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 4 of 23

Jun 25, 2026

New EU €3 Customs Fee Reshapes Cross-Border E-commerce for Shoppers

A new €3 customs duty for all parcels entering the EU from July 2026 may make cross-border online shopping more expensive. Shoppers seem to be changing their habits by buying items in larger orders or choosing goods already in the EU to avoid extra fees. Trust still appears to be an issue in social commerce, with some shoppers facing long deliveries or products not matching what was promised. Many younger shoppers might trust social shopping more, but a lot still use traditional marketplaces for actual purchases. The use of AI for product recommendations is growing, but many people want clearer information about how these recommendations work.

Jun 24, 2026

Sanders bill proposes 50% public stake in AI firms, $7 trillion fund

Sanders has proposed a bill that would give the public a 50% ownership stake in large AI companies and create a $7 trillion fund. The bill would require these companies to give half their stock to the government, which would be managed by a special commission. Supporters say this might help control how AI is used and provide yearly payments to citizens, but critics warn it could hurt investment and is unlikely to pass Congress. Experts suggest that while public ownership could attract private co-investment, it may also weaken oversight and create risks. There are also suggestions that other partnership models might offer similar benefits with fewer problems.

Jun 24, 2026

Enterprises can cut LLM overruns with new procurement checklist

The article suggests that using a careful checklist may help procurement teams check if LLM vendors are the right fit, safe and fairly priced before they sign contracts.

Jun 23, 2026

Brands Face "Trust Penalty" for Undisclosed AI Content, Study Finds

A recent study suggests that brands may face a trust penalty if they do not clearly disclose when AI is used to create content. Research finds that people trust AI-generated ads less than human-made ones, and labeling content as AI-made can lower purchase intent. Simple disclosures and visible proof of AI involvement might help ease some concerns, but brands should also use human review and follow clear rules. Full transparency appears to be important, as many consumers worry about misinformation in AI content.

Jun 23, 2026

Sanders proposes $7 trillion AI fund, wants 50% public stake in AI firms

Senator Sanders has proposed a plan where companies making at least $200 million a year from AI would give half their shares to a public fund, which might be worth about $7 trillion. A special commission would manage these shares and could give out about $1,000 a year to each person, but experts say this amount is not certain and depends on company profits. The bill does not have support from Republicans or other Democrats, and many say it is unlikely to pass in the current Congress. Some people worry the plan could lower company values and mix up government roles, while others say it could give the public more control over AI.

Jun 22, 2026

Investors Adopt Hybrid AI Stacks, Blend LLMs and Finance Platforms

Investors are starting to use both general large language models (LLMs) and finance-specific platforms together, but picking the right tool for each task can be unclear. Studies suggest general LLMs like GPT-4o may have about 47% accuracy on finance questions, while platforms like AlphaSense could offer more reliable and easier-to-check data. A combination approach seems to work best: use LLMs for flexible tasks and finance platforms for trusted sources. Adoption of AI agents in finance appears to be growing but is still cautious due to data and oversight challenges. Experts recommend keeping humans in charge of final decisions and using clear steps to manage risks and trace responsibilities.

Jun 22, 2026

Microsoft's 2026 Work Trend Index Reveals How AI Affects Culture

Microsoft's 2026 Work Trend Index suggests that when managers model how to use AI, employees see more value and trust in AI systems. The study indicates that organizational factors may matter more than individual attitudes for most AI impacts. Experts recommend clearly defining what AI can and cannot do, keeping team rituals to preserve culture, and encouraging safe experimentation with AI. Upskilling employees and tracking culture health regularly may help prevent problems and build trust as AI becomes part of daily work.

Jun 22, 2026

How Enterprises Adopt Auditable AI Agents for Workflow Automation

Enterprises adopting AI agents for workflow automation may face strict requirements for tracking and explaining every action. To build trust, experts suggest using strong policies, detailed logs, and careful expansion. Systems usually involve checking risks, limiting permissions, logging all activity, and reviewing regularly. Reliable systems often use contract-based APIs and phased rollouts, with human checks and rollback steps for high-risk actions. Experts believe these steps help keep automation safe and maintain operator trust.

