Master your proprietary data for AI to unleash new products and services

Artificial intelligence is reshaping product development by improving cost, quality, and sustainability. Enterprises across all sectors, from consumer goods to high-tech, are leveraging AI to design new products and services aligned with their strategic roadmaps. However, success isn’t hinged only on the AI models but on the underlying data. Models based on public datasets can only take innovation so far. Proprietary organizational data is a major driver of AI differentiation. Organizations betting on AI for innovation must ensure their data is accessible and well-structured while tightly aligning AI models and the broader innovation process with their strategic goals.

The long-term direction of sustainability hasn’t changed—AI done well can accelerate it

Sustainability remains a key differentiator for businesses in 2025 (see Exhibit 1 and our detailed assessment here), and AI is increasingly proving its role in that differentiation. IBM and L’Oréal’s recent collaboration exemplifies how advanced AI models, trained specifically for the cosmetics industry and using L’Oréal’s proprietary data, streamline product formulation. This collaboration and its models integrate enterprise data into AI architectures to optimize future product formulations for cost, quality, environmental, and social impact.

Exhibit 1: Sustainability is a leading differentiator for 2025

Sample: 2024 HFS Pulse Research, N=605 executives across Global 2000 enterprises
Source: HFS Research, 2025

AI is a catalyst for product innovation—but data is the real differentiator

IBM has refined sustainable AI models since 2017, but integrating enterprise-specific data with AI frameworks presents a technical challenge. L’Oréal’s structured data transformation over the past decade has made this feasible, linking input parameters directly to business outcomes. This transformation enables L’Oréal to overcome the data and technical debt getting in the way of the best emerging technology, alongside other barriers from culture to process to skill (see Exhibit 2). The R&D functions of both partners support significant AI investments to foster a diversified product portfolio. IBM Consulting is specifically engaged in operationalizing and scaling the adoption of new AI technology and innovation processes.

Exhibit 2: Data debt leads the spectrum of enterprise barriers to adopting GenAI

Sample: 550 Enterprise Leaders
Source: HFS Research, 2025

AI’s role in product development: The why and so what

AI in product development must deliver multiple outcomes, including cost efficiency, improved quality, reduced environmental footprint, and positive social impact. L’Oréal’s sustainability targets include sourcing most of its product formulas based on bio-sourced materials and/or the circular economy by 2030 and advancing personalized cosmetics for inclusivity. Achieving these goals requires integrating proprietary enterprise data with AI to build specialized models that deliver targeted outcomes. Publicly trained models lack the industry-specific context, decades of L’Oréal data, and the accuracy needed for complex formulation tasks.

L’Oréal’s investment in structured formulation databases over the past decade provides a strong foundation for AI-driven product innovation. Their mature R&D function supports AI adoption, maximizing the return on investment. Consumer demand for authenticity and sustainability reinforces the focus on responsible sourcing and formulation. AI acceleration is proving more efficient than simply expanding human research teams or robotic capabilities, though these elements remain critical to applying AI effectively.

IBM’s partnership with L’Oréal clearly intends to go beyond a proof of concept—it is an initiative designed to scale AI across the business.

AI partnerships are transforming consumer goods

AI-driven product development is not limited to L’Oréal. Other major consumer goods firms are also making strategic moves:

• Mars and Microsoft: Mars partnered with Microsoft to develop an in-house generative AI tool, ‘Brahma,’ capable of analyzing consumer insights and generating up to 50 product concepts daily. This has accelerated Mars’ innovation cycle and responsiveness to consumer preferences.
• Reckitt and BCG: In collaboration with BCG, Reckitt’s AI pilot programs have reduced concept development time by 60% and advertising localization costs by 30%. AI integration has streamlined operations and enhanced personalized product offerings.
• Mondelez: Mondelez leverages AI to refine recipes and improve product development speed. Machine learning models optimize flavor, appearance, cost, and environmental impact, enabling faster time-to-market.

These examples highlight how AI partnerships are transforming product development, boosting efficiency, personalization, and sustainability.

The tech sector’s ambitious AI applications

Beyond consumer goods, the technology sector is driving groundbreaking AI applications for materials science:

• Google DeepMind’s GNoME project: This initiative has used AI to discover 2.2 million new crystalline structures, of which 380,000 have been identified as highly stable and applicable to technological advancements.
• Microsoft’s MatterGen and MatterSim: These AI tools generate and simulate new materials with specific properties, accelerating innovation in materials science.
• XtalPi’s AI-driven research: Originally focused on drug discovery, XtalPi now applies quantum computing and AI to identify new materials for solar panels and EV batteries, tackling scalability and efficiency challenges.

These efforts illustrate AI’s ability to revolutionize industries beyond traditional software applications, ranging from consumer goods to advanced materials science.

The Bottom Line: AI’s most impactful future lies in proprietary data and aligned outcomes in the innovation process.

L’Oréal and IBM’s partnership underscores an emerging but fundamental truth—AI’s greatest value emerges when combined with the best possible data and aligned with clear business outcomes. The broader industry, along with technology and academic institutions, is moving in the same direction. Leading firms must adopt AI and redefine their value chains through strategic collaboration and scaled adoption of AI-driven innovation.

To stay ahead, organizations must:

• Align AI initiatives with sustainability and business priorities.
• Clean, structure, and integrate proprietary data for AI applications to achieve results beyond those of public models and data sets.
• Partner strategically—throughout their own organizations and ecosystems—to scale AI across business functions and whole industries.