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These AI rules are frequently presented at international conferences, corporate training sessions, and industry events, helping businesses stay ahead in a rapidly evolving landscape.
ChatGPT is a user friendly way to interact with OpenAI's GPT models. Essentially, it takes a model of GPT that is fine tuned for dialogue and turns it into a chat bot that responds in a human-like manner.
Many people hesitate to rely on AI, fearing it will replace human decision-making or lead to lazy dependence. However, the real problem isn’t using AI, it’s using it blindly.
Just like any other tool, AI should be leveraged intelligently to enhance productivity and decision-making.
AI has been championed as a huge productivity booster, which it is. However, people blindly trusting AI outputs, or preferring them over theirs, can lead to 'automation bias'.
Let's talk about what leads to creating AI slop:
Until November 2026 this rule recommended Sora and DALL-E 3. Both had already been shut down. A hand-written list of "the best AI tools" goes stale fast, and every vendor's launch post claims their tool is number one.
The fix is to check independent leaderboards, then test the top few on your own work. This rule shows you which leaderboards to trust, where they mislead, and the tools that are winning right now.
You paste 12 files into ChatGPT, ask for a refactor, then copy the answers back one by one. Or you spend a week wiring up the OpenAI API for a report you only need once.
The same models sit behind very different tools. Each one suits a different kind of work, and picking the wrong one wastes more time than it saves.
ChatGPT is most useful when it's easy to access throughout your day. Whether you're working at your desk, using your phone, or on the go, having quick access to ChatGPT helps you capture ideas, ask questions, and get assistance without interrupting your workflow.
Using the desktop and mobile apps also provides convenient features such as keyboard shortcuts, voice conversations, and quicker access compared to opening a browser.
Your company has Microsoft 365 Copilot licences, and half the team tried it once. They typed "help me with this email", got a bland answer, and went back to doing it by hand.
The fix is a better prompt. Tell Copilot what you want and where to look, then test it on work you'd be doing anyway. The prompts below are a starting point for each app.
“I asked Copilot to pull the sales deck, but nothing happened.”
Sounds familiar? Chances you were using the personal Copilot, while the deck lives in your Office 365 tenant. In the Microsoft world, not all Copilots are equal. Add Agents and Connectors to the mix and you can easily get lost.
AI search tools such as Perplexity, ChatGPT search, Google AI Mode and Copilot answer a question in a paragraph with citations, instead of giving you a list of links to read. That saves a lot of time on research, but the answer is only as good as its sources, and AI search tools still get those wrong surprisingly often.
Leading questions can result in biased or unhelpful answers from ChatGPT. To obtain objective and useful responses, it's important to ask open-ended, unbiased questions that allow the model to explore a topic without being influenced by the phrasing of the prompt.
Your AI solution needs your support docs and business information. Feed it too little and it misses the answer. Feed it everything and you can pay more and wait longer.
Choose how the model accesses that data: long context, retrieval or agentic search. Reuse preparation for repeated tasks, and add graphs or fine-tuning when they solve a measured problem.
AI tools like ChatGPT, Claude, and Copilot are great for drafting emails, reports, LinkedIn posts, and documentation quickly. They're not always great at sounding human.
Left unchecked, AI-generated text comes across as overly formal, overly dramatic, or just a bit fake.
AI is a productivity force multiplier, and in some situations, it is good to be transparent that it helped you. Other times, it is more important that the message feels like it came personally from you.
So, in what situations should you attribute AI? Let's find out.
Typing is the bottleneck now. An agent will happily take 3 times the context you'd ever bother typing: your constraints, your edge cases, the things you explicitly don't want. Almost nobody gives it that. Typing a paragraph is a chore, so you send a one-liner and get a one-liner's quality back.
Dictation fixes it. Most people quit before they find a tool that works, because the first ones you try are slow, clip the start of your sentence, and can't spell your own product names.
Generating charts is essential in conveying data effectively. While creating charts can sometimes be a tedious process, ChatGPT can assist by generating various types of charts quickly, saving you time and ensuring accuracy. This is especially useful when you need to visualize data for reports, presentations, or data analysis.
An AI assistant builds an operations dashboard in an afternoon. It looks finished, but nobody can trace its 77% accuracy figure to a source or find how many answers were checked. The AI commentary already tells the product owner to roll back a release.
Build the dashboard on saved calculations and require evidence for its insights. Let code refresh the figures and use AI to suggest what a person should investigate.
Microsoft Forms is a popular survey tool used by many organizations. When forms receive numerous responses, digesting the results becomes challenging.
Starting on an unfamiliar project used to be a stressful experience - it could take developers months to learn the architecture, code patterns, and libraries that are used. And that's not even talking about the application's features and functionality.
But with intelligent use of AI, this on-boarding time can be reduced to almost zero. You can now get on-demand analysis into projects, bugs, and features in minutes, and be operating at maximum velocity in your first Sprint. Learn how to do it right.
An AI agent finishes one task, then waits. You repeat your preferences, chase progress, and still have to check what it delivered. Clear goals and standing orders help it do more useful work with less repeated prompting.
Previously, users have needed to BYO devices to host their AI agents. Whether that's running Claude Code et al locally on your machine, running OpenClaw/Hermes on a local server, or setting up a VPS in the cloud, there weren't many out-of-the-box solutions that gave people an always-on, always-available agent host.
Recently, things have changed. With the release of Claude Tag, Grok Bot, Meta Muse, and ChatGPT Dots, more and more providers are now offering subscription-based agents you don't need to host yourself.
This opens up a lot of new opportunities for AI automation, but where (and how) do cloud-based agents fit into the mix? When is the best time to reach for them over your existing local setup?