10 ChatGPT Plugins and GPTs That Actually Improve How You Work

ChatGPT in 2026 looks nothing like ChatGPT in 2024

The plugin marketplace shut down. 

The GPT Store opened, expanded, and got rebuilt around Apps in ChatGPT, with the Apps SDK released as an open standard on the Model Context Protocol. 

Connectors arrived for Plus and Pro users and now support write actions, not just read. 

Built-in capabilities (Search, Advanced Data Analysis, Image Generation, Tasks, Agent Mode) swallowed a large chunk of what third-party plugins used to do.

But the underlying need has not changed. 

People want ChatGPT to do more than answer questions. 

They want it to:

• Pull data
• Run analysis
• Automate work
• Generate assets
• Act on what they ask for 

This is the same itch the original AI data analytics tools landscape tries to scratch from a different angle.

Here are the 10 tools to actually use in 2026, organized by the outcome you need.

Built-in capabilities, third-party Apps, custom GPTs, and connectors all count.

What "ChatGPT plugins" actually means in 2026

The original plugin marketplace is gone. 

OpenAI shut it down in April 2024 and the functionality migrated into three different surfaces inside ChatGPT, each with its own purpose.

Custom GPTs in the GPT Store

Specialized versions of ChatGPT for narrow tasks.

Built by OpenAI, partners, or anyone with a ChatGPT account.

Still available, still useful for focused workflows.

Apps in ChatGPT

Launched at OpenAI DevDay in October 2025.

Interactive apps that appear inline in conversations.

Pilot partners include:

• Booking.com
• Canva
• Coursera
• Figma
• Expedia
• Spotify
• Zillow.

All built on the Apps SDK and released as an open standard on the Model Context Protocol.

Connectors

Integrations that let ChatGPT search files and pull content from services you already use, including:

• Google Drive
• Gmail
• SharePoint
• Dropbox
• Notion
• GitHub
• Linear
• Slack

Available to Plus and Pro users.

Write actions enabled in Business and Enterprise plans as of March 2026.

ChatGPT's built-in capabilities

On top of those three surfaces, ChatGPT's built-in capabilities have grown to subsume most of what generic plugins used to do. 

• Search reaches the live web with citations. 
• Advanced Data Analysis runs Python against any file you upload.
• Image Generation is native.
• Tasks runs scheduled prompts.
• ChatGPT Agent and Codex Agent execute multi-step work end to end.

So the 2026 question isn't "which plugins are best?" It is:

What's the right mix of apps, connectors, GPTs, and built-in capabilities for the outcome you need? 

For anyone trying to actually connect ChatGPT to their own data and get useful answers back, that question matters more than the marketing label any individual tool carries.

Each pick below names the outcome first, then the tool that delivers it.

1. Advanced Data Analysis in ChatGPT

Analyze any spreadsheet without writing code

Upload a CSV, XLSX, or any structured file. 

Ask a question. 

ChatGPT runs Python in the background to clean the data, compute metrics, and generate charts. 

No SQL. No setup. No separate tool.

Ask "which region had the highest revenue growth last quarter?" and the model returns a chart with the answer. 

Ask it to:

• Find outliers
• Identify trends
• Pivot the table a different way

And it iterates without you touching the file.

What it covers well:

• Quick exploratory analysis of a single dataset
• Cleaning messy spreadsheets and finding outliers
• Presentation-ready charts on the fly
• Sanity-checking numbers before they go into a report

What it does not cover: 

• Live business data
• Ongoing monitoring
• Analysis across multiple files simultaneously

Each conversation works against the files you upload to it. 

Best for: 

• Business teams
• Analysts
• Product managers

2. Pull live data from Drive, Gmail, and SharePoint into your chat

ChatGPT Connectors

This is the biggest functional change since the original plugin store closed.

‍ChatGPT Connectors let the model search files and pull content from services you already use, without uploading anything manually each time.

Current connector list covers most enterprise stacks:

• Google Drive, Docs, Sheets, and Slides
• Microsoft SharePoint, OneDrive, Outlook, and Teams
• Gmail
• Dropbox
• Notion
• GitHub and Linear
• Slack

Setup takes a few minutes:

• Open Settings
• Choose the app, authorize access, and ChatGPT can reference files from that source in any new conversation.

