6 real-life Business Process Automation examples for Product

6 real-life Business Process Automation examples for Product

Automation in the product function can help because teams are continuously translating messy customer signals, research, delivery data, and technical constraints into decisions about what to build next.

The best automations accelerate synthesis, documentation, experimentation, and internal coordination, but they have to preserve product judgment around prioritisation, trade-offs, and what actually creates customer value.

Industry

Sector

Genentech Automating Drug Research and BioMarker Validation

Genentech has developed an advanced generative AI system, known as the gRED Research Agent, which empowers scientists to automate the arduous process of drug research and biomarker validation, transforming tasks that previously took weeks into operations completed in minutes.

Use Case
Automating Drug Research
Tools
gRED Research Agent & Claude
Input
Complex scientific queries (e.g. identifying cell surface receptors in specific diseases) and vast data sources including PubMed journals and internal proprietary repositories.
Process
The system utilises autonomous agents to decompose complex research tasks into dynamic, multi-step workflows. By employing RAG, the agents search across multiple knowledge bases and Genentech's internal APIs, adapting their approach based on information gathered at each stage to synthesise high-level findings.
Output
Synthesised scientific findings accompanied by cited summaries and data-driven insights.
Outcome
Expected automation of over 43,000 hours of manual effort in biomarker validation, significantly reducing time-to-target identification and accelerating the delivery of new medicines to patients.
Pharma and Biotech
Product

Nestlé KitKat's Autonomous Process Optimisation

This AI automation enables Nestlé KitKat production lines to self-regulate and optimise processes autonomously. By monitoring real-time production parameters, the system ensures consistent product quality and significantly reduces downtime, contributing to Nestlé’s broader objective of accelerating product development across all categories.

Use Case
Autonomous Process Optimisation
Tools
IoT Sensors & Internal Tools
Input
Real-time production data including line speed, temperature, coating thickness, wafer quality metrics, and machine status.
Process
AI continuously monitors production parameters and autonomously adjusts machine settings to maintain quality and throughput, triggering automatic corrections for any deviations without human intervention.
Output
Consistent product quality, reduced downtime from human-triggered stoppages, and a decrease in quality defects reaching the packaging stage.
Outcome
Improved production efficiency and a 64% reduction in average project duration since AI integration began.
Food Processing and Packing
Product

Unilever's Intelligent Recipe Tool

Unilever Food Solutions (UFS) has launched an AI-powered Recipe Intelligence tool that acts as an "indispensable kitchen companion" for professional chefs and restaurant operators. By utilising a bespoke chatbot interface, the system provides trend-led recipe inspiration and menu optimisation, helping culinary businesses stay competitive and culturally relevant.

Use Case
Food and Menu Optimisation
Tools
GenAI Chatbot
Input
Data derived from the expertise of 250 UFS in-house chefs, including over 30,000 recipes and product applications. It also incorporates "Future Menu Trends" research, which involves social listening across 312 million global searches and feedback from 1,100 chefs.
Process
The tool analyses user queries via a chat interface to generate tailored recipes and cooking techniques. It performs menu analysis by evaluating uploaded PDF menus, suggests optimised preparation steps, and conducts a "Gen Z compatibility test" to score and adapt menus for younger demographics based on trends like "modernised comfort" and "borderless cuisine".
Output
Personalised recipe inspiration, ingredient lists, nutritional insights, and Gen Z appeal scores with specific optimisation recommendations.
Outcome
The system has achieved a 96% user satisfaction rate, with 30% of operators returning for repeat usage. Furthermore, user engagement has tripled, with average chat durations extending to 13 minutes.
Grocery and FMCG Retail
Product

Nestlé and IBM's Packaging Efficiency

This strategic collaboration between Nestlé and IBM Research utilises advanced generative AI and chemical language models to rapidly discover sustainable, high-barrier packaging materials, effectively compressing years of traditional laboratory R&D into digital simulations.

Use Case
Identifying Novel High-Barrier Sustainable Packaging Materials
Tools
IBM Watson
Input
Public and proprietary documents on packaging materials, molecular structure data, and physical-chemical property datasets
Process
IBM AI learns molecular structures from a vast knowledge base while a regression transformer correlates structural features with properties to propose entirely new materials that resist moisture, temperature, and oxygen
Output
In-silico generation of novel packaging candidates evaluated for cost, recyclability, and functionality
Outcome
Drastic compression of R&D timelines, supporting the goal of 100% recyclable or reusable packaging by 2025
Food Processing and Packing
Sustainability
Product

Mercedes-Benz's Virtual Voice Assistant for Drivers

Mercedes-Benz has expanded its partnership with Google Cloud to integrate a specialized Automotive AI Agent into its MBUX Virtual Assistant. By leveraging the multimodal reasoning of Gemini models, the assistant can now engage in complex, multi-turn dialogues and access real-time data from Google Maps to provide highly contextual travel recommendations.

Use Case
Virtual Voice Assistant for Drivers
Tools
Google Gemini & Internal Tools
Input
Natural language verbal commands, multi-turn follow-up questions, and real-time data from Google Maps Platform (covering 250 million places with 100 million daily updates).
Process
The system utilizes Gemini’s natural language understanding and multimodal reasoning to process complex queries. It employs "contextual memory" to retain information throughout a journey, allowing users to ask follow-up questions (e.g., asking for a restaurant's signature dish after initially asking for directions) without repeating previous details. The agent is specifically tuned for automotive environments to handle diverse accents and minimize driver distraction.
Output
Sophisticated, human-like verbal responses, personalized points-of-interest (POI) suggestions, and dynamic navigation updates displayed via the vehicle’s native interface.
Outcome
Significant enhancement of the in-car user experience through more intuitive and helpful interactions. The automation reduces the cognitive load on drivers by allowing hands-free, conversational control over complex navigation and search tasks, debuting in the new CLA-Class and rolling out across the MB.OS-equipped fleet.
Engineering, Architecture and Design
Product

Nestle's Faster Ideation Cycles

Nestlé S.A. leverages AI-driven concept engines and machine learning to revolutionise the R&D cycle, enabling the rapid translation of social media trends and consumer data into viable product proposals while minimising the need for costly physical prototyping.

Use Case
Accelerating New Product Development
Tools
Internal Tools
Input
Social media data, consumer preference datasets, historical R&D data, and market trend signals
Process
ML models analyze historical data and social insights to generate product concepts while AI clusters trend data into actionable proposals and facilitates virtual prototyping
Output
Faster ideation cycles, reduced physical trials, and unbiased ingredient exploration
Outcome
64% reduction in development time (from 33 months down to 12 months)
Food and Beverage Manufacturing
Product
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