If you've been on LinkedIn lately, you've probably seen your fair share of posts about how "AI will replace your analysts!"

"ChatGPT just made my whole data team obsolete!"

We got curious. Not about whether AI can theoretically do analysis, we know it can be a very useful tool, but whether it can deliver an actual work product. The kind of thing a marketing director might hand to their exec team. So we ran a simple test.

We seen it used on several Subreddits, it made rounds on Facebook, and of course it has been shared ad nauseam by LinkedIn thought-leaders. But do the charts, as one poster on LinkedIn put it, make it obvious that we are not in a bubble?

The charts seems legit, although they have been so overshared at this point the pixel quality has clearly degraded. The numbers seem authoritative. The argument feels data-driven.

But is it obvious?

Let's break down exactly what's going on here, and more importantly, how you can spot similar red flags in any analysis you encounter.

The claim.

“If this were a bubble, we'd see massive cash burn and unsustainable growth. But the numbers show real adoption, real revenue, and real productivity. What matters is net profit growth, something that frankly wasn't there at all in the dot com bubble. This is what separates AI from past 'bubbles'."

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Optimizely Opal's Custom Tools feature lets you extend the Opal AI agent's capabilities by connecting it to your own services and APIs. In this guide, we'll walk through creating a simple custom tool from scratch, deploying it to the cloud, and integrating it with Opal.

By the end of this tutorial, you'll have a working custom tool that responds to natural language queries in Opal chat. We'll keep it straightforward, no complex integrations, just the essentials you need to get started.

This article is about code generation, but the lessons apply to everything AI does in optimization. The difference between "CSS-only" AI and "almost everything" AI isn't the model you're using. It's the infrastructure you build around it. And that infrastructure—documentation, constraints, institutional knowledge—transforms how teams work with AI on any complex task.

With AI, the temptation to move fast is almost irresistible. You get instant outputs, the thrill of novelty, the satisfaction of "adopting AI" before your competitors. It's a quick high.

But like any quick high, it wears off fast and leads to disappointment. Three months later, you're back where you started—except now you've also eroded team confidence in AI and wasted time on generic outputs that didn't move the business forward.

Most enterprise experimentation teams know they lack formalized process. They feel it daily in the endless prioritization debates, the ad-hoc QA checks, the experiments that launch without clear success criteria, the results that spark more questions than answers.

If you're an analyst who's ever felt that nagging doubt when reviewing data that "looked right" but felt wrong, then this game is for you.

If your reason for implementing these technologies is "because everyone else is doing it," or "because our board is telling us we need to," or "because my friend at another company is doing it" - my experience has shown me, over and over again, that you're destined to fail.

I need to tell you something that might change how you see yourself: you already have the capacity to be an analytical thinker. You don't need a statistics degree. You don't need to know how to query a database. You don't need to become an analyst.

The CRO industry has it backwards. While agencies push "big, bold changes" and platforms promise "30% conversion lifts," the most sophisticated enterprise programs are finding massive value in subtle improvements that inexperienced teams would dismiss as trivial. This isn't about settling for small wins, it's about understanding how human psychology, business maturity, and compound growth actually work at scale.

For Wambui, choosing 33 Sticks wasn't just about fulfilling an internship requirement, it was about finding the right environment for meaningful growth. With her experience, she sought a boutique agency with specialized offerings and a reputation for excellence.

"At this stage in my career, joining a boutique agency felt essential, somewhere with a tailored service offering and a reputation for excellence. I wanted more than just 'checking the box' for my master's internship, I wanted an enriching, really focused experience that truly aligned with my goals."

Perhaps most importantly, she was drawn to our people-first philosophy. "Success to me, is about building with integrity and people at the center. That people-first approach made me confident this is the right place to grow and receive invaluable mentorship."

As data practitioners, our ability to spot patterns and extract insights can become our greatest weakness when overconfidence overrides curiosity. This article explores how even experienced analysts can fall into the trap of drawing sweeping conclusions from limited data, missing crucial business context that completely changes the story the numbers tell.

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At 33 Sticks, we believe ethics cannot be optional. Our manifesto commits us to objectivity, transparency, and context because without objectivity, the true value of data is lost.

For the past fifteen years, i’ve worked with data practitioners across industries, retail, healthcare, tech, finance, entertainment. Different businesses, same story. Teams feel frustrated and underappreciated. They’re buried in requests for reports and dashboards, treated as service providers, and excluded from strategic conversations.

The complaints are familiar:

• “We don’t have a seat at the table.”

• “The business doesn’t respect us.”

• “Leadership ignores our insights.”

Yet beneath the frustration lies a paradox. The very behaviors and beliefs that define how most data teams work are the same ones sabotaging their influence. We want respect but avoid accountability. We want to be strategic, but we refuse to step into the role.

Earlier this month, Cracker Barrel unveiled a new brand identity. The familiar imagery of a man leaning on a barrel disappeared. Within hours, social media lit up and headlines followed:

• “Board member under fire for DEI background after restaurant ditches traditional logo”

• “Conservative activist slams Cracker Barrel; company left reeling after logo redesign”

• “Marketing fiasco shows investors are making ‘woke’ a massive risk factor”

The narrative was clear, the redesign wasn’t about business strategy, it was about politics.

Analytics teams will always be understaffed, that's not a problem to solve, but a reality to work with. This article explores how analytics leaders can build powerful virtual teams through evangelism and relationship building rather than waiting for budget approvals.