AI Application Planning: Choosing Between Traditional ML and Generative AI

As Generative AI’s (GenAI’s) popularity surges, C-suite executives and board members increasingly recognize AI as critical to their business strategies. In fact, 80% of executives expect it to affect their bottom line within five years—but many GenAI POCs fail to gain traction, draining budgets and slowing progress. Before diving into AI application planning, it’s crucial to choose the approach that truly fits the needs at hand. This blog provides a practical guide to compare GenAI and traditional ML across key decision points, ensuring a solid foundation by selecting the right tool for each use case.

Rather than diving straight into GenAI application planning, which will be covered in subsequent posts, in this post we will explore key decision points that can help you understand when to choose GenAI or traditional ML for your business use cases. We will explore dimensions such as explainability and transparency, latency, cost vs performance, task specific, and data.

GenAI versus Traditional ML comparison

The following table is an executive summary, containing the main factors discussed in this blog. Each subsequent section explores each decision factor in more detail.

Task Specific

The type of task or goal of the use case directly impacts whether a GenAI model or traditional ML model should be used.  

Generative AI models can work for traditional NLP tasks like text classification, sentiment analysis, and summarization. LLMs are transformer-based models that predict the next word in a sentence. This makes GenAI shine with generative tasks where it generates text such as personalized marketing emails, synthetic data when data is not readily available, and even code generation. A common use case that GenAI supports is chatbots and virtual assistants, where the AI assistant can have a back-and-forth conversation with the user and can provide contextually relevant information to address conversations. Typically, for this type of use case a Retrieval Augmented Generation (RAG) solution is used, which will be discussed in further detail in the Data section. Another common use case is text-to-SQL where the user can ask a question and get a response based on a table in their database. In this case, the LLM will translate that question into a SQL query, execute the query, and then provide the user with a human interpretable response. For example, a user may ask what where "How many laptops are in my New Jersey warehouse?” and the LLM would respond “There are 121 laptops in our New Jersey warehouse.”. 

Recently there has been a rise in the use of LLM as Agents. Two common use cases for LLM as agents are function tool calling and routing. In function tool calling an LLM will operate as an agent and select which tools to call and in what order to answer a user’s question. Tools could include a Vector Database, an external API, or a calculator for math. On the other hand, an LLM for routing is where the LLM decides what pipelines or workflows to run to answer the question. While routing logic can sometimes be hard-coded or rule-based, using an LLM allows the system to select among various paths intelligently. For example, if a query is about how many sales the company had in Q2 it might route the user’s query to the text-to-SQL module instead of the RAG which hosts the company’s internal knowledge documents. 

While LLMs can work for solving problems that involve complex reasoning; they are limited in their ability to handle mathematical patterns and even have challenges in interpreting time series patterns like seasonality. Therefore, tasks like predictive analytics, classification, segmentation, and optimization are well suited for traditional ML approaches. For prediction use cases like forecasting sales or predicting maintenance and repairs for manufacturing equipment using ARIMA or LSTM might make sense. Likewise, to optimize workforce scheduling a solver like Gurobi might be leveraged to identify the mathematically optimal solution given the constraints (e.g. maximum weekly working hours) unlike an LLM which would likely come up with a less-than-optimal solution given its probabilistic nature. 

To summarize, traditional NLP, Q&A, and advanced reasoning are all great use cases for GenAI, whereas traditional ML works best for use cases like predictive analytics, classification, segmentation, and optimization.

Data

The type of data available is critical in considering whether it makes sense to move forward with a GenAI use case. Traditional machine learning models typically leverage structured data like tables from databases and CSVs. Although GenAI can handle structured data, it may sometimes be an overly complex solution for straightforward tasks like classification or forecasting. However, for text classification, where training data is limited, GenAI could be more applicable, especially by using an LLM with few-shot learning techniques, which involve providing classification examples within the prompt context.

Before GenAI's emergence, much of the potential of unstructured data remained largely untapped. The advent of GenAI has led to a significant increase in unstructured data use cases (including documents, images, audio, etc.), as GenAI excels at processing and generating such data. For instance, multi-modal LLMs (MLLMs) can comprehend and produce various forms of data, including text, images, audio, and video. A user might prompt GPT-4o, an MLLM, to create an image of a cat riding a surfboard. Traditional ML can also process unstructured data, but it demands extensive data preprocessing and feature engineering, adding complexity. In some cases, organizations may choose to employ familiar computer vision or natural language processing techniques to save costs or reduce latency for simpler tasks like image or text classification. 

In addition to MLLMs, Retrieval-Augmented Generation is a common design pattern for GenAI use cases with unstructured data. This pattern tackles issues related to outdated information and inaccuracies. By including relevant company or domain-specific context in the prompt, the LLM generates responses that are more precise and current. The RAG architecture operates using a vector database that stores unstructured data as embeddings, utilizing semantic search to find the most pertinent text chunks (such as internal company documents) or images for context. This method is frequently applied in conversational chatbots and internal knowledge-sharing applications.

