AI in Business 2026: What Actually Works and What Just Sounds Good
We've implemented AI systems in over ten projects in the last two years. Here's what works, what fails, and when you don't need AI at all.
Document classification achieves 90–95% accuracy with a well-prepared model. An insurance broker (~300 emails/day) reduced manual work by 80% after AI implementation.
Honest Talk About AI Hype
In 2023–2024, every company wanted "AI." Now, after many failed implementations, clients ask smarter questions: "Will this actually work in our case?"
Here's our honest experience.
What Works Well
Document Processing and Classification
If your company receives hundreds of emails, contracts or forms per day — AI can classify them, extract key information and route them to the right person. Accuracy: 90–95% with a well-prepared model.
Real example: an insurance broker with ~300 emails per day. After AI implementation, manual classification time dropped by 80%.
Internal Copilots
An AI assistant that knows your product catalog, pricing rules, and contract conditions — and answers your sales team's questions in seconds. Not ChatGPT. Your data, your rules.
Code Generation and Review
Our team uses AI as a senior assistant — not for writing code, but for boilerplate fragments, test generation, and first-draft documentation. We save ~30% of time on routine tasks.
Content Management and Localisation
Generating product descriptions for large catalogs, contextual translation, SEO metadata — AI does this well when the process is correctly designed.
What Doesn't Work (or Works Poorly)
Customer Service Chatbots Without Proper Data
90% of "AI chatbots" on the market are just GPT with a system prompt. Without a structured knowledge base, they hallucinate, make mistakes and frustrate customers. Worse than no chatbot.
Decision-Making Without Human Review
AI is excellent at supporting decisions. It's a bad idea to let it make decisions without human review — especially in finance, legal, or healthcare.
When You Don't Need AI
- When the process happens 10 times a month (too infrequent)
- When you already have a simple rule that works perfectly
- When data quality is poor and there's no budget to fix it
- When the team isn't ready to change how they work
Our Approach
We don't start with "which AI to use." We start with "where does your team waste the most time?" and only then decide if AI is the answer.
Often it is. But not always.
Have questions or want to discuss your project?
Get in touch