Building AI Products, Part 1: How To Find AI Use Cases Worth Building in the First Place

Signup Write New Story Building AI Products, Part 1: How To Find AI Use Cases Worth Building in the First Place by sneh by sneh | @ sneha Curious about life, whimsical by nature, prone to existential detours and always searching for truth - when laziness doesn't win:)

Curious about life, whimsical by nature, prone to existential detours and always searching for truth - when laziness doesn't win:)

In February 2024, Klarna announced its AI assistant was handling two-thirds of customer chats. It was doing the work of 700 agents and cutting resolution time from 11 minutes to 2. It looked like the AI success story everyone wanted. Fifteen months later, Klarna was hiring humans again. The CEO admitted that a focus on cost had led to "lower quality" and customers still wanted a person for complex cases. The AI stayed and alongside people.

So which Klarna got it right: the one that automated 700 agents' worth of work or the one that hired people back? Picture two chats. "Where's my refund?" has one answer, sitting in a database. "Why was I charged twice?" needs someone to hear the customer out and make a call. AI can handle the first on its own. The second needs a person or at least a person checking.

Most AI roadmaps skip that distinction. They list places to add AI, not jobs to split between AI, software and people. The rest of this piece shows how to make that split: a map of where AI value sits, then three tests for any idea. Is it worth it? Is it suitable? How do we build it?

The AI economy has four layers and value grows as you move from answering to doing:

Then run the strategic test: if the AI disappeared tomorrow, what's left?

Each position calls for a different strategy, so know which one you're in before you go further.

Using AI is easy. Earning from it? McKinsey's 2026 State of AI survey finds nearly 9 in 10 organisations use AI but only 37% see any EBIT impact(Earnings Before Interest and Taxes) and about 6% earn more than 5% of EBIT from it.

Alexa is the cautionary tale: used daily for timers and weather, rarely for shopping. As one former senior Amazon employee told the Wall Street Journal: "We worried we've hired 10,000 people and we've built a smart timer."

Heavy use doesn't always equal value. And unlike normal software, every AI answer costs money. Whether you charge per seat, per call, per outcome or a hybrid of these, a flat price can lose money on your heaviest users, so model them first.

Before building, run a 60-second TASQ screen:

Next, put a rupee number on it: Value = volume × value per task × automation rate − cost

A support bot handling 50,000 tickets a month, resolving 60% and saving ₹40 each, is worth about ₹9.75 lakh a month after ₹2.25 lakh of AI cost. Note that the AI cost applies to every ticket it attempts, not just the ones it solves. One more catch: if nobody leaves the team, you have freed capacity, not saved cash. Be honest about which one you're claiming.

The useful question to ask is "where should AI sit in this system?" Split the task into parts, then score each part from 1 to 5 on four dimensions:

Use the total to compare ideas. The answer is rarely yes or no.

It's a role: AI can assist (it drafts, a person checks), recommend (it scores, a person decides) or execute (it acts, within set limits). Who should approve a ₹4,500 expense claim? Not the AI. AI reads the receipt, and rules approve it within caps. The pattern that falls out of all this is worth memorising: AI for ambiguity, software for certainty, humans for judgment.

Pick the simplest route that still has lasting value, and stop at the first yes:

Take a WhatsApp bot that refunds up to ₹2,000 for missing food items. Buy the conversational platform. Build the order lookup, per-customer caps, fraud checks and refund rules. Send disputes to a person.

Why not just prompt it? Because a prompt alone can't promise facts, policy or privacy. Ask a bare chatbot "where's my refund?" and it may invent a status. Ask it to delete your data and it may say "done" when nothing was deleted. Those guarantees live in the system around the model, not in the prompt.

On 2 May 2023, Chegg's share price fell 48% in a day. Students wanted homework help, and ChatGPT gave it free, instantly. When a free AI can do your customer's job, features are no defence.

Here is the whole method as a one-page exercise. Pick one painful, high-volume problem from your work and answer six lines:

Klarna eventually found its balance. By Q3 2025, its CEO said the AI was doing the work of 853 agents, while promising customers that "there will always be a human if you want". That's the lesson in one line: let AI carry the volume and keep people where judgment matters.