the company ensures a stable and efficient energy supply while continuously modernizing its power grid infrastructure. Their focus on innovation and digital transformation drives their interest in AI-based solutions for optimizing energy distribution, predictive maintenance, and operational efficiency.

    And when they do so, they look for the right balance between risking the unproved or being late adopters. They often struggle to choose between risky trials with the potential to overinvest in promises never to be realized or coming late and onboarding technology already used by the competition.

    For us at Vstorm, this is the perfect moment to step in—grounding customers’ thinking in tangible and practical AI applications while dispelling all myths and uncertainties.

    Dispelling the myths about AI in business

    During our workshops, participants frequently raise concerns about AI that press and media are all about. We experienced constant re-emerging of typical questions from the leadership, such as:

    • When will AI replace human jobs?

    This fear-based question often comes from a fundamental misunderstanding of what those new technologies are. It is, perhaps, the easiest one to tackle with examples of companies that already embraced MLL-based solutions to save time and costs — and by doing that, increased their team satisfaction by minimizing the mundane and repetitive tasks people did.

    • How much of General Purpose Technology is in the acronym ‘GPT’?

    Very often, what is feasible thanks to MLL-based solutions is confused with a magic wand, similar to the times when early Oracle transactional databases were confused with Delphic Oracle. Nope, not the same thing.

    • How does AI adoption differ between large enterprises and smaller companies?

    Contrary to popular belief, AI solutions are increasingly accessible to businesses of all sizes. Many of our clients, including smaller organizations, have successfully implemented AI in targeted use cases to achieve measurable improvements.

    By addressing these questions early in the workshop, we help participants gain a clearer perspective on AI’s true capabilities.

    Our approach

    Instead of presenting AI as a futuristic magic bullet, we focused on real-world applications instead. We speak of:

    • What can MLL and Gen-AI systems effectively do today vs. what may still be considered experimental?
    • How AI aligns with business objectives in the reality of today,
    • The importance of measurable outcomes rather than vague expectations.

    Thanks to this approach, workshop participants can avoid both blind optimism and excessive skepticism toward AI. Instead, their perspective shifts to embrace AI’s transformative potential without unnecessary risks or unrealistic expectations.

    AI in business

    A practical strategy for AI implementation

    Adopting AI successfully requires a well-defined approach. Based on our work with various organizations, we recommend the following structured process:

    1. Identify key areas where AI can bring value. Look for repetitive processes, inefficiencies, or customer pain points that AI could optimize.
    2. Engage stakeholders early. AI adoption works best when decision-makers and operational teams collaborate from the outset.
    3. Start with a Proof of Concept (PoC). Testing AI in a controlled environment allows teams to evaluate its feasibility before scaling.
    4. Evaluate results and iterate. Tracking AI performance over time enables businesses to refine their approach and maximize impact.
    5. Scale strategically. Once the AI model proves its value, expanding its application gradually ensures long-term sustainability.

    Following this structured approach ensures that AI delivers tangible business benefits rather than becoming just another technology experiment.

    Workshop outcomes

    By the end of the session, we make sure that teams take home the following know-how:

    ✔ Understood what AI can realistically achieve for their business.

    ✔ Identified specific areas where AI could provide real value.

    ✔ Developed a clear action plan for AI adoption.

    Talk to a knowledgeable expert, not a chatbot

    Bart, PhD economist and our co-founder, is ready to leverage his hands-on experience of:

    • Entrepreneurial, and C-level roles
    • Exited and supported startups
    • Executive Consulting background

    to discuss your project on a 20-minute introductory call.

    Dr. Bart Gonczarek

    Vice President

    Stages of AI adoption in business

    If you consider following four stages of AI adoption — where is your company stand on the journey of exploration to final deployment?

    Tabelka

    For example, decision-makers might think of onboarding new tech by saying, “We need a chatbot.” However, after implementing it, they usually realize their actual goal wasn’t that simple. The intent was rather to offload the support team from repetitive tasks that could be automated yet maintain high satisfaction levels by not removing humans from the equation. It’s not anymore a need for a chatbot, but a successful blend of human chatbot routines.

    To answer where your company stands in the AI adoption process requires — Exploration, Proof of Concept, and Testing that Vstorm can support to get to the real goals and Deployment stage quicker.

    The Need for Entrepreneurship & Intrapreneurship – AI in Business

    Adopting AI requires a different frame of mind than pertaining to the needs of existing processes. What is required is to see how things could be done differently, which requires Intrapreneurial skills. Luckily, at Vstorm, we come with a set of Entrepreneurial experiences that fit right in. Here’s how both compare:

    Tabelka

    It is crucial to have workshops with people who not only tell you how to do things (such as consultants) but also are real entrepreneurs who have experienced the pain of creating and launching innovative products themselves.

    After all, any new technology adoption in customer service, marketing, or sales is an innovation in itself, that comes with potential challenges entrepreneurs know best of.

    • Entrepreneurs can help you craft a bold path forward while minding the risks
    • Intrapreneurs can ensure that AI aligns with company strategy, knowing its operation best
    • A balanced approach leads to both mindsets working in tandem

    Final Thoughts: Finding the middle ground

    Is your company avoiding AI due to uncertainty, or are you adopting it without a clear set of objectives? Successful AI adoption isn’t about extremes—it’s about strategic, well-planned implementation that is grounded in what is feasible as of now.

    Start with a Proof of Concept (PoC) to test feasibility.

    Measure success using clear business metrics (ROI, efficiency, customer satisfaction).

    Balance innovation with practicality for long-term success.

    We help businesses turn AI uncertainty into AI opportunity. Where do you see your company in this journey?

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