Perspectives

Edition #38

From rescue to resilience: AI's role in product futures

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Executive summary

New technologies, the pace of change and competitive demands are all forcing businesses to develop, release and upgrade products faster – and quality and user experience can suffer as a result. As more companies grapple with product rescues and recoveries, they are learning these interventions can be delicate, and come with challenges of their own.

In this edition of Perspectives Thoughtworks product development and AI leaders explore how to rehabilitate products that are on the brink, and coax successes out of near-failures. Based on their experiences with leading enterprises in financial services and retail, they outline the frameworks, practices and AI-enabled tools that reduce the number of products that need rescuing, and build long-term capacity for innovation.

Key takeaways

Product problems often begin in the design stages. ‘Soft’ factors such as communication breakdowns, failure to consider the context of the end-user or a lack of clear end-goals can do more to damage a product’s prospects than any talent or technology shortfalls.

Products need governance backed by engineering to thrive. The rise of AI means companies need to create and streamline a product lifecycle that’s capable of distilling the best ideas, delivering quickly to support multiple experiments, and building on, adapting or discarding early-stage products based on the user response.

Structure informs effective product decision-making. Clearly defined processes like the loops framework and decision factories provide lenses through which teams can identify where product frictions or failures originate, and apply the right approaches to address them. These frameworks have become more important than ever in a competitive environment where product and strategic decisions need to be made more often, and faster.

AI agents create, as well as solve, product problems. As more enterprises come under board pressure to adopt AI, and the hype around AI agents grows, a new phenomenon has emerged: ‘agent-washing.’ This refers to the adoption of AI agents to fix or prevent product challenges that would often be better addressed by traditional methods. When products aren’t AI-ready, agent-washing can damage functionality or experience to the extent that a rescue is required.

AI is an accelerant, but no replacement for engineering excellence. Whether by disseminating vast amounts of information, automating testing or credibly modelling the responses of user groups, AI presents multiple opportunities to streamline and elevate product development and rescue practices. But unless these advantages are underpinned by a coherent product vision and engineering effectiveness, it’s likely that they’ll only speed up the rate at which the enterprise builds fragile products, or the wrong things altogether.