Ideating in The New Default: A New Operating Model for Building Products

In our previous articles, we introduced The New Default as a new way of building software in an AI-driven world. The message was clear: traditional ways of designing, developing, and scaling products no longer match the reality teams face today. Now, let's focus on one of the core pillars of this new operating model: Ideating. 

More than just brainstorming, ideating in The New Default is about applying AI strategically, moving faster with smarter experimentation, and shaping ideas around real business outcomes, not just features. Let's explore the mindset behind modern ideation, practical principles teams can use immediately, and real-world perspectives straight from The New Default, supported by video expert insights that show how ideation works in practice.

What Ideating Means in The New Default

Ideating, according to The New Default, means designing with applied AI in mind from the outset, prioritizing workflow integration, and empowering teams with distributed intelligence. What makes this approach fundamentally new is its shift from abstract brainstorming to execution-oriented thinking: ideas are shaped by what AI enables, where automation fits naturally, and how teams can move faster by integrating intelligence directly into the systems.

Traditionally, ideating has been understood through the lens of design thinking as the phase where teams explore a wide range of possible solutions before narrowing them down. In classic product development, this is the moment when creativity is encouraged over constraints, allowing ideas to flow freely before they are refined into viable concepts.

The New Default reframes ideating entirely by embedding it into an AI-native development lifecycle rather than treating it as a standalone creative exercise. Ideation becomes an operational capability, tightly connected to tooling, workflows, and delivery. Not something that ends once sticky notes come down. 

Instead of treating ideation as a discrete step before development, this approach merges discovery and creation into a continuous, insight-driven cycle in which clear intent and constraints guide fast experimentation, early proof-of-value tests, and quick convergence on the concepts most likely to succeed in real workflows.

Why Ideation Is the Strategic Core of AI-Native Teams

In AI-native teams, ideation becomes the strategic core of delivery because execution is no longer the bottleneck. Decision quality is. As AI accelerates research, design, and build work, the primary constraint shifts to identifying the right problems, framing them effectively, and selecting the highest-value opportunities. Ideation is where this strategic work happens: it is the system through which signals become direction, and direction becomes action.

Unlike traditional product models, where ideation is a one-time "brainstorm" between research and delivery, AI-native teams treat it as a continuous, operational capability. AI expands the possibility space - enabling rapid synthesis of research, generation of alternatives, simulation of outcomes, and fast iteration of concepts - but that only increases the importance of human judgment in shaping intent, prioritising outcomes, and defining what should be built.

This marks a break from legacy workflows that rely on a single ideation phase followed by a handoff to design and engineering. That approach often creates misalignment, long delivery cycles, and features that fail to reflect real user or workflow needs. AI-native teams replace this with an ongoing ideation loop, where insight, experimentation, and delivery evolve together,  keeping strategy, design, and execution tightly coupled.

In this model, ideation is no longer a support work. It is the control system for speed, relevance, and leverage in AI-powered organisations.

How does AI change the Ideation Phase

AI shifts the balance by enabling rapid prototyping, immediate feedback loops, insight generation from existing domain data, and efficient validation of multiple directions simultaneously; it makes ideation more dynamic, allows specification and constraints to be refined early (primarily when AI tools can act on them), and supports autonomous teams running parallel experiments with light guardrails rather than waiting for a top-down plan.AI transforms ideating from a slow, linear process into a dynamic system of experimentation and validation. Instead of relying on one-off brainstorming followed by long delivery cycles, teams can now prototype ideas quickly, test assumptions in real time, and generate insights directly from domain data, enabling the move from opinion-driven planning to evidence-driven decision-making. 

AI also makes ideation more executable: constraints and specifications can be refined early because ideas can be turned into working experiments almost immediately. Results from McKinsey research show that AI-powered experimentation accelerates innovation and improves decision quality by enabling faster iteration at scale. Most importantly, ideation no longer has to be centralized. Autonomous teams can run parallel experiments with lightweight governance, reducing risk, cutting waste, and helping organizations invest in the ideas that actually work.

Redefining the Ideating Phase

The Ideating pillar of The New Default brings together operators, product leaders, and technologists who are actively shaping how modern teams build with AI. Rather than theorists, the voices featured are practitioners embedded in real organizations dealing with scale, complexity, and rapid change. Their shared experience gives this pillar its practical credibility: these are the experts who have seen what breaks traditional product development and what actually works in AI-native environments.

Across the featured talks, one theme stands out clearly: ideation is no longer a creative phase. It's more like an operational capability. Speakers consistently emphasize the move away from centralized planning and rigid product discovery cycles toward continuous experimentation, real-time decision-making, and team autonomy. Ideating, in this view, is not about generating ideas in isolation but about designing systems where insight, tooling, and execution are deeply connected. 

These insights directly reflect the core shift behind The New Default: ideation must evolve from brainstorming into a strategic function powered by AI, data, and workflow integration.

What Are the Core Ideating Principles of The New Default?

• Bet on Applied AI, Not Moonshots

The New Default makes a strong case for focusing on applied AI, practical use cases that leverage existing data, workflows, and distribution,  rather than chasing speculative breakthroughs. This principle reflects a pragmatic approach: teams should use AI when it immediately improves speed, quality, or economics, rather than investing heavily in unproven ideas that may never reach production.

• Build Thin, Integrate Smart

Rather than building massive AI systems from scratch, The New Default encourages thin layers of integration that embed intelligence directly into existing workflows. The message is clear: value comes from making AI usable, not from engineering complexity. Lightweight integrations reduce risk, speed up adoption, and make innovation sustainable at scale.

• Let Teams Choose Their Tools

Tooling decisions, according to The New Default, belong as close to the work as possible. Autonomous teams are best equipped to evaluate what fits their workflows, their constraints, and their goals. Instead of enforcing tooling from the top, organizations are encouraged to let teams experiment and standardize only after value has been proven in practice.

• Work in Real Time, Not in Phases

One of the strongest ideas across the ideating pillar is abandoning phased delivery models in favor of real-time collaboration and iteration. Ideation, validation, and execution are no longer sequential — they happen in parallel. AI enables this by shortening feedback loops and turning concepts into testable outputs immediately.

• Design Outcomes, Not Just Features

Finally, The New Default reframes product thinking around outcomes rather than deliverables. Ideation should focus on solving real problems, improving workflows, and creating measurable impact, not shipping features for their own sake. This principle reinforces the strategic nature of ideation: success is defined by results, not output.

How Does Ideation Actually Work in The New Default?

Autonomy Beats Central Control

One of the strongest messages across The New Default's ideating videos is that innovation does not scale through central planning. It scales through empowered teams. Instead of pushing ideation into strategy decks and annual roadmaps, The New Default argues for distributed intelligence: small, autonomous teams running experiments close to real work. When decision-making sits where execution happens, ideas are tested in reality rather than debated in abstraction.