AI Has Made Building Software Easier. Building the Right Product Is Still Hard

By Ayush Yadav, Founder, AY Technologies. This is an Op-Ed on Operators' Stack, Menlo Times.

There has probably never been a better time to build software. What once required weeks of engineering can increasingly be prototyped in days, sometimes hours. AI can generate code, debug problems, create interfaces, explain unfamiliar technologies, and help small teams experiment with ideas that previously required significant time and money.

As someone building software products for startups and businesses, I see this as more than a technology shift. It is changing where the real difficulty of product development lies. At AY Technologies, we work with startups and businesses to build custom software and digital products.

One lesson has become increasingly clear: The easier it becomes to build software, the harder it becomes to decide what is actually worth building.

AI is reducing the cost of implementation. But the difficult questions remain: What problem should we solve? For whom? What should the experience look like? What should happen when users succeed, struggle, or change their minds? The bottleneck is moving from writing code to making better product decisions.

The MVP Is Getting Easier to Build.

Traditionally, turning an idea into a usable product required a substantial investment in design, engineering, and infrastructure. That created a natural constraint. Founders had to be selective because every experiment was expensive.

AI is changing that equation.

A founder can now create a prototype with far less technical support. Developers can move through repetitive implementation much faster. Product teams can test multiple ideas rather than spending weeks committed to one approach. This is an enormous advantage.

But it creates a new problem: When everyone can build faster, speed alone becomes less of a competitive advantage. An MVP can now be created quickly. The harder question is whether the MVP solves a meaningful problem.

This is why I increasingly think of an MVP not simply as a Minimum Viable Product, but as a Minimum Viable Learning system.

The first version should help a team answer questions such as:

• Who actually uses this?

• Where do users struggle?

• What makes them continue?

• Where do they drop off?

• What do they expect that we did not anticipate?

  
  

AI can accelerate the building of the first version. It cannot guarantee that the first version is the right one.

The Product Is More Than Its Features.

One of the biggest lessons from building different types of software is that customers do not experience a product as a collection of features.

They experience a journey.

Consider a simple booking flow.

From a technical perspective, the product team may think about search, availability, payment, and confirmation as separate features.

The customer experiences something very different. They begin with intent. They may become excited when they find what they want. They may become uncertain while entering payment details. They may feel frustrated when a transaction fails. They may feel reassured when a booking is confirmed. These moments matter.

Imagine asking a customer for an app-store rating immediately after they have successfully completed a booking or finished an important journey. That request arrives at a moment of achievement and confidence.

Now imagine asking for the same rating after a failed payment.

Technically, both are interactions with the same application.

From a product perspective, they are completely different moments. Good products understand not just what the user is doing, but what state the user is in while doing it.

That is what I mean by understanding the customer's highs and lows.

A successful action may be the right moment to introduce the next relevant capability. Repeated failure may be the right moment to offer help. Abandonment may be a signal to simplify the journey. A long pause may indicate confusion. The product should respond to these moments differently.

Product-First Development Starts With the Journey.

This is where product-first development becomes important.

A traditional software process often looks like:

Requirements → Design → Development → Testing → Launch

A product-first process asks more fundamental questions throughout the process:

Problem → Customer → Journey → Friction → Solution → Measurement → Learning

The difference is subtle but important. Instead of starting with: “We need a notification feature.” A product team should ask: “What does the user need to know, and what should they do differently after receiving this notification?”

Instead of: “We need a dashboard.” Ask: “What decisions should this dashboard help someone make?” Instead of: “We need AI.” Ask: “Where is the customer currently spending time, effort, or attention that technology could meaningfully reduce?”

At AY Technologies, this product-first thinking has become an important part of how we approach custom software development. Clients often come to us with a business requirement rather than a perfectly defined product.

Our role is not simply to translate that requirement into code.

It is to understand the business, understand the user, challenge assumptions where necessary, and determine what should actually be built.

That distinction can make a significant difference to the final product.

