Introduction: Why Pain Comes First
A lot of AI projects sound great on paper. They start with good intentions, promising features, and excitement around the possibilities. But then something happens. The feature ships, adoption is low, the ROI is unclear, and slowly, quietly, the initiative loses steam.
This is more common than you might think. In fact, according to studies, around 70 percent of AI initiatives fail to deliver meaningful business value. And one of the biggest reasons? Teams skip the first and most important step: identifying a real pain worth solving.
That’s why we created the PAVE framework. It’s a practical tool for product leaders to go from AI hype to real impact. PAVE stands for Pain, AI fit, Value, and Effort. And it starts with P for a reason.
This post is about the first step – Pain. Because before you jump into building a chatbot or integrating an LLM, you need to ask: what is the actual problem? What’s broken? What are people frustrated by? Where is time being wasted? Where are we losing customers?
If you can zero in on a real, validated pain point, the rest of the process gets easier. You will know what to build, who it’s for, and why it matters. If you skip this step, there’s a good chance you’ll build something smart that no one really needs.
In this post, we’ll walk through how to find the right kind of pain – deep enough to matter, common enough to justify solving, and sharp enough that people are willing to try something new.
Because in the end, the best AI ideas don’t start with the technology. They start with the problem.
The Temptation Trap: “It’s Cool, Let’s Build It”
Every product team has felt it. Someone on the team shares a demo of the latest LLM. It summarizes documents in seconds, generates flawless meeting notes, even answers support questions with spooky accuracy. The room lights up.
“We should build this into our product.”
This is the temptation trap. It is exciting. It feels cutting edge. But it skips the hard question: is this actually solving a problem for our users?
Too many AI features get built because they seem impressive, not because anyone is asking for them. And in the absence of real user pain, these features become novelty layers. They get launched with fanfare, then slowly gather dust. No usage. No impact.
This does not just waste time. It chips away at your team’s confidence and the organization’s trust in AI. Now the next project is viewed with more skepticism. It becomes harder to get buy-in. And soon, AI becomes that thing we tried that never really worked.
Here is the hard truth: just because something is technically possible does not mean it is worth building.
The most successful GenAI features feel almost boring. They solve real, specific pain in a way that is faster, cheaper, or easier than before. That is the bar.
If you are feeling tempted to build something just because it is cool, take a breath and go talk to your users. Watch them work. Ask them where they are struggling. Then, and only then, come back and ask if AI is the right tool to help.
If it’s not solving pain, it’s just a party trick.
What Does “Pain” Really Mean in a Product Context?
Pain is not just someone saying “this could be better.” Pain is that recurring frustration your users feel. It is the thing that slows them down, causes errors, or keeps them up at night. Real pain shows up in behavior, not brainstorming sessions.
If someone is hacking together a clunky workaround with spreadsheets. If they are constantly pinging support for the same issue. If they churn after a few months and say your tool was too hard to use. That is pain.
Pain has three defining qualities: it is frequent, it is frustrating, or it is costly. Ideally, it is all three.
When you find something your users do every day that makes them sigh, you are getting close. If it also causes your company to miss SLAs, lose revenue, or deal with customer complaints, you are right on top of it.
Here is the kicker: users will not always tell you directly. They might say everything is fine in an interview. But watch them work. Notice the tools they keep open in the background. Ask what they wish was faster. Look at your usage data and NPS comments. That is where the pain lives.
The best GenAI features do not chase futuristic dreams. They fix the stuff users hate doing today.
Build something that solves that, and people will not just use it. They will thank you for it.
The line to hold onto:
Pain is not what users say, it is what they do when they think no one is watching.
How to Unearth Real Pain
If you want to build something people truly value, you have to go where the pain lives. Not in a brainstorm. Not in a whiteboard session. In the wild.
Start with user interviews but not the kind where you just ask what features they want. Sit with them. Watch them work. Ask them to show you how they get a task done from start to finish. Notice where their voice tightens, where their mouse pauses, where they sigh.
Shadowing a user for an hour can teach you more than a week of dashboard data.
