Alchemy Cloud
May 15, 2023
Artificial intelligence has the potential to transform product development in numerous industries, including consumer packaged goods (CPG). But the promise of AI can only pack a punch if it’s fed good data. To ensure accurate and useful AI predictions, it's essential that your AI projects are built on a data structure that produces clean, consistent, conformed, current, and comprehensive laboratory data.
In this blog post, we'll discuss the challenges of achieving reliable AI predictions in CPG product development, outline a framework for addressing these difficulties, and highlight the role of a B2B SaaS solution like Alchemy in ensuring data quality and, ultimately, accurate AI predictions.
Generative AI tools create human-like text by predicting the next word in a sequence based on context, AKA data. However, the quality of the output is dependent on the data that the tools have been trained on. If the training data contains inaccuracies, inconsistencies, or biases, the AI will likely produce flawed outputs as well. For example, by some estimations, Chat-GPT responses are made up 20% of the time.
In the context of research and development, a rate of unreliability that high is simply unacceptable. For laboratory and technical personnel in CPG product development, accurate data is essential for ensuring product safety, quality, and consistency. It’s, therefore, crucial to maintain a high standard of data quality to prevent inaccuracies from affecting AI-based predictions and decision-making processes.
Five unique challenges to achieving reliable AI predictions in the CPG product development space exist. They include:
Poor data quality can lead to inaccurate AI predictions, impacting product safety, stability, and regulatory compliance, which is why It’s essential to ensure data quality across various stages of the product development lifecycle.
Integrating data from disparate sources, such as supply chains, manufacturers, regulatory databases, and laboratories, can be complex and time-consuming. That makes seamless data integration vital to achieving comprehensive analysis and accurate AI predictions.
CPG product development generates large volumes of data that must be managed, analyzed, and stored efficiently to make informed decisions and accurate predictions. The larger the data pool, the more the AI model has to pull from to improve the chances of a valuable AI recommendation.
The CPG sector is subject to various stringent regulations that require accurate and up-to-date data for compliance purposes. Non-compliance can result in costly fines, product recalls, or damage to a company's reputation.
Sensitive product and consumer information must be safeguarded to maintain privacy and prevent data breaches, which can lead to significant financial and reputational consequences.
To tackle these five challenges, laboratory and technical personnel can follow a structured framework that focuses on data quality, integration, and management:
Implement data quality standards to ensure the 5 Cs of data: clean, consistent, conformed, current, and comprehensive data. Regularly review and update these standards to maintain high-quality data.
Use data integration tools to consolidate data from various sources, ensuring seamless data flow and analysis across the organization.
By employing ML-driven Experimental Design and implementing recommended new experimental designs, CPG companies can gather efficient data for AI.
Stay up-to-date on regulatory requirements and maintain accurate, well-structured data to facilitate compliance reporting and auditing.
Artificial intelligence stands to change how the CPG industry formulates and innovates completely. Establishing a reliable data structure as early as possible and prioritizing data quality, integration, and security is crucial to ensure accurate AI predictions and regulatory compliance.
A B2B software solution like the one Alchemy provides can help CPG companies collect and store quality data easily. Alchemy's platform can provide AI-powered insights to help optimize product formulations. By processing customer lab data and helping customers structure their data in an AI-ready way, Alchemy can predict formulating outcomes, enabling product developers to make informed decisions and help create better products.
