In 2023, using AI was basically asking ChatGPT to summarize a news article or draft an email. Three years later, AI can work inside office software, create a website without code, and connect complex tasks across different systems.
But where do these AI tools make sense in real work? Instead of asking another chatbot, here is our practical guide to the AI tools that can help you work smarter in 2026.
AI productivity tools: Copilot and Gemini
Copilot (Microsoft) and Gemini (Google) are AI assistants built into productivity ecosystems many teams already use every day: Microsoft 365 and Google Workspace. That integration is what makes them especially useful for everyday work.
If you were out sick and missed the latest project updates, Copilot can search across Outlook, Teams, and shared Microsoft 365 files to help you catch up. Gemini can do something similar across Gmail, Drive, and Docs. You can simply ask what was decided or what is still pending.
These tools become especially useful when a task moves across several apps. Imagine a client workshop that leaves you with a 50-page transcript. Gemini can help identify the key takeaways, organize the information in Google Sheets, and use it to build a presentation in Google Slides. The time saved can go into preparing what you want to say in the meeting.
In fact, a 2025 NBER field experiment found that employees who used AI integrated into their work applications spent around two fewer hours per week on email. And email is only one part of the working day where these tools can help.
AI app builder: Replit Agent
Replit Agent lets you describe what you want to build in plain language and turn it into a working website or application, without coding every part from scratch. You can test it as you build, ask for changes, and keep refining it until it is ready to be published. Replit Agent 4 can create websites, web apps, and mobile apps in the same environment.
For product and operations teams, this means they can test an idea with a working prototype before committing more time and development resources. When it is ready, they can publish it as a live website or connect to their own domain.
AI coding agents: Claude Code, Codex and Cursor
Until recently, using AI for coding often meant getting code suggestions or snippets and then applying and reviewing them yourself. Coding agents such as Claude Code (Anthropic), Codex (OpenAI), and Cursor can work across a codebase rather than only suggest individual snippets. They can identify where changes are needed and apply them.
If a developer prompts “Add a search bar to the product catalogue,” the agent can implement the change and then run its own tests to check that it works. Some tools, such as Cursor Cloud Agents, also support human review by attaching videos, screenshots, and logs that show what changed.
An operations manager who knows the sales dashboard well could, for example, ask the agent to add a new filter to compare sales by region, without having to write every line of code.
According to OpenAI, around 20% of Codex users are not developers, including analysts, marketers, operations professionals, designers, and researchers.
OpenAI API
OpenAI provides APIs that let companies integrate its models into software and workflows, so AI can process information as part of a larger process without someone copying it manually from one tool to another.
Imagine a support team receiving 10,000 requests. AI could read each one, classify it as billing, technical, or account-related, set a priority, and send unusual cases for human review. With Structured Outputs, the results can come back in the same format every time, ready for another system to use.
Open and local AI models: Hugging Face and Ollama
Companies may need to compare different models or ways of running them, especially when cost, privacy, or control matter.
Hugging Face is a platform and ecosystem for finding and working with AI models such as Llama, Gemma, and Qwen. Ollama makes it easier to download many models and run them locally on your own computer or server.
Take a finance team working with hundreds of supplier invoices and contracts. They could test several models on documents where the correct result is already known and compare how accurately each one extracts amounts, dates, or clauses. Running a model locally with Ollama also means those documents can be processed on the company’s own machine instead of being sent to an external service.
A smaller local model may be enough for a repetitive task. For something more demanding, a cloud-based model may still perform better. Which option works best will depend on the task, so testing them matters.
AI workflow automation: n8n
n8n connects apps, APIs, databases, AI models, and human approvals inside the same workflow, so information can move between them without someone handling every step manually.
AI might classify a request or prepare a response, while n8n takes care of what happens around it: retrieving data, applying rules, updating another system, or notifying the right person. It can also stop a workflow when human approval is needed.
With n8n, AI can move from an isolated task to part of a process that runs across several tools.
From AI tools to practical skills
Learning different AI tools gives you more options at work. The more important skill is knowing which one fits the problem, how to test it, and when several tools need to work together.
If you want to keep building those skills, there are different ways to go further. Our AI Professional training builds a practical foundation in AI, from models and data to agent essentials. The AI Agents training focuses on building and orchestrating AI-driven workflows with tools such as n8n and OpenAI.
For professionals who want to connect AI with business strategy, governance, and implementation, our AI Business Specialist program brings these areas together and prepares you for the Swiss federal examination at Professional Bachelor level.
The names and interfaces will keep changing. Those skills will remain useful as new tools appear.