Then go spelunking in your support tickets and escalation logs. These are gold mines. Look for patterns. What are people complaining about over and over again? What gets escalated to the product team again and again? These are not edge cases, they are pain points waiting to be solved.
Sales call recordings are another treasure trove. When prospects walk away from a deal, listen to why. What made them hesitate? What did they not believe your product could do? Sometimes the pain is not in what your product has, but in what it cannot yet help them avoid.
And of course, look at the data. Where are users dropping off? Which workflows have the longest time to resolution? What tasks get started but never completed? These metrics are your trail of breadcrumbs. Follow them.
As you listen and watch, tune your ear for signals like:
“It takes forever.”
“I hate this part.”
“We just deal with it.”
“It is always wrong.”
These are not throwaway lines. They are neon signs pointing at opportunity.
The things users tolerate but secretly resent? That is where the best products are born.
The pain worth solving is rarely loud but it is always there if you know where to look.
Signals That It’s Not a Pain Worth Solving (Yet)
Sometimes an idea sounds promising. People nod. Someone even says, “That would be nice to have.” And that is exactly when your alarm bells should start ringing.
“Nice to have” is the product equivalent of a polite shrug. It means the problem exists but no one is losing sleep over it. No one is hunting for a workaround. No one’s job is on the line if it doesn’t get fixed.
Real pain shows up differently. It comes with frustration. It comes with urgency. It comes with stakes.
If you cannot tie the problem to a business impact like churn, revenue leakage, missed SLAs, or inefficiency that costs time and money, you may be looking at an inconvenience, not a priority.
Another sign: stakeholders are indifferent. You mention the idea and no one pushes back, but no one leans in either. They are not invested because, to them, the status quo is just fine. That is not the foundation you want to build a GenAI initiative on.
Also pay attention to the frequency and friction. If the issue happens once a month and takes two minutes to deal with, it might annoy a few people, but it will not move the needle. Solving it might even create more complexity than it removes.
Here is the truth:
The best product decisions often come from knowing what not to solve.
Examples of Strong Pain Points for AI to Solve
Let’s make this real. What does a worthwhile problem look like especially one that AI is actually good at solving?
In healthcare, it shows up in claims processing. When every claim needs a manual review, delays pile up, patients wait, and providers get frustrated. It is not just slow, it is expensive and error-prone. The cost is real. So is the burnout.
In insurance, agents spend hours after every client call just summarizing notes. It is not strategic work. It is necessary, but it pulls them away from the conversations that actually drive revenue. Every hour they spend typing summaries is an hour they are not selling or helping a customer.
In HR, high-volume recruiting creates an avalanche of resumes. Recruiters scan hundreds to find just a few that make it to the next round. They are overwhelmed, timelines stretch, and great candidates slip through the cracks. It is a bottleneck with real impact on hiring goals and team productivity.
What ties all of these together? They bleed time. They cost money. They create compliance risks and customer pain. And they are high-volume, repetitive, and ripe for automation, the perfect setup for AI to step in and help.
If the problem sits where human time is being wasted on low-leverage work, where delays are hurting outcomes, or where people are drowning in tedious tasks, AI is not just a nice idea. It is a force multiplier.
Because when you find pain at the intersection of scale, cost, and urgency – you are no longer solving a problem. You are unlocking value.
Wrap-Up: No Pain, No Product
At the end of the day, even the most advanced AI cannot rescue a solution that has no real problem to solve. GenAI is not magic dust. It is a tool. A powerful one but only when pointed at something real, urgent, and human.
The best AI products do not start with models or data pipelines. They start with a person sighing at their screen. With a task that eats up hours. With a manager who keeps seeing the same mistake. With a team that says, “There has to be a better way.”
If we skip the pain, we skip the point.
So before you brainstorm features or write a single line of code, ask the hard questions. Go talk to the people. Feel the friction. And build with your feet on the ground.
In the next post in the PAVE series, we will tackle the second step: Is it an AI fit? Not every problem needs AI, and forcing it where it does not belong only creates more pain. But when the fit is right, magic can happen.
Let’s get to work.