The new beauty battleground: Turning AI insights into products at speed

Execution is the new differentiator

AI is now combining social, search and commerce signals into a near real-time view of demand, often before it shows up in sales. For brands, this means visibility is no longer the constraint. They can see emerging ingredients, viral routines and creator-led demand forming as it happens.

The challenge is what happens next. Most organizations are not set up to respond at the same speed. The constraint has shifted from insight to execution. The time it takes to translate signals into product, content and distribution decisions is now the primary barrier to growth.

When insight outpaces action

AI adoption across beauty is accelerating, particularly in demand sensing, social listening and content generation. Leading organizations are already using these capabilities to move beyond passive insights and into commercial action.

Estée Lauder’s AI-enabled Trend Studio, for example, detects emerging trends, recommends products and generates marketing content to accelerate response time from signal to market action.

Most organizations can see what is happening. Fewer can respond at the speed required. What we therefore see consistently is an increasing asymmetry:

• Brands can now detect demand faster than they can act on it
• Trends are spotted early, but products arrive late
• Content is approved after the moment has passed
• Media spend is locked before demand shifts

Inventory fails to align with where interest actually materialises. The result is a widening gap between signal and execution; and this gap is becoming the biggest barrier to the growth of beauty brands

Demand is shaped by culture and captured in real time

In beauty, demand is increasingly created through cultural mechanisms that are observable and measurable.

These include:

• Creator endorsement driving rapid product adoption
• Short-form content amplifying specific ingredients, formats and routines
• Consumers discovering, validating and purchasing within the same interaction

Platforms describe this as ‘discovery commerce,’ where content, search behavior and purchase intent are tightly linked.

This matters because it changes how demand behaves:

• Demand emerges and scales quickly
• Relevance is short-lived and continuously contested
• Brand loyalty is conditional on current performance
• Demand is harder to forecast using historical patterns

Discovery, validation and purchase are increasingly collapsing into a single interaction. As a result, the window to capture demand is shrinking. If organizations cannot respond within that window, demand does not wait, it simply moves to competitors that can.

Organizations are still built for a slower market

While demand has become faster and less predictable, most organizations are still structured around older assumptions. Planning assumes stability, innovation assumes time and governance assumes that more control reduces risk. Those assumptions no longer hold consistently.

Consumers are less predictable, switch brands more freely and make decisions based on relevance, availability and value in the moment. At the same time, 54% of beauty executives cite uncertain demand as the biggest risk to growth.

The result is a structural mismatch between how demand behaves and how organizations operate.

This creates friction at every stage:

• Insight is generated but not embedded into decisions
• Marketing, product and supply respond at different speeds
• Decisions move through multiple layers and approvals
• Innovation is validated internally before reaching the market

AI amplifies this tension. It increases the volume and speed of insight, but does not remove the constraints that slow execution. In fact, many organizations are still optimized for risk reduction rather than responsiveness when it comes to AI.

The bottleneck has moved from insight to speed

For years, organizations invested in improving visibility. Today, many have more insight than they can act on. The constraint has shifted. Success increasingly depends on how quickly the organization can orchestrate coordinated action across product, marketing and supply chain.

That shift has direct commercial consequences:

• Demand spikes are missed rather than captured
• Media investment becomes misaligned to actual demand timing
• Discounting is used reactively to correct inventory imbalances
• Working capital is tied up in the wrong products

At the same time, organizations that move quickly are being rewarded.

e.l.f. Beauty delivered 28% net sales growth in FY2025, supported by its ability to respond rapidly to trends and cultural signals. The company’s $800 million acquisition of Rhode (with a further $200 million earnout) reflects the premium placed on brands that can create and capture demand at speed. The economics are shifting. The cost of being slow exceeds the cost of being imperfect.

What differentiates organizations that keep up

The difference is rarely access to better insight. It is how the organization is designed to respond.

Organizations that consistently capture demand, share four characteristics:

• They align around a single, connected view of demand across teams
• They move from insight to decision with clear ownership and minimal delay
• They test and learn in market rather than relying on lengthy internal validation
• They continuously adapt products, content and investment based on real-world outcomes

In effect, they create a continuous cycle of sensing, acting and learning. Challenger brands have adapted faster, often because they are structurally simpler. They focus on fewer priorities, move quickly and stay close to demand signals.

Incumbents rarely lack capability. More often, they struggle because scale introduces complexity, slowing the translation of signals into action.

Planning shifts from fixed cycles to continuous reallocation

Improving speed requires changing how the organization works in practice. Static plans cannot keep pace with dynamic demand. Leading organizations are moving towards a rolling planning model that:

• Integrates social, sales and supply signals into a single demand view
• Reallocates media spend, inventory and assortment focus dynamically
• Adjusts channel prioritization based on real-time performance
• Defines clear guardrails for what can change quickly

This allows resources to move with demand, rather than being fixed in advance.

Decision-making is designed around speed and ownership

The primary constraint is often not identifying opportunities, but acting on them.

Delays occur when:

• Ownership is unclear
• Decision rights are fragmented
• Approvals are sequential rather than parallel
• There are no expectations on decision speed

Organizations that move faster address this directly because they define clear decision rights, reduce approval layers and introduce expectations for how quickly decisions should be made and executed. Crucially, they operate with an acceptance that decisions will often be made with incomplete information.

Test-and-learn happens in market, not before it

Traditional innovation models prioritize validation before launch. This slows down response.

Leading organizations shift towards in-market learning:

• Launching quickly with a focused proposition
• Using real consumer response to guide iteration
• Scaling what works and exiting what does not early

This approach prioritizes speed over perfection. AI supports this by accelerating upfront testing and iteration, but the impact comes from embedding it into execution rather than treating it as a separate capability.

Insight is embedded into how work happens

Many organizations have access to the same demand signals, but those signals often sit outside the flow of decision-making. The organizations creating the most value are those that embed insight directly into workflows, planning processes, prioritization forums and execution decisions, rather than treating it as a separate research function.

Closing the execution gap requires:

• Connecting data across social, sales and supply into a unified view
• Embedding that view into weekly decision forums
• Using AI outputs as direct inputs into product, marketing and supply decisions

This depends on having a foundation of connected and trusted data. The goal is to reduce the distance between seeing and acting.

Speed is becoming the defining advantage

Beauty is not short of demand. It is short of organizations that can respond to the insights they are gathering in time by reducing the distance between signal, decision and execution. The differentiator is turning insight into action at pace.

Organizations that can respond to signals as they emerge, adapt continuously and stay aligned to culture will capture disproportionate value. Those that cannot will continue to generate insight without capturing its value. Speed is not an operational metric, it’s a strategic imperative.