What changes when you AI empower a Product
In this article, I will address What changes when you AI empower a Product.
When an Agile Professional thinks about AI, it is often as a “productivity enhancer”. We look at AI as something that can reduce our effort. AI indeed does that job. But more importantly, AI is rapidly changing the products we use every day. For example, search engines summarize answers instead of simply displaying links. Business applications recommend actions instead of just presenting dashboards. Streaming platforms recommend what show you can watch next. Development tools generate code. And now in addition, AI agents are beginning to execute tasks on our behalf.
It is tempting to look at this as simply another technology shift—similar to moving applications to the cloud or making them mobile-enabled.
But I believe the change is more fundamental.
When we add AI to a product, we don’t just change the technology behind the product. We often need to change entire product behavior
Traditional software was largely designed to follow predefined instructions. AI-empowered products can interpret, generate, recommend, reason, and, increasingly with Agentic AI coming in picture, these products can act.
As Agile Professionals or even Product Specialists, we need to understand how AI-empowered products transition from traditional data crunching and display to understanding impact. Read on the article to understand this shift better
From Executing Commands to Understanding Intent
Traditional software expects users to understand how the application works. The user performs action, and the Product or Software responds
Imagine booking a holiday using a conventional travel application. (Think MakeMyTrip or something similar) You, as the user, have to select a destination, enter dates, apply filters for price and hotel category, compare options, and gradually build your itinerary.
The application is very good at executing the instructions you provide. But it does not customize the itinerary for you.
Now imagine telling an AI-empowered travel application:
“We have five days in Sri Lanka. We enjoy wildlife and history, don’t want long drives and would like two days near a good beach. Suggest an itinerary.”
The user has expressed an intent, not provided a sequence of commands.
The AI needs to interpret that intent, understand the constraints, consider alternatives and generate an appropriate response.
This represents an important shift:
Traditional products ask: “What does the user want to do? / what steps will help the user”
AI-empowered products can increasingly ask: “What is the user trying to achieve?”
With this shift from user actions to user intention, we now need to rethink entire customer journeys rather than simply adding AI to existing screens.
From Deterministic to Probabilistic Behavior
This is another fundamental difference.
Traditional software is predominantly deterministic.
If a banking system is programmed to calculate interest using a particular formula, we expect the same inputs to produce the same output every time. Even if we make a mistake in the formula, the same wrong answer will be displayed every time
We can broadly describe this as:
Input → Rules → Expected Output
Generative AI does not always behave this way.
Ask an AI system to summarize a customer complaint twice, and you may receive two slightly different summaries. Both could be perfectly acceptable.
Ask it to recommend the next best action for a salesperson, and there may be several reasonable answers.
And occasionally, AI hallucinates. This means that the AI may produce an answer that sounds convincing but is simply wrong.
AI product behavior therefore looks more like:
Input + Context + Model → Probable Output
This has a huge shift in product behavior.
For many AI capabilities, we cannot simply ask:
“Did the system produce the correct answer?”
We also need to ask:
“How good was the answer?” (Was the itinerary suitable ?/Was the new show recommended by the streaming service relatable? / Did the AI-based customer agent really help the complainant)
This shift will eventually impact the entire product life cycle. It will change
- How we write requirements. – Now we will have to write requirements that capture how do we know if user intent was met?
- How we test the product. –
- How we measure quality
- How we decide what is an acceptable range of correctness?
- Last but not least, how do we decide our feature is ready for release?
AI empowered Intent driven products will not be merely right or wrong – we need to find a way to capture if the user intent was fulfilled
From Fixed Expectations to Context-Aware Experiences
For decades, we have designed digital products with fixed expectations. You navigate a certain menu, click a certain button – each time same action happens. This is a fixed expected behavior. We discussed above that Intent makes a key shift from expected behaviors for AI – But Context is an equally important factor to consider
Imagine a project manager opening a portfolio dashboard. With a traditional, fixed-expectations product, she expects to scan each of his 20 dashboards daily. Maybe she even has an alert built in to highlight dashboards with “red” items. But in the end, that alert is based on a fixed set of rules
But when working with an AI-empowered, intent-driven dashboard, instead of examining twenty charts, the manager might ask:
“What has changed since last week, and which three things need my attention?”
For the product to answer well, however, it needs context.
What projects does this person manage? What happened last week? Which risks were previously discussed? What does this organization consider critical? What information is this person permitted to access?
We can now see that context is becoming an increasingly important part of product design.
