How to guide your engineering team through the generative AI hype

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Over the past six months, AI, especially generative AI, has been thrust into the mainstream by OpenAI's release of ChatGPT and DALL-E to the mainstream. For the first time, anyone with an internet connection can interact with an AI that feels smart and useful - not just a cool prototype that's interesting.

With this elevation of AI from sci-fi toy to real-life tool comes a mixture of widely publicized concerns (should we pause AI experiments?) and excitement (four-day work week!). Behind closed doors, software companies are scrambling to integrate AI into their products, and engineering leaders are already feeling the pressure of higher expectations from boards and customers.

As an engineering leader, you will need to prepare for the increasing demands placed on your team and make the most of new technological advancements to outpace your competition. Following the strategies outlined below will set you and your team up for success.

Turn your ideas into realistic projects

Generative AI is nearing the peak of the Gartner Hype Cycle's Inflated Expectations. Ideas start flowing. Your peers and the board will suggest new projects that they see as opportunities to ride the AI ​​wave.

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Whenever people think big about what's possible and how technology can enable them, that's good for engineering! But here is the difficult part. Many ideas that come to your desk will come with a how, which may not be rooted in reality.

Presumably you can just plug a model of OpenAI into your application and, presto, high-quality automation. However, if you unpack the how and extract the what from the idea, you might discover realistic projects with strong stakeholder support. Skeptics who previously doubted that automation was feasible for certain tasks can now consider new possibilities, regardless of the underlying tool you choose to use.

Opportunities and challenges of generative AI

All-new headline-capturing artificial intelligence is really good at quickly generating text, code, and images. For some applications, the potential time savings for humans is enormous. Yet it also has serious weaknesses compared to existing technologies. Let's take ChatGPT as an example:

ChatGPT does not have...