Generative artificial intelligence: how to sort between promise and reality before the bubble bursts? – Forbes in France


Currently, the excitement surrounding generative artificial intelligence is such that vendors are flooding the market with big promises, sometimes too good to be true. If we believe some, just a few would be enough call so that all your problems are solved.

Posted by Alan Trefler, CEO of Pega

While the power of generative AI is real, there is no single solution that can meet all business challenges. And generative AI is not the only artificial intelligence on the market. Different types of AI, each with their own shortcomings and qualities, can meet different business goals.

Unfortunately, too many vendors are taking advantage of the current craze to present their generative AI tools as a “Swiss Army Knife” that can solve ALL your problems. In addition to false promises and confusion, many of these generative AI products lack the control, management, and scalability that large enterprises need.

To avoid making bad choices, it’s important to learn about the different types of AI available for each type of problem. Artificial intelligence can be divided into two categories.

“Artificial intelligence with the right brain” for creativity

The first category of AI, “right brain”, is concerned with creative tasks. Generative AI falls into this category.

He developed at a dizzying pace. It is most often used to create content: articles, images, audio files, videos or even apps. (I may have even used a bit of generative AI to write this article…but that’s between us.) It also excels at summarizing content (like meeting minutes) and multilingual translation. She’s even able to code, although that’s not necessarily a good idea, but that’s a topic for a future article.

“Left-brained artificial intelligence” for rational thinking

While all eyes are on “right brain” generative AI, we’re forgetting a crucial point: it doesn’t help make decisions based on logic and analysis. For that you need another category of AI, the “left brain” category.

It is essential to optimize business decisions. She is able to evaluate situations, predict results and take decisive measures, either in cooperation with people or autonomously in less critical cases. It can go by different names: predictive analytics, process mining, logical reasoning, real-time decision making, etc.

This artificial intelligence will be able to recommend a product to a customer, approve a bank loan, assign a damage report to the appropriate agent or even resolve customer service issues. Some companies rely on its analytics capabilities to automate millions of business decisions every day, making it a central part of their day-to-day operations.

Use both hemispheres of the brain

If you use creative (right-brain) AI for tasks better suited to analytical (left-brain) AI, or vice versa, you may be disappointed, if not worse. More and more organizations are making this serious mistake and risk facing criticism.

But there’s a balance to be struck: to harness the full power of AI, you need to use your left brain to drive analytical decisions and your right brain to create content. This association is necessary to achieve all the promises of an autonomous enterprise.

For example, left brain AI will be able to analyze customer engagement data and provide it observations in real time on their journey with your brand while the right brain AI will be able to use them observations create interesting content or offers tailored to the needs of the given customer, thereby significantly strengthening the relationship with the customer. This collaborative approach with AI helps optimize the customer experience and the company’s growth potential.

Towards an AI speculative bubble?

As the AI ​​hype peaks, organizations that have invested in the wrong type of AI risk having to start from scratch, and some of their suppliers may disappear. When the bubble bursts, there will be great disillusionment.

But the story doesn’t end there. We’ve barely scratched the surface of AI’s potential, whether creative or analytical. To keep up, organizations must continue to implement and test all types of AI to find a pattern that works for them.

However, they must remain cautious and carefully examine the various promises of these AIs. This is the best way to avoid the consequences of the bubble and be ready for the era of autonomous business.


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