AI Should Improve the Product, Not Just Build It.

There is another distinction that I believe will become increasingly important: Using AI to build software is not the same as building software that is meaningfully AI-native. The first is already becoming mainstream.

Developers use AI to write code, generate tests, troubleshoot issues, and accelerate development.

The second is much more interesting from a product perspective.

Imagine a business process where employees spend hours reading documents, classifying information, moving data between systems, and making repetitive decisions.

Adding a chatbot to that workflow does not necessarily solve the underlying problem. An AI-native product might instead understand the documents, extract relevant information, identify exceptions, recommend actions, and automate routine decisions-while bringing a human into the loop when judgment is required.

That is a product transformation, not simply an AI feature.

The same principle applies to intelligent search, recommendation, personalization, workflow automation, decision support, and AI agents.

The question should not be: “Where can we add AI?” It should be:

“Where does AI create a fundamentally better customer experience or business outcome?” Sometimes AI will be the right answer.

Sometimes a simple rule, database query, or conventional software workflow will be better. Product maturity means knowing the difference.

What Good Product Teams Do Differently?

As building becomes cheaper, some established product-development practices become even more valuable.

1. Design for outcomes, not features

Features describe what the software contains. Outcomes describe what the customer is trying to achieve. The second is usually the better starting point.

2. Map the complete customer journey

Don't focus only on the happy path. Understand what happens during onboarding, success, confusion, failure, cancellation, repetition, and abandonment.

3. Design around moments that matter

Not every interaction deserves the same response. A successful transaction, a failed payment, and an abandoned workflow represent different customer states and should be treated differently.

4. Instrument the product

Teams should know where users drop off, where they retry, what they ignore, which workflows take too long, and where users consistently need help. Data turns assumptions into evidence.

5. Close the feedback loop

Customer conversations, support tickets, analytics, usage patterns, and business outcomes should continually influence the roadmap. A product should evolve from evidence rather than internal opinion alone.

6. Build for edge cases

The happy path makes the demo look good. Real products are defined by what happens when information is missing, payments fail, users make mistakes, permissions conflict, or systems behave unexpectedly.

7. Avoid unnecessary complexity

AI makes it easier to build sophisticated systems. That does not mean every product should become sophisticated. Sometimes the best technology is the technology the user barely notices.

What Should Founders Do Differently in the AI Era?

For founders, the opportunity is not simply to build more software.

It is to learn faster. Start with the problem, not the AI model. Use AI to reduce the cost and time of experimentation.

Build the smallest version that can generate meaningful learning.

Put the product in front of real users as early as practical.

Watch what users do, not only what they say.

Pay particular attention to their highs and lows-where they feel successful, where they hesitate, where they get frustrated, and where they abandon the journey.

Invest in analytics and data from the beginning. And don't assume that every new AI capability belongs on your roadmap. The strongest products will likely be built by teams that can combine AI-assisted execution with disciplined product thinking.

The Bottleneck Has Moved Up the Stack.

AI is making software development faster, cheaper, and more accessible.

That is likely to continue. But the value of software has never come from code alone. It comes from helping someone accomplish something better.

As the barriers to building decrease, the importance of deciding what to build, for whom and why increases.

The most valuable product teams will need to understand technology-but also customer behaviour, business objectives, data, user experience, and the moments that shape the customer journey.

At AY Technologies, this is increasingly how we think about custom software development. The question isn't simply: “What can we build?”

It is: “What should we build, for whom, and what should the customer experience at every important moment?”

AI can help us build the answer faster. It still cannot decide what the answer should be. And that may be the most important shift happening in software today: The competitive advantage is moving from the ability to build software to the ability to build the right product.

About the Author:

Ayush Yadav is the Founder of AY Technologies, a custom software development company that builds digital products, business applications, and AI-enabled solutions for startups and businesses. His work focuses on combining product thinking, technology, and customer understanding to turn business problems into practical, scalable software products.

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