Pick One: 5 AI Assistants Your Employees Will Actually Use in 2025

Walk into any office today and ask employees how they use AI. You'll get two answers: the official one (our internal tool) and the real one (ChatGPT on my phone when no one's looking).

Shadow AI is real. 93% of employees admit to putting information into AI tools without approval. While IT was busy building a "secure ChatGPT clone," most employees moved on.

That’s why in 2025 every company needs to offer ONE general-purpose AI assistant that employees actually want to use. Not because it's perfect, but because the alternative is losing control entirely.

Today, I'll show you 5 proven options that solve the problem without the headache of building your own.

Let's dive in!

Why ONE AI Assistant Matters

Before we jump into tools, let's get the strategy straight.

When I say every company needs one AI Assistant, I'm not talking about building the ultimate AI-everything platform. I'm talking about putting the Integration-Automation Framework into practice – specifically the Assistant layer.

As I mentioned in the post below, there are essentially 4 ways to build any AI use case. Most companies try to do all 4 at once – and wonder why nothing works. The trick is to find a lean set of tools which allows you to flexibly build and evolve between those 4 types without getting “all-in” into one platform or collecting AI Tools like they’re Pokemon cards. (Check out my workshop if you need help with that).

Assistants are the easiest way to get started into the new AI era.

Why? 3 reasons:

1. Employees are already using them. You're not creating new behavior – you're making existing behavior official.

2. Legacy systems make copilots hard. Try integrating AI into 15-year-old enterprise software. Good luck with that.

3. Assistants build AI literacy. Before your team can work with AI agents, they need to learn how to work with AI at all.

The goal isn't to solve every AI use case. It's to give your people a safe, approved way to get stuff done with AI.

The Make vs. Buy Reality Check

To provide that safe, internal general-purpose assistant, a lot of companies got lured into building their own internal "ChatGPT clones".

"It's just a chat wrapper around an API – how hard can it be?"

Turns out: very hard.

Building a simple chatbot is easy. But building a great general-purpose AI chatbot is deceptively hard.

ChatGPT is a technical masterpiece. Fast. Responsive. Feature-rich. Always available.

And they keep shipping. In the last 6 months alone ChatGPT released:

• Deep Research Mode

• 3 new AI models

• Model picker in Custom GPTs

• Better web search

• Memory

• New connectors

• and 20 more 

And it’s not just ChatGPT.

For a general-purpose AI Assistant, you're competing with teams at OpenAI, Anthropic, and Microsoft who have hundreds of engineers working on user experience alone.

Meanwhile, your internal team is trying to:

• Keep up with model improvements every few months

• Handle enterprise security requirements

• Manage user feedback and feature requests

• Scale infrastructure as usage grows

• Debug issues that OpenAI already solved months ago

All while building something employees actually prefer over the real ChatGPT.

Most often, the results are ghost towns. Expensive internal tools that very few actually use.

The other mistake is trying to build deep integrations at the same time.

"If we're building this assistant anyway, why not connect it to our internal systems?"

That's a dangerous cocktail. Using an off-the-shelf tool that already connects to Google Drive or OneDrive where it makes sense is fine. But building these connectors yourself so they can work with your messy company data? You'll spend months in integration hell instead of focusing on AI.

At the beginning, most use cases don't need deep integration. They need a reliable, fast, user-friendly AI assistant that people can access when they need it.

That's an AI make-vs-buy decision. And in 2025, buying general-purpose assistants almost always wins.

5 AI Assistant Options That Actually Work in 2025

Here are the proven options that I’ve seen solving different business needs:

ChatGPT Team & Enterprise

Let's start with the obvious choice. ChatGPT is still the most popular AI assistant with reportedly 400+ million weekly users. Most of your employees already know how to use it. Little training required, easy adoption curve.

But there are also lots of concerns, especially around privacy. OpenAI is famously known for "move fast, break things", which isn’t really how most other businesses operate. A court recently ordered OpenAI to preserve all historic chats that affects also enterprise clients. So yeah, with OpenAI it feels like there’s always a little drama around the next corner.

Make no mistake – ChatGPT does cater to enterprise needs, offering the quite popular ChatGPT Team & Enterprise Plan. These tiers offer better privacy (no training on your data, GDPR compliance) and admin controls. Plus, you have advanced sharing options like sharing custom GPTs just within your organization – which can be a pretty big deal.

ChatGPT Team starts at $30/month for 2+ users. It comes with basic admin features, that have some quirks – like users being able to invite others without approval. Enterprise plans starts at 150 seats, ~$100K/year. Contact sales.

Bottom line: ChatGPT is great for companies that want to make existing behavior official without changing how people work. If your team is already using ChatGPT and you can live with the tradeoffs, this is the path of least resistance. The familiarity factor is huge. Sometimes the best tool is the one people actually want to use.