As of March 2026, the Microsoft and Google connectors support write actions in Business and Enterprise plans: ChatGPT can draft an email in Outlook, create a calendar event, or update a document.

The honest limit:

Connectors search one source at a time and rely on keyword matching, not semantic search.

You still need to know roughly where the information lives.

There are also gaps for specific use cases.

The case study on how the HubSpot connector struggles with basic business questions is a useful look at how connectors fall short when the underlying data model isn't built for free-text querying.

Best for:

‍Knowledge workers tired of re-uploading the same documents into every new conversation.

3. Zapier

Automate workflows across thousands of apps

Zapier remains one of the most useful third-party additions in the ecosystem, even after ChatGPT picked up native automation features.

It connects to over 6,000 apps including Slack, HubSpot, Google Sheets, Notion, and Gmail, and lets you trigger workflows directly from a chat prompt.

Tell it "log this lead to the CRM and send a welcome email" and Zapier executes the chain.

The difference between Zapier and ChatGPT Agent (covered below): Zapier shines on trigger-based, cross-app automation with explicit if-this-then-that logic, while Agent is better for ad-hoc, multi-step tasks the user describes in natural language.

Where Zapier still wins:

• Long-running automations with a clear trigger event
• Workflows that fan out across many apps at once
• Anything the team has already built and trusts in Zapier

For teams already running on Zapier, the ChatGPT integration is a thin layer on top of automations they already trust. A good adjacent example: running analytics and deal flow through Slack, which shows what cross-tool AI workflows look like in practice.

Best for:

• ‍‍Operations managers
• RevOps teams
• Founders managing repetitive cross-platform work

4. ChatGPT Tasks

Run scheduled or recurring work

Tasks let you set up recurring or scheduled work directly inside ChatGPT.

Configure it once, and the model delivers on a schedule via push notification or email.

Examples that work well in practice:

• "Summarize my unread emails every weekday at 8 AM"
• "Pull the top three competitor news stories every Monday"
• "Remind me to review Q3 forecasts on the first of each month"
• "Run a weekly digest of changes in this Google Drive folder"

Plus users get 40 active tasks per month. Pro users get 400.

This is the closest ChatGPT comes to "always-on" work, but with one important caveat:

Each task is a discrete prompt that runs on a clock.

It is not continuous monitoring.

ChatGPT is not watching anything in the background between scheduled runs.

That distinction matters when the underlying need is true operational monitoring rather than periodic reminders.

The difference between a scheduled query and an investigation workflow is the difference between knowing what to ask and being told what you didn't think to ask.

Best for:

Knowledge workers who run the same checks every day or week.

5. Delegate multi-step work end to end

ChatGPT Agent and Codex Agent

ChatGPT Agent runs multi-step work on its own.

Tell it "research the top three competitors in this space, pull their pricing pages, and put the comparison into a doc", and then it:

• Browses
• Extracts
• Assemble the output without further prompts

Codex Agent is the developer-focused sibling, optimized for coding tasks:

• Scaffolding repos
• Running tests
• Making structured changes across files

Both share a foundation:

The model can plan, execute, and recover when something fails along the way.

What Agent closes that plain ChatGPT plus connectors does not:

• Sequential dependencies where step 3 needs the result of step 2
• Recovery and retry when an intermediate step fails
• Browser interaction (filling forms, clicking through pages, scraping content behind logins)

Where it still hits a wall: Agent is reactive.

You prompt it, it runs.

It does not decide on its own to investigate something it noticed last week.

The agentic data analyst paradigm sits a step beyond reactive multi-step execution.

Best for:

‍Anyone who can describe a multi-step workflow in plain language and wants the model to actually execute it.

6. Canva

Build presentations and visual assets from prompts

Canva is one of the original Apps in ChatGPT launch partners.

Inside a conversation, you can describe a presentation, social graphic, or document, and Canva builds a draft you can then edit in its own interface.

What changes versus the old Canva GPT:

• The app renders inline in the chat
• You see the design as it builds
• You can iterate without leaving the conversation
• Then jump into Canva for final edits

Workflows that get fast:

• Turning a blog post or article outline into a designed deck
• Generating social graphics from a prompt and brand color palette
• Producing event posters, one-pagers, or infographics from a written brief

Canva + Advanced Data Analysis:

• Run your numbers
• Get the chart
• Ask Canva to turn the findings into a deck.

