The most popular AI tools right now are ChatGPT, Google Gemini, Microsoft Copilot, Claude, and Perplexity. Between them they account for the overwhelming majority of AI tool usage in professional settings, and ChatGPT alone has more than 700 million people using it every week. If you’ve been hearing about AI tools and want to understand what they actually are and what people use them for, this is a reasonable place to start.
The most popular AI tools and what they do
All of the major popular AI tools share a common architecture: they are large language models trained on vast amounts of text, which means they can generate fluent, contextually relevant responses to an enormous range of requests. The differences between them are more about integration, context window size, and specialization than fundamental capability.
ChatGPT
ChatGPT is the most widely used AI tool in the world by a significant margin. It handles drafting, summarizing, brainstorming, coding, research, data analysis, translation, and a wide range of other tasks through a conversational interface. You describe what you want, it generates a response, you can refine it from there.
It is popular partly because it was first: OpenAI launched ChatGPT in late 2022 and it reached 100 million users within two months, faster than any consumer application in history. That early lead created a network of integrations, plugins, and familiarity that compounds over time. Being first is not the same as being best for every task, but ChatGPT’s breadth makes it a sensible starting point for most people.
Used most for: Writing, editing, research, coding assistance, summarizing documents, brainstorming.
Google Gemini
Gemini is Google’s AI assistant, and its practical advantage over ChatGPT for many people is integration rather than raw capability. It lives inside Gmail, Google Docs, Sheets, Drive, and Search, which means you do not have to open a separate tool to use it. For anyone whose work runs through Google Workspace, that integration removes the friction that causes most AI tools to go unused after the initial novelty fades.
Its connection to real-time Google Search also gives it an edge for tasks where current information matters. ChatGPT has training data cutoffs; Gemini can pull from live search results.
Used most for: Google Workspace users wanting AI assistance inside Gmail, Docs, and Sheets without context switching.
Microsoft Copilot
Copilot does for the Microsoft 365 stack what Gemini does for Google: it puts AI inside Outlook, Word, Excel, Teams, and PowerPoint. For organizations whose work happens inside Microsoft tools, it is the most frictionless AI option because it requires no new software. It summarizes email threads in Outlook, drafts document sections in Word, answers data questions in Excel, and recaps meetings in Teams.
Used most for: Microsoft 365 users wanting AI across Word, Excel, Outlook, and Teams.
Claude
Claude is made by Anthropic and has a reputation for producing careful, nuanced output, particularly on long documents and complex reasoning tasks. Its context window handling is strong, which matters when you need to analyze a lengthy contract, synthesize multiple research papers, or produce writing that needs to stay coherent over many pages. Less widely known than ChatGPT but preferred by many professionals for analytical and editorial work.
Used most for: Long-form writing, complex analysis, legal or technical documents, tasks where precision and consistency over length matter.
Perplexity
Perplexity operates differently from the others. Where ChatGPT generates responses from its training data, Perplexity retrieves from current web sources and presents cited answers. For research tasks where you need up-to-date information and want to verify where it came from, Perplexity is more reliable than asking a standard language model. The output is generated text, but grounded in verifiable references.
Used most for: Research requiring current information, competitive analysis, fact-checking, anything where source verification matters.
What people are actually using these tools for
It is worth knowing what the popular tools are primarily being used for in practice, because it shapes how you think about them. OpenAI’s own usage research found that writing accounts for around 40% of professional ChatGPT messages, with information retrieval and decision support making up most of the rest. Coding, which gets significant press coverage, is a minority use case relative to writing and research.
The pattern makes sense. Writing is the most universal professional task, and the friction of going from blank page to first draft is something a general AI assistant addresses immediately and reliably. The productivity gains from this are real. A 2024 survey by economists at the Federal Reserve Bank of St. Louis found that generative AI users saved an average of 5.4% of their working hours. Among daily users, a third saved at least four hours per week.
