Almost every tech tool I have been using for the past three years has some AI feature baked into it now.
It all started with ChatGPT, then Claude, and then we had an explosion of AI apps for literally every use case you can think of.
Video editing, voice generation, coding, search, automation, presentations, SEO, you name it.
Tools promising to make us more productive.
Some were simple "ChatGPT wrappers" while others were genuinely new products that used AI in ways that were not possible a few years ago.
The problem is that there are so many AI tools out there now that it's hard to know which ones are actually worth your time. I have personally tested over 70 of them, and most I used once and never opened again. But there are a handful that I genuinely cannot live without at this point.
AI orchestration and automation (Zapier)
Chatbots (ChatGPT, Claude, Meta AI)
AI agent builders (Zapier, Botpress)
Search engines (Perplexity, Komo, Brave)
Content creation (Jasper, Anyword, Writer)
Grammar checkers and rewording tools (Grammarly, Wordtune, ProWritingAid)
Video creation and editing (Runway, Descript, Google Veo 3)
Image generation (ChatGPT, Midjourney, Ideogram)
Vibe-coding tools (Lovable, Bolt)
Social media management (FeedHive, Flick, Buffer)
Voice and music generation (ElevenLabs, Hume, Speechify)
Knowledge management and AI grounding (Mem, Notion, Evernote)
Task and project management (Asana, ClickUp, Hive)
Transcription and meeting assistants (Fireflies, Avoma, Granola)
Scheduling (Reclaim, Clockwise, Motion)
Email (Shortwave, Microsoft Copilot Pro for Outlook, Gemini for Gmail)
Slide decks and presentations (Gamma, Canva, Beautiful.ai)
Resume builders (Teal, Enhancv, Kickresume)
Google Cloud offers free usage of many AI products up to monthly limits, including Translation, Speech-to-Text, Natural Language, and Video Intelligence. There is no charge to use these products up to their specified free usage limit. The free usage limit does not expire, but is subject to change.
Additionally, there are several core generative AI tools that offer defined free usage:
Google AI Studio makes it easy to start building with Gemini, including free tiers across our family of multimodal generative AI models.
With NotebookLM, you can create a personalized AI assistant that surfaces insights and provides Audio Overviews on data you upload, including text, video, and audio. It is available on a free plan with set usage limits.
Gemini Code Assist for individuals is available at no cost and offers defined daily and monthly request limits for code completion, generation, and chat within supported IDEs.
Zapier is built to help your tools and AI work together seamlessly across 9,000+ apps. It's a hub that lets you do everything from analyzing business data with AI to creating entire automated systems that react instantly (and intelligently) to real-time triggers.
AI chatbots allow you to chat with a large language model, letting you ask questions and get answers in a conversational style. The best chatbots use advanced reasoning and logic, write code, and make mathematical calculations.
Hallucinations are one of the biggest issues with AI. To counter this risk, some tools build web searches into their apps, using the results as sources to support the AI-generated answer. And that's an AI search engine in a nutshell.
Entire movies have been made about writer's block. These apps are here to break it. By entering your prompts, starting from templates, or using recipes, you can use AI models as a co-writer, helping you put together first drafts faster.
Best for: All-around AI assistant for writing, coding, research, and productivity
Pricing: Free plan available, Pro starts at $20/month, Max starts at $100/month
How I use it: Proofreading blog posts, building internal tools with Claude Code, running SEO campaigns with MCP integrations, and creating custom skills for every project I work on.
There is a good chance you already know what Claude is, the popular AI assistant built by Anthropic, but specifically I want to talk about the desktop app. I have been using Claude almost every day for everything from helping refine my ideas to using it as an AI writing tool to proofread blog posts to integrating it with MCP servers to pull in analytics or keyword data for SEO campaigns. There is just a broad range of use cases you can do with Claude.
My favorite way to use it is to create Claude skills and for every campaign I work on, save it as a project and add those skills as instructions within those projects. It basically turns Claude into a custom assistant for whatever you are working on.
The biggest change I have noticed in AI productivity tools is that they are no longer limited to generating text. Modern AI applications can understand documents, images, audio, video, code, spreadsheets, websites, and other types of information. This makes them useful across almost every part of a typical workday.
Instead of thinking about AI as a replacement for individual applications, I find it more useful to think about AI as a productivity layer that sits across the tools you already use.
For example, an AI assistant can summarize a long document, turn meeting notes into tasks, analyze a spreadsheet, create an outline for an article, generate code, research a topic, or help organize information. The real productivity benefit comes from reducing the amount of repetitive work between those activities.
Research is one of the areas where AI productivity tools have become especially useful.
Traditional research often requires opening dozens of browser tabs, comparing information from different websites, taking notes, and eventually turning those notes into something useful. AI search and research tools can reduce some of that manual work by combining search, summarization, and question answering.
Perplexity is a good example of this approach. Instead of simply returning a list of search results, AI search tools can provide a synthesized response and point users toward the sources used to construct the answer.
This can be particularly useful when researching unfamiliar subjects. You can start with a broad question, identify important concepts, and then use follow-up questions to explore specific areas.
However, I still recommend checking the original sources when accuracy matters. AI-generated summaries can contain mistakes, misunderstand context, or present outdated information as current.
The best workflow is therefore not "AI instead of research." It is "AI to accelerate research."
Writing is another area where AI has become part of my normal workflow.
I don't necessarily want an AI tool to write everything from beginning to end. In many cases, the more useful role is helping me move through the parts of writing that consume time without adding much creative value.
For example, AI can help create an initial outline, identify gaps in an argument, suggest alternative headlines, simplify complicated sentences, improve transitions, or turn notes into a rough first draft.
