5 Ways Household & Personal Care Brands Can Future-Proof AI Investments

Sunil Sanghavi • NobleAI

Artificial intelligence transforms the way we live, study and work; the household and personal care industry is no exception. From AI-powered ingredient discovery and personalized product formulation to virtual try-on experiences and predictive trend analysis, the potential applications are vast, but so are the risks of investing in technologies that don’t live up to the hype. Brand leaders face mounting pressure to adopt AI solutions that deliver immediate results while maintaining long-term relevance. 

How do you balance innovation imperatives around sustainability and transparency while building flexible capabilities that adapt to tomorrow’s market demands of complex global regulations, ethical sourcing requirements and rapidly shifting consumer preferences? 

This article provides household and personal care executives with five practical frameworks for evaluating and implementing AI investments that withstand the test of time. As consumer consciousness around ingredient transparency intensifies and regulations like the EU’s chemical safety requirements become more stringent, these approaches help brands distinguish between essential AI capabilities worth investing in today and overhyped applications likely to disappear, creating sustainable competitive advantage in an increasingly tech-driven beauty ecosystem. 

1. Leverage AI for Lasting Impact Across Diverse Applications

As with any emerging technology, AI is sparking a wide range of applications, from transformative tools critical to business operations to innovative consumer solutions that may captivate but not always endure. Rather than focus solely on trendy novelties, prioritize AI solutions that address high-value challenges—such as developing superior-quality, cruelty-free products with clean beauty transparency—while recognizing that some applications, though less critical, may still offer value to specific audiences before fading into the background.

For example, Unilever leverages AI to optimize its supply chain, using predictive analytics to streamline raw material sourcing and reduce waste, ensuring flexibility to meet sustainability goals. Similarly, L’Oréal employs AI-driven platforms to accelerate product development, analyzing consumer data to create formulations that align with clean beauty trends, rather than investing heavily in marketing-focused AI like image generation tools. By targeting persistent challenges like supply chain efficiency or faster product innovation, brands can invest in AI that adapts to evolving technologies while delivering measurable impact.

2. Navigate AI Buy-vs-Build Development Tradeoffs for Maximum Value

The build-versus-buy decision hinges on capability complexity and strategic value. Sophisticated no-code and low-code AI platforms reshape what makes sense to build internally. For solutions with well-defined business processes and clear data inputs, such as predictive analytics or customer segmentation, building AI models may appear deceptively straightforward. But organizations easily fall into the maintenance trap. Industry estimates attribute 60-70% of software lifecycle costs to maintenance. For AI systems, this burden intensifies as teams continuously update models, integrate APIs and adapt to regulatory requirements.

On top of this, complex applications—molecular simulation, chemical formulation and advanced supply chain modeling—require specialized algorithms and scientific expertise unfamiliar to typical data science teams and DIY tools. Expert vendors with deep scientific knowledge and domain-focused AI experts can design, test and deploy solutions exponentially faster while spreading maintenance efforts across larger user bases.

The strategic question isn’t build-versus-buy, but where to invest internal resources for maximum impact. Companies like Estée Lauder partner with vendors, evaluating integration capabilities and regulatory compliance support, maximizing speed and impact while reducing long-term obsolescence and maintenance risks.

3. Leverage AI Expertise for Competitive Advantage

AI literacy empowers organizations to evaluate and adapt solutions as technologies evolve, driving faster development and iteration. Whether leveraging internal data science expertise or partnering with external AI specialists, companies gain a competitive edge by integrating AI effectively into their workflows. The key is fostering collaboration between AI experts—whether in-house or external—and operational teams like R&D, product development and strategic procurement to understand their challenges and processes. For example, when AI identifies replacements for potentially banned ingredients, close collaboration between data scientists, formulators who understand ingredient interactions, and regulatory chemists ensures compliant, high-quality outcomes.

Equally important is building AI literacy across operational teams, regardless of whether dedicated data scientists are on staff. Companies like Colgate-Palmolive embraced upskilling programs, training employees in R&D, marketing and supply chain to understand AI applications. This enables teams to assess tools like predictive trend analysis and integrate them seamlessly. Similarly, Shiseido collaborates with external AI training providers to enhance internal capabilities, equipping teams to make informed decisions and implement solutions aligned with their goals. Organizations with strong AI literacy—whether developed internally or through external partnerships—can rapidly adopt new approaches, making them more resilient to technological shifts and better positioned to meet consumer demands.

4. Leverage AI for Consumer-Centric Sustainability and Transparency

Sustainability and ingredient transparency are non-negotiable for many consumers. AI can help brands meet these expectations by optimizing sustainable practices and providing clear communication. However, multi-tiered supply chains make this increasingly challenging as brands must verify that every downstream raw material aligns with their sustainability and ethical standards. AI-powered traceability is essential for tracking ingredients across multiple supplier tiers, ensuring compliance with clean beauty and ethical sourcing requirements.  

5. Adopt Modular AI Architectures for Scalability and Adaptability

To future-proof AI investments, brands should adopt modular architectures that allow seamless integration of new technologies. Johnson & Johnson uses modular AI systems for predictive trend analysis, enabling them to swap out components as better algorithms emerge without overhauling entire platforms. This approach supports scalability, allowing them to expand AI use from R&D to consumer engagement. Modular designs reduce dependency on single vendors and enable brands to stay agile amid regulatory changes, such as the EU’s evolving chemical safety rules, ensuring AI investments remain effective over time.

By implementing these strategies, household and personal care brands can navigate the complexities of AI adoption and balance innovation with sustainability and transparency to create a lasting competitive advantage.

About the Author

Sunil Sanghavi is CEO of NobleAI, a pioneer in science-based AI solutions for chemical and material informatics. Sunil has a rich operating background in deep-tech companies. Most recently, he was Senior Investment Director at Intel Capital, investing in AI/ML hardware and software companies including Motivo, Untether AI, Syntiant and Kyndi.