Microsoft Copilot

With many companies running on Microsoft anyway, Copilot feels like the obvious choice. Your IT team loves it because it's "already in the stack."

But Copilot might be the most confusing AI assistants on the market.

There's the free Microsoft Copilot built into Windows. There's Copilot Pro, Microsoft 365 Copilot Business and Microsoft 365 Copilot Personal. Then there’s Copilot Studio for building custom Copilots.

If you need a consultant just to figure out which Copilot you need, welcome to Microsoft's universe. Entire blogs have been written about the pricing model. The basic version is fairly limited – comparable to free ChatGPT. Paid licenses start around $20/month per user, but there's a whole ecosystem of upsells waiting for you.

In reality, Copilot can feel like a slippery slope. Easy to adopt, not so simple to walk away from. Many people who bought in told me the experience felt beta at best. You're not just buying a tool – you're buying deeper into Microsoft's ecosystem.

And once you're in, switching is hard. Microsoft is very good at making their tools feel "integrated" in ways that make alternatives seem like more work. Good luck doing something that’s not planned for.

Bottom line: Copilot works if you're already all-in on Microsoft and don't mind the complexity. But don't assume "bundled" means "better." Sometimes the obvious choice is obvious for the wrong reasons.

Claude

Claude is ChatGPT's more thoughtful cousin. It's more privacy-focused (no training on your data unless you opt in), SOC 2 compliant, and offers more integrations with Anthropic pioneering the MCP standard that connects their AI to 3rd-party tools. The models are often on-par or better than OpenAI's, especially for writing and coding tasks.

Overall, Claude feels more... careful. Better at following complex instructions, and genuinely impressive at tasks like document analysis and creative writing. Plus, features like Artifacts let you create and iterate on code, documents, and visualizations right in the chat.

Claude For Work plans start at $25/month with a minimum of 5 users. Enterprise features like Single Sign-On, role-based access, and SCIM come at a similar price point like ChatGPT, but fewer minimum seats (I've heard 50).

Despite being technically excellent, I personally haven't seen any company that went all-in with Claude as their primary general-purpose assistant. A lot of them use it as a second option for people who specifically want better writing capabilities. But maybe that’s just my biased observation. Part of that might be that Claude is simply much less well-known compared to ChatGPT. There aren’t any official numbers, but reports suggest around 19 million monthly active users. So when you bring in Claude, expect that a lot of your team members haven’t used it before and there might be a steeper learning curve.

Bottom line: Claude is excellent if you want a more enterprise-focused approach and don't mind introducing a tool most employees haven't used before with very limited customization options.

Langdock

Langdock is what most companies think they're building internally – but often better. It's a fully managed, customizable, enterprise-grade AI platform that was purpose-built for European enterprise needs. GDPR-compliant and hosted in the EU, no data used for training, plus you get built-in features that would take your internal team months to build:

• Multiple AI models in one interface (GPT-4o, Claude, Gemini, etc.)

• Easy setup of custom AI assistants and even agent-style workflows

• Built-in spreadsheet analysis and charting right in the chat

• Lots of pre-built, internal connectors

• Easy setup with proper admin panel

Being a YC23 startup, Langdock has made quite some waves and landed partnerships with notable names like Merck, plus lots of smaller businesses. From what I've seen, it gets closest to the original ChatGPT experience – but with more security and more business-friendly positioning. If you're evaluating internal chatbots, Langdock should definitely be on your shortlist.

Bottom line: Langdock feels like what happens when someone actually listens to enterprise IT requirements instead of trying to retrofit consumer tools. Worth a serious look if you need strong data control, integration possibilities, and a clean UX that actually works.

LibreChat

If you have a dev team and strict data requirements, LibreChat might be your answer. It's an open-source ChatGPT clone that supports both hosted and on-prem AI models out of the box which can be fully self-hosted.

What makes it compelling:

• 100% self-hosted = full data ownership

• Highly customizable UI and backend logic

• Role-based access and multi-user support

• No vendor lock-in – you own the entire stack

• Active open-source community backing development

Of course, the tradeoff is that you have to run it yourself. It’s not plug-and-play — setup, maintenance, and integration are all on you. But if you’ve got the technical muscle and strict data requirements, LibreChat gives you ChatGPT-like power without vendor lock-in. Check it out if you’re in a regulated industry, or you wants full-stack control.

Bottom line: LibreChat is a viable "build" option that doesn't require building from scratch. Just don't underestimate the ongoing maintenance overhead.

Conclusion

The era of "just build your own ChatGPT" is ending. The 5 tools above prove that secure, enterprise-ready AI assistants already exist – and they’re just the tip of the iceberg. Most are better than anything your internal team could realistically build and maintain.

The key is picking one that fits your constraints and getting people to actually use it. Start with the assistant layer, prove value, then evolve to more sophisticated AI applications.

Don't try to solve everything at once. Pick one tool. Get adoption. Build from there.

See you next Friday!
Tobias