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Business & Ethical AI

Pieces on AI’s impact on business processes, ROI, leadership decisions, plus the risks, ethics, and reliability of these technologies.

343 articles • Page 7 of 23

Coca-Cola taps AI to boost retail growth, not just cut costs

Coca-Cola taps AI to boost retail growth, not just cut costs

Coca-Cola is using artificial intelligence (AI) to help grow its retail business, not just to save money, according to CFO John Murphy. The company may use AI to improve how it targets customers, sets prices, and chooses products for both premium and value shoppers. Some case studies suggest AI tools have led to higher sales and better store performance in different countries. Coca-Cola also appears to use AI to help manage inventory and suggest restocking through apps, which may boost sales. The company's approach suggests AI might help make its products appealing to a wide range of shoppers, focusing on growth instead of cutting jobs.

New Laws Force Brands to Disclose AI-Generated Celebrity Likenesses by 2026

New Laws Force Brands to Disclose AI-Generated Celebrity Likenesses by 2026

New laws in some U.S. states will require brands to clearly say when they use AI-created versions of celebrities in ads by 2026. There is no single federal rule yet, but states like Tennessee, California, and New York have put in or will put in special rules to protect celebrity rights and require clear labels. Brands may need written consent from the celebrity or their estate if the fake image or voice can be recognized and looks like an endorsement. Companies are encouraged to keep careful records, add labels, and be ready to act if there is confusion or complaints. This approach suggests that safe AI advertising may depend more on good permission, open labeling, and record-keeping than on new technology.

New York Law Adds Disclosure Duty for AI Synthetic Performers in Ads

New York Law Adds Disclosure Duty for AI Synthetic Performers in Ads

New laws in New York and other places may require companies to clearly label ads that use AI-generated performers and get consent from people whose likenesses are used. Different countries and states have different rules; for example, the EU focuses on transparency, while the U.S. may treat likeness as a kind of property. Companies might need to follow special contract rules and give visible warnings to consumers when AI is used in ads. Platforms could face deadlines to remove flagged content, and not following these rules could lead to legal trouble. There does not yet appear to be a single global approach, so compliance may need to match each area's laws.

Coca-Cola expands AI to boost retail growth, not cut costs

Coca-Cola expands AI to boost retail growth, not cut costs

Coca-Cola is using artificial intelligence (AI) mainly to help grow its retail business, not just to cut costs. The company says AI may help them make better pricing decisions and suggest the right products for different stores, which could increase sales in both premium and value product segments. Early results suggest AI tools, like sending personalized product suggestions to retailers, might lead to more orders and faster price changes. Coca-Cola appears to focus on using AI to support its workers and keep its products appealing to both budget and higher-income shoppers. Overall, the company suggests AI can help them adapt quickly and make smarter business choices, but it does not claim to replace jobs.

Microsoft Foundry Unveils AI ROI Metrics for Enterprises

Microsoft Foundry Unveils AI ROI Metrics for Enterprises

Microsoft Foundry has introduced a way for businesses to measure whether AI agents provide more value than they cost. This process may involve tracking each AI run, attaching quality and cost evaluators, and showing the ratio of benefits to costs on dashboards. Experts suggest using at least 90 to 180 days of data and control groups to avoid misleading results. Reports indicate that organizations might be giving AI access only when the return on investment can be shown. Dashboards appear to help both technical teams and executives make faster, clearer decisions about their AI projects.

Zvi Mowshowitz Ranks 2026 AI Alignment Controls: Governance Over Math

Zvi Mowshowitz Ranks 2026 AI Alignment Controls: Governance Over Math

Zvi Mowshowitz says AI alignment often fails when fixes only hide problems instead of really changing how the AI works. He suggests that teams may only need to focus on a few key parts of the AI to make it safer, but this needs careful tracking of what has been checked. Zvi also thinks good habits and clear ownership matter more than fancy tools and recommends simple steps like logging activity, regular reviews, and human checks for important decisions. He argues that governance and oversight may be more important than technical details, and there is no single number that shows if alignment is working. Some studies suggest that with the right habits, making AI safe may not slow down work as much as people fear.

Microsoft details how to measure AI ROI with Azure tools

Microsoft details how to measure AI ROI with Azure tools

Microsoft suggests measuring AI ROI with Azure should start before building any solution, by setting one clear goal for each use case. Teams may use Azure tools to collect data on costs, usage, and business results, making sure to tag each event with business context. Calculating ROI means comparing money saved or earned against all costs, using a clear formula and treating "time saved" as uncertain unless it leads to real savings. The guidance also warns about common mistakes, like ignoring some costs or missing a baseline, and notes that continuous measurement might help teams adjust for better results, even though it does not guarantee success.

