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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.

166 articles • Page 5 of 12

Navigating the AI Paradox: Why Enterprise AI Projects Fail and How to Build Resilient Systems

Navigating the AI Paradox: Why Enterprise AI Projects Fail and How to Build Resilient Systems

Many big companies are spending a lot on AI, but most of their projects do not work out. They often fail because of poor planning, messy data, unclear goals, hidden mistakes, and leaders hoping for more than the company can really do. Problems like missing safety checks, calling simple software "AI," and skipping security steps make things worse. To succeed, companies need to set up strong rules, test new systems carefully, and have teams from different areas work together. Following

The AI Chasm: Bridging the Gap Between Ambition and Impact in Enterprise

The AI Chasm: Bridging the Gap Between Ambition and Impact in Enterprise

Many big companies dream of using AI everywhere, but most projects fail because of leadership confusion, bad data, and teams not working well together. Without clear goals, good data, and strong rules, AI projects often get stuck and never help the business. Workers are also scared of losing jobs, and old systems make adding AI very hard. To succeed, companies need to set clear goals, clean up their data, teach workers about AI, and build flexible, secure systems that can change with new technol

Building an Enterprise AI Assistant in 6 Steps: The 2025 Workflow

Building an Enterprise AI Assistant in 6 Steps: The 2025 Workflow

Building an enterprise AI assistant in 2025 is a clear, stepbystep journey. First, pick one important job for your assistant and measure how well it helps. Next, choose an easy, secure platform to build your assistant without lots of coding. Make your questions and answers smart using special prompt tricks, and connect the assistant to the tools your team already uses. Always keep user data safe and follow rules about privacy. Finally, launch the assistant to a small group, collect feedback, and

Bridging the AI Orchestration Gap: How IT Drives Secure, Scalable Innovation

Bridging the AI Orchestration Gap: How IT Drives Secure, Scalable Innovation

Many companies struggle to make their AI projects bigger because it's hard to connect people, technology, and rules in a safe way. IT teams are in a special spot to help, since they understand both the tech and how to keep things secure. To fix this, IT can bring different teams together, use better tools, and make sure everyone follows the same rules. When IT leads the way, companies can use AI more safely, quickly, and with better results.

The AI Experimentation Trap: Strategies for Driving ROI in Generative AI Investments

The AI Experimentation Trap: Strategies for Driving ROI in Generative AI Investments

Most companies fail to get real value from generative AI because they run scattered, small projects that aren't tied to big business goals. These "AI experiments" often stay stuck as pilots and never grow into real solutions. Real success happens when businesses use AI to solve important, everyday problems that affect many people. Winning teams work across departments and have the power to make changes. Companies that link AI to their core strategy, culture, and ways of working

Beyond the Hype: Scaling GenAI for Enterprise-Level ROI

Beyond the Hype: Scaling GenAI for Enterprise-Level ROI

Most generative AI projects in big companies fail because they aren't joined up, don't focus on making money, and staff aren't ready to use them. Only a few companies succeed by starting small, making sure each project has a clear goal and owner, and teaching workers new skills. Winners measure real business results, not just fancy numbers, and quickly stop what isn't working. To truly get value, companies must focus on one strong use case, work as a team, and grow only when real success shows.

The Unseen Cost of AI: Navigating the Water Footprint of Generative Models

The Unseen Cost of AI: Navigating the Water Footprint of Generative Models

Generative AI models like ChatGPT need a lot of fresh water to keep their computers cool. Huge data centers can use up to 2 million litres of water every day, which adds up to 560 billion litres each year around the world. This heavy water use is a problem in places that already don't have enough water. Some new technology and rules are trying to help, but every time you use AI, it quietly uses water behind the scenes. When you see AI answers like this, remember there's a hidden river maki

The IC CEO: How Airtable Leveraged AI for a $100M Turnaround

The IC CEO: How Airtable Leveraged AI for a $100M Turnaround

Airtable's CEO, Howie Liu, led a bold AI transformation after a viral tweet challenged the company's direction. He became the main user and builder of Airtable's AI tools, focusing on rapid product changes and cutting out layers of management. Teams worked faster, launching new AI features weekly and slashing the time to build new products. After a year, these changes helped Airtable make over $100 million in cash flow while reducing costs and outpacing competitors with smarter, faster software.