The quality of an AI experience may depend not only on the model being used but also on what the product Context – What the Product knows, remembers and retrieves at the right moment.
From Providing Features to Providing Intelligent Capabilities
Consider a traditional sales application.
It may provide customer records, reports, dashboards, filters and alerts. These features help a salesperson understand what is happening. And using the information, the salesperson then makes a decision about which customers are likely to turn to a competitor product this month
Instead, an AI-empowered version of the sales application could possibly answer:
“Which of my customers are likely to turn to a competitor product this month and why? What can I do to reduce the impact?
The product is no longer simply retrieving or representing information.
It analyzes information, identifies patterns /trends and/or anomalies , and then recommends possible action.
We can see similar changes across industries.
A traditional HR product stores employee information. An AI capability may help managers identify recurring themes in employee feedback.
A project management product tracks tasks and dependencies. An AI capability may identify emerging delivery risks and suggest possible interventions.
A customer-support product displays previous interactions. An AI capability can summarize the customer’s history and recommend a response.
The conversation when we think about AI products moves beyond: “What features should our product provide?”
To Goes towards “What intelligent capabilities should our product have?”
From Assistance to Increasing Autonomy
AI products are also changing in another significant way: they can increasingly act.
Imagine an expense-management product. If we want to limit AI use to analytics, AI could identify unusual expenses. If we want to go ahead and provide recommendation abilities, AI could explain why an expense appears unusual. It could then recommend an action.
But an AI agent takes this even further. It could potentially investigate supporting documents, contact the employee for missing information, update the case and route it for approval.
The products are now going from providing intelligence to taking coordinated action toward an objective.
The difference between an AI that says “Here is what I recommend” and an AI that says “I have done it” is often the differentiator.
As products move along this spectrum, autonomy itself becomes a product-design choice. We need to decide what action an AI agent should be allowed to take and where we need to insist on human intervention. For example, it may be OK to have AI approve a standard insurance claim, but you may want AI to flag any unusual claims to human or maybe you want all rejections to be vetted by a human. Going further you may want AI to suggest a possible diagnosis to a physician but never to a lay person.
The decisions on where AI acts and when it recommends… Or even what it can recommend what to whom are now the Product Design decisions
Products Can Now Fail in New Ways
Perhaps the most important shift in behavior appears when AI fails. Traditional software certainly fails. Applications crash, integrations break and calculations contain defects.
But AI introduces different kinds of failure. An AI system may misunderstand a perfectly valid request. It may generate information that doesn’t exist. (Hallucinations!).It may use outdated or inappropriate context. It may make a poor recommendation while sounding extremely confident. An autonomous agent may correctly execute an action that it should never have been allowed to take.
This creates an unusual situation: The software may be functioning exactly as designed, while the product is still producing an unacceptable outcome. Data – availability /correctness and retrieval will now impact a lot of outcomes . With data, we now have to worry about the data security / ethics
Concepts such as trust, uncertainty, boundaries, human oversight and acceptable failure increasingly important product considerations.
Snehamayee’s Perspective – Empowering your product with AI is more than adding an AI patch
The most common way people try to adopt AI is to see how it can make current work more productive. How can AI automate my MoM? Can we use AI write user stories / acceptance criteria or even code for me? How can AI write test cases and analyze test coverage?
A few people go ahead and try to incorporate AI in their products- what they do is to take an existing product and ask: “Where can we add AI?”
People may add a chatbot to their Product. Sometimes they add data summarization or trend analytics. They may even add recommendations. These certainly add value to the product.
But the more interesting question is:“If this product had access to intelligence from the beginning, would we design the product differently?”
Would users still navigate through ten screens? /Would they still analyze the dashboard themselves? /Would they still manually coordinate five different activities?
Or could the product understand their objective, assemble the necessary context, recommend an approach and—with appropriate safeguards—take some of those actions for them?
That is where AI stops being merely another feature and starts changing the product.
And this leads to an important question for those of us working in Agile product development.
If our products are becoming probabilistic, context-aware, adaptive and increasingly autonomous, can we continue building them exactly as we built deterministic software?
- Do our requirements need to change?
- What does “Done” mean?
- How should we test something when there isn’t always one correct answer?
- What new risks should teams consider?
And what does all of this mean for Product Owners, Scrum Masters and Agile teams?
Those are the questions I will explore next. while I do that please refer to this document for the design guideline when we build AI empowered Products
Because when the nature of the product changes, the way we build the product must evolve too.