Jun 21, 2026

Microsoft's Nadella updates AI strategy: build learning loops to avoid commodity models

Satya Nadella, Microsoft's CEO, warns that if only a few AI models control most of the value, it may not be accepted by society and could harm entire industries. He suggests that companies should focus on building their own learning loops, where feedback and human oversight help improve models, instead of relying only on outside AI models. Reports suggest that if AI becomes too concentrated, companies might lose control and value to a few big models. Early examples show firms using human checks and their own data to keep improving their AI systems. This approach may help companies stay strong even if AI models themselves become widely available and similar.

Jun 21, 2026

AI Crisis Playbook: How Companies Manage Reputational Disasters

The text suggests that managing public relations crises has become a top priority for AI companies after several high-profile incidents from 2024 to 2026. Common problems may include viral misinformation, safety failures, and bias, which can quickly lead to lawsuits and loss of trust. Experts recommend having a crisis playbook with clear steps for the first hour, including gathering facts, making coordinated statements, and monitoring for rumors. Working with regulators appears to require clear roles, transparency, and careful record-keeping. Companies that prepare and respond quickly may limit reputational damage better than those who rely only on good messaging.

Jun 21, 2026

Nadella Warns Enterprises: Own AI Workflow, Data to Avoid Commoditization

Microsoft CEO Satya Nadella warns that generative AI may quickly become a commodity, making it harder for companies to stand out. He suggests businesses should keep control of their own data, workflows, and feedback loops so their systems stay valuable even if the AI model changes. Analysts say value might shift away from the models themselves to the ways companies use and improve them with human judgment and outcome tracking. Experts suggest that collecting feedback and verifying AI outputs may lead to better results, though this may vary by industry. Nadella's main message appears to be that owning data and learning processes helps companies avoid losing their advantage to outside AI providers.

Jun 20, 2026

Nadella's Test: How Companies Build AI Moats With Learning Loops

Satya Nadella warns that relying too much on outside AI models may hurt companies by making them lose their unique expertise. He suggests that lasting value comes from a learning loop made of private data, human input, and feedback, not just the AI model itself. Reports suggest that companies should keep their own data, use human checks for risky decisions, and make sure their systems are always being tested for problems. Firms that manage their own learning and data systems may be better prepared if AI models become easy for everyone to get. The research suggests that the real advantage comes from how companies use and protect their knowledge, not from owning the AI model itself.

Jun 20, 2026

Mars integrates $250M fund, capex to boost decarbonization

Mars is combining capital spending for factory upgrades with a $250 million sustainability fund to help cut emissions while continuing to grow. This approach may help Mars and its suppliers meet energy goals by making it easier to finance low-carbon technologies. Reports suggest that more CFOs expect bigger returns from sustainability projects and plan to increase green spending, though many still see these efforts as a cost. Mars appears to align its investments with equipment renewal, and uses supplier finance programs to encourage emissions cuts in its supply chain. These actions might become common as more companies see benefits in linking capital planning with climate goals.

Jun 19, 2026

Anthropic ban on foreign nationals sparks AI talent risk playbook

Reports suggest that Anthropic has been ordered by the U.S. to block its advanced AI models from all foreign nationals, even those already working in the U.S. on visas. This move appears to make managing AI talent risks an urgent issue for many companies, who now need clear plans to keep projects on track and protect their teams. The suggested playbook includes steps like diversifying hiring, upskilling local staff, monitoring legal compliance, and preparing for sudden policy changes. Experts warn that if more restrictions spread, it could make daily collaboration much harder, so firms may need to quickly adapt their processes and advocate for clearer policies.

Jun 19, 2026

Firms adopt NIST, ISO 42001 to close AI governance gap by 2026

Many large companies are using NIST and ISO 42001 frameworks to help close gaps in how they manage AI, especially as AI use grows faster than oversight. Reports suggest that these frameworks, along with real-time tracking of AI systems and better board oversight, may help firms meet new rules like the EU AI Act. Early results from some firms show fewer compliance delays and fewer fines, but experts note that missing basic controls can still lead to problems and loss of trust. As regulations become stricter, accurate classification of AI risk and ongoing monitoring appear to be essential, although these activities might increase costs, especially for smaller firms.