Products change. Shouldn't the way we make decisions change too?

New tools, increasingly accessible technologies, and artificial intelligence embedded in everyday workflows are transforming how organizations design, build, and evolve their products.

Development cycles are getting shorter, data is more accessible, teams can automate repetitive tasks, explore new opportunities, and deliver solutions faster than ever before.

But there is a question worth asking: are organizations evolving at the same pace as the products they create?

Technology can accelerate execution without necessarily improving the decisions behind it: organizations may adopt agile methodologies, introduce AI-powered tools, and invest in digital transformation while continuing to rely on unclear responsibilities, fragmented information, and decision-making processes that struggle to keep up.

If our ability to execute changes, but the way we decide what to do remains the same, how much are we really innovating?

This question goes beyond product management. It concerns how organizations distribute responsibility, evaluate opportunities, manage uncertainty, and turn information into meaningful action. And as AI becomes increasingly integrated into our work, rethinking organizational decision-making is becoming just as important as adopting new technologies.

Why organizational change matters for product innovation

When we talk about innovation, we often focus on what is most visible: new features, improved interfaces, emerging technologies, and increasingly sophisticated digital experiences. Yet products don't evolve independently of the organizations behind them.

Every product decision reflects a broader system of priorities, responsibilities, processes, and relationships. The way teams identify problems, allocate resources, evaluate opportunities, and measure results is deeply influenced by how their organization operates.

A company might invest in modern development practices while maintaining rigid approval processes. A product team might be encouraged to experiment but have limited autonomy to act on what it learns. Data might be available across the organization without being meaningfully integrated into strategic decisions.

In these situations, technology evolves, but the organizational conditions needed to benefit from it remain largely unchanged. This creates a gap between an organization's technological capabilities and its ability to translate those capabilities into value.

Real transformation requires more than better tools. It requires better ways of working, collaborating, and making decisions.

How to improve organizational decision-making processes

An organizational decision-making process is the set of practices, responsibilities, and criteria through which an organization identifies problems, evaluates alternatives, and determines what actions to take.

In product-driven organizations, decision-making happens continuously.

Which opportunities deserve investment? Which customer problems should be prioritized? When should a team change direction? What should be measured to understand whether a solution is working?

These decisions involve different perspectives, incomplete information, competing priorities, and varying degrees of uncertainty. An effective decision-making process therefore needs more than speed.

It requires several conditions:

• Clarity: people understand which decisions need to be made, who is responsible for them, and what criteria should guide them.
• Access to relevant information: decisions are informed by evidence, user insights, business objectives, and technical constraints.
• Appropriate autonomy: teams can make decisions within clearly defined boundaries without unnecessary escalation.
• Accountability: ownership remains clear, even when decisions involve multiple stakeholders.
• Continuous learning: outcomes are evaluated, assumptions are revisited, and new information informs future choices.

These elements help organizations move beyond decision-making as a sequence of approvals and toward a process of continuous learning and adaptation.

Centralized vs. distributed decision-making

Traditional organizational structures often concentrate decision-making authority within a limited number of roles.

This can provide consistency and control, particularly when decisions involve significant risks or strategic implications. However, excessive centralization can also create bottlenecks, slow down responses, and disconnect decisions from the people closest to the problem.

Product teams, for example, frequently have direct access to user feedback, product analytics, and technical knowledge. Yet they may still depend on multiple approval layers to act on that information.

The challenge is to determine which decisions should be made where, by whom, and with what level of autonomy.

A more effective model aligns decision-making authority with the knowledge, responsibility, and potential impact associated with each choice. And this becomes particularly relevant when artificial intelligence enters the picture.

How AI supports organizational decision-making

Artificial intelligence is changing how organizations access, process, and interpret information.

AI-powered systems can help teams analyze large datasets, summarize complex documentation, identify patterns, compare alternatives, and explore possible scenarios. In product management, these capabilities can support activities such as user research synthesis, opportunity assessment, prioritization, and product performance analysis.

But improving access to information is not the same as improving the quality of decisions.

An AI system can produce a convincing analysis based on incomplete or unreliable data, it can generate multiple recommendations without understanding the organizational context in which they will be applied, it can accelerate a workflow that was poorly designed in the first place. This is why AI adoption should not begin and end with the introduction of new tools.

Organizations need to understand where AI can provide meaningful support, what information it can reliably process, and which decisions require human judgment, contextual knowledge, and explicit accountability.

AI decision support vs. human judgment

One of the most promising applications of AI is its ability to support human decision-making rather than simply automate it. AI can help teams examine a broader range of information, challenge initial assumptions, and reduce the time spent on repetitive analytical tasks.

For example, a product team evaluating a new opportunity might use AI to organize customer feedback, identify recurring needs, and compare possible approaches.

However, deciding whether that opportunity aligns with the company's strategy, justifies an investment, or creates meaningful value still requires a clear understanding of objectives, trade-offs, and responsibilities. AI-generated insights also need to be assessed for accuracy, bias, relevance, and potential risks.

The real opportunity is to design processes in which human expertise and AI capabilities complement each other.

And doing so requires organizations to rethink how decisions are structured, not merely how quickly information is produced.

Product Management is a continuous decision-making practice

Product management offers a particularly useful perspective on this challenge, because it involves making decisions under uncertainty. Teams must continuously determine which problems are worth solving, which opportunities deserve attention, and how to balance user needs, business goals, and technical feasibility.

Discovery, prioritization, roadmapping, experimentation, and outcome measurement are not isolated activities. They are interconnected parts of a continuous decision-making process.

An outcome-driven approach makes this relationship even more explicit.

Instead of measuring progress primarily through completed tasks or delivered features, teams focus on the changes they want to generate and the evidence needed to understand whether those changes are happening.

This shifts the conversation from How quickly can we build this? to Why should we build it, what outcome do we expect, and how will we know whether it worked?

AI can support this process by making information easier to access and analyze, but it cannot compensate for unclear objectives, misaligned incentives, or the absence of shared decision-making principles.

For organizations seeking to become more product-oriented, improving decision-making is therefore not a secondary concern. It is part of building the organizational capabilities needed to create sustainable value.

Rethinking decision-making at 20tab

At 20tab, we believe that evolving products also means questioning and improving the processes and organizational models behind them.

This perspective shapes how we approach digital transformation, product development, and innovation: connecting strategy, technology, and organizational change rather than treating them as separate challenges.

🟡 This experience will be at the center of the talk by Gabriele Giaccari, Head of Product & Delivery at 20tab, at Product Management Day 2026.

Rather than presenting AI as a shortcut to faster decisions, the discussion will explore how organizations can rethink the way decisions are made, supported, and evaluated.