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Question: What product should use machine learning (ML)?Project manager answer: Yes.
Jokes aside, the advent of generative AI has upended our understanding of what use cases lend themselves best to ML. Historically, we have always leveraged ML for repeatable, predictive patterns in customer experiences, but now, it’s possible to leverage a form of ML even without an entire training dataset.
Nonetheless, the answer to the question “What customer needs requires an AI solution?” still isn’t always “yes.” Large language models (LLMs) can still be prohibitively expensive for some, and as with all ML models, LLMs are not always accurate. There will always be use cases where leveraging an ML implementation is not the right path forward. How do we as AI project managers evaluate our customers’ needs for AI implementation?
The key considerations to help make this decision include:
I put together a quick table below, summarizing the considerations above, to help project managers evaluate their customer needs and determine whether an ML implementation seems like the right path forward.
— Generate a new artwork per click
—StumbleUpon (remember that?) discovering a new corner of the internet through random search
Yes–Image generation LLMs
–Recommendation algorithms (collaborative filtering)
Repetitive tasks where a customer needs the same/similar output for different inputs–Grading essays–Generating themes from customer feedbackDependsIf the number of input and output combinations are simple enough, a deterministic, rules-based system can still work for you.
However, if you begin having multiple combinations of inputs and outputs because a rules-based system cannot scale effectively, consider leaning on:
–Classifiers –Topic modelling
But only if there are patterns to these inputs.
If there are no patterns at all, consider leveraging LLMs, but only for one-off scenarios (as LLMs are not as precise as supervised models).
Repetitive tasks where a customer needs different outputs for different inputs –Answering customer support questions–SearchYesIt’s rare to come across examples where you can provide different outputs for different inputs at scale without ML.
There are just too many permutations for a rules-based implementation to scale effectively. Consider:
–LLMs with retrieval-augmented generation (RAG)–Decision trees for products such as search
Non-repetitive tasks with different outputsReview of a hotel/restaurantYesPre-LLMs, this type of scenario was tricky to accomplish without models that were trained for specific tasks, such as:
–Recurrent neural networks (RNNs)–Long short-term memory networks (LSTMs) for predicting the next word
LLMs are a great fit for this type of scenario.
The bottom line: Don’t use a lightsaber when a simple pair of scissors could do the trick. Evaluate your customer’s need using the matrix above, taking into account the costs of implementation and the precision of the output, to build accurate, cost-effective products at scale.
Sharanya Rao is a fintech group product manager. The views expressed in this article are those of the author and not necessarily those of their company or organization.
