Newsletter

How to overcome obstacles, or: how I learned not to worry and love artificial intelligence

Why do so many companies fail to adopt AI? The main barrier is not technological but human. The article identifies six critical barriers: resistance to change, lack of management involvement, data security, limited budget, compliance, and continuous updating. The solution? Start with pilot projects to demonstrate value, train staff, protect sensitive data with dedicated systems. AI enhances, not replaces-but it requires process transformation, not simple digitization.

Breaking down barriers: the algorithm inside us

Artificial intelligence (AI) changes work. Many companies face difficulties in adoption that can undermine the successful adoption of these new tools in their processes. Understanding these obstacles helps organizations leverage AI while maintaining efficiency.

The challenge of continuing education

The rapid development of AI creates new challenges for professionals and companies. Workers fear replacement by AI. However, AI functions as an empowering tool, not a replacement, through:

  • Automation of repetitive tasks
  • Space for strategic activities
  • Decision support with data

Presenting AI as a collaborative tool reduces resistance and encourages adoption of this technology. Undoubtedly some tasks will disappear over time, but fortunately only the most tedious ones. This actually entails not only an adoption of the technology within processes, but a total change in them. In short, the difference between digitization and digital transformation. Insight: https://www.channelinsider.com/business-management/digitization-vs-digitalization/

Data protection and security

Privacy and security pose major obstacles. Companies must, or should, protect sensitive data by ensuring the accuracy of AI systems. The risks of breaches and incorrect information require:

  • Regular security checks
  • Evaluation of suppliers
  • Data protection protocols

In particular, the adoption of "automatic filters" in the management of the most sensitive data, and the use of dedicated systems in case the entirety of corporate data is to be managed or analyzed, is essential, not only as a matter of security, but also to avoid "giving away" data of very high value to third parties. Just as has happened before in other contexts, however, this kind of attention will remain the "enlightened" approach of only a few organizations. In short, everyone does what he or she wants, aware of the trade-offs that different choices entail.

Below is a short list of Key Points

Managing resistance to change

Adoption requires management strategies that include:

  • Communication of benefits
  • Continuing Education
  • Practical coaching
  • Feedback management

Top-down approach

Decision makers require evidence of the value of AI. Effective strategies:

  • Show competitor success stories
  • Pilot demonstration projects
  • Clear ROI metrics
  • Demonstrate employee engagement

Managing constraints in the budget

Inadequate budget and infrastructure hinder adoption. Organizations can:

  • Starting with contained projects
  • Expand based on the results
  • Allocate resources carefully

Legal and ethical aspects

Implementation must consider:

  • Impartiality and fairness
  • Regulatory compliance
  • Rules for responsible use
  • Monitoring legislative developments

Continuous updating

Organizations must:

  • Monitor relevant developments
  • Participate in industry communities
  • Use authoritative sources

Perspectives

Effective adoption requires:

  • Strategic approach
  • Attention to organizational change
  • Alignment with corporate goals and culture
  • Focus on practical value

Effective change improves operations and workforce capacity through targeted and sustainable choices.

Resources for business growth

November 9, 2025

Regulating what is not created: does Europe risk technological irrelevance?

Europe attracts only one-tenth of global investment in artificial intelligence but claims to dictate global rules. This is the "Brussels Effect"-imposing regulations on a planetary scale through market power without driving innovation. The AI Act goes into effect on a staggered timetable until 2027, but multinational tech companies respond with creative evasion strategies: invoking trade secrets to avoid revealing training data, producing technically compliant but incomprehensible summaries, using self-assessment to downgrade systems from "high risk" to "minimal risk," forum shopping by choosing member states with less stringent controls. The extraterritorial copyright paradox: EU demands that OpenAI comply with European laws even for training outside Europe-principle never before seen in international law. The "dual model" emerges: limited European versions vs. advanced global versions of the same AI products. Real risk: Europe becomes "digital fortress" isolated from global innovation, with European citizens accessing inferior technologies. The Court of Justice in the credit scoring case has already rejected the "trade secrets" defense, but interpretive uncertainty remains huge-what exactly does "sufficiently detailed summary" mean? No one knows. Final unresolved question: is the EU creating an ethical third way between U.S. capitalism and Chinese state control, or simply exporting bureaucracy to an industry where it does not compete? For now: world leader in AI regulation, marginal in its development. Vaste program.
November 9, 2025

Outliers: Where Data Science Meets Success Stories.

Data science has turned the paradigm on its head: outliers are no longer "errors to be eliminated" but valuable information to be understood. A single outlier can completely distort a linear regression model-change the slope from 2 to 10-but eliminating it could mean losing the most important signal in the dataset. Machine learning introduces sophisticated tools: Isolation Forest isolates outliers by building random decision trees, Local Outlier Factor analyzes local density, Autoencoders reconstruct normal data and report what they cannot reproduce. There are global outliers (temperature -10°C in tropics), contextual outliers (spending €1,000 in poor neighborhood), collective outliers (synchronized spikes traffic network indicating attack). Parallel with Gladwell: the "10,000 hour rule" is disputed-Paul McCartney dixit "many bands have done 10,000 hours in Hamburg without success, theory not infallible." Asian math success is not genetic but cultural: Chinese number system more intuitive, rice cultivation requires constant improvement vs Western agriculture territorial expansion. Real applications: UK banks recover 18% potential losses via real-time anomaly detection, manufacturing detects microscopic defects that human inspection would miss, healthcare valid clinical trials data with 85%+ sensitivity anomaly detection. Final lesson: as data science moves from eliminating outliers to understanding them, we must see unconventional careers not as anomalies to be corrected but as valuable trajectories to be studied.