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Introduction: Higher speeds, higher stakes

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The product development process is undergoing a major shift as organizations strive to launch new offerings with all the hallmarks of greatness under tighter cost and time pressures. Many are looking to generative AI (GenAI) to meet these demands, but it’s not always clear how to apply rapidly evolving technology to create products with unique value propositions that foster a sustainable competitive advantage.��

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In this issue, ������ AI experts discuss key GenAI applications across the product development lifecycle, and share the best practices that enable businesses to drive product differentiation and business value with GenAI while avoiding its very real and present risks.

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Generative AI adoption in product development set to accelerate through 2032

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Section i. Augmentation along the product development spectrum����

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Unlike classic machine learning models, GenAI has the capability to be creative, and can partner with product development teams across all stages of innovation, from synthesizing large amounts of information quickly, to proposing, visualizing and even validating ideas. GenAI applications vary across industries, with those that face less regulatory, IP and privacy constraints having more flexibility and room to experiment.

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Section ii. Best practices and principles; balancing the AI and human roles��

Contrary to the oft-held belief that AI adoption requires significant data resources and advanced skills, the barriers to leveraging GenAI for tangible product development gains are lower. The biggest hurdles lie in the human mind, and letting go of established principles and preconceived notions is needed to collaborate with GenAI and produce the best results. Though AI’s presence as a copilot is changing the role of developers and the makeup of product teams, ultimate creative direction – and the associated responsibilities – will rest with humans.

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Section iii. Avoiding negative consequences

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Recognizing GenAI’s flaws and limitations and taking steps to mitigate them are as critical as the need to foster AI fluency. Organizations must identify where guardrails and human intervention are necessary to uphold ethical and compliance standards. On the positive side, while GenAI will create governance problems, it also has the potential to help solve them.

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Section iv. How the use of generative AI will evolve����

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As GenAI technologies and organizations’ ability to use them mature, product development roles are likely to converge and drive deeper integration with disciplines such as data science, behavioral economics and even philosophy. Design methodologies will also need to evolve to take AI into account, while models themselves will become more specialized and industry-specific. The reward for enterprises that manage the journey will be the ability to harness more minds and collective creativity in a fast, effective and highly decentralized product decision-making process.

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