Not every app needs AI. However, when implemented correctly, the right AI features can boost efficiency, enhance user experience, and unlock new value. So, how do you know if it’s the right time and the right fit? 

The AI Buzz Is Loud, But Is It Useful? 

AI is everywhere. From AI-powered chatbots to intelligent automation and recommendation engines, product teams are under pressure to incorporate AI in order to remain competitive. However, it is often perceived as a mere checkbox rather than a strategic initiative to enhance the user experience or drive significant outcomes. 

The risk? Building something that your target audience doesn’t need. 

Before rushing to integrate the latest language model or machine learning system into your app, it’s advisable to take a step back. Here’s how to assess whether AI is the right tool for the right job. 

1. What Problem Are You Solving? 

This is the starting point. Before you even mention AI, ask: 

  • What user problem are we solving? 
  • What business outcome are we targeting? 

If the problem isn’t clearly defined, any solution, AI or otherwise, is likely to fall flat. 

We start every project with product validation. Uncovering real user needs, mapping goals and identifying friction points. Only then do we explore whether AI is the right fit, because technology should serve the solution, not be the solution itself. 

2. Could AI Deliver Clear Value? 

If you’re confident in the problem, now it’s time to explore whether AI can help solve it better. 

Ask yourself: 

  • Would automation reduce manual workload or improve speed? 
  • Is there a large dataset that, if analysed, could drive smarter decisions? 
  • Do your users need predictions, suggestions or intelligent flows? 

If yes, AI might be worth exploring. If not, improvements to user experience, performance, or information architecture could deliver a more significant impact, faster. 

Example: A real estate platform handling thousands of property listings may benefit from AI-driven recommendations. But a simple booking app? It may require better design and clearer navigation. 

3. Are You Ready for the Complexity? 

AI is not plug-and-play, at least not if you want meaningful results. 

Here’s what’s involved: 

  • Data: You need clean, structured and relevant data to train or fine-tune models. 
  • Resources: AI development requires specialist knowledge and thorough testing. 
  • Ethics: Decisions made by machines affect real people. Transparency, bias mitigation and explainability all matter. 

So, don’t just ask, “Can we build it?” – ask, “Should we?” 

At this moment, a simpler rule-based system or an intelligent user interface can often perform just as effectively without the added overhead of AI. While the gap is closing rapidly, and AI will increasingly become more accessible, capable, and integrated, it remains essential to seek out the most straightforward solution. 

4. Where Is the ROI? Prioritise the Right Use Cases 

AI should be treated like any other product feature, with an eye on value. 

Use a simple 2×2 matrix: Impact (High/Low) vs. Effort (High/Low). 

  • High Impact, Low Effort = quick wins (e.g. automated support ticket triaging) 
  • High Impact, High Effort = strategic bets (e.g. predictive analytics) 
  • Low Impact, High Effort = avoid 
  • Low Impact, Low Effort = maybe nice to have, but not a priority 

Don’t waste your budget on AI features that sound clever but won’t move the needle for your users or your business. 

5. Can You Validate It with a Proof of Concept? 

Once you’ve identified a strong use case, do not dive into full-scale development. 

Instead: 

  • Build a prototype or proof of concept (PoC) 
  • Validate the interaction with real users 
  • Test the performance and feasibility of the AI component 
  • Gather feedback and iterate 

A PoC de-risks your investment and gives you clarity on whether the AI feature works in practice, not just in theory. 

Final Thought: AI is a Tool, not a Strategy 

AI is not magic. It will not fix a broken product or solve a problem you have not clearly defined. However, when used intentionally, it can genuinely transform your app – automating tasks, enriching experiences, and driving new forms of value. 

At Sonin, we help clients determine whether AI is worthwhile and where it fits into the broader context because building the right product means asking the right questions, not just chasing trends. 

Want to explore whether AI has a role in your product? 

[Book a Discovery Call →] 
Let’s identify the opportunities that matter and turn the right ideas into reality. 

TL;DR 

  • Start with the problem, not the tech 
  • Only use AI where it creates real user or business value 
  • Understand the costs, data needs and ethical responsibilities 
  • Use impact vs. effort to prioritise the right use cases 
  • Always validate with a proof of concept before scaling