In conclusion, while there are specific nuances regarding the use of GenAI versus traditional ML, GenAI generally excels with unstructured data, whereas traditional ML is well-suited for structured data.

Explainability & Transparency

When developing a model for a specific use case, it's essential to consider the level of interpretability that the end user requires from the model's decision process. Some use cases will trade interpretability for speed, or for a less cluttered product, but critical applications may require a high level of transparency -- which is not always available.

Examples where the user is less concerned with the background process: 

• Spell checking: users expect immediate, low-stakes corrections — they manually approve appropriate fixes and aren’t concerned with the logic behind it 
• Friendly chatbot: users only care about the quality of output, not necessarily the process behind it

Examples where the user needs to know the model’s reasoning: 

• Cancer detection model: tools like Paige.ai augment physicians by accelerating image review and must guide doctors through their reasoning for each flagged anomaly so clinicians can verify and approve the final diagnosis
• Loan issuing model: a big sum of money is at stake, so it is vital to understand the ‘Why’ for auditing and risk management purposes

When a model assists critical decision making, especially those with significant health, investment, or legal implications, stakeholders must be able to explain their conclusions in a few sentences. “Because the model said so” is not sufficient when decisions require accountability and justification. Instead, transparency enables stronger explanations such as “the model flagged this loan because income-to-debt ratios exceed 40% and credit utilization is at 85%”. 

Simpler algorithms in traditional ML—linear regression, decision trees, logistic regression—offer inherent interpretability: it’s straightforward to trace which features drive outputs. Moreover, as deep models have been around for a few decades, there are quite a few explainability methods (feature permutation, SHapley Additive exPlanations or SHAP) we can use in production when needed.

GenAI uses powerful, massive, models, making them less predictable and harder to interpret. Implementing transparent guardrails can bridge that gap. For example, a legal question-answering bot can leverage retrieval-augmented generation (RAG) by grounding its responses in a verified legal corpus, providing document citations such as statutes, case law, or regulations that experts can review and verify. Another approach is to implement a hybrid system with traditional ML. For instance, machine learning models can be leveraged to predict the lower and upper limits of house prices. A Generative AI system could utilize these thresholds to identify when a prediction from a large language model falls outside these bounds, preventing it from delivering an inaccurate estimate to the end user. Moreover, for more complex GenAI system frameworks like LangSmith offer observability into the entire GenAI workflow, from input and prompt construction to the final LLM-generated output. Thus, by implementing a combination of GenAI system guardrails and traceability, we gain visibility into the decision-making process, debugging, and auditing, which enhances the trustworthiness of the final response for users.   

Cost versus Performance

For tasks that can be tackled by both GenAI and Traditional ML, a decision factor to consider is the trade-off between cost versus performance. A use case's cost entails both engineering effort, and the price of a solution.

Traditional ML models are generally cheap to train, deploy, and maintain—both in compute and engineering effort. They’re also easy to monitor and debug, reducing long-term operational costs.

GenAI unlocks advanced capabilities, particularly in unstructured data, but comes at a higher price, as they involve ongoing inference costs. As with every expensive technology, applications will bring value when planned with care. At Aimpoint, a crucial step of GenAI application planning is an initial strategy phase, to focus on business use cases where the added cost aligns with real-world impact.

A good strategy is to first consider whether a simpler traditional ML solution might be suitable, as these tend to be cheaper to run and often perform remarkably well. If there are grounds for improvement, we can build a GenAI proof of concept, evaluate the system, and decide based on a cost-performance analysis. For example, improving text classification accuracy from 92% to 94% with a large language model might look good on paper, but if it triples your cloud bill, the tradeoff isn’t always justified.

Latency

Another factor is how quickly the model needs to respond. Examples where latency is vital include:

• High-frequency trading: speed is a massive competitive advantage
• Augmented Reality: low latency is essential to synchronize real-world actions with virtual elements

Examples that don’t require low latency include: 

• Offline document processing
• Periodic predictive modeling
• Scientific and simulation models

Traditional ML models tend to be much smaller than GenAI models, so they are faster and scale better. Some of these models can produce outputs in milliseconds, which makes them ideal for real-time systems with strict response-time requirements.

Every GenAI component in an application adds a latency burden, which can be mitigated if the LLM calls are parallelized. For a user facing chatbot, chaining sequential LLM calls could make a user lose interest and leave the conversation, so ideally, we would stream the model responses, and keep the process short. For backend applications that do offline batch processing, pipelines can be made more complex, and a multi-step system could increase the accuracy, and the added latency wouldn’t be an issue.

Conclusion

In an age where GenAI is so novel and popular, and many are rushing to integrate LLMs into their businesses out of fear of falling behind, we make a case that the best results come from choosing the right tool for the right task: GenAI for complex, unstructured, or generative use cases, and traditional ML for structured, cost-sensitive, or high-precision applications. By understanding the strengths and trade-offs of each approach, organizations can avoid costly missteps and build AI solutions that are not only impressive, but also sustainable and effective over time.

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