19 real-life Business Process Automation examples for Product
19 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.
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
Spire Healthcare's AI-enabled MRI scanning
Spire Healthcare rolled out AI technology across MRI scanners in 21 hospital sites in 2025, halving scan times for certain orthopaedic studies and increasing scan throughput from 1.9 to 2.3 patients per hour.
Use Case
AI-Accelerated Diagnostic Imaging
Tools
Philips SmartSpeed AI
Input
Raw MRI scan data captured by Philips scanners across 21 Spire hospital sites, covering a range of orthopaedic and diagnostic imaging protocols.
Process
Philips SmartSpeed AI applies deep learning reconstruction algorithms to MRI data in real time, removing noise and enhancing image resolution without extending scan duration.
Output
Sharper, higher-resolution MRI images delivered in around 15 minutes for orthopaedic studies, with scan rates increased to 2.3 patients per hour.
Outcome
Average patient wait time for MRI fell to 3.5 days, orthopaedic scan times halved from 30 to 15 minutes and diagnostic confidence for radiographers and consultants improved.
Hospitals and Clinics
Product
Corteva's AI-Accelerated Seed and Crop Protection R&D
Corteva Agriscience applies AI to speed up plant breeding and the discovery of crop protection molecules, developing disease- and drought-resistant seed varieties faster than traditional field trials allow.
Use Case
AI-Assisted Agricultural R&D
Tools
Google Cloud
Input
Genomic, phenotypic and environmental data, alongside molecular screening data for candidate crop protection compounds.
Process
Machine learning models predict breeding outcomes and identify promising molecules, narrowing vast search spaces that previously depended on extensive trial-and-error.
Output
Prioritised hybrid seed candidates and crop protection compounds advanced into Corteva's development pipeline.
Outcome
Corteva identifies viable crop protection molecules and disease-resistant seed traits faster, aiming to raise production and cut waste across a smaller farming footprint.
Farming and Agribusiness
Product
Khan Academy's Khanmigo AI Tutor and Teaching Assistant
Khan Academy deployed Khanmigo, an AI tutoring and teaching assistant to provide personalised conversational support to hundreds of thousands of students and teachers across its free online learning platform.
Use Case
AI Conversational Tutoring and Teaching Support
Tools
OpenAI
Input
Student learning histories, in-progress exercise attempts, and teacher requests for lesson planning support submitted through the Khan Academy platform.
Process
Khanmigo uses OpenAI's LLMs to engage students in Socratic dialogue, guiding them through problem-solving rather than providing direct answers, while offering teachers tools to generate lesson plans, assessments and differentiated content aligned to curriculum objectives.
Output
Personalised conversational tutoring responses and hints delivered to students in real time, plus lesson plans, quiz drafts, and instructional materials generated for teachers on demand.
Outcome
Hundreds of thousands of students and teachers have adopted Khanmigo with early data showing improved student engagement and comprehension rates, validating AI tutoring at scale on a free platform.
Edtech Platforms
Product
Novo Nordisk's AI-automated clinical study report drafting
Novo Nordisk uses Claude to automate the drafting of clinical study reports, compressing a process that traditionally required around 50 medical writers working for 15 weeks into minutes of machine generation followed by review by just three human experts.
Use Case
AI-Generated Regulatory Clinical Documentation
Tools
Claude
Input
Structured clinical trial data, statistical analysis outputs, and past clinical study report examples provided as context to the AI model.
Process
Claude ingests structured medical data and historical report examples to produce high-quality draft clinical study report content, which is then reviewed and refined by a small team of regulatory experts.
Output
Near-complete draft clinical study reports ready for expert review, covering the required regulatory narrative, tables and summaries for submission.
Outcome
An operation requiring over 50 person-months is compressed to minutes of machine time plus a few hours of human oversight, potentially saving weeks per regulatory submission cycle.
