The deployment of AI-assisted development tools in compliance-relevant, large-scale industrial environments represents significant gaps in academic literature, despite growing industry adoption. We report on the industrial deployment of WhatsCode, a domain-specific AI development system that supports WhatsApp (serving over 2 billion users) and processes millions of lines of code across multiple platforms. Over 25 months (2023-2025), WhatsCode evolved from targeted privacy automation to autonomous agentic workflows integrated with end-to-end feature development and DevOps processes. WhatsCode achieved substantial quantifiable impact, improving automated privacy verification coverage 3.5x from 15% to 53%, identifying privacy requirements, and generating over 3,000 accepted code changes with acceptance rates ranging from 9% to 100% across different automation domains. The system committed 692 automated refactor/fix changes, 711 framework adoptions, 141 feature development assists and maintained 86% precision in bug triage. Our study identifies two stable human-AI collaboration patterns that emerged from production deployment: one-click rollout for high-confidence changes (60% of cases) and commandeer-revise for complex decisions (40%). We demonstrate that organizational factors, such as ownership models, adoption dynamics, and risk management, are as decisive as technical capabilities for enterprise-scale AI success. The findings provide evidence-based guidance for large-scale AI tool deployment in compliance-relevant environments, showing that effective human-AI collaboration, not full automation, drives sustainable business impact.

We examine evaluation of faithfulness to input data in the context of hotel highlights: brief LLM-generated summaries that capture unique features of accommodations. Through human evaluation campaigns involving categorical error assessment and span-level annotation, we compare traditional metrics, trainable methods, and LLM-as-a-judge approaches. Our findings reveal that simpler metrics like word overlap correlate surprisingly well with human judgments (Spearman correlation rank of 0.63), often outperforming more complex methods when applied to out-of-domain data. We further demonstrate that while LLMs can generate high-quality highlights, they prove unreliable for evaluation as they tend to severely under- or over-annotate. Our analysis of real-world business impacts shows incorrect and non-checkable information pose the greatest risks. We also highlight challenges in crowdsourced evaluations.

The Product Security Threat Analysis and Security Standards team is responsible for the structured security analysis of Zoox products and the judicious application of security standards to the System Development Life Cycle at Zoox. The ideal candidates will have a strong general systems engineering background and demonstrated passion and concrete expertise in cybersecurity. A demonstrated skill in turning the analysis into high-quality written deliverables (such as TARA). Carry out security analysis, threat modeling, and risk assessment for a complex product ecosystem consisting of a custom-designed and built vehicle fleet as well as a portfolio of cloud services. Produce high-quality, readable, structured artifacts such as TARAs that reflect the security analysis performed (previous bullet) and help guide the company's efforts in the cybersecurity domain.

Supply chain and logistics industries worldwide lose over $1 trillion a year due to out-of-stock or overstocked items1. Shifting demands and shipping difficulties make the situation worse. Challenges in inventory management, demand forecasting, price optimization, and more can result in missed opportunities and lost revenue. The retail marketplace has become increasingly complex and competitive. Keeping pace with the connected consumer, embracing emerging trends in shopping, or staying ahead of the competition--these challenges bear down on retailers and manufacturers greater than ever before.

If you're a data scientist or you work with machine learning (ML) models, you have tools to label data, technology environments to train models, and a fundamental understanding of MLops and modelops. If you have ML models running in production, you probably use ML monitoring to identify data drift and other model risks. Data science teams use these essential ML practices and platforms to collaborate on model development, to configure infrastructure, to deploy ML models to different environments, and to maintain models at scale. Others who are seeking to increase the number of models in production, improve the quality of predictions, and reduce the costs in ML model maintenance will likely need these ML life cycle management tools, too. It's all technical jargon to leaders who want to understand the return on investment and business impact of machine learning and artificial intelligence investments and would prefer staying out of the technical and operational weeds.

Our services alliance with OpenAI brings clarity to the expanding array of its potential business applications, combining OpenAI's technology with our deep understanding of business strategy and social responsibility. Our services alliance combines OpenAI's industry-leading AI tools and platforms and Bain's strategic guidance and digital implementation capabilities, helping you harness the power of generative artificial intelligence to transform your business.

"The best way to have a good idea is to have a lot of ideas." Let's assume we are running an e-commerce search engine that uses machine learning on user-issued queries to identify the intended product category. Say the model in production incurs a 20ms prediction latency and has 90% accuracy. A natural next goal from a modeling perspective would be to drive the accuracy higher, say to 95% or beyond. However, we know that improving the accuracy almost always requires the consumption of more computational resources for training models and may also increase the inference latency.

Responsible AI is an umbrella term encompassing fairness and equity, social and environmental impact, and privacy and safety. A more precise definition depicts responsible AI as systems that are consistent with a company's organizational values, while still delivering transformative business impact, according to Steven Mills, chief AI ethics officer, managing director and partner at Boston Consulting Group. "The end there is really important," said Mills, co-author of the report. "I can build it responsibly, considering my values and deliver business impact. Some people think you can do one or the other and I'd argue that's a false choice. You can absolutely do both and you should do both."

Decision intelligence (DI) is a new field aimed at transforming and improving the way business decisions are made. It uses technology to support, augment or automate business decisions. DI combines technologies such as machine learning, optimization, analytics and process automation. It helps with business decisions by leveraging three modes: decision support, where the machine provides analytics and other tools to assist human decision-making; decision augmentation, where the machine suggests decisions for a human to review and accept; and decision automation, where the whole process is automated and the machine executes its decisions autonomously, under human supervision. Implementing DI provides significant business impacts to organizations.

I hope that you enjoy the latest AI news, insights, and the Web3 section at the end! AI4Diversity is planning to run a video production marathon, just submit 2-3 mins long video about a Tech related topic and get viewed by millions. Artists Aren't Happy: Tools released this year -- with names like DALL-E 2, Midjourney, and Stable Diffusion -- have made it possible for rank amateurs to create complex, abstract or photorealistic works simply by typing a few words into a text box. If Yes, Is It Moral? Like a "Black Mirror" episode, Microsoft introduced earlier this 12 months it had secured a patent for a software that would reincarnate folks as a chatbot, opening the door to even wider use of AI to carry the lifeless again to life. IT leaders and industry observers lend insights on how to get a clear idea of whether your AI efforts are paying off.