AI has fundamentally changed how product decisions are made. Hypotheses can be tested faster, user feedback can be synthesised in minutes, and early signals are available almost on demand. For product managers, this promises speed, autonomy and momentum – all critical in modern product organisations.

This acceleration is not driven by AI alone. Connected and smart products now generate continuous streams of usage data, behavioural signals and in-product feedback. Combined with AI-powered analysis, these continuous signals are no longer episodic or project-based – they are always on.

As a result, product teams can observe, interpret and act in near real time – often faster than organisations can align on what those signals actually mean. Decisions that once required formal research cycles are now made within days or even hours. From a product perspective, this feels like genuine progress.

And in many ways, it is.

However, progress at the level of individual teams does not automatically translate into better decisions at organisational level. As continuous signals multiply and AI accelerates interpretation, shared market understanding does not always keep pace. What appears as clarity locally can, over time, fragment across the organisation.

The challenge is therefore not a lack of data or tools. It is how this new speed reshapes collaboration between product teams and insights – and whether organisations are prepared to design that collaboration deliberately.

Why continuous signals and AI feel like a breakthrough for product teams

From a product management perspective, the appeal of continuous signals amplified by AI is clear. Insights are no longer something to be requested, waited for and interpreted retrospectively. They are embedded directly in day-to-day product work.

Usage data from connected products, in-app feedback and customer comments can be combined and analysed quickly. AI helps structure qualitative input, surface recurring themes and translate signals into actionable hypotheses. Product teams gain a level of immediacy and control that was previously difficult to achieve.

This changes how product teams operate. Decisions become more iterative, experimentation accelerates and ownership shifts closer to those building the product. In fast-moving environments, this autonomy is not just attractive – it is rational.

Seen from this angle, AI-enabled DIY insights are not a problem. They are a logical response to new capabilities, growing data availability and increasing time pressure. The more important question is how these locally generated signals connect back to shared learning, long-term market understanding and organisational decision-making.

When local clarity undermines global learning

Continuous signals work exceptionally well at the level of individual product teams. They create clarity and momentum exactly where day-to-day decisions are made. For product managers, this can be powerful – and often career-defining.

At organisational level, however, the perspective shifts. Senior decision-makers are rewarded not for optimising individual decisions, but for building sustainable success over time. That requires shared market understanding, comparability across products and a clear view of how customer needs evolve.

When insights are generated locally – using similar data sources but different questions, prompts and timeframes – organisations may move faster in the short term, yet struggle to learn systematically. What looks like strong evidence for one product decision becomes harder to integrate into a coherent picture that supports portfolio, investment and strategy decisions.

For product managers aspiring to broader responsibility, this distinction matters. Long-term impact is not created by speed alone, but by turning continuous signals into cumulative organisational learning.

The hidden risks of continuous signals amplified by AI

The risks of AI-supported DIY insights rarely surface immediately. Early results often look convincing. Over time, however, structural issues tend to emerge.

Confirmation bias becomes easier to scale as teams naturally explore questions aligned with their current hypotheses. Comparability erodes when similar data is analysed with different prompts and lenses. Decision logic also becomes harder to explain when AI-generated summaries replace transparent analytical steps.

More alignment meetings do not resolve these issues. They are not communication problems, but structural ones. Continuous signals amplified by AI require shared decision logic, not just shared discussion.

Designing the interface: when speed is enough – and when it isn’t

The solution is not to slow product teams down or to recentralise insight generation. Speed is a competitive advantage, and continuous signals supported by AI are here to stay.

What organisations need instead is a deliberately designed interface between product teams and insights. That interface clarifies when speed is sufficient – for example in early exploration or tactical optimisation – and when decision-grade insight is required, such as for major investments or scaling decisions.

In this model, insights teams do not act as gatekeepers. Their role is to make continuous signals comparable, contextualised and decision-ready. Product teams retain autonomy, while organisations preserve their ability to learn over time.

The shift is subtle, but powerful: from generating more insights to designing how insights flow into decisions.

Continuing the conversation in practice — at succeet26, March 18–19

These and many other pressing insight-related topics are discussed at succeet26 [link to https://www.succeet.de/en/succeet26-visitors/welcome/], where corporate insights leaders, product decision-makers and research experts come together to explore how AI, governance and decision quality play out in practice.

The event features an extensive conference programme with 165 sessions [link to https://succeet-event.de/frontend/page/program], more than 60 of them in English, alongside over 130 exhibitors from across the insights industry, offering perspectives that go beyond tools and trends.

As a courtesy to the Insights Platforms community, readers are invited to attend the trade show free of charge via the registration link below. Free tickets [link to https://succeet-event.de/registration] for succeet26 are available with voucher code SUC26-MRS.

About the Author:

Joachim Klink is managing partner of succeet and brings around 30 years of experience in international corporations, with global P&L responsibility for products, offerings, and market segments. He has worked both as a buyer of market research and insights and as a leader in building and scaling data-driven products and services.

He holds a degree in Mechanical Engineering (Dipl.-Ing.) from the University of Stuttgart and spent ten years in consulting roles at the Fraunhofer Society and T-Systems. This was followed by more than 20 years in senior leadership positions at Hewlett-Packard and Deutsche Telekom / T-Systems. His focus is on translating insights, data, and technology into measurable business impact