Decision Velocity: The New Competitive Advantage in an AI-Powered Market
- Bo Roe
- May 9
- 4 min read
In an operating reality punctuated by supply chain disruptions, rapid shifts in consumer behavior, and unpredictable competitor moves, what separates the leading organizations from the strugglers isn't their ability to read the tea leaves and predict the changes, but rather their ability to respond to them with speed and precision. HBS organizational decision-making research found that the average time from insight to action in large consumer companies is 3-5 weeks. This eternity in today's fast-moving markets isn't primarily driven by a lack of analytics capability but by slow, outdated decision processes.
A series of MIT and McKinsey studies showed CPG brands with the highest decision velocities (the speed of moving from insight to action) outperformed their peers by 25% or more on key financial metrics. Seems obvious, but the underlying implication is compelling – faster smaller adjustments commercially beat slower more ‘correct’ adjustments. AI is fundamentally changing what's possible here, in unexpected ways. This isn't just about better data or analytics – it's the ability of AI to empower people to act at unprecedented speed.
From Insight Rich to Decision Advantaged
Most consumer brands today are drowning in data but starving for decisions. The explosion of not only the number of signals but the number of directions those signals are coming from has made organizations theoretically "insight rich" without necessarily making them more responsive. This gap is an insidious competitive vulnerability.
Decision advantaged organizations have redesigned their operating systems for decision speed rather than simply layering new analytical capabilities onto existing processes. Despite substantial investments in AI-powered [insert functional area], we’ve seen organizations missing critical commercial opportunities because insights sat in dashboards rather than driving action and a sense of immediacy.
Where Traditional Processes Neutralize AI Investments
Three problematic patterns appear consistently across organizations:
Hierarchical approval chains create bottlenecks that delay even simple decisions. Accenture's study of decision processes found that 62% of commercial decisions in consumer products companies require three or more levels of approval, with each level adding 3-5 days to the timeline.
Consensus-driven cultures prioritize alignment over speed, often waiting for perfect information rather than acting on sufficient information.
Periodic review cycles (weekly, monthly, quarterly) create artificial delays in decision-making, regardless of market urgency. Having a calendar driven decision-making paradigm makes event-triggered insights fairly useless.
The result is a frustrating (and familiar) pattern: companies invest heavily in AI-powered analytics that identify opportunities in real-time, only to have those insights languish in decision processes designed for a less dynamic era.
Considerations for Making the Shift
AI presents three dimensions to consider – when are we making decisions, how are the boundaries of those decisions defined, and what signals get ignored.
Move from periodic reviews to always-on decision frameworks. Set up continuous decision processes triggered by specific conditions or thresholds.
Change the guardrails of decision making dynamically. Consider shifting from hierarchical approvals to distributed decision authority set inside AI-informed guardrails. Rather than route every decision through approval layers, push authority outward, using AI to adjust guardrails to manage risk. The key is using AI not to make the decisions but to empower teams to act quickly. AI adjusts the decision bands based on changes and predefined stops.
Actively manage the noise. One of the biggest risks to manage is algorithm-driven overreactions to short-term static. Hard stops and safe defaults are all assumptions to prevent algorithmic overreaction and maintain bounded autonomy, just make sure your technologists has them covered.
More Human Judgment, Not Less
Perhaps the most counterintuitive takeaway here is that properly designed systems often enable more human judgment, not less. The popular narrative around AI assumes it will increasingly replace human decision-makers. In practice, the most effective implementations create new divisions of labor. AI handles routine decisions while elevating humans to focus where context and experience matter most.
The Defining Competitive Advantage
Decision velocity is emerging as a defining competitive advantage of the next decade. The kicker here is this advantage compounds for those who embrace it. As you consider how to build decision velocity in your organization, I'd suggest a few priorities.
This should go without saying by now, but I’ll say it anyway. Technology is an enabler, not a solution. This is a human problem.
The bottleneck is rarely analytical capability – it's almost always the decision processes that translate insights into action. If analytics IS the problem, that’s a direct solve. Be willing to fundamentally redesign decision rights, approval chains, and meeting cadences to enable speed.
Look to AI to go beyond just generating insights. It’s a tool to enable safe decision zones with thresholds that make distributed authority possible and expected.
The solutions are increasingly commoditized and accessible, and the embracing them sooner pays compounding dividends. The true differentiation comes from how you integrate it into your operating model and decision culture.
In our final post of this series, we'll examine why this wave of AI innovation is fundamentally different from previous technology cycles and why it's likely to drive more lasting transformation in consumer categories.