Pharma and Biotech
Product
GE Healthcare's AIR Recon DL Deep Learning MRI Reconstruction
GE Healthcare's AIR Recon DL applies deep learning to MRI reconstruction, simultaneously improving image sharpness and reducing scan times by up to 50%, enabling radiology departments to scan more patients per day without compromising diagnostic quality.
Use Case
AI-Powered Medical Image Reconstruction
Tools
AIR Recon DL
Input
Raw MRI signal data acquired during the scan, spanning all anatomies and compatible GE scanner hardware.
Process
A deep learning neural network trained on millions of MRI scans removes noise and ringing artefacts from raw k-space data in real time at the operator console, improving signal-to-noise ratio without additional scan time.
Output
High-resolution, artefact-reduced MRI images delivered to the radiologist at the point of scan, ready for diagnostic review.
Outcome
Scan times reduced by up to 50%, with image sharpness improved by approximately 60%, enabling providers to address patient backlogs and increase throughput without additional hardware investment.
Medical Devices
Product
Harvard's AI physics tutor
Harvard physics lecturers built a custom GPT-based AI tutor for a large introductory physics course, replacing part of the active-learning classroom model with personalised, self-paced tutoring conversations at home.
Use Case
AI-Tutored Introductory Physics Instruction
Tools
OpenAI
Input
Research-based lesson content and pedagogy from course instructors, alongside live student questions during at-home study sessions, feed the custom tutor.
Process
Instructors encode content-rich, pre-vetted prompts and scaffolding into a GPT API-based framework so the tutor behaves like a seasoned instructor rather than default ChatGPT.
Output
A guided, conversational tutoring session that students work through independently in place of one weekly active-learning lecture.
Outcome
Preliminary results show learning gains roughly double those of the in-class lecture group, alongside higher self-reported engagement and motivation.
Schools and Universities
Product
Pfizer's AI-accelerated clinical trial data quality and analysis
During the pivotal PAXLOVID clinical trials, Pfizer deployed AI and machine learning to automate quality checks and analyse large volumes of patient data, compressing timelines that had historically required weeks of manual effort.
Use Case
Automated Clinical Trial Data Quality Control
Tools
Internal Tools
Input
Structured and unstructured patient data from global trial sites, including electronic health records, adverse event reports and lab results across thousands of participants.
Process
ML models run automated quality-check routines across incoming trial datasets, flagging anomalies, inconsistencies, and protocol deviations for human review in near real time.
Output
Validated, analysis-ready clinical datasets with exception reports surfaced to trial statisticians and regulatory affairs teams.
Outcome
Pfizer's clinical teams performed essential quality checks and analysed trial data 50% faster than with previous methods, saving an estimated month of development time on the PAXLOVID programme.
Pharma and Biotech
Product
The AP's Automated Earnings Report Generation
The Associated Press use the Automated Insights Wordsmith natural language generation platform to automatically produce thousands of quarterly earnings articles from structured financial data, scaling coverage without adding editorial headcount.
Use Case
Automated Financial News Article Generation
Tools
Automated Insights Wordsmith
Input
Structured financial data from company earnings releases, including revenue figures, EPS, and analyst consensus expectations.
Process
The platform maps incoming structured data to pre-approved editorial templates, generating factually accurate earnings articles in AP house style without manual writing.
Output
Published earnings articles distributed through the AP wire, covering thousands of companies each reporting season.
Outcome
AP scale quarterly earnings coverage from approximately 300 articles to over 3,700 annual reports, expanding financial journalism at near-zero marginal cost per article.
Consumer Internet Platforms
Product
Bloomberg's Cyborg AI Financial Report Drafting
Bloomberg deploy Cyborg, an internal AI system that automatically generates initial drafts of financial news articles from structured market data and earnings releases, with human journalists verifying and refining content before publication.
Use Case
Automated Financial News Report Drafting
Tools
Bloomberg Cyborg
Input
Structured financial data including earnings releases, market price feeds, analyst estimates, and corporate announcement data ingested in real time.
Process
The Cyborg system applies natural language generation to structured financial data, producing initial article drafts in Bloomberg house style that journalists review, contextualise, and publish.
