Why High-Growth Companies Must Build Decision Cultures in the AI Era

At the Exceptional Women Alliance, we help senior women leaders mentor one another through shared insight. As founder, chair, and CEO, I speak with executives shaping how organizations evolve and perform. This month, I spoke with Jennifer Renaud, CEO of Kradle LLC and a board director with more than 30 years of experience in digital innovation, commercial strategy, and customer-centered growth. She has guided companies through operating model transformation and post-integration growth.

As artificial intelligence becomes embedded across organizations, Renaud believes companies must rethink how decisions are made. Traditional hierarchies, designed for stability and control, often slow organizations when speed and adaptability matter most. Here are highlights from our discussion.

Why Traditional Decision Hierarchies Are Failing

Q: How are traditional decision hierarchies becoming less effective?

Jennifer Renaud: Hierarchies were built for predictability. They worked when markets moved slowly and information traveled through limited channels. Today, customer expectations shift quickly, competitive advantages disappear faster, and organizations are expected to respond almost immediately.

Many companies still assume better decisions come from additional layers of approval. In reality, too many approvals often create delays. When decision authority sits too high in the organization, teams wait for alignment while customer and market signals lose relevance.

Organizations rarely fail because of one bad decision. More often, they struggle because they make too few decisions to keep pace with change. Leaders are increasingly recognizing that decision quality improves when authority sits closer to the insight itself. The people closest to customers, products, and operations often understand emerging tradeoffs best.

How to Balance Speed and Alignment in Decision-Making

Q: How can companies move faster without losing alignment?

Renaud: I think about this through the lens of decision proximity—how close decision authority sits to the information needed to make a strong decision. When decisions move too far from the source of insight, context weakens and response times slow. Leaders may gain consistency, but they often lose accuracy and speed.

High-growth companies intentionally shorten the distance between signal and response. Amazon’s distinction between reversible and irreversible decisions is a strong example. Teams are encouraged to move quickly on decisions that can later be adjusted, rather than waiting for perfect consensus. Not every decision needs executive involvement or has to be perfect the first time.

Decision proximity improves both speed and judgment because the people closest to the issue usually understand the tradeoffs most clearly.

How AI Is Reshaping Organizational Decision-Making

Q: How is AI changing decision-making inside organizations?

Renaud: AI is dramatically increasing the number of signals organizations can act on. It is not just automating tasks; it is continuously generating insights across pricing, forecasting, supply chains, customer engagement, and operations. Signals that once took months to identify now appear in real time, forcing organizations to make decisions faster.

Teams can evaluate multiple variables simultaneously and identify opportunities that would have been difficult to detect manually. For example, companies can analyze supplier constraints, production efficiencies, and product compatibility together to determine the most effective manufacturing combinations.

The advantage is not simply better analysis. It is the ability to act on insight while it still matters. As AI expands the number of decisions that can be made in real time, organizations must adapt their decision-making processes—or risk falling behind.

Key Takeaways for Building a Decision Culture

• Decentralize decision authority to the teams closest to the insight.
• Shorten approval chains to improve response times and relevance.
• Use AI to generate and act on real-time signals before they lose value.
• Distinguish between reversible and irreversible decisions to balance speed and risk.
• Focus on making more decisions, not just perfect ones, to keep pace with change.