Those gains are concentrated in writing and research tasks done by people using tools that fit those tasks. The professionals getting the most from AI are not necessarily using the most tools or the most sophisticated ones. They have found two or three things their tool handles well and use it consistently for those.
Where popular tools work well and where they plateau
General AI assistants are built for breadth. They handle an enormous variety of tasks adequately, which is exactly what makes them popular: almost anyone can find something useful to do with ChatGPT within the first five minutes of using it.
The limitation of breadth is depth. A general assistant generates an email when you ask it to. It does not know your communication history, your relationships, the context of previous conversations, or the tone you use with different people. It produces something plausible. Whether it produces something you can actually send without significant rewriting depends on how much context you can provide in a prompt.
The same pattern shows up across categories. A general assistant writes code when you ask, but does not know your codebase. It summarizes a document when you paste it in, but does not remember last week’s version or the conversation that led to this draft. The tool is capable but contextless, and context is usually where professional value lives.
This isn't a criticism of these tools; it's how they are designed. The question it raises is whether breadth is the right criterion for choosing an AI tool, or whether fit to a specific task matters more.
From popular to purposeful: a different way to evaluate
Once you have used a general AI assistant enough to understand what it does well, a more useful question tends to emerge: what is the single biggest time drain in my working week, and is there a tool specifically built for that?
This is how most professionals who get consistent value from AI end up thinking about it. Here are the most common task-specific tools worth knowing, organized by what they are actually for.
If the task is email and communication management: Fyxer
Email is where the most professional time goes, and a general assistant only helps when you ask it to.
Fyxer connects to Gmail or Outlook and handles the inbox before you open it: organizing by priority, drafting replies in your own voice from thread and meeting context, managing scheduling, and joining calls to produce follow-up emails. The next section covers this in more detail because it is the most universally relevant task in professional life.
If the task is coding: GitHub Copilot or Cursor
GitHub Copilot integrates directly into code editors and understands your codebase, suggesting completions and generating functions in context. ChatGPT can also write code, but without that environment awareness it is essentially blind to how your project is structured. Cursor goes further, enabling multi-file edits from plain language instructions across the whole codebase.
If the task is research requiring current information: Perplexity
Perplexity retrieves from live web sources and presents cited answers, which means you can verify what it tells you. Asking ChatGPT about a recent development risks getting a confidently worded response drawn from training data that predates the event. For competitive research, market trends, or anything time-sensitive, Perplexity is more reliable.
If the task is working from a specific set of documents: NotebookLM
Google’s NotebookLM generates answers exclusively from the documents you upload, which means it cannot invent information from outside them. Feed it a set of research papers, contracts, or meeting transcripts and ask questions about them, and the answers stay grounded in your material. Useful for analysts, lawyers, and consultants working from a defined source set.
If the task is creating visuals: Canva AI or Adobe Firefly
Canva AI generates images and removes backgrounds inside a design interface, so outputs slot directly into presentations and social assets without switching tools. Adobe Firefly integrates into Photoshop and Illustrator with the added advantage that it is trained on licensed content, which matters for commercial use. ChatGPT includes image generation, but the outputs require exporting and placing manually, and the commercial licensing position is less clear.
If the task is sales outreach: Clay or Lavender
Clay pulls from dozens of data sources to build enriched prospect records automatically, identifying trigger events and personalizing outreach at a scale that manual research cannot match. Lavender coaches outbound email quality in real time, scoring each message for clarity and likely response rate as you write it. Both are considerably more targeted than asking a general assistant to write a cold email.
If the task is meeting notes: Otter.ai, Fathom, or Fireflies
These tools join your calls automatically, transcribe in real time, and produce structured summaries with action items. Fathom has a generous free tier. Fireflies pushes notes directly into Salesforce and HubSpot. Otter supports collaborative annotation across teams. A general assistant cannot attend a meeting; these can.
None of these tools are especially popular in the ChatGPT sense. They are used by people who have identified a specific constraint and found the tool built to address it. That shift in how you ask the question tends to produce better outcomes than starting with what is most well-known.
