Product & Innovation Testing: From Guesswork to Prediction

Key Takeaways from the Product & Innovation Testing Showcase

What Is Product & Innovation Testing and Why Does It Matter?

Product & Innovation Testing is the set of research and validation practices used to evaluate ideas, concepts, products, packaging, and creative before they reach the market. At its core, it exists to answer one critical question: Should we move forward, and if so, how?

Historically, innovation testing relied heavily on surveys, concept tests, and staged consumer feedback. While effective, these approaches were often slow, expensive, and backward-looking. In today’s environment, where innovation cycles are compressed and portfolios are under constant pressure, that model no longer holds.

Modern product and innovation teams need:

• Earlier signals before development dollars are committed

• Faster feedback loops without sacrificing rigor

• Predictive confidence, not just descriptive insight

That evolution was the central theme of the Greenbook Insights Tech Showcase on Product & Innovation Testing, held January 14, 2026. Each participating company addressed a different point along the innovation lifecycle, collectively demonstrating how AI, predictive analytics, and operational scale are transforming testing from a reactive safeguard into a proactive decision engine.

From Idea to Evidence: How Innovation Testing Is Evolving

Across the showcase, one message was consistent: the future of innovation testing is not about running more research, but about moving from idea to evidence faster and with greater certainty.

Here’s how four technology providers are reshaping that journey.

Predicting Demand Before the First Prototype

Black Swan Data

Innovation stage: Idea screening and early concept evaluation

One of the most costly failure points in innovation is advancing ideas that never had real demand. Black Swan Data addressed this challenge by showcasing its NEW Concepts Tool, developed in partnership with PepsiCo.

Rather than relying on historical data or stated intent alone, Black Swan analyzes more than 500,000 real-time trend signals using generative AI to evaluate concepts at the earliest stage possible.

Why this matters

• Concepts are scored against live consumer demand signals

• Weak ideas are filtered out before development begins

• PepsiCo is already using the tool to support its “Fewer, bigger, better” innovation strategy, ensuring ideas start closer to future demand, not past behavior

This approach reframes innovation testing as a predictive gate, not a post-hoc checkpoint.

From Drivers to Decisions

Cambri

Innovation stage: Concept refinement and launch decision-making

Once a concept is viable, teams must understand why it is likely to succeed or fail. Cambri demonstrated Launch AI, a platform designed to move teams from insight to action with speed and clarity.

What differentiates Cambri is its grounding in real-world Point of Sale (POS) data, rather than relying solely on survey-based feedback.

Why this matters

• The platform reports 75%+ accuracy in predicting launch success

• Driver analysis pinpoints which elements strengthen or weaken a concept

• Teams receive clear direction on what to optimize before a product reaches market

Here, innovation testing becomes a decision system, not just a diagnostic tool.

Physical Product Testing at Digital Speed

Highlight

Innovation stage: Product validation and in-market readiness

While AI can predict trends and success drivers, physical products still need to be used in real-world conditions. Historically, In-Home Usage Testing (IHUT) has been slow and operationally complex. Highlight showed how this no longer has to be the case.

Its Product Intelligence Platform manages everything from participant targeting to logistics and data integration, enabling faster, higher-quality physical testing.

Why this matters

• 90%+ completion rates ensure reliable data

• Turnkey logistics allow brands to test performance, positioning, and messaging simultaneously

• Scalable governance modernizes the traditional “Testing Mountain”

Highlight’s approach shows how innovation testing can maintain human realism without operational drag.

Democratizing Neuroscience for Creative Optimization

Neurons

Innovation stage: Creative and communication effectiveness

Neuroscience has long been viewed as the gold standard for predicting attention and impact, but cost and complexity limited its use. Neurons demonstrated how AI trained on the world’s largest proprietary neuroscience database is changing that reality.

During the showcase, Neurons showed how brands can access predictive attention metrics without lab studies or long timelines.

Why this matters

• Immediate insights into advertising effectiveness

• Creative assets are optimized before launch, not after

• Brands bypass the traditional 15-year barrier associated with neuro-marketing

This expands innovation testing beyond products into communication and brand impact.

What This Means for the Future of Product & Innovation Testing

The overarching shift highlighted at the showcase is clear: innovation testing is moving from guessing to predicting.

Across all four approaches, the goal is the same:

• Faster go/no-go decisions

• Reduced risk earlier in the process

• Greater confidence across innovation portfolios

Whether through trend prediction, POS-grounded AI models, scalable physical testing, or accessible neuroscience, these platforms demonstrate how modern innovation testing reduces cost while increasing creative confidence.

As organizations continue to scale the “Testing Mountain,” the message from this showcase is unmistakable: with the right technology, innovation becomes less about luck and more about foresight.

Ready to see what’s next?

Register for an upcoming Greenbook Insights Tech Showcase to see live demos and the tools shaping the future of research. Register here 👉