The workflow that used to take three tools and four context switches now lives in one place.

The Scoop Canva integration works on a similar principle, but for analytics output rather than general design.

Best for:

• ‍‍Marketers
• Content creators
• Small business owners producing visual assets at volume

7. ChatGPT Image Generation

Generate original images on demand

Image generation is now native.

There is no separate DALL-E plugin to install.

You describe what you want, and ChatGPT produces it.

Quality has improved substantially since 2024.

What the current model handles that older versions did not:

• Readable text inside images (logos, posters, signage)
• Brand-consistent style across multiple generations within one chat
• Reference uploads: give it a sketch or rough mockup, ask it to refine
• Style transfer: apply the look of one image to a different subject

Common use cases:

• Blog headers and inline visuals
• Product mockups and concept art
• Presentation visuals and pitch deck illustrations
• Social media graphics when you do not need the precision of a Canva template

Best for:

Content teams producing visual assets alongside written content.

8. ChatGPT Search

Pull live web information with citations:

ChatGPT's built-in search tool reaches the live web with citations.

The base model has a knowledge cutoff.

Search fills the gap.

Useful for:

• Quick fact-checks and competitor scans
• Pulling current statistics into a draft
• Validating dated claims before they go into a report
• Light research that does not require deep statistical analysis

Citations link back to the source, so you can verify what the model pulled.

For anything heavier, there is also Deep Research mode, which spends 5 to 30 minutes building a more comprehensive report across dozens of sources.

Search is the floor for live information, not the ceiling. When the question is "what is happening in my business right now and why," AI investigation that goes beyond the dashboard is a different kind of work than a single web query can deliver.

Best for:

• Researchers
• Marketers
• Anyone who needs current information without leaving the chat

9. Wolfram

Handle precise computation and STEM work:

Wolfram is one of the few legacy plugins that survived the marketplace shutdown and still earns its place.

It integrates Wolfram Alpha's computational engine into ChatGPT.

What it handles well that base ChatGPT does not:

• Complex symbolic math (calculus, differential equations, linear algebra)
• Unit conversions across scientific systems
• Lookup of constants, formulas, and reference data
• Graph generation with mathematical precision

For most everyday math, the current ChatGPT thinking models are accurate enough on their own.

For work where "approximately right" is not acceptable (financial modeling, engineering calculations, scientific research), Wolfram is still the right tool.

Best for:

• ‍‍Engineers
• Researchers
• Analysts working with quantitative data where precision is non-negotiable

10. File uploads and File Library

Read and query long documents inside ChatGPT

File handling has moved entirely into the native ChatGPT interface.

• Drag a PDF, contract, research paper, or internal report into the chat
• Ask questions
• The model reads and returns the relevant sections

File Library, now available on Free and Go plans as well, makes uploaded files reusable across conversations.

No more re-uploading the same document every week.

Use cases that used to require a separate plugin:

• Querying long contracts for specific clauses
• Pulling key findings from a 50-page research paper
• Comparing two documents side by side
• Building a Q&A workflow against a static reference document

Compared to connectors, file uploads are best for documents you need to query once or a few times.

For documents that update regularly (a shared CRM export, a quarterly board deck), a connector is the better fit.

Best for:

• Legal teams
• Analysts
• Ops leads working with dense documents on a regular basis

Where ChatGPT plugins still fall short for business analytics

Even with connectors that read your live data, Agent mode that can act, and Tasks that run on a schedule, ChatGPT is still a prompt-response system.

You sit down, ask a question, and get an answer.

Or you set up a scheduled prompt that runs the same query on a clock.

That's a productivity tool.

It is not a monitoring or investigation system.

The gap is visible in two specific places:

Knowing what to ask

ChatGPT answers what you ask.

It does not know what questions to ask on its own.

If a store starts to underperform on a metric you were not watching, ChatGPT will only catch it if someone scheduled a Task that watches that exact metric.

Interpreting what the numbers mean in the context of your business

Even with connectors that pull live data, ChatGPT tells you what happened.

It does not tell you what it means in the context of how your business actually runs.

