Is SaaS Dead? Why the Answer Matters for Physical Retail

There's a question making its way through investor Zooms and tech boardrooms right now that I think deserves a straight answer.

Is SaaS dead?

The short answer is no. But the longer answer is more interesting, and I think it matters a lot for anyone building or buying software that touches the physical world.

The reason the question is being asked at all is AI. The argument goes like this: if artificial intelligence can build, replicate, and iterate on software faster and cheaper than any development team, then the product itself stops being defensible. A competitor with the right model and the right prompt can close the gap on years of product development in a fraction of the time. If that is true, then a subscription to a software product is not really a moat. It is just a head start that is getting shorter.

There is something to that argument. For pure software businesses with no physical layer, no proprietary data, and nothing underneath the code that cannot be reconstructed, AI does compress the competitive advantage. The product alone is not enough anymore.

But the businesses that will navigate this well are the ones that have something underneath the software. Something that cannot simply be rebuilt by a well-funded team with access to the same AI tools.

For Storeplay, that conversation is one I find genuinely energising. Because we have been building that foundation, largely without naming it as such, for 36 years.

The hardware layer nobody talks about

Storeplay has physical hardware operating inside tens of thousands of retail locations across more than 40 countries. Music delivery hardware. Scent diffusion systems. Digital signage infrastructure. These are not peripheral to what we do. They are the mechanism through which everything we build actually reaches the store.

That physical presence changes the nature of the relationship between Storeplay and the retailers we work with. It is not a subscription you cancel and forget. It is infrastructure that is in the room, day after day, shaping the environment that customers walk into. That creates a depth of integration that a software-only competitor cannot replicate quickly, regardless of how good their product is or how sophisticated their AI tools are.

It also creates accountability that I think is good for everyone. When your hardware is in the store, you are genuinely invested in what happens there. You are not watching metrics on a dashboard from a distance. You are part of the experience.

New entrants can build software. AI can help them build it faster. But building a global hardware footprint with trusted retailer relationships takes considerably longer than any model can accelerate.

25 years of record label relationships that cannot be recreated overnight

This one does not get talked about enough, and I think it should.

Storeplay has spent more than 25 years building direct licensing relationships with record labels around the world. Major labels. Independent labels. Publishers. Rights holders across dozens of territories. These relationships were built over a very long time, through genuine partnership, through showing up consistently, and through demonstrating that we understood how to use music responsibly and commercially inside retail environments.

The music licensing landscape today is significantly more complex and significantly more expensive than it was when we started building these relationships. Many of the deals we hold would be very difficult for a new entrant to obtain at all, and where they could be obtained, the cost would be substantial. The labels know us. They trust how we operate. That trust took decades to earn.

For any competitor thinking about entering the retail music space today, this is not a gap they can close with better software or smarter AI. It requires time, relationships, and a track record that simply does not exist yet. That is a moat that compounds quietly in the background while everything else gets faster.

36 years of data that cannot be purchased

The third asset is less visible but equally valuable.

Over three and a half decades, operating across retail categories and geographies, Storeplay has accumulated a proprietary data set about what actually works inside physical stores. What music generates longer dwell times in fashion retail versus grocery. How different sensory combinations affect customer behaviour across store formats. How atmosphere interacts with brand identity to influence purchasing decisions across different customer profiles.

This is not data that exists in a public dataset. It is not data a new competitor can acquire through research or purchase, and it is not data any AI model was trained on. It is the result of a very long time being present inside the retail environment, paying close attention to what happens when you get the experience right and what happens when you get it wrong.

For most of Storeplay's history, that data existed in a form we could learn from but could not fully activate. The tools to do something genuinely intelligent with it at scale did not exist. That has changed.

What AI actually makes possible for us

The conversation around AI in software tends to focus on replacement and replication. For pure software businesses without a proprietary data layer, that framing is probably accurate and probably a problem.

For Storeplay, the dynamic runs in a different direction.

AI is the capability that finally allows us to fully activate what we have built up over decades. The data we hold about retail environments becomes dramatically more useful when you can run intelligent systems across it. We can now deliver music programming that responds dynamically to what is happening in a store rather than running a static schedule. We can deliver advertising content that adapts based on real inputs from the environment. We can build software that learns over time rather than executing a fixed set of rules.

The precision of what we can offer a retailer has improved significantly. The ability to demonstrate the connection between the in-store environment and commercial outcomes has improved significantly.

And the honest operational reality is this: AI allows a lean development team to build and maintain software at a level of sophistication that previously required a much larger one. For a business that has always run efficiently, that is not a threat. It is an advantage.

The model that holds up

I started Storeplay as a university assignment. I got a pass, not even a credit. I was not thinking about building a global retail experience platform. I was thinking about solving a specific problem for a specific client.

What has made the business work over a long time is not any single product decision or technology investment. It is the combination of being physically present in the retail environment, holding licensing relationships that took decades to build, accumulating knowledge that cannot be bought, and paying enough attention over a long enough period to understand what actually makes a difference in a store.

The businesses that will do well in the next phase of retail technology are not the ones with the cleanest SaaS metrics. They are the ones with real infrastructure in the physical world, proprietary rights and relationships that new entrants cannot easily access, and the intelligence to use all of it well.

SaaS is not dead. But software alone was never really enough. The retailers who have been buying point solutions and stitching them together are starting to realise that. The question they are increasingly asking is not which software to buy. It is which partner actually understands the store.

We have spent 36 years trying to earn that answer.