Output
Draft financial news articles covering earnings results, market movements, and corporate announcements, ready for journalist review and publication on the Bloomberg terminal.
Outcome
Bloomberg significantly increase the volume and speed of financial news output, enabling journalists to focus on analysis and contextualisation while automated drafting handles high-volume structured reporting.
Consumer Internet Platforms
Product
Exactimo's Automated Job Market Insight Engine
Exactimo, a training business helping people and companies thrive in an AI world, built a Claude Code and Claude Skills-powered automation that pulls job and industry data via APIs, analyses it for fast-growing companies and industries, and turns the findings into social posts and career guides for job seekers.
Use Case
Automated Job Market Research and Content Generation
Tools
Claude Code, Claude Skills & Job-Data APIs
Input
Job listings, hiring trends, and industry growth data pulled from job-data APIs.
Process
Claude Code and Claude Skills automate the data pull, analyse it to identify fast-growing companies and industries, and draft social media posts and career guide content from the findings.
Output
Ranked insights on fast-growing companies and industries, published as social media posts and career guides for job seekers.
Outcome
The time job seekers need to research which companies to apply to fell by 95%.
Professional Training Providers
Product
Zillow's Neural Zestimate Automated Property Valuation Model
Zillow's Neural Zestimate uses deep learning and neural networks to provide near-instantaneous automated valuations for over 104 million homes across the United States, replacing manual appraisal processes with continuously updated AI-driven estimates.
Use Case
Automated Residential Property Valuation
Tools
Internal Tools
Input
Public records including tax assessments and ownership history, MLS transaction data, property attributes such as square footage and location and user-submitted property details across 104 million US homes.
Process
A neural network trained on hundreds of data points per property analyses structured and unstructured data sources, incorporating deep history and market trends to continuously refine valuations in near real time via cloud computing infrastructure.
Output
Automated property value estimates displayed to buyers, sellers, and lenders on the Zillow platform, updated dynamically as new market data becomes available.
Outcome
National median error rate of approximately 2.4% for on-market homes, creating a data asset that powers Zillow's broader marketplace and enables cash offer programmes requiring no manual appraisal.
Residential Real Estate and Lettings
Product
Argonne National Laboratory's RAPID Autonomous Discovery Labs
Argonne National Laboratory runs robotic RAPID labs where AI systems design experiments, direct robotic arms to run them around the clock, and analyse the results, accelerating materials science and biosciences research beyond what human-only teams can achieve.
Use Case
Autonomous AI-Directed Scientific Experimentation
Tools
RAPID Autonomous Discovery Labs
Input
Scientific literature, prior experimental results, and researcher-defined optimisation goals for materials and biosciences problems.
Process
AI systems trained on scientific literature plan experiments, direct robotic arms to prepare and run them continuously, then analyse results to decide the next experiment in a closed loop.
Output
Continuously generated experimental data and refined material or biological candidates, run and analysed without pausing for human shifts.
Outcome
Argonne reports the RAPID labs have the potential to accelerate scientific discovery by tens to hundreds of times compared with unautomated research.
Research Institutes
Product
UCLH's Live AI Guidance in Brain Tumour Surgery
Surgeons at UCLH's National Hospital for Neurology and Neurosurgery used an AI system that reads the live operating video feed and marks critical anatomy, completing the world's first brain tumour removal guided by AI in real time.
Use Case
Real-Time Surgical Guidance
Tools
Internal Tools
Input
The live surgical video feed from the endoscope during removal of a tumour at the base of the brain.
Process
A computer vision model trained on hundreds of recorded pituitary operations segments each frame to recognise the gland, tumour margins, blood vessels and nerves.
Output
Colour-coded overlays on the surgeon's live video display marking the critical structures to avoid and the boundary of tumorous tissue.
Outcome
The tumour threatening the patient's sight was removed and his vision improved, opening a route to fewer complications in high-risk skull base surgery.