Why Businesses Need to Embrace AI Now

AI is no longer an emerging idea or future opportunity. It is already producing measurable business results for global brands that are using it with intent. L’Oréal, a century-old leader in beauty, is a clear example. Its success with AI shows how practical, focused adoption can reshape customer experience, accelerate decision-making, and create a foundation for long-term growth.

This is not about hype. It is about execution.

L’Oréal’s AI initiatives span diagnostics, product recommendations, marketing personalization, and supply chain forecasting. The company is not trying to reinvent itself as a tech startup. It is solving real problems using available tools, integrated into its operations. This approach is what most businesses need right now, not more strategy decks but action tied to outcomes.

Delaying AI adoption has real costs. While some companies are still evaluating options, others are already learning what works, gathering data, improving accuracy, and delivering value. That advantage compounds over time.

The risk is not falling behind on technology. It is falling behind on decision speed, customer relevance, and operational flexibility. Every manual process, every missed signal, every slow decision becomes more expensive when your competitors are moving faster with better tools.

L’Oréal did not wait for perfect infrastructure or ideal conditions. It started with specific use cases where impact could be measured and scaled.

How L’Oréal Is Using AI to Win

SkinConsult AI

L’Oréal built a skin diagnostic engine trained on thousands of clinical images and selfies. It powers personalized skincare recommendations across multiple markets. Customers get product advice based on visible skin concerns, increasing confidence and reducing hesitation. The result is higher engagement and stronger conversion in digital channels.

ModiFace Virtual Try-On

This tool allows users to test makeup and hair color using their smartphone camera. Powered by deep learning and computer vision, it is integrated into L’Oréal’s websites and platforms like Amazon and Snapchat. Customer interaction surged, and the company captured valuable behavioral data that now feeds back into marketing and product planning.

Appier for Targeted Marketing

L’Oréal uses Appier’s AI to personalize ecommerce campaigns based on user behavior. It automates recommendations and offer timing, resulting in a measurable increase in revenue without increasing media spend.

Tidal for Paid Media

In the Nordics, L’Oréal automated campaign optimization using Tidal. The platform improved media efficiency and campaign performance while reducing manual input from marketing teams.

Beauty Genius and Noli Platform

From virtual beauty assistants to end-to-end product recommendations powered by AI, L’Oréal is using layered data such as purchase history, skin diagnostics, and sentiment to refine everything from messaging to fulfillment. These tools are practical and tied directly to business outcomes.

Generative AI with IBM

Partnering with IBM, L’Oréal is now building models to accelerate sustainable product development. These models predict ingredient performance and compatibility to speed up formulation processes. This is not just innovation. It is product pipeline efficiency.

What This Means for Your Business

L’Oréal’s approach is not exclusive to the beauty industry. The tools it uses such as diagnostics, personalization engines, campaign optimizers, and predictive models are available across sectors. What matters is how they are applied.

Most businesses have entry points that look similar. Processes that are manual and repetitive. Decisions based on partial data. Customer interactions that are too slow or too broad. These are the areas where AI can drive clear gains.

You do not need to replicate L’Oréal’s tech stack. But you can adopt its mindset.

• Start with one business challenge that impacts revenue, efficiency, or experience

• Use accessible tools with proven results

• Focus on measurable outcomes

• Build internal buy-in by sharing early wins

• Let customer data inform your next move

Action Plan: A Practical Starting Template

Use this structure to move from interest to implementation

1. Define the business goal

Choose a problem worth solving. Example: reduce cart abandonment, improve lead conversion, or shorten resolution time

2. Choose the right entry point

Look for repeatable processes or underused data. Prioritize tasks that are already high-effort or error-prone

3. Set boundaries

Use existing data. Focus on one region or product line. Limit scope to a short pilot. Keep resources lean

4. Assign ownership

Make one team responsible for delivery. Align roles across business and data teams

5. Measure clearly

Track a small set of metrics tied to the business goal. Make sure everyone involved understands how success will be defined

6. Learn and expand

If the pilot delivers value, apply the same model in other areas. If not, adjust based on what you have learned. Either way, progress continues

Final Thought

L’Oréal is not ahead because of its size. It is ahead because it uses AI with discipline and purpose. It focuses on problems that matter, deploys tools that work, and makes decisions faster than its competitors.

That opportunity is open to every business willing to act.