09/20/2024 | Press release | Distributed by Public on 09/19/2024 23:54
\r\nSeptember 20, 2024
September 20, 2024
From ideation through launch, generative AI can introduce levels of productivity to product engineering that product businesses simply cannot ignore.
\r\n"}}" id="text-6cdf7e051b" class="cmp-text">From ideation through launch, generative AI can introduce levels of productivity to product engineering that product businesses simply cannot ignore.
Product engineering has seen dramatic change in the last few years, from agile methodologies, DevOps and design thinking in software development, to modular design principles, digital twins and 3-D printing in hardware.
\r\nThe result has been a product engineering renaissance that's given rise to newly invigorated and turbo-charged product companies.
\r\nBut even more change is ahead for hardware and software businesses with the arrival of generative AI. From ideation through launch, generative AI can introduce levels of productivity to product engineering that product businesses simply cannot ignore.
\r\nUsing generative AI, for instance, product designers can explore ideas beyond the realm of their own imaginations and experiences, then develop initial design concepts significantly faster than with traditional methods.
\r\n"}}" id="text-f313f51e02" class="cmp-text">Product engineering has seen dramatic change in the last few years, from agile methodologies, DevOps and design thinking in software development, to modular design principles, digital twins and 3-D printing in hardware.
The result has been a product engineering renaissance that's given rise to newly invigorated and turbo-charged product companies.
But even more change is ahead for hardware and software businesses with the arrival of generative AI. From ideation through launch, generative AI can introduce levels of productivity to product engineering that product businesses simply cannot ignore.
Using generative AI, for instance, product designers can explore ideas beyond the realm of their own imaginations and experiences, then develop initial design concepts significantly faster than with traditional methods.
In product research and design alone, McKinsey estimates gen AI could unlock $60 billion in productivity.
\r\n"}}">In product research and design alone, McKinsey estimates gen AI could unlock $60 billion in productivity.
This applies to both new products and adaptations of existing ones. For example, if a business needs to modify an existing product for a particular customer segment, product engineers could ask a gen AI agent to analyze the existing product and suggest alternatives. As the product modifications took shape, the gen AI agent could continue to refine the product, check it against regulatory requirements, generate blueprints and prototypes, and keep other team members in the loop, including sales and marketing.
\r\nAs for software development, it's not a question of whether product businesses should embrace generative AI-but how best to do it.
\r\n
From requirements analysis and systems design and coding, to testing, to deployment and maintenance, we're seeing productivity gains of 30% to 40% in the software development lifecycle with the use of generative AI. We expect that in the next few years, those gains will spike even higher.
\r\n
This applies to both new products and adaptations of existing ones. For example, if a business needs to modify an existing product for a particular customer segment, product engineers could ask a gen AI agent to analyze the existing product and suggest alternatives. As the product modifications took shape, the gen AI agent could continue to refine the product, check it against regulatory requirements, generate blueprints and prototypes, and keep other team members in the loop, including sales and marketing.
As for software development, it's not a question of whether product businesses should embrace generative AI-but how best to do it.
From requirements analysis and systems design and coding, to testing, to deployment and maintenance, we're seeing productivity gains of 30% to 40% in the software development lifecycle with the use of generative AI. We expect that in the next few years, those gains will spike even higher.
By 2025, 50% of all software product development teams will leverage AI-based design tools, according to Gartner, optimizing workflows and accelerating time-to-market.
\r\n"}}">By 2025, 50% of all software product development teams will leverage AI-based design tools, according to Gartner, optimizing workflows and accelerating time-to-market.
The most common areas in which we're seeing businesses apply generative AI to software development include:
\r\nThe most common areas in which we're seeing businesses apply generative AI to software development include:
We worked with a healthcare client, for instance, with highly complex health plan requirements. Traditionally, the company would need to print out the requirements document and manually highlight all the business rules that needed to be extracted, coded, tested and deployed.
\r\nNow, a generative AI program automatically extracts the business rules, creates a configuration file, applies it to the system and tests it. Software developers are still involved, but the job is far less manual, resulting in dramatic acceleration and productivity gains.
\r\nIt's important to note that realizing generative AI productivity gains in software development requires more than just giving a developer a gen AI assistant or copilot. Developers should also be armed with reusable templates and libraries of leading frameworks and models to streamline workflows and ensure consistency. Automated security reviews and compliance checks can further ensure adherence to standards, and code explainability can improve resilience and reliability.
\r\nProduct engineers should also be thoroughly trained in how and when to use these tools, as well as how and when to check gen AI outputs. For instance, it may be tempting to provide junior-level staff with these tools, as they may jump to higher levels of productivity than more senior developers. This was apparent in a recent Stanford and MIT study in which call-center reps who used gen AI were 14% more productive on average than those who didn't. The gains were even greater among workers who had been on the job for less than a few months. However, junior-level staff will also need more assistance in vetting what the tools produce.
