EU AI Act Risk Categories: Which One Is Your System In?
The EU AI Act sorts AI systems into four risk categories. What each means, classification examples from banking, HR and marketing, and the high-risk exceptions.
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Field notes and guides on enterprise AI, GenAI, MLOps, regulation and AI in banking.
What Is Shadow AI? Risks and 10 Essential Controls
Shadow AI is employees using AI tools without approval. Why it happens, the risks it brings and how to bring it under control without simply banning it: 10 essential controls.
Read more →MLOps Maturity Levels: A Roadmap from Level 0 to 4
MLOps maturity determines how fast and reliably your models reach production. The levels in Microsoft's and Google's models, what characterises each level and the next step.
Read more →What Is AI Literacy and Why Do Your Employees Need It?
AI literacy means understanding AI, using it safely and recognising its risks. Its definition in the EU AI Act, what it covers and how to build it across your organisation.
Read more →AI Maturity Models: What Level Is Your Organisation At?
AI maturity models show where your organisation stands and what to do next. Gartner, MITRE and Cisco models, 6 core dimensions, 4 levels and actions for each level.
Read more →The EU AI Act and Turkish Companies: Scope, Timeline and Penalties (2026)
The EU AI Act covers not only EU companies but any AI system whose output is used in the EU. Scope, risk categories, the updated 2026 timeline and penalties for Turkish companies.
Read more →GenAI in Banking: On-Premise or Cloud?
Banks adopting GenAI must balance data localisation, security, model quality and cost. A comparison of on-premise, cloud and hybrid approaches, with a focus on Türkiye.
Read more →Using ChatGPT at Work: 10 Data Protection Rules for Employees
ChatGPT and similar tools save time at work, but careless use can lead to personal data breaches and leaks. Ten practical rules for employees, with a Turkish data protection lens.
Read more →Machine Learning for Fraud Detection: From Rules to Models
Rule-based fraud systems can't keep up with new schemes. How machine learning improves fraud detection: approaches, imbalanced data, the right metrics and hybrid architectures.
Read more →What Is ISO/IEC 42001? Does Your Company Need It?
ISO/IEC 42001 is the first international standard for AI management systems. Its structure, the 38 Annex A controls, how it relates to the EU AI Act and how to prepare for certification.
Read more →How to Validate Credit Scoring Models: A Practical Guide
Validating a credit scoring model is more than checking the Gini coefficient. Conceptual soundness, discriminatory power, calibration, stability, explainability and monitoring.
Read more →How to Plan Corporate AI Training: A 5-Step Guide
Corporate AI training isn't a one-off presentation; it's a measurable capability programme. A five-step planning guide, from setting goals to measuring impact.
Read more →What Is MLOps? Why Models Never Reach Production
MLOps is the discipline of getting machine learning models into production reliably, and keeping them there. Its components, maturity levels, LLMOps and how it differs from AIOps.
Read more →Deploying LLMs On-Premise: Hardware, Model Choice and Cost
You can use large language models without your data leaving the building. Model choice, GPU sizing, serving software, security and the cost case for on-premise LLM deployment.
Read more →What Is Prompt Engineering? 8 Techniques for Better Results at Work
When one person gets mediocre results from an AI tool and another gets excellent ones, the difference is usually the prompt. Eight prompting techniques you can use at work today.
Read more →What Is RAG? Building an AI Assistant on Your Company Documents
RAG lets language models answer based on your organisation's own documents. How it works, how it differs from fine-tuning, what to watch when building it and how to measure quality.
Read more →Which AI Training Do Your Employees Need? A Role-Based Guide
Executives, HR specialists, analysts and developers all need different AI training. A practical guide to matching each role with the right programme.
Read more →Türkiye's AI Action Plan 2026–2030: What It Means for Companies
Türkiye's AI Action Plan, in force since 18 August 2026, creates no direct obligations for companies but sets the direction. What is changing, and what should companies do now?
Read more →What Are AI Agents? How Companies Are Using Them
AI agents don't just chat; they use tools and run workflows. How agents differ from chat assistants, enterprise use cases, the risks and how to get started.
Read more →What Executives Need to Know About AI: 7 Critical Topics
Executives don't need to build models, but to make good AI investment decisions they need to understand these seven topics: value, data, cost, risk, regulation and people.
Read more →How to Measure the Impact of AI Training
Participant satisfaction doesn't show whether AI training worked. A four-level measurement framework based on the Kirkpatrick model, sample metrics and a 30-60-90 day plan.
Read more →How to Build an AI Team: Roles and Operating Models
Which roles does your first AI team need, in what order should you hire, and which operating model fits: centralised, federated or hybrid?
Read more →How to Write an AI Acceptable Use Policy for Your Company
Your employees are already using AI tools. The sections of a short, workable AI usage policy, a sample data classification table and the most common mistakes.
Read more →Why AI Projects Fail: 6 Common Mistakes
Most AI projects never make it past the pilot or fail to create value in production. The six most common mistakes, and how to avoid each one.
Read more →10 Questions to Ask Before Starting an AI Project
Before budgeting for an AI project, answer these 10 questions on the problem, value, data, legal basis, users, integration, cost and scale. A checklist you can use with your teams.
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