Claude's Transparency Playbook: Redefining AI Accountability for the Enterprise

Claude's Transparency Playbook: Redefining AI Accountability for the Enterprise

Claude, the AI from Anthropic, is changing how companies think about honesty and safety in AI. Unlike most AIs, Claude explains its answers, admits mistakes right away, and shares safety reports with everyone. Anthropic created new rules for showing how Claude works, including full reports on any problems or abuse. This open way of working is now used as a standard by important groups and companies. Claude proves that being open and truthful makes AI more trustworthy for everyone.

The AI-Driven Decision Environment: Architecting Competitive Advantage in 2025

The AI-Driven Decision Environment: Architecting Competitive Advantage in 2025

In 2025, companies that combine AI agents, realtime data, and human experts into a single decision system make better and faster choices than those using only traditional leadership. This special AIdriven setup helps teams move 20 - 35% quicker, make fewer mistakes, and stay ahead in the market. As data from billions of devices grows, old ways of working just aren't fast enough. Smart businesses are building clear, fair, and secure systems where people and AI work together, making sure every decis

Machine Unlearning: Navigating AI Governance and Data Privacy in 2025

Machine Unlearning: Navigating AI Governance and Data Privacy in 2025

In 2025, machine unlearning has become essential for AI companies to erase sensitive or copyrighted information from their models. Strict privacy laws now force businesses to quickly and efficiently remove private data or risk huge fines. New techniques let companies erase data from AI without retraining everything, saving time and energy. However, "forgotten" data can sometimes still pop up, so the process is not perfect. Big tech firms are racing to adopt these tools to protect use

GM's Marketing Metamorphosis: Driving Profit and Innovation with AI and Data-Centric Strategies

GM's Marketing Metamorphosis: Driving Profit and Innovation with AI and Data-Centric Strategies

General Motors is using smart computers and lots of data to make its marketing stronger and more profitable. By using AI, GM creates ads that change quickly, finds out what customers want, and predicts when cars need service. This has helped them sell more cars, save money on ads, and earn more from services. People from different teams work together, using both data and human ideas, to make sure everything is clear and smart. Now, GM is leading the way, showing how car companies can use technol

AI Transformation in 2025: Navigating Critical Bottlenecks for Enterprise Success

AI Transformation in 2025: Navigating Critical Bottlenecks for Enterprise Success

In 2025, big companies face three main problems with using AI: keeping important knowhow before employees leave, making AI work all over the business instead of just in test projects, and getting people to trust AI decisions. Most companies struggle with these issues, which can hurt results if not fixed. MIT Sloan created a helpful guide with tools and examples to solve these challenges, including ways to save expert knowledge, grow AI projects, and build trust. The guide is free to download wit

From Pilot to Profit: Scaling AI for Enterprise Impact in 2025

From Pilot to Profit: Scaling AI for Enterprise Impact in 2025

In 2025, companies are rushing to use generative AI across their whole business, not just small pilot tests. CIOs are making AI a top priority and seeing big returns, but only if they focus on strong data systems, clear goals, and helping people adapt. Getting rid of extra tools, building better data rules, and tying each AI model to real business results are key steps. Without these, projects get stuck, budgets go over, and gains stay small. The right moves can turn AI from an experiment into r

Anthropic's Talent Playbook: Redefining AI Retention Through Culture, Not Compensation

Anthropic's Talent Playbook: Redefining AI Retention Through Culture, Not Compensation

Anthropic keeps top AI talent by focusing on a strong, ethical company culture instead of huge salaries. They use special interviews to find people who care deeply about safety, ethics, and teamwork. Engineers join Anthropic even though other companies offer more money, because they value autonomy and the chance to do good work. Their unique culture helps them keep more engineers than big names like Google, Meta, or OpenAI. Other companies are starting to copy Anthropic's focus on mission and va