AI & SaaS: The future is here

I’ve been meaning to write this blog for a while…

There is no more of a pressing topic for data professionals at this time than AI. You can’t escape the conversation. The topic of AI is in every news cycle, every LinkedIn post, and even in conversations with friends.

There are a lot of concerns with AI – and I truly believe that engaging in conversation around all aspects of these concerns is productive, whether it be the impacts on society economically, ecologically, ethically, or other.

As a business owner servicing SaaS clients, this topic is obviously a very important one for me to understand. This blog is written through that lens and is mainly focused on the impact that AI is going to have/is having on the BI & Analytics ecosystem in the SaaS space.

Let’s jump in 🙂

We’ve been here before

AI is here to stay. That’s the reality. Whether that is a net positive or negative for society is still to be seen, but the reality is our understanding of how to interact with, leverage, and understand data has forever been changed.

But here’s the thing…I think we’ve been here before. Last time, we called it self-service analytics.

And before anyone jumps down my throat on this – I am not equating AI to self-service analytics. What I am saying is that I remember a time when everyone’s wildest dreams were going to be realized with self-service analytics.

Tableau even had a marketing tagline that was called “Analytics at the speed of thought”. At the speed of thought!? I’ve built hundreds of Tableau dashboards in my life, and even though my brain doesn’t move very quick, Tableau still wasn’t keeping up with my thoughts…

What did the self-service analytics wave bring us? On the positive side, it brought about the expectation of data fluency and a baseline for most employees to understand the basics of how to use and interpret data. It also brought about increased access and use of data by federating it to individual users.

Self-service also left behind countless unused data products. Countless refreshes of data for users that would never consume it. There are countless examples of sprawl that was unmanageable and extremely costly to organizations.

And this was just for self-service tools like Tableau and Power BI…

Imagine the impact that AI vibe coded tools on a company’s ecosystem if they are managed in the same way as self-service tools.

What this means for SaaS

“The only constant in life is change.” — Heraclitus

They used to say that sex sells. In 2026, the only thing selling better than sex is fear.

Across the board, SaaS valuations have rapidly declined. One of the reasons being is that proprietary software development used to be an advantage – it was how products differentiated themselves. Now with AI, software development is at everyone’s fingertips.

You saw the Super Bowl commercials. 5 minutes to build a CRM? 10 minutes to build your own version of Google Maps? This has to be the end of SaaS right!?

I don’t believe so. Just in the way that self-service didn’t kill off centralized data teams or make us see insights at the speed of thought.

However, it will change the way that SaaS is understood and leveraged.

SaaS tools are going to be put to the test to see how fast they can adapt to this new landscape. BI is no longer a product to hang your hat on – it’s table stakes. Analytics tools must now be seen as software products.

It will not be long until AI is developing much of the code that will be used to build reports, dashboards, and applications. This doesn’t mean that BI teams will become obsolete, but it does mean that their roles will shift from pointing and clicking buttons on a dashboard to:

• Prompt engineering – crafting and refining the prompts that develop your applications
• Output validation – reviewing and verifying AI-generated code, which can be a very tedious process
• Data modeling oversight – ensuring the right models are in place and structured in a way that actually answers user questions
• Agent testing – testing and validating functionality of the AI produced workflows. As a trusted colleague said recently, “people will lead to production”.
• UI/UX review – evaluating the design and usability of the applications your team develops
• Access governance – managing permissions to applications and their data sources as well as monitoring cost and impacts on infrastructure
• Systems integration – connecting applications across user bases, organizational teams, and use cases
• Application Engineering – working closely with data engineering so they understand the intent and needed architecture to support and develop business applications
• Maintenance & incident response – keeping applications running and fixing them when they break (and they will, especially on edge cases)
• User enablement — training teams to use agents and applications effectively

The list could actually go on, but I’ll stop there.

Hopefully you can see that there will still be a LOT of work for BI teams to do, but the work IS going to change.

Now what does this mean for AI integrated BI platforms like Sigma?

How Sigma shines in this era of AI & BI

Vibe coding an app in 10 minutes is now possible – and that’s awesome.

IMO, from an enterprise data strategy perspective this is no different than people making dashboards in Tableau or Power BI.

Does it happen quicker? Yes.

Are they arguably more effective? Yes.

Does this still create the issue of decentralized product sprawl hampering people’s clarity around SST, bloated cost, tech debt, and loss of centralized governance? Yes.

The same problems with unfettered access to data and development tools exist whether it’s Power BI or AI. You still need governance of your data. You still need secure access to data in well curated and maintained models in your CDW. You still need guardrails around consumption and the ROI that you receive from that consumption. And you still need a unified organizational strategy so that you don’t end up with AI sprawl. Arguably, with the speed to development and power of AI, AI sprawl could end up being far more devastating and costly than BI sprawl.

This intersection of AI & BI is where Sigma truly stands out.

Sigma used to be a BI platform. Now it’s a data application company. Sigma’s ability to create apps that are easily embedded and seamlessly integrated with AI means that it’s capable of being a platform that allows for the creation of vibe coded applications with the governance, security, and scalability of a modern BI platform. As Sigma continues to move towards becoming an application development platform with a true software development lifecycle, its value will only increase.

Just like with any large charge in human history, it’s a pendulum. At first the pendulum swings way to one side and eventually it finds itself back into moderation. We see a platform of Sigma being where that pendulum comes to rest.

Conclusion

Good news – I think I’ll have a company in two years. I also think the type of work that we’ll be doing will look much different than it does today. AI will not be end of BI platforms. Rather, I think it’s the beginning of what they were always meant to be.

The organizations that win in this new era won’t be the ones that let their teams vibe code random apps in isolation. They’ll be the ones that leverage the power of AI with the structure, governance, and trust that a mature data platform provides. The ones that succeed with be the ones that learned from the self-service analytics era instead of repeating it.

The pendulum always finds the middle. I think we’re looking at it.

Contact Us

If you would like to talk to someone at Maverick Data about maximizing your usage of the Sigma platform, please email us at spencer@maverickdata.io for more information!