ChatGPT is impressive. But defaulting to it for every single task is like using a Swiss Army knife when you actually need a scalpel.
A LinkedIn creator recently shared something that genuinely shifted my thinking on AI workflows. The insight: Specialised tools built for specific tasks will almost always outperform a general-purpose model doing the same job. The original poster backed it up with a full breakdown of 16 tools worth adding to your stack, and the logic is hard to argue with.
I went through the full list. Here’s what each tool actually does and why it matters.
16 Specialised AI Tools Worth Adding to Your Workflow
- Research: SciSpace. Built specifically for navigating academic papers, it explains dense research, highlights key findings, and answers questions directly from scientific literature. Far more reliable than asking a general chatbot to summarise a study it may have never seen.
- Presentations: Gamma. Gamma turns ideas into polished slide decks without the usual formatting pain. You describe what you need, and it structures the content visually. Not just pretty output, it’s a structured storytelling tool that saves serious time.
- Brainstorming: Claude. Claude shines here because of its ability to hold nuanced, context-rich conversations. It explores ideas from multiple angles, pushes back thoughtfully, and helps you stress-test concepts before you commit to them.
- Learning: NotebookLM. Upload your own documents and have a conversation with them. PDFs, reports, textbook chapters, all fair game. It’s like having a tutor who’s already read everything you gave them and is ready to answer questions on demand.
- Data Analysis: Rows. Rows brings AI directly into your spreadsheet workflow. Describe what analysis you need in plain language, and it handles the formulas, queries, and chart generation. Particularly useful for non-technical users who live in spreadsheets but dread complex functions.
- Data Visualization: Julius. Julius takes raw data and turns it into clear, professional charts and graphs. Designed for people who need to communicate data visually without spending hours fighting formatting in Excel or Tableau.
- Video Generation: Freepik. Freepik has expanded well beyond stock images. Its AI video generation tools let you create short visual content without footage or a production team. Useful for social posts, ads, or quick explainers on a tight deadline.
- Image Generation: Higgsfield. Higgsfield focuses on high-quality AI image generation with stronger control over style and composition. If you’re doing brand work or creative production and need visual consistency across outputs, this one is worth exploring.
- Marketing Copy: Writesonic. Writesonic is trained specifically on conversion-focused writing. It understands the patterns that make ads, landing pages, and email subject lines perform. A general model can write copy, but Writesonic writes copy with clear commercial intent behind it.
- Scriptwriting: Sandcastles AI. Purpose-built for scripts, whether YouTube videos, podcasts, or short-form content. It understands pacing, structure, and how written content translates to spoken word, which is a very different skill set from standard copywriting.
- Content Creation: Veed. Veed handles the full video content pipeline: subtitles, editing, repurposing long-form content into clips, adding captions. It collapses what used to be a multi-step, multi-tool process into a single workflow.
- Development: Replit. Replit gives developers and non-developers alike an AI-assisted coding environment directly in the browser. Build, run, and deploy code without setting up a local environment. Especially powerful for rapid prototyping and testing ideas fast.
- Website Development: Webflow. Webflow combines visual website building with the kind of customisation that used to require a dedicated developer. Its AI features help with layout, copy suggestions, and design decisions, significantly reducing the gap between idea and live site.
- Customer Support AI: Expertise AI. Designed specifically for building customer-facing support systems, it can be trained on your own documentation and handle queries intelligently, without needing a full engineering team to configure it.
- Deep Research: Perplexity Deep Research. When you need to go deep on a topic, this is one of the strongest tools available. It searches the web, synthesises sources, and returns structured, cited responses. A meaningful upgrade over asking a general chatbot to research something and hoping for the best.
- Meeting Notes: Granola. Granola records and transcribes meetings, then generates clean summaries with action items. No more scrambling to take notes while trying to stay present in the conversation.
The Bigger Takeaway
The LinkedIn creator frames this well: the productivity unlock isn’t using more AI. It’s using the right AI tools together. Combining a few purpose-built tools into a single workflow is where the real efficiency gains happen, and according to the mind behind this post, the results compound fast once you start stacking them intentionally.
Instead of forcing one AI to do everything, use the best AI for the job, combine multiple AI tools, and build faster workflows.
The Rules to Live By
The post’s author rounds things out with a sharp set of guidelines worth keeping close:
Do’s:
- Use specialised tools for specialised tasks
- Combine multiple AI tools in one workflow
- Verify important outputs with reliable sources
- Use AI to speed up thinking, not replace it
- Experiment with different tools regularly
Don’ts:
- Do not rely on a single AI tool for every task
- Do not skip fact-checking for research outputs
- Do not assume AI-generated data is always correct
- Do not ignore domain-specific AI tools
- Do not replace human judgement with AI
Check the full LinkedIn post for the infographic breakdown, and drop a comment there with your own favourite ChatGPT alternative for a specific task.