Not every problem needs generative AI — using it incorrectly can set back your organization’s goals.
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Misusing GenAI diminishes the value of AI in organizations
While generative AI (GenAI) has seen a surge in adoption and now accounts for a significant portion of AI initiatives in organizations, this rapid uptake can create challenges. The hype surrounding GenAI often leads businesses to overlook whether it’s truly the AI techniques that are the best fit for their needs, risking higher complexity and missed opportunities for value. Focusing too heavily on GenAI can cause organizations to ignore alternative, often more reliable AI techniques that may be better suited for most use cases.
GenAI is only one piece of the much broader AI landscape, and most business problems require a combination of different AI techniques. Ignore this fact, and you risk overestimating the impacts of GenAI and implementing the technology for use cases where it will not deliver the intended results.
How to identify when there is a better solution than GenAI
Use this guidance to evaluate when to use GenAI, when to use alternative AI techniques, and when to lean on a combination of AI solutions.
Determine whether GenAI makes sense for your use case
Start by figuring out whether the use case is value-driving for the business and feasible to execute on, regardless of the AI technique. This is important because some use cases are not a good fit for AI and do not merit further consideration.
Map your use case against the relevant use case family. As a primary technique, GenAI is:
Highly useful for content generation, conversational user interfaces, knowledge discovery
Somewhat useful for segmentation/classification, recommendation systems, perception, intelligent automation, anomaly detection/monitoring, autonomous systems
Hardly useful for prediction/forecasting, planning, decision intelligence
GenAI may also be a poor fit for your use case if the risks that come with it are unacceptable and cannot be effectively mitigated. These include unreliable outputs, data privacy, intellectual property, liability, cybersecurity and regulatory compliance, either alone or in combination with one another.
Consider alternative AI techniques
For areas where generative AI is not highly useful, consider other AI techniques — including established options like nongenerative machine learning (ML), optimization, simulation, rules or heuristics, and knowledge graphs, as well as emerging approaches such as causal AI, neuro-symbolic AI and first-principles AI.
Trying a simpler alternative AI technique before diving into generative AI can be a smart move; they are often less risky, less expensive and easier to understand. Recent developments, such as reasoning models and AI agents, have improved GenAI’s capabilities in certain areas like planning and autonomous systems. However, these advances still fall short of the performance achievable with traditional AI approaches for many use cases.
Combine GenAI models with other AI techniques
AI techniques are not mutually exclusive; they can often be combined in a way that delivers better accuracy, transparency and performance, while also reducing costs and need for data.
The potential combinations of AI techniques are endless. Strong combinations and use cases include:
Nongenerative ML and GenAI models for segmentation and classification, synthetic data generation, computer vision
Optimization/search and GenAI models for enterprise search
Simulation and GenAI models for simulation acceleration
Graphs and GenAI models for knowledge management, retrieval-augmented generation
Rule-based systems and GenAI models for chatbots, robo-advisors, specialized natural language generation
When not to use generative AI FAQs
When is generative AI not the right fit for my use case?
GenAI is not a good fit as a primary technique for use cases requiring prediction/forecasting, planning and optimization, or decision intelligence. It’s also poorly suited when you need exact calculations, reliable outputs or full autonomy without human supervision. Additionally, if the risks around output unreliability, data privacy, intellectual property, liability, cybersecurity or regulatory compliance are unacceptable for your use case, GenAI may not be appropriate.
What AI techniques should I consider instead of generative AI?
Common established alternatives include nongenerative machine learning (ML), optimization, simulation, rule-based systems and knowledge graphs. Emerging techniques, such as causal AI, neuro-symbolic AI and first-principles AI, are also worth tracking. These alternatives are often more reliable, better-understood and less risky than GenAI for many use cases. For example, optimization techniques excel at planning, while nongenerative ML is superior for prediction tasks.
How have AI agents and reasoning models changed when to use generative AI?
Recent developments in AI agents and reasoning models have improved GenAI’s usefulness for autonomous systems — moving them from “hardly useful” to “somewhat useful.” These advances particularly benefit semiautonomous systems operating in software environments. However, reasoning models’ planning performance still falls short of traditional optimization and search techniques. The best approach often combines GenAI with other AI techniques to create more robust systems.
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