For multi-location operations

• Retail chains
• Hotel groups
• Franchise concepts
• Property portfolios

The time it takes to diagnose a struggling location is usually the bottleneck, not the time it takes to query the data.

By the time someone gets around to asking "why is store 47 off this month?" three weeks have passed, and the moment to act is gone.

That is where Domain Intelligence fits, and where it differs from a productivity tool by design.

Domain Intelligence

Domain Intelligence runs autonomous investigation cycles across every location every week.

Scoop Analytics interprets the business:

• What the operator checks first
• What thresholds matter
• What signals they act on versus ignore

And lastly it encodes that logic into the system.

Once live, the Domain Intelligence runs against your existing data warehouse and Business Intelligence stack. It never replaces them.

Every Monday, a report arrives in the operator's inbox showing:

• What's flagged
• What changed
• What the most likely drivers are

Nobody logs in. Nobody builds a query. The report just shows up.

It is a different paradigm than a ChatGPT plugin.

The distinction between monitoring and investigation matters most when "what the data means" lives in the heads of one or two senior operators and needs to scale to everyone else.

Frequently asked questions

Are ChatGPT plugins still available in 2026?

Not in their original form. OpenAI shut down the plugin marketplace in April 2024. The functionality migrated to three surfaces: custom GPTs in the GPT Store, Apps in ChatGPT (launched at DevDay 2025), and Connectors. Most of the tools that existed as plugins are now available as GPTs, Apps, or official integrations. A broader picture of AI data analysis tools that actually work for small businesses covers what survived the transition.

What is the difference between a ChatGPT App, a Connector, and a custom GPT?

Three different surfaces with different jobs.

• Custom GPTs are specialized versions of ChatGPT configured with specific instructions, knowledge files, and tools. Built and shared in the GPT Store. Best for narrow, repeatable workflows.
• Apps in ChatGPT are interactive, third-party experiences that render inline in conversations. Built on the Apps SDK and Model Context Protocol. Best for tasks that need a partner service (booking, design, navigation).
• Connectors are integrations that let ChatGPT read (and increasingly write) data in services like Google Drive, Gmail, SharePoint, and Notion. Best for grounding answers in your own files and data.

For business teams trying to get analytical answers out of any of these, the Scoop AI data analyst walks through what genuine analytical depth requires.

What are Apps in ChatGPT?

Apps in ChatGPT are third-party experiences that appear directly inside a conversation. Launched at OpenAI DevDay in October 2025 with pilot partners including Booking.com, Canva, Coursera, Figma, Expedia, Spotify, and Zillow. Built on the Apps SDK, released as an open standard on the Model Context Protocol so developers can build for ChatGPT (and other model hosts) without vendor lock-in. For a related look at where connector-style integrations work and where they hit a wall, see the breakdown of what HubSpot's ChatGPT connector can and cannot answer.

Can ChatGPT analyze my live business data?

Partially. The built-in Advanced Data Analysis tool can work with files you upload (CSVs, spreadsheets, documents). Connectors can pull from Google Drive, Microsoft 365, Notion, Slack, GitHub, Linear, and a handful of other services. ChatGPT Agent can take multi-step actions across those sources. For ongoing, automated investigation of live business systems (sales, inventory, location performance, customer churn), a platform built for that purpose handles the work differently. Scoop Self-Serve is one example of what that kind of platform looks like.

What is the best ChatGPT tool for productivity?

It depends on where your time goes. Zapier is the strongest for cross-app workflow automation. Advanced Data Analysis is the best for working with your own data files. Connectors are the best for grounding answers in your existing tools. ChatGPT Agent is the best for delegating multi-step work. The honest framing: the right answer is rarely a single tool. It is a combination. For why this distinction matters, this take on why Scoop was built as a real AI data scientist gets at the difference between productivity and analytical depth.

What is the difference between a ChatGPT plugin and an AI analytics platform?

A ChatGPT tool, however configured, responds to what you ask. An AI analytics platform built for a specific business context investigates proactively, surfaces patterns you did not think to look for, and delivers findings on a schedule with the evidence behind every conclusion. One is a tool you operate. The other runs while you are doing something else. For multi-location operators in particular, the Scoop Domain Intelligence offering for retail is built on that distinction.