The most common professional time drain and the tool built for it
For most professionals, the biggest single consumer of working time is communication: email and meetings. Fyxer’s research across more than 355,000 inboxes puts the average at 4.3 hours per day on email. That is not writing and reading. It is triage, drafting, scheduling, chasing, and following up on meetings. The hidden cost of that admin on professional output is significant and consistent across roles.
A general AI assistant addresses part of this. If you open ChatGPT and describe an email you need to write, it will produce a draft. The process still requires you to open the tool, provide the context, generate the response, copy it across, and edit it to sound like you. For one email that is manageable. For the volume of correspondence that fills a professional inbox, the process adds a step rather than removing one.
This is the gap that Fyxer is built for. It connects directly to Gmail or Outlook and works inside the inbox rather than as a separate tool you switch to. Incoming messages are organized by priority before you open them. Draft replies are prepared in your own voice, drawn from your actual sent email history and meeting context, before you have asked for them. Scheduling requests are handled. Post-meeting follow-ups are drafted from call notes.
The difference from a general AI tool is that Fyxer acts before you ask, and it knows how you specifically write. A ChatGPT draft sounds professional. A Fyxer draft sounds like you. For professionals where the quality and tone of communication carries weight, that specificity matters. Try Fyxer free to see what your inbox looks like when the drafting and organizing is handled before you sit down.
How to choose your first AI tool
The most common mistake when starting with AI tools is trying several simultaneously. The result is superficial familiarity with many tools and genuine productivity from none of them. Most AI tools require two to three weeks of consistent use before the output quality improves enough to save real time: prompting habits improve, the tool’s patterns become familiar, and for tools that learn from your behavior, the calibration takes time.
A more productive approach: identify the one task that takes the most predictable, repeatable time in your working week. For most knowledge workers, that is writing-related work, with email being the single largest category. Pick one tool built specifically for that task and use it every day for a month before evaluating whether to add anything else.
If you are starting from zero and the popular tools are new to you, ChatGPT is still the most reasonable first stop. Its breadth means you will find uses for it quickly, and learning to prompt well on a general tool transfers to every other AI tool you use later.
Once you have a feel for what general AI can do, the next question is where it is not enough. That is usually where a more purpose-built tool for email and meeting workflows starts to make more sense than any general assistant.
Popular AI tools FAQs
Is ChatGPT the best AI tool?
It depends what you mean by best. ChatGPT is the most widely used and the most versatile, which makes it a sensible starting point. But versatility is not the same as being best for any specific task. For writing long documents requiring careful reasoning, many professionals prefer Claude. For research requiring current sources, Perplexity is more reliable.
For coding, GitHub Copilot outperforms ChatGPT in context-aware assistance. For email and meeting management, purpose-built tools handle the work ChatGPT can only assist with when prompted. Best depends on what you are trying to do.
Are popular AI tools free to use?
Most offer free tiers with meaningful functionality. ChatGPT’s free tier handles most writing and research tasks. Gemini and Copilot are included in Google Workspace and Microsoft 365 respectively for users on those platforms. Claude and Perplexity both have free tiers.
The paid tiers unlock more capable models, higher usage limits, and features like extended memory and larger context windows, which matter for professional use at volume. If you are evaluating whether an AI tool is worth the investment, using the free tier seriously for two to three weeks is the right test.
How do AI tools fit together if I want to use more than one?
The tools that work well together tend to handle different things. A general assistant like ChatGPT or Claude handles varied writing, research, and analysis tasks. A communication tool like Fyxer handles the email and meeting layer that general tools only address when prompted.
A coding tool like GitHub Copilot handles the development environment that general assistants lack the context for. The failure mode is not using multiple tools; it is using multiple tools that overlap on the same tasks. The question to ask before adding a second tool is whether it handles something the first tool cannot, or just handles the same things in a slightly different way.
