Paul Evans, a B2B positioning expert and Founder of V2RSION, recently posted something that made me stop scrolling. He wrote:

I tested multiple AI tools this year.

If you still don't know where to start, here's what you need to know.

AI isn't as complicated as people make it.

He went on to say that the hardest part is not learning AI itself, but simply knowing where to begin. That simple distinction is powerful, because it shifts the challenge from technical skill to strategic focus.

In this post, I want to build on Paul Evans's idea and walk through how to start using AI in a way that is practical, purposeful, and aligned with what you are actually trying to achieve.

AI is not the hard part. Direction is.

Paul Evans explained that you do not need to understand everything about AI to benefit from it. You just need to know which tools are fit for your purpose, and which ones can make your ideas sharper, your processes more efficient, and your systems more effective.

That framing is liberating.

Most people get stuck in one of two traps:

They feel they have to understand the underlying models, math, and architecture before they can use AI meaningfully.
They bounce between shiny tools without a clear idea of what problem they are trying to solve.

Both lead to overwhelm and very little progress.

Instead, start with a simple question: What work am I already doing that AI could help me do faster, better, or more consistently?

Once you can answer that, choosing tools becomes much easier.

Start with your purpose, not the platform

Before we dive into the resources Paul recommended, it is worth underlining his main point: you do not have to master every AI platform to stay ahead. You have to understand how to use the right ones with intent.

That means:

If you are a founder, you might care most about strategic thinking, positioning, customer research, and faster content creation.
If you work in operations, you probably want better documentation, more reliable processes, and small automations that save hours each week.
If you are in marketing or sales, you want sharper messaging, faster drafts, smarter personalization, and better research.

The same model can support all of these, but how you use it, and which tools you lean on, will look very different.

That is where curated learning matters. Rather than trying everything, you pick a handful of high quality resources and go deeper.

Paul Evans highlighted six that are worth your attention.

1. OpenAI Academy: learn from the source

When you are starting out, it is tempting to piece together your AI understanding from random tweets, YouTube videos, and half-finished threads. That is a recipe for confusion.

OpenAI Academy is a cleaner path. As Paul notes, it is the best place to start if you want to learn from the team that built ChatGPT, with short, practical lessons instead of dense theory.

Use it to:

Understand the basic capabilities and limitations of large language models.
Learn how prompts really work, with guided examples.
Get a sense of what is safe, what is not, and how to think about reliability.

Even a few hours here will give you a foundation that makes every other tool easier to use.

2. Perplexity Labs: research with receipts

Paul calls Perplexity his go-to for research, and with good reason. It behaves like a blend of search engine and chat assistant: fast answers, clear citations, and useful context.

This matters for anyone doing strategy, content, or product work. Instead of opening ten tabs, skimming each, and trying to stitch together a view of the landscape, you can:

Ask for overviews with cited sources.
Compare perspectives quickly.
Drill into specific papers, reports, or posts without getting lost.

Think of Perplexity Labs as your AI research analyst. You still make the decisions, but it dramatically compresses the time from question to informed answer.

3. Claude from A to Z: deep thinking, cleanly explained

Claude has earned a reputation for thoughtful, structured responses and strong reasoning. The Claude from A to Z guide that Paul links to is exactly what the name suggests: a simple walkthrough of how to write, plan, and reason with Claude.

If you work with complex ideas, long-form content, or strategic documents, this is gold. Use Claude to:

Draft and refine positioning statements or value propositions.
Structure long documents, from pitch decks to whitepapers.
Explore scenarios and edge cases before you commit to a decision.

The guide gives you patterns, not just tips. Once you internalize a few of those patterns, you can reuse them across almost any knowledge work.

4. Gemini prompting: Google's secret weapon

Google's Gemini often feels underrated in the conversation, which is why Paul calls its prompting guide a best-kept secret. The documentation breaks down prompting into real examples that you can borrow and adapt.

If you are already in the Google ecosystem, Gemini can slide into your existing workflows: Docs, Sheets, Slides, Gmail, and more.

The prompting guide will help you:

Turn vague requests into precise instructions.
Chain prompts together to handle multi-step tasks.
Improve the quality of your outputs without guessing what to type next.

Learning to prompt well in one tool will also transfer to others. The mental model is more important than the logo.

5. Guide to AI agents: make AI work for you

At some point, you will want AI to do more than answer questions or draft text. You will want it to act on your behalf: summarizing inboxes, updating spreadsheets, generating reports, triggering workflows.

That is where AI agents come in.

The Guide to AI Agents that Paul recommends focuses on small, useful automations. Not science fiction assistants that run your company, but concrete helpers that:

Move data between tools.
Alert you when something important changes.
Handle repetitive tasks that chew up your time.

Start with one tiny agent that saves you 10 to 30 minutes a week. The goal is to experience what it feels like when software quietly does work for you in the background.

6. Deep dive into LLMs: connect the dots

Finally, when you are ready to go deeper, Paul points to a long-form video that explains how large language models actually work, and what that means for how you should use them.

You do not need this on day one. But after you have experimented a bit, understanding the basics of how these systems are trained, where they fail, and how they generalize will make you a more confident and critical user.

You will be better able to:

Judge when to trust an answer and when to double-check.
Design prompts and workflows that play to the model's strengths.
Explain AI decisions to colleagues and stakeholders in plain language.

Start small, then layer on sophistication

The thread that runs through Paul Evans's post is simple: you stop overthinking AI by using it, on real work, with clear intent.

A practical way to put this into action:

Pick one resource from his list that matches your current goal.
Give yourself a single week to explore it with a real project in mind.
Capture what works, what feels clumsy, and what you want to try next.
Only then, add a second tool or resource.

Instead of trying to master everything, you are building a stack of skills and systems around your actual needs.

Over time, that is how you move from dabbling with AI to quietly compounding its impact across your ideas, processes, and business.

This blog post expands on a viral LinkedIn post by Paul Evans, Redefining how businesses position and grow in the AI era | B2B Positioning Expert | Founder of V2RSION. View the original LinkedIn post →