When Not to Use Generative AI | Gartner ThinkCast

Summary

Generative AI (Gen AI) has gained immense popularity, but its application is not always appropriate. Gartner experts discuss when to use Gen AI, such as for content generation, conversational interfaces, and knowledge discovery. However, Gen AI is not suitable for tasks like planning and optimization, forecasting, decision intelligence, or autonomous systems. These areas require more reliable, precise, and explainable AI techniques. Businesses should avoid over-relying on Gen AI and explore a combination of AI tools to optimize solutions and unlock greater potential.

Takeaways

• 😀 Generative AI (Gen AI) has rapidly gained popularity in organizations, becoming the most widely adopted AI technique, but it is currently at the 'peak of inflated expectations' in Gartner's hype cycle.
• 😀 A common misunderstanding is equating all AI with generative AI, leading to improper use of Gen AI in areas where other AI techniques would be more effective.
• 😀 Gen AI excels in content generation, including text, images, video, and even code, as well as enhancing conversational interfaces and enabling knowledge discovery through systems like Q&A.
• 😀 However, Gen AI is not suitable for tasks like planning and optimization, where precise calculations and optimization models are required, such as marketing allocation or supply chain planning.
• 😀 For forecasting use cases (e.g., sales predictions, inventory management), Gen AI is not the ideal tool. Predictive machine learning and simulation techniques are better suited for this purpose.
• 😀 When it comes to decision intelligence, such as recruitment, Gen AI may be unreliable due to its lack of explainability and potential for bias. Rule-based systems can offer better explainability in these cases.
• 😀 Gen AI is also not a good fit for autonomous systems, like algorithmic trading or robotics, as it lacks the robustness required for full autonomy. Human oversight is still necessary with Gen AI.
• 😀 Businesses should avoid over-relying on Gen AI, as it can result in missing out on other valuable AI techniques that could better address specific use cases.
• 😀 Combining different AI techniques, such as using Gen AI for conversational interfaces linked to optimization algorithms, can lead to more robust and effective solutions than using Gen AI alone.
• 😀 The main takeaway is that while Gen AI is powerful, it is not a silver bullet. Businesses should use it selectively and be mindful of its limitations, especially in areas like decision-making and forecasting.

Q & A

What is the main takeaway from the discussion on Generative AI (Gen AI)?

The main takeaway is that while Generative AI (Gen AI) is powerful, it is not a one-size-fits-all solution. Businesses should carefully evaluate use cases and not default to Gen AI for every problem, as other AI techniques might be more suitable for certain tasks.

Why is Generative AI currently at the peak of the Gartner Hype Cycle?

Generative AI is at the peak of the Gartner Hype Cycle due to widespread adoption and the public’s inflated expectations about its capabilities. There is a disconnect between the technology’s actual capabilities and what the media and vendors are promising.

What is a common misunderstanding regarding AI and Generative AI?

A common misunderstanding is equating all AI with Generative AI. Many businesses assume that Gen AI is the solution for all AI-related problems, which can lead to misapplication and missed opportunities to use other, more appropriate AI techniques.

What are some alternative AI techniques that can be used instead of Generative AI?

Some alternative AI techniques include machine learning (especially predictive models), simulation (for business scenario modeling), optimization (to maximize business objectives), knowledge graphs (for understanding relationships between entities), and rule-based systems (for explainable automated decision-making).

When is Generative AI most appropriate to use?

Generative AI is most appropriate for tasks like content generation (e.g., text, images, audio), improving conversational interfaces (e.g., chatbots), and knowledge discovery (e.g., building question-answer systems from internal documents).

Why is Generative AI not ideal for planning and optimization tasks?

Generative AI is not ideal for planning and optimization because it struggles with reasoning ahead, making precise calculations, and explicitly optimizing for specific objectives. Techniques like optimization models and predictive machine learning are more reliable for these tasks.

What alternatives should businesses use for forecasting tasks instead of Generative AI?

For forecasting tasks such as sales or inventory predictions, businesses should use traditional machine learning models (especially predictive models) or simulation techniques, which are more suited for making reliable forecasts based on historical data.

Why is Generative AI risky to use for decision intelligence or decision-making tasks?

Generative AI is risky for decision intelligence tasks because it lacks explainability and can be prone to bias. Critical decisions, like recruitment, require transparent and reliable systems, and other AI techniques such as rule-based systems are more appropriate for these use cases.

What challenges do Generative AI models face in autonomous systems?

Generative AI models are not yet capable of handling fully autonomous systems. They are prone to errors and require human oversight, which makes them unsuitable for high-stakes applications like algorithmic trading or robotics, where full autonomy is required.

What are the potential risks of over-focusing on Generative AI in a business?

Over-focusing on Generative AI can lead to failure in tasks where other AI techniques are better suited. It also risks missing out on the synergy between different AI techniques and may result in less robust solutions. A combination of Gen AI and other techniques is often more powerful.