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How do you decide which problems are truly worth solving with AI versus traditional software approaches?

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4 Answers
Product Development Process
  1. Puja Hait
    Puja Hait

    Google Group Product Manager • 6mo

    My litmus test are the following: 1) Can AI do this better - not just the first time but over time? If yes, then its worth the initial investment of training for quality, safety, guardrails etc. 2) Is there lack of data and more judgement required? if so, then AI is an assistance and not a replacement. 3) Does the solution need to deterministic or probabilistic is okay? Perhaps Personalized is better and hence probabilistic? Consumer expectations are changing and what we considered Traditional s ...Read More

    1,819 Views
  2. Derek Ferguson
    Derek Ferguson

    GitLab Group Product Manager • 6mo

    That’s a great question. I’ve seen a lot of people and companies building AI features simply to have AI features and say they are “AI powered”. That’s a losing strategy. It’s expensive, doesn’t provide any meaningful value over traditional features, and shows that the company cares more about chasing trends than delivering real value. So, for me, when I’m evaluating whether AI is an appropriate tool, I start by ignoring the “AI” part entirely. The first question is always: Does this solve a real ...Read More

    350 Views
  3. Ruchi Aggarwal
    Ruchi Aggarwal

    Former BILL Director, Product Management - Payments • 7mo

    I use a simple filter: if the problem is deterministic, rules-based, and predictable, traditional software is better. AI shines when there’s lots of data, high variability, and many “it depends” scenarios where different users need different answers. I also check if we can reliably measure correctness i.e. AI must be evaluable. If the task benefits from pattern-matching, reasoning, or personalization at scale, it’s a strong candidate for AI.

    547 Views
  4. Subu Baskaran
    Subu Baskaran

    Splunk Director of Product Management • 7mo

    In B2B environments, AI can dramatically simplify complex workflows—whether through chat-based interfaces, dynamic wizards, or fully contextual recommendations. But context is everything. In technical products that deal with terabytes or even petabytes of data, AI’s effectiveness depends heavily on personalization, training data quality, and latency expectations. Without these, AI can produce suboptimal results or slow experiences that degrade user trust. When deciding whether to use AI or a tra ...Read More

    384 Views

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