In a world where everyone thinks the answer to everything is AI, the truth is not even close to that. Every week, I chat / work with data scientist and the reality is that AI is a disruptive technology but it’s really more of an enhancing one than something that will replace the field (at least in its current state).

To explain this, I’m going to borrow a concept from finance called Alpha and Beta.

Example: You buy Apple shares and it’s up 25% this year.

Beta → The beta of a portfolio is how much of your returns are related to the broader market. Example : The S&P 500 is up 10% this year. That would generally be considered “the market” and so that’s the Beta

Alpha → The alpha of a portfolio is how much of your returns are related to your decisions. You chose to only buy Apple instead of the broader market and so you’ve created a 15% Alpha.

10% (Beta) + 15% (Alpha) = 25%.

So, how does this work with Data Science? The Beta will essentially be the expected and the average performance. The Alpha is what you can layer on top to create better performance than your average peer. Using this concept, we’re going to explore how Data Scientist should use AI by breaking down the core work flow of a data scientist (more detail on the core work flow here) and how you can find Alpha and Beta.

Core work flow:

The first step for every data professional should be learning how to prioritize their work which is a nice way of saying “learn how to say no”.

The reason prioritization is challenging is for a few reasons:

So, let’s now revisit the Alpha + Beta concept.

If you’re below the Beta (aka below the average) at prioritization then AI can kinda somewhat sorta help you. I mean that to be intentionally vague because AI has been trained on enough data to sense what may feel like a bigger priority that it should be able to tell you on average what is worth prioritizing.

However, it doesn’t necessarily have context on your company and leadership dynamics. For example, when you’re founder comes in and says “Hey, I need you to work on XYZ by this weekend for an investor deck”, the reality is that it’s not objectively a priority. It’s emotionally a priority.

Gen AI also doesn’t have the dynamics of understanding your relationship with the person asking / determining the priority. There have been many instances where I’d feel comfortable saying “No, we don’t need this now” and other times where I haven’t.

Within the Beta + Alpha framework:

Beta (average) → Use Gen AI to help you prioritize knowing that it’s taking the average and assuming the priorities. It can also help you with exercises like T-Shirt sizing if you provide it the right context.

Alpha (beat the average) → The best way to do this is to shift to a more human approach. Make sure you have a seat at the table and are aligning with stakeholders as partners and not as a subservient query monkey. This is where the best data scientist separate themselves which AI can’t do yet.

Summary: Good at getting to Beta but not for finding Alpha

The analysis step is where Gen AI has the most advantage for a data scientist / analyst.

Breaking down an analysis into a scoring rubrik:

In all 3 steps, you can leverage AI to significantly improve (in the realm of at least 20%).

Speed → This is the most obvious one that EVERY data professional should be using Gen AI for. In 2025, there is no reason you should be handwriting SQL at all. I’ve tried this out multiple times and it has saved me hours every time. It’s not perfect because your prompt is likely not perfect but there are other times where it just writes the wrong SQL (again can be fixed with better prompting) . Speed for a data scientist / analyst is so important because it allows you to do more analyses which therefore creates more impact and gives you bigger opportunities and so the cycle continues.

Quality → Quality of an analysis comes down to whether you did the data cleaning right and whether you avoided any big mistakes. Again, Gen AI is fantastic at this because it’s good at spotting patterns. This means you can essentially use Gen AI as a fellow analyst to spot check your work and make sure you’re avoiding embarrassing mistakes. It helps you automate away the errors that are solely because we’re human and make mistakes.

Completeness → Completeness is the third and final step and it’s where AI has mixed results. Completeness requires context and as we’ve discussed before AI doesn’t always have all the context. So, you can use Gen AI to verify whether you’ve thought of everything in your current analysis and it’ll do the best it can. Again, use it like a sounding board. However, it has limitations because it only knows what you provide it and often we’re lacking context that would help us be better data scientist / analysts.

Within the Beta + Alpha framework:

Beta (average) → Use it to do standard SQL queries but quicker so that you’re spending more time on other components.

Alpha (beat the average) → Use Gen AI to make you a faster analyst and double down on quality and completeness.

Summary: Great at getting to Beta and decent for finding Alpha

Communication is such a complex human activity that I’m really curious to see how AI continues to change the way we interact with each other. Within the context of data scientist / analyst, I’m more bullish on the impact of AI because this is where the data industry struggles the most.

Let’s think about all the ways you communicate:

I’m not going to break all of them individually but it’s safe to say we can split this up into verbal and written communication.

Let’s start with where AI can’t help you all that well: Verbal. Yes, it can write a speech for you but at the end of the day it’s not going to speak for you. Verbal communication though is where most trust is built in a relationship so you’re not going to be create much “Alpha” here with AI. This is something you have to work on.

Now, the written is where Gen AI excels. It helps make writing more succinct. It helps non native English speakers. It speeds up the process.

This is where data analysts / scientist can use Gen AI the most .

Example:

Above is an example of a slide deck I found online (slide 1) that I then rewrote (Slide 2). This is a great example where instead of manually doing it, I could have put the content of the first slide (or even just the slide itself) and asked Gen AI to rewrite it . It’ll come up with something better / simpler than you had before. As it gets better at imitating tone of voice and not sounding the same for everyone, this will be a huge unlock for teams.

Email summaries is another great example of how Gen AI can do much of the heavy lifting. If you provide a template and the underlying message it can format it for you.

Within the Beta + Alpha framework:

Beta (Average) → Use Gen AI to create summaries, text for decks etc. Use it if English is not your native language.

Alpha (Beat the average) → Use Gen AI to create multiple versions of copy so you can pick the best version. Use it to templatize communication. Use it to review your work.

Summary: Must use for getting to Beta and good for finding Alpha

However, you’ll need to practice your verbal communication outside the context of Gen AI. This is also where Data Scientist can find even more Alpha than their peers if they really focus on this.

Finally, we get to the last section which is where many data scientist / analyst stumble and it’s why it’s the shortest section here. Execution is about influencing and learning how to persuade stakeholders. It’s another example of a very complex human action that’s predicated on trust. While the other steps mentioned above help you build trust, this is the phase where you then cash in your chips and the trust you’ve built up.

Because Data Scientist / Analyst usually can’t pull levers (we don’t build code or run marketing campaigns), execution becomes a bit more challenging for us to use Gen AI to do. The idea of agents here isn’t realistic so here’s where I think Gen AI currently will fail for data scientist / analyst.

You have to go talk to people and get them to do what you propose is the best for the company. Sorry. No way around it for now.

Within the Beta + Alpha framework:

Beta (Average) → 🤷

Alpha (Beat the average) → 🤷

I really don’t see how Gen AI can help in execution in it’s current form, but if you do then please let me know!

Gen AI is an incredible tool but I’m not fully bullish on its ability to replace a data scientist / analyst. However, data professionals can take advantage of Gen AI and find “Alpha” in their work separating them from the rest of the pack by improving key workflows.

Here it is: The 5 laws of Marketing

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