Clear-eyed AI: real cases and practical perspective
In-depth analysis of artificial intelligence for business. Use cases, technologies, real challenges, lessons learned, and perspectives that help you make better decisions.
Automating document management with AI: a practical guide
How to automate business documents with AI: data extraction, classification, and approval workflows with no manual data entry.
Data quality in AI projects: the factor nobody wants to look at
Data quality in AI projects: how to assess it, how to improve it, and how to build a solid foundation for artificial intelligence.
Business chatbots: what nobody tells you before you build one
Business chatbots: what actually works, maintenance, limitations, and how to design them so customers don't get frustrated.
ChatGPT vs. Claude for business: the differences that matter
A practical comparison of ChatGPT and Claude for business use: capabilities, privacy, and when to use each one depending on the use case.
How to choose an AI vendor without getting it wrong
How to evaluate and choose an AI vendor: the key questions, the red flags, and the criteria the market rarely mentions.
How to tell if your company is ready for AI
An AI readiness assessment for companies: data, processes, people, and culture. The signs that tell you the time is right.
Microsoft 365 Copilot for business: what works and what doesn't
Microsoft 365 Copilot for business: its real capabilities, its specific limitations, and when it makes sense to switch it on.
How much does it cost to implement AI in a company?
The factors that drive the cost of implementing AI: what makes it more expensive, what makes it cheaper, and how to evaluate the investment.
What data does your company need to get started with AI?
What data a company needs for AI projects: quality, volume, structure, and what to do when the data you have isn't enough.
EU AI Act: what your company needs to know
A practical guide to the EU AI Act for companies: risk categories, specific obligations, the implementation timeline, and how to prepare.
AI and employees: how to manage the change without losing anyone
How to bring AI into your teams: training, handling resistance, and turning the change into an opportunity.
Generative AI in business: the use cases that really work
A practical look at generative AI in business: which tasks it really improves, where it needs oversight, and how to implement it with judgment.
Free AI for business: what's real and what isn't
Free AI tools for business: which ones actually work, their limitations, and when it's worth paying for a subscription.
Machine learning vs. generative AI: which does your company need?
Machine learning vs generative AI: the practical differences, and how to choose the right approach for each use case.
AI predictive maintenance: what real projects show
AI predictive maintenance: how it works, what data it needs, and when it makes sense to apply it in manufacturing and industry.
Why AI projects fail in business
The real reasons enterprise AI projects fail — data, expectations, and organization — and how to avoid the most common mistakes.
AI demand forecasting: what nobody explains well
AI demand forecasting: how it works, what data it needs, and when it actually beats the methods you already use.
What nobody tells you before your first AI project
Lessons from first AI implementations: the real challenges, the usual surprises, and how to prepare your company to succeed.
AI and data privacy in business: real risks and how to manage them
Data privacy in business AI projects: GDPR, personal data in language models, the real risks, and the practices that keep them in check.
Prompt engineering for business: techniques that make a difference
A practical prompt engineering guide for business teams: how to instruct AI models to get consistent, useful results every time.
GDPR and AI in business: how to handle data responsibly
GDPR and AI: what the regulation requires, how it affects artificial intelligence projects, and how to build compliance in from the start.
How to calculate the ROI of an AI project before you launch
How to calculate the ROI of an AI project: what to measure, how to quantify the value, and which estimation mistakes to avoid.
RPA vs. AI agents: when to use each technology
The practical differences between RPA and AI agents in business: when to use each technology, their real limits, and how to combine them.
Computer vision in manufacturing: real cases and what it takes
Computer vision for manufacturing: how it works, what it detects, what data it needs, and which industries see the biggest impact.
Ask us about AI
Our articles cover the most common questions. To assess your specific situation, the first step is a free initial conversation.