HomeA Framework to Assess When AI Solutions Outperform Traditional Methods
Question: Which products are suitable for machine learning?
Project manager's response: Yes.
All jokes aside, the rise of generative AI has dramatically reshaped our perspective on which use cases are best suited for machine learning. Traditionally, ML has been applied to repetitive, predictable patterns in customer experiences. Today, however, it's even possible to use certain forms of ML without needing a massive training dataset.
Even so, the answer to "Do these customer needs call for an AI solution?" isn't always affirmative. Large language models (LLMs) can still be too expensive for some projects, and like all ML models, they aren't guaranteed to be accurate. There will always be situations where implementing ML isn't the best approach. So how should AI project managers assess whether a customer's needs warrant an ML solution?
Key factors to guide this decision include:
- Required inputs and outputs to meet customer needs: A customer provides an input to your product, which then delivers an output. For example, a Spotify playlist generated by ML (the output) might rely on inputs like user preferences, liked songs, favorite artists, and preferred genres.
- Combinations of inputs and outputs: Customer requirements differ depending on whether they expect the same or different outputs from the same or varying inputs. As the number of possible input-output combinations grows, especially at scale, the case for ML over rule-based systems becomes stronger.
- Patterns in inputs and outputs: Recognizing patterns in how inputs and outputs relate helps determine the right type of ML model. If there are clear patterns—such as analyzing customer feedback to determine sentiment—supervised or semi-supervised models may be more cost-effective than LLMs.
- Cost and precision: At scale, LLM calls can be expensive, and their outputs aren't always precise, even with fine-tuning and prompt engineering. In some cases, supervised models or neural networks that classify inputs using a fixed label set—or even a rules-based system—may be a better choice than an LLM.
To help project managers evaluate customer needs and decide if ML is the right direction, I've summarized the considerations above in a simple table.
Type of customer need Example ML Implementation (Yes/No/Depends) Type of ML Implementation Repetitive tasks requiring the same output for the same input Autofill email across online forms No A rules-based system is adequate for these outputs Repetitive tasks requiring different outputs for the same input Customer is exploring and expects a fresh experience with each repeated action (e.g., logging in):
— Generate new artwork per click
— StumbleUpon (remember that?) finding a random new corner of the internet Yes – Image generation LLMs
– Recommendation algorithms (collaborative filtering) Repetitive tasks requiring the same or similar output for different inputs – Grading essays
– Identifying themes in customer feedback Depends If input-output combinations are straightforward, a deterministic, rules-based system may suffice.
When scaling leads to too many combinations for a rules-based system, consider:
– Classifiers
– Topic modeling
But only if patterns exist in the inputs.
If no patterns are present, LLMs can be used for one-off tasks (though they are less precise than supervised models). Repetitive tasks requiring different outputs for different inputs – Responding to customer support inquiries
– Search functionality Yes It's uncommon to handle varied inputs and outputs at scale without ML.
Rule-based approaches can't efficiently manage the sheer number of permutations. Consider:
– LLMs with retrieval-augmented generation (RAG)
– Decision trees for search products Non-repetitive tasks with different outputs Writing a hotel or restaurant review Yes Before LLMs, such tasks often required specially trained models like:
– Recurrent neural networks (RNNs)
– Long short-term memory networks (LSTMs) for next-word prediction
LLMs are now an excellent fit for this scenario.
The main takeaway: Don't use a lightsaber when scissors will do. Use the matrix above to evaluate customer needs, considering both implementation costs and output precision, to build scalable, accurate, and cost-efficient products.
Sharanya Rao is a fintech group product manager. The views expressed in this article are the author's and do not necessarily reflect those of their employer or affiliated organizations.
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Interesting framework, but I wonder how they define 'outperform'? In my experience, traditional methods often win when data is scarce. 🤔 #AIvsTraditional
Question: Which products are suitable for machine learning?
Project manager's response: Yes.
All jokes aside, the rise of generative AI has dramatically reshaped our perspective on which use cases are best suited for machine learning. Traditionally, ML has been applied to repetitive, predictable patterns in customer experiences. Today, however, it's even possible to use certain forms of ML without needing a massive training dataset.
Even so, the answer to "Do these customer needs call for an AI solution?" isn't always affirmative. Large language models (LLMs) can still be too expensive for some projects, and like all ML models, they aren't guaranteed to be accurate. There will always be situations where implementing ML isn't the best approach. So how should AI project managers assess whether a customer's needs warrant an ML solution?
Key factors to guide this decision include:
- Required inputs and outputs to meet customer needs: A customer provides an input to your product, which then delivers an output. For example, a Spotify playlist generated by ML (the output) might rely on inputs like user preferences, liked songs, favorite artists, and preferred genres.
- Combinations of inputs and outputs: Customer requirements differ depending on whether they expect the same or different outputs from the same or varying inputs. As the number of possible input-output combinations grows, especially at scale, the case for ML over rule-based systems becomes stronger.
- Patterns in inputs and outputs: Recognizing patterns in how inputs and outputs relate helps determine the right type of ML model. If there are clear patterns—such as analyzing customer feedback to determine sentiment—supervised or semi-supervised models may be more cost-effective than LLMs.
- Cost and precision: At scale, LLM calls can be expensive, and their outputs aren't always precise, even with fine-tuning and prompt engineering. In some cases, supervised models or neural networks that classify inputs using a fixed label set—or even a rules-based system—may be a better choice than an LLM.
To help project managers evaluate customer needs and decide if ML is the right direction, I've summarized the considerations above in a simple table.
| Type of customer need | Example | ML Implementation (Yes/No/Depends) | Type of ML Implementation |
|---|---|---|---|
| Repetitive tasks requiring the same output for the same input | Autofill email across online forms | No | A rules-based system is adequate for these outputs |
| Repetitive tasks requiring different outputs for the same input | Customer is exploring and expects a fresh experience with each repeated action (e.g., logging in): — Generate new artwork per click — StumbleUpon (remember that?) finding a random new corner of the internet | Yes | – Image generation LLMs – Recommendation algorithms (collaborative filtering) |
| Repetitive tasks requiring the same or similar output for different inputs | – Grading essays – Identifying themes in customer feedback | Depends | If input-output combinations are straightforward, a deterministic, rules-based system may suffice. When scaling leads to too many combinations for a rules-based system, consider: – Classifiers – Topic modeling But only if patterns exist in the inputs. If no patterns are present, LLMs can be used for one-off tasks (though they are less precise than supervised models). |
| Repetitive tasks requiring different outputs for different inputs | – Responding to customer support inquiries – Search functionality | Yes | It's uncommon to handle varied inputs and outputs at scale without ML. Rule-based approaches can't efficiently manage the sheer number of permutations. Consider: – LLMs with retrieval-augmented generation (RAG) – Decision trees for search products |
| Non-repetitive tasks with different outputs | Writing a hotel or restaurant review | Yes | Before LLMs, such tasks often required specially trained models like: – Recurrent neural networks (RNNs) – Long short-term memory networks (LSTMs) for next-word prediction LLMs are now an excellent fit for this scenario. |
The main takeaway: Don't use a lightsaber when scissors will do. Use the matrix above to evaluate customer needs, considering both implementation costs and output precision, to build scalable, accurate, and cost-efficient products.
Sharanya Rao is a fintech group product manager. The views expressed in this article are the author's and do not necessarily reflect those of their employer or affiliated organizations.
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Interesting framework, but I wonder how they define 'outperform'? In my experience, traditional methods often win when data is scarce. 🤔 #AIvsTraditional
