Opinion Article by Naguib Maoulida | February, 2026
There’s more talk about AI than ever, but it’s also becoming harder to understand what really matters. With new models, hype about agents, and prompt tricks everywhere, it’s tough to know what’s actually important.
My goal in this article is straightforward: highlight the main trends that will shape how we use AI in 2026 and what they mean for real-world use.
Trend #1: AI Models Are Converging and Becoming Commodities
In recent years, picking the best model seemed crucial. There were big differences in performance, so benchmarks were important. But that’s starting to change.
Recent benchmark studies show that top models, whether closed or open, are now performing at similar high levels. While all models keep getting better, the differences between them are getting smaller.
Meanwhile, the cost to run these models is falling quickly, thanks to:
- Hardware efficiency gains.
- Optimized architectures.
- And, competitive pressure among providers.
As models perform more alike and become cheaper to use, they start to feel like basic commodities. When that happens, the focus of competition changes.
It’s no longer just about raw intelligence. Instead, it’s about:
- Distribution.
- Integration into existing tools.
- Reliability and trust.
- And overall user experience.
Practical implication: Don’t focus only on benchmarks anymore. Instead, pick AI systems that fit well with your actual workflows and current tools.
Trend #2: 2026 will be about AI workflows, not autonomous agents
Public conversations moved quickly from chatbots to talk of fully autonomous AI agents. But the reality is more down-to-earth.
Industry surveys show that only a few organizations have managed to use fully autonomous agents on a large scale. On the other hand, many companies already use workflow-based AI, where AI helps with certain tasks but people stay in control.
This pattern is the same across different industries:
- AI handles repetitive, predictable tasks.
- Humans remain responsible for validation and decision-making.
- Results are measurable, reliable, and easier to govern.
Calling every system an agent leads to confusion and sets expectations too high. Autonomous agents might become common in the future, but there are still big challenges with security, reliability, and accountability. We’re probably entering a decade of agents, not just a single year.
Practical implication: In 2026, you’ll get the most value by turning good prompts into repeatable workflows: one clear deliverable, well-defined steps, AI assistance where variability is low, and human judgment where it matters.
Trend #3: The Shift From Prompting to Context
Prompt engineering used to set you apart. It still matters, but not as much as it once did. Today’s models are better at understanding unclear or incomplete instructions. But they all have one big limitation: they don’t know your private context. AI systems know public information extremely well. They know almost nothing about your:
- Internal documents.
- Goals.
- E-mails.
- Prior decisions.
This lack of context is now the main thing holding AI back. That’s also why big tech companies are rushing to add AI directly into productivity tools. Whoever manages your documents, calendar, and messages controls the context, and how useful your AI can be.
This leads to strong ecosystem lock-in, which can be good or bad.
Practical implications: If you want to get value from AI, organizing your files is now a must; when your information is scattered across different tools, AI becomes less useful, and things get harder. If AI can’t access your information, it can’t work with it.
In Closing
AI’s future in 2026 won’t be shaped by one big model or a sudden rise of autonomous agents. Instead, it will be shaped by convergence, workflow redesign and context integration.
Prompting still plays a role, but the bigger question now is:
Does your AI have access to the information it needs to understand your world?
About the Author
Naguib Maoulida is a Data & AI Consultant specialized in the design of modern, scalable data platforms and end-to-end analytics solutions. He advises organizations across the full data value chain, from strategy and architecture to advanced analytics, data science, and AI implementation.
With deep expertise in cloud ecosystems (Microsoft Azure, AWS, Google Cloud) and enterprise technologies such as Microsoft Fabric, Databricks, Snowflake, Power BI, and SAP BusinessObjects, he delivers pragmatic, high-impact solutions tailored to complex business environments.
Naguib has led data initiatives across industries including luxury watchmaking, transportation, pharmaceuticals, energy, sustainability, and insurance, helping organizations unlock measurable value from their data and accelerate their AI journey.