Tools such as Grammarly, Wordtune, ProWritingAid, Claude, and ChatGPT can all be useful at different stages of the writing process.
The important distinction is between generating content and improving communication.
A good AI writing workflow might look like this:
Start with your own idea or research.
Create a rough structure.
Use AI to identify missing sections.
Write the important ideas in your own voice.
Use AI for editing and clarity.
Fact-check important claims.
Perform a final human review.
This approach keeps the writer involved while still benefiting from AI's ability to process and improve large amounts of text quickly.
AI coding assistants have also changed how quickly developers can move from an idea to a working prototype.
Instead of manually writing every function, developers can describe what they want to build and let an AI assistant generate an initial implementation. The developer can then review, modify, test, and improve the result.
Tools such as Claude Code, GitHub Copilot, Lovable, and Bolt demonstrate different approaches to AI-assisted development.
For experienced developers, these tools can help with repetitive coding tasks, debugging, documentation, testing, and understanding unfamiliar codebases.
For beginners, AI coding tools can make experimentation much easier because users can describe an idea in natural language instead of needing to understand every technical detail before getting started.
The biggest productivity gain comes when AI handles repetitive implementation work while the human focuses on architecture, requirements, testing, and decision-making.
Meetings are another area where AI can eliminate a surprising amount of manual work.
Instead of taking detailed notes throughout a meeting, transcription and meeting assistants can record conversations, generate transcripts, summarize discussions, and identify important action items.
Tools such as Fireflies, Avoma, and Granola are designed around this type of workflow.
The useful part isn't simply having a transcript. A long transcript can be difficult to read and may contain hundreds or thousands of words.
The real value comes from turning the conversation into structured information:
Important decisions
Action items
Assigned responsibilities
Questions that still need answers
Key topics discussed
Follow-up tasks
This can make meetings significantly easier to manage, especially when multiple projects and teams are involved.
Creating presentations traditionally requires several separate steps: planning the structure, writing slide copy, selecting visuals, designing layouts, and formatting everything consistently.
AI presentation tools can reduce much of that work.
Gamma, Canva, and Beautiful.ai can help turn an idea, outline, or written document into a presentation structure.
This is particularly useful when the goal is to communicate information clearly rather than create highly customized visual artwork.
I still recommend reviewing every slide manually. AI-generated presentations can sometimes contain unnecessary text, repetitive layouts, weak visual hierarchy, or inaccurate information.
The best approach is to let AI handle the first version and then spend your time improving the message and presentation quality.
Another interesting category is AI-powered knowledge management.
Most people accumulate information across documents, notes, bookmarks, emails, conversations, PDFs, and project files. The problem is not always a lack of information. It is finding the right information at the right time.
Tools such as Notion, Mem, Evernote, and NotebookLM are useful because they can help users organize and interact with their existing information.
NotebookLM is particularly interesting for research-heavy workflows because you can provide source material and then ask questions about that material.
This changes the way I think about personal knowledge management. Instead of simply storing information and hoping to remember where it is, AI can make stored information easier to search, summarize, and understand.
Scheduling is another category where AI can reduce small decisions that accumulate throughout the day.
Tools such as Reclaim, Clockwise, and Motion can help organize calendars, tasks, meetings, and available working time.
The concept is simple: instead of manually deciding when every task should happen, AI can consider your schedule and help find suitable blocks of time.
This is particularly useful for people who work on multiple projects simultaneously.
The goal isn't necessarily to fill every available minute. Good AI scheduling should help protect focused work time, reduce unnecessary context switching, and make deadlines easier to manage.
Social media management can also involve a significant amount of repetitive work.
Creating ideas, writing captions, repurposing content, scheduling posts, reviewing analytics, and maintaining consistency can consume hours every week.
Tools such as FeedHive, Flick, and Buffer can help organize parts of this workflow.
AI can be particularly useful for repurposing one piece of content into multiple formats. A long article could become a short social post, a series of posts, an email idea, or a video script.
However, automation shouldn't remove the human voice from social content. Audiences can quickly recognize generic AI-generated posts, so editing and personalization remain important.
With hundreds of new AI products appearing every month, I don't think the goal should be collecting as many tools as possible.
In fact, having too many tools can make you less productive.
Every new application introduces another interface, another account, another workflow, and another place where information can become fragmented.
Instead, I look for tools that solve a recurring problem.
A useful AI productivity tool should ideally do at least one of these things:
Save significant time on repetitive tasks
Make difficult work easier
Reduce the number of applications needed for a workflow
Help organize large amounts of information
Improve the quality of an existing process
Automate tasks that previously required manual effort
Help turn ideas into usable outputs faster
If an AI tool doesn't provide a meaningful improvement after several uses, there is usually little reason to keep it in the workflow.
AI productivity software is moving quickly toward systems that can perform multiple steps instead of simply responding to individual prompts.
The next generation of tools will increasingly combine AI models with automation, memory, integrations, search, and specialized workflows.
That means the most useful AI assistant may not be the one with the most impressive individual response. It may be the one that can understand the context of a project, access the information it needs, complete several related tasks, and return a useful result with minimal supervision.
For me, this is the most exciting part of AI productivity.
The technology is moving from simply asking an AI a question toward building workflows where AI becomes part of the process itself.
At the same time, human judgment remains important. AI can generate ideas, analyze information, automate tasks, and accelerate production, but users still need to decide what is correct, useful, ethical, and worth publishing.
The best productivity setup in 2026 isn't necessarily the one with the largest collection of AI applications.
It's the one where a small number of reliable tools work together to remove friction from the work you already do.