New Tutorial Helps Enterprises Measure AI ROI in Azure

New Tutorial Helps Enterprises Measure AI ROI in Azure

A new tutorial may help businesses measure the return on investment (ROI) of their AI projects in Azure. It guides teams on tracking costs, mapping them to different applications, and linking these expenses to business results using key performance indicators (KPIs). The tutorial suggests using dashboards for clear reporting, and it might make it easier for finance, product, and governance teams to see the same data. Experts note that reliable financial signals may only appear after 90 to 180 days. The approach appears designed to help companies understand value and spot issues quickly, though exact results could vary by industry.

Enterprises Formalize Shadow AI, Cut Hours, Shorten Cycles

Enterprises Formalize Shadow AI, Cut Hours, Shorten Cycles

Generative AI tools are being used in many workplaces before official rules are set, which may boost productivity but can also increase risks if not managed. Some evidence suggests that when companies formally add approved AI tools and train their teams, they can save time and shorten work cycles. However, these benefits might not be fully realized unless leaders change roles and track how time saved is used. There are also signs that sharing AI successes openly helps build trust and reduces employee resistance. Overall, the text suggests organizations should guide and measure AI use to balance innovation with security and compliance.

Enterprises cut LLM costs and risks with new governance strategies

Enterprises cut LLM costs and risks with new governance strategies

Enterprises using large language models (LLMs) may face high costs and risks if they do not have strong controls. Governance strategies suggest that tracking model changes, using approved models, and monitoring spending can help reduce wasted budgets and manage risks. Protecting data through automatic masking, encryption, and location controls appears important for privacy. Security measures like role-based access and logging every prompt are recommended, and regular security reviews may help uncover new risks. Following these practices might help companies use LLMs more safely and affordably as rules around AI become stricter.

Enterprises adopt new models to govern always-on AI agents

Enterprises adopt new models to govern always-on AI agents

Enterprises are increasingly using always-on AI agents for tasks like emails and finance, which may raise new security and control questions. Treating each agent like an employee - with unique credentials and clear ownership - appears to be a key step for safety and traceability. Organizations might set rules so that low-risk tasks happen automatically, but actions with more risk require human approval. Reports suggest that strong logging, runtime checks, and clear data rules are needed to meet legal and compliance demands. By 2026, about 40% of enterprise apps may use these agents, so companies seem to be moving toward structured, layered oversight instead of ad hoc solutions.

Enterprises Adopt Governance, Budget Controls for LLM Costs, Data Risks

Enterprises Adopt Governance, Budget Controls for LLM Costs, Data Risks

Enterprises using large language models may face risks of high costs and data exposure. Experts suggest that having clear rules and real-time controls, such as tracking usage and setting spending limits, can help manage these risks. Many companies follow official guidelines like NIST AI RMF and the EU AI Act to build strong programs. Regularly checking and updating policies, as well as working across different teams, appears to make these programs more resilient as rules and needs change.

Enterprise AI Adoption Reaches 88% in 2026, Reshaping Workflows

Enterprise AI Adoption Reaches 88% in 2026, Reshaping Workflows

By 2026, surveys suggest that 88 percent of organizations may use AI in at least one business function, and adoption appears to be rising quickly, especially in large firms and financial services. Many companies report seeing better productivity and lower costs, but most are still experimenting with how AI fits their work. Privacy and data protection are highlighted as important, with advice to limit data collection and keep humans involved in big decisions. New rules, like the EU AI Act, may soon require firms to manage risks and document how AI systems are used, especially in areas that affect job decisions. Overall, AI is becoming more common, but full automation does not seem to be happening yet for most teams.

Report Maps How AI Changes Investor Jobs, Skills, and Governance by 2026

Report Maps How AI Changes Investor Jobs, Skills, and Governance by 2026

The report suggests that AI may change investor jobs by automating routine tasks, but key decisions and judgment are still led by humans. Investors might need new skills like using AI tools safely, checking for bias, and clear communication with clients. Regulations are expected to tighten, and firms may need better oversight and training for safe AI use. Pilot programs appear to keep humans involved in important decisions, and logs may be needed to track how AI is used. Overall, AI integration seems to mean shifting tasks rather than completely replacing investor jobs.

Anthropic commits $200B to Google Cloud, testing vendor lock-in

Anthropic commits $200B to Google Cloud, testing vendor lock-in

Anthropic may spend up to $200 billion on Google Cloud and chips, which could be over 40% of Alphabet's cloud revenue backlog, but Reuters could not confirm the contract details. The reported commitment appears to include data-center space, TPU chips, and cloud services, though exact terms and payment schedules are not public. Analysts say the deal highlights how big AI companies try to secure resources and discounts, but it might also create risks around relying on one vendor and lead to regulatory questions. There are still uncertainties about what the $200 billion covers and how the contract works. Policymakers and investors are watching for more details before making further decisions.