I've been in 12+ implementation projects this year, and the conversation always starts the same way.

"So... which AI tool should we actually use?"

Followed by a spreadsheet comparing ChatGPT vs Claude vs Copilot vs Gemini vs Perplexity. Feature by feature. Price by price.

And I always stop them right there.

Because, the question isn't "which is best" - it's "which fits our workflow?"

Let me show you exactly how I approach this with clients.

Here's what most teams get wrong: they start with the tool and try to fit their work into it.

I do the opposite.

I map out what the team actually does. Content creation? Data analysis? Customer support? Research?

Then I match the tool to the workflow.

Here's my quick guide:

ChatGPT: The Swiss Army knife. If you have budget for just one tool, go here. Text, image, video, agents, deep research.

Copilot: The Microsoft ecosystem play. Does it have all the advanced features? Nope. But if your business runs on Microsoft 365, the integration compensates for everything else.

Claude: The writer and coder's choice. For writing and coding tasks, I still prefer Claude. The ‘Claude skill’ feature has made my work significantly easier. (Like how I'm writing this newsletter with minimal input.)

Perplexity: The deep researcher. I've tried deep research features across all LLMs. Perplexity's depth is unmatched when you need thorough research with proper citations.

Gemini: The Google ecosystem specialist. Need large context windows? Working in Google Workspace? This is your tool.

But before you commit: Check the security and compliance requirements

This is non-negotiable for enterprise.

Here's what I verify with every client:

This is where most implementations fail.

Teams pay for enterprise accounts and then... everyone uses it like the free version.

Most chatbots now have built-in automation capabilities. Custom GPTs. Projects. Team workspaces.

These aren't nice-to-haves. They're the reason you're paying for enterprise.

Custom GPTs / Projects: Create standardized prompts for recurring tasks. Your entire team uses the same prompt for client proposals. Same prompt for meeting summaries. Same prompt for content generation. Result? Consistent output quality across the organization.

Team workspaces: Everyone collaborates in shared spaces. No more "Can you share that prompt?" No more inconsistent results because everyone's winging it.

A marketing team I worked with created custom GPTs for campaign generation and market research. Their associates now produce work at senior associate quality because they're using the team’s best practices embedded in the prompts.

This is the difference between "we use AI" and "AI is integrated into our workflow."

Once you've mastered your core tool, this is where productivity gains accelerate.

Third-party integration tools like n8n or Make connect your AI chatbot to the rest of your business.

My own workflow: Webinar recording gets uploaded, n8n triggers Claude to generate summary and social posts, content goes to my calendar. What used to take 3-4 hours now takes 20 minutes of review time.

The Real Insight 📖📖

It isn't that complicated when you realize that AI tools need to work in your business, not on it.

Stop chasing features. Start mapping workflows.

Pick one tool (max two). Pay for enterprise. Master the collaboration features. Then add integrations.

That's the framework that's worked for 12+ implementations this year.

My advice if you're just starting:

Most teams skip steps 3-4 and wonder why their "AI transformation" didn't work.

I work with mid-size companies on AI implementation projects - from tool selection to team setup to building automated workflows.

Reply to this email or DM me on LinkedIn.

Until next time,

Pooja