Digital Products Are Becoming Digital Services
For most of the software industry’s history, we thought about products as things people used directly.
You opened an application.
You clicked buttons.
You navigated screens.
You filled forms.
The software provided tools, and the user performed the work.
Even when products became more sophisticated, the relationship remained largely the same. The software exposed functionality, and the user operated it.
Lately, while building and experimenting with AI-powered products, I’ve started noticing a shift that feels more significant than simply adding AI features to existing applications.
Many digital products are beginning to behave less like tools and more like services.
The distinction may sound subtle, but it changes how we think about product design, user expectations, and software delivery.
Consider tax preparation software.
Traditional software helps you prepare your taxes. It provides forms, calculations, validations, and workflows. The user still performs most of the work.
A tax advisor, on the other hand, delivers an outcome. The client is usually less interested in the process than in having the taxes completed correctly.
Historically, software occupied one side of that spectrum and services occupied the other.
AI is starting to blur the boundary.
When a user asks an AI system to create a travel itinerary, summarize research, generate a meal plan, analyze a document, or prepare a report, they are often not looking for a tool to help them perform those activities. They are looking for the activity itself to be completed.
The interaction starts feeling less like software usage and more like service consumption.
I noticed this while experimenting with products of my own.
When thinking about something like Founder, the original instinct is often to design screens, workflows, settings, and features. That’s the traditional software mindset. But many users may not actually care about most of those things. They care about receiving useful scenarios, meaningful decisions, and interesting feedback. The underlying mechanics become secondary.
The same pattern appears in many AI-native products. Users increasingly evaluate products based on outcomes rather than features.
Nobody asks how many screens an AI writing assistant has.
Nobody asks how many database tables support a research agent.
Nobody cares whether a recommendation came from a workflow engine, a prompt chain, or a collection of microservices.
They care whether the result is useful.
This creates an interesting challenge for builders.
For years, software teams optimized for usability.
Today, many teams are being pushed to optimize for successful outcomes.
These are related but different goals.
A beautifully designed application that consistently produces mediocre results may lose to a less elegant product that reliably delivers value.
The shift also changes how we think about product architecture.
Traditional SaaS applications often resemble digital factories. Users move information through a sequence of screens, and the software records, stores, and displays data.
AI-native systems increasingly resemble digital operations.
Requests arrive.
Specialized components perform work.
Information is gathered.
Content is generated.
Decisions are proposed.
Actions are executed.
The system becomes less of a database with a user interface and more of an operational engine capable of producing outcomes.
This is one reason why discussions about agents have gained so much attention. The technology itself is interesting, but the larger trend is that software is moving closer to performing work rather than simply facilitating it.
That does not mean traditional applications disappear.
A school management platform such as Blest still requires records, workflows, permissions, reporting, and all the fundamentals of business software. Most enterprise systems will continue to need structured interfaces and predictable processes.
What changes is that more parts of those systems may start acting on behalf of users.
Instead of helping an administrator create a report, the system generates it.
Instead of helping a teacher communicate with parents, the system drafts the communication.
Instead of helping a manager analyze data, the system prepares an initial assessment.
The user increasingly reviews, adjusts, and approves rather than performing every step manually.
This has implications beyond technology.
Pricing becomes different.
Support becomes different.
Reliability becomes different.
When users purchase software, they often tolerate imperfections in the process.
When users purchase outcomes, expectations rise dramatically.
A project management tool that occasionally requires extra clicks may be acceptable.
An AI service that delivers incorrect project plans may not be.
The closer software moves toward performing work, the more it inherits the expectations traditionally associated with professional services.
I suspect many discussions about AI products are actually discussions about this deeper transition.
We are not simply adding intelligence to software.
We are gradually changing what software is.
For decades, software primarily provided capabilities.
Increasingly, it may provide completed work.
The technology enabling this shift is fascinating, but the more interesting question is what happens when users stop buying tools and start buying results. I’m not convinced we fully understand the implications yet, but it feels like one of the most important product shifts currently underway.
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