2026: The Year AI Decides Beauty’s Winners and Losers
Apr 3rd, 2026
As trend cycles accelerate, assortments sprawl and omnichannel complexity becomes the norm, AI is emerging as a core operating lever, not a shiny experiment.
Chanelle Malambo/peopleimages.com at Adobe Stock
AI in beauty is officially past the hype phase—and 2026 is shaping up to be the year it starts separating winners from everyone else. As trend cycles accelerate, assortments sprawl and omnichannel complexity becomes the norm, AI is emerging as a core operating lever, not a shiny experiment. In this Q&A, Kevin Tong, a leader in Nesuite’s consulting AI services organization, breaks down why now is the inflection point, where AI delivers real P&L impact across forecasting, inventory and M&A, and what beauty executives must do to move from pilots to durable competitive advantage.
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AI in beauty is officially past the hype phase—and 2026 is shaping up to be the year it starts separating winners from everyone else. As trend cycles accelerate, assortments sprawl and omnichannel complexity becomes the norm, AI is emerging as a core operating lever, not a shiny experiment. In this Q&A, Kevin Tong, a leader in Nesuite’s consulting AI services organization, breaks down why now is the inflection point, where AI delivers real P&L impact across forecasting, inventory and M&A, and what beauty executives must do to move from pilots to durable competitive advantage.
"Beauty faces faster trend cycles, SKU proliferation and omnichannel complexity than most CPG categories, and by 2026 AI is mature enough to manage that volatility at scale," says Tong. "Leaders operationalize AI across forecasting, personalization and supply chains, while laggards stay stuck in campaign-level experiments."NetSuite
Q: Why is 2026 the inflection point for AI adoption in beauty and what separates leaders from laggards?
Tong: Beauty faces faster trend cycles, SKU proliferation and omnichannel complexity than most CPG categories, and by 2026 AI is mature enough to manage that volatility at scale. Leaders operationalize AI across forecasting, personalization and supply chains, while laggards stay stuck in campaign-level experiments.
Q: How can beauty brands use data- and AI-driven approaches to unlock revenue growth while improving capital allocation?
Tong: AI helps beauty brands predict trend-driven demand, optimize shade and SKU assortments and reduce overproduction that erodes margins. This enables capital to shift from excess inventory and markdowns into high-performing products and growth channels.
Q: What does a strong business case for AI look like in a beauty organization and how should ROI be defined?
Tong: A strong case links AI to fewer stockouts on hero SKUs, lower obsolescence from trend misses and higher conversion through personalization. ROI should be defined through margin lift, inventory turns and speed-to-market, not vanity AI metrics.
Q: Where should AI be embedded first in planning and core processes to deliver measurable impact?
Tong: The highest impact starts in demand forecasting, inventory planning and promotions where beauty brands struggle with volatility and short product lifecycles. These processes already hold rich data and directly affect revenue and cash flow.
Q: How can AI meaningfully support M&A decisions, from target evaluation to post-acquisition integration?
Tong: AI can analyze brand velocity, SKU profitability, customer loyalty and channel mix to identify which indie or premium brands can scale. Post-acquisition, it accelerates ERP alignment, demand planning and reporting consistency across portfolios.
Q: Why are industry-specific AI agents particularly effective for automating tasks like retail inventory management?
Tong: Beauty-specific agents understand shade depth, seasonal launches, retail sell-through patterns and replenishment constraints. This domain context allows AI to make practical inventory decisions that generic models cannot.
Q: What role do cross-functional teams play in ensuring AI initiatives move from pilots to scaled adoption?
Tong: In beauty, success requires tight alignment between merchandising, supply chain, marketing and finance, so AI reflects real-world trade-offs. Cross-functional ownership ensures AI improves execution, not just analytics.
Q: How does model context protocol (MCP) enable secure, personalized AI insights grounded in a brand’s real business data?
Tong: MCP allows AI to reason over live ERP data such as sales, inventory and financials without exporting sensitive beauty IP. NetSuite AI Connector grounds insights in actual product, customer and channel data, keeping recommendations accurate and secure.
Q: What governance, data discipline and security considerations must be in place before scaling AI across the enterprise?
Tong: Beauty brands must standardize product hierarchies, shade and SKU definitions and customer data while enforcing role-based access. AI should operate within existing ERP controls to protect formulas, supplier terms and pricing strategy.
Q: What mindset shift do beauty brand leaders need to make to move from experimental AI use to AI-driven competitive advantage?
Tong: Leaders must view AI as a core operating capability for managing trend volatility, not a digital innovation project. Competitive advantage comes from redesigning planning, merchandising and supply chains around AI-first decision-making.
