Thanks to the magic of AI, developers can complete tasks in half the usual time. It’s not just a dream; it’s happening in businesses’ product and engineering departments like yours.

But hold on—it’s not all about splashing the cash on AI. Getting AI to work like a charm in your job isn’t just luck—it’s about smart moves and experimenting.

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• Product and engineering departments lead AI investment, with AI potentially doubling developer productivity.
• Planning for AI is like getting ready for a big party: you need the right amount of snacks (budget), the best music (training), and guests who love to dance (experienced staff).
• Trying out AI first is like test-driving a car: you want to ensure it’s cool and worth the money before getting it for the team.

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Enterprises Gain Efficiency with AI in Engineering and Product Development

Key Points:

• Imagine AI as a speed booster, making developers do their work twice as fast!
• Smart planning for AI means thinking about money and learning needs—it’s super important.
• Newbies might get an even bigger kick from AI tools than the tech wizards.
• Conducting a proof of concept is essential before full AI implementation.

Business teams making cool stuff are using AI to work smarter and faster. It’s like giving their work a turbo boost! A McKinsey report highlights that with generative AI, developers may see a reduction in task completion time by as much as half.

However, the successful integration of AI is not guaranteed by investment alone.

Companies need to think about many things to get the most out of AI. They have to figure out how much money to spend, whether AI is better than hiring more people, and how to teach their teams to use it correctly. And guess what? New devs might learn more cool stuff from AI than the old pros.

If they don’t plan well, things could go south. They could waste money, end up with projects that don’t wow anyone, or worse, their team members could pack up and leave.

Over the past year, software analytics firm Waydev has been actively experimenting with the best practices for implementing generative AI in software development.

Implementing a Proof of Concept:

Enterprises should validate AI tools through a proof of concept before total commitment.

An Engineering Management Platform (EMP) or Software Engineering Intelligence Platform (SEIP) should be utilized to monitor advancements and results.

You need a bunch of different checks to make sure everything stays top-notch.

A proof of concept should encompass a range of tasks and developer roles for a comprehensive assessment of AI tool performance.

Big bosses in charge of tech stuff, CIOs, have a tough choice: hire new people or spend money on AI gadgets. Trying out AI first helps them see if it’s as awesome as they hope so they can make smart money moves.