\r\nGenerative AI in software development will also require a fundamental culture change. Traditional coding is very deterministic: you write a set of instructions, and those instructions produce an output.
\r\nWith gen AI, the process is much more organic. As inputs change and the models continue to learn, the outputs need to be checked and validated on an ongoing basis. This requires a whole new way of thinking and a new approach to building out the software engineering organization.
\r\nFinally, businesses must ensure the code the AI generates is secure and free of copyright issues. If the code base the AI was trained on had a security issue, the code that's generated will potentially have it too.
\r\nBusinesses can avoid such scenarios by understanding where their code comes from, what it was trained on, the models that were used, and where it's deployed. And if a problem is detected, they need the ability to stop it in its tracks.
\r\nThe immediate productivity benefits of generative AI in product engineering will collectively contribute to a more streamlined and rapid development process in which teams can build, maintain and update applications at an accelerated pace.
\r\nBut the end goal isn't necessarily cost savings or labor reductions. Instead, it's the ability to do more in the same amount of time. This is essential for product businesses-getting features and functions and products into the market more quickly. In a McKinsey study, generative AI accelerated product time-to-market by 5%, improved product managers' productivity by 40% and boosted employee experience by 100%.
\r\nFurther, the use of gen AI in product engineering will improve the quality of life for software developers, freeing them from mundane tasks to focus on more complex and engaging ones-such as problem solving and working with generative AI to get it done.
\r\nIn the end, it will be less a matter of competitive advantage for product businesses that best use generative AI than a vast disadvantage for those that don't.
\r\nTo learn more about how product engineering organizations can take advantage of generative AI, tune into our product innovation podcast series.
\r\n"}}" id="text-1bd28802cc" class="cmp-text">We worked with a healthcare client, for instance, with highly complex health plan requirements. Traditionally, the company would need to print out the requirements document and manually highlight all the business rules that needed to be extracted, coded, tested and deployed.
Now, a generative AI program automatically extracts the business rules, creates a configuration file, applies it to the system and tests it. Software developers are still involved, but the job is far less manual, resulting in dramatic acceleration and productivity gains.
It's important to note that realizing generative AI productivity gains in software development requires more than just giving a developer a gen AI assistant or copilot. Developers should also be armed with reusable templates and libraries of leading frameworks and models to streamline workflows and ensure consistency. Automated security reviews and compliance checks can further ensure adherence to standards, and code explainability can improve resilience and reliability.
Product engineers should also be thoroughly trained in how and when to use these tools, as well as how and when to check gen AI outputs. For instance, it may be tempting to provide junior-level staff with these tools, as they may jump to higher levels of productivity than more senior developers. This was apparent in a recent Stanford and MIT study in which call-center reps who used gen AI were 14% more productive on average than those who didn't. The gains were even greater among workers who had been on the job for less than a few months. However, junior-level staff will also need more assistance in vetting what the tools produce.
Generative AI in software development will also require a fundamental culture change. Traditional coding is very deterministic: you write a set of instructions, and those instructions produce an output.
With gen AI, the process is much more organic. As inputs change and the models continue to learn, the outputs need to be checked and validated on an ongoing basis. This requires a whole new way of thinking and a new approach to building out the software engineering organization.
Finally, businesses must ensure the code the AI generates is secure and free of copyright issues. If the code base the AI was trained on had a security issue, the code that's generated will potentially have it too.
Businesses can avoid such scenarios by understanding where their code comes from, what it was trained on, the models that were used, and where it's deployed. And if a problem is detected, they need the ability to stop it in its tracks.
The immediate productivity benefits of generative AI in product engineering will collectively contribute to a more streamlined and rapid development process in which teams can build, maintain and update applications at an accelerated pace.
But the end goal isn't necessarily cost savings or labor reductions. Instead, it's the ability to do more in the same amount of time. This is essential for product businesses-getting features and functions and products into the market more quickly. In a McKinsey study, generative AI accelerated product time-to-market by 5%, improved product managers' productivity by 40% and boosted employee experience by 100%.
Further, the use of gen AI in product engineering will improve the quality of life for software developers, freeing them from mundane tasks to focus on more complex and engaging ones-such as problem solving and working with generative AI to get it done.
In the end, it will be less a matter of competitive advantage for product businesses that best use generative AI than a vast disadvantage for those that don't.
To learn more about how product engineering organizations can take advantage of generative AI, tune into our product innovation podcast series.
We're here to offer you practical and unique solutions to today's most pressing technology challenges. Across industries and markets, get inspired today for success tomorrow.
"}}" id="text-7b77c4d702" class="cmp-text">We're here to offer you practical and unique solutions to today's most pressing technology challenges. Across industries and markets, get inspired today for success tomorrow.