Read below for a roundup of interesting sites, resources, and articles from around the web, curated and contextualized by unbiased analytics experts at IIA. Highlights include a case study on Mayo Clinic's AI adoption journey, a piece on data preparedness for GenAI, and an article on how to tackle AI bias. Follow us on Twitter and LinkedIn to receive daily updates on IIA content and curated content as it becomes available.

Featured Articles on Analytics and AI Use Cases

Mayo Clinic’s Healthy Model for AI Success (MIT Sloan Review)

Mayo Clinic's journey into AI adoption has been remarkable, fueled by its size, tradition of medical research, and commitment to enhancing patient care. In this article, Ajai Sehgal, the Chief Data and Analytics Officer, sheds light on the organization's AI success, emphasizing an enablement-focused approach over heavy-handed governance. With a sprawling infrastructure and innovative initiatives, Mayo Clinic is pioneers in AI applications, from clinical advancements to administrative efficiency.

Will A.I. Boost Productivity? Companies Sure Hope So (NYT)

Economists doubt that artificial intelligence is already viable in productivity data. Big companies, however, talk often about adopting it to improve efficiency. Check out this article for a glimpse into what large companies are looking to for AI use cases.

Featured Articles on Analytics and AI Strategy

Is Your Company’s Data Ready for Generative AI? (Harvard Business Review)

While CDOs and data leaders are excited about generative AI, they have much work to do to get ready for it. A recent survey of 334 CDOs and data leaders — and a series of interviews with these executives — reveals that companies have not yet created new data strategies or begun to manage their data in the ways necessary to make generative AI work for them. Despite excitement, companies have yet to see clear value from generative AI and need to do significant work to prepare their data.

Getting Your Company’s Data Program Back on Track (Harvard Business Review)

Many senior managers find themselves wondering: If data is such a game changer, why is it so hard to extract any value from it? For companies struggling to actually see results from their data program, it might be time to make a fresh start. There are three important issues managers need to understand as they restart: 1) Too many companies have made the mistake of viewing data as a technology problem, but data is a management problem, and it cannot be solved with technology alone. 2) Many companies try to tackle issues that are too difficult straight out of the gate. 3) Data is a different sort of asset: intangible, nuanced, both potentially valuable and dangerous. People need to spend time working with it to understand its properties.

Featured Articles on Analytics and AI Leadership and Culture

5 AI Management Tools for Business Leaders (ODSC)

For many organizations, especially startups and small businesses, the cost of enterprise-grade AI tools can be a barrier that makes it seem that AI-powered tools are out of reach. This article explores five AI tools that either have free plans or run on freemium models and can revolutionize the business leaders operate on a day-to-day basis.

What Can a CIO Do About AI Bias? (InformationWeek)

In a world filled with bias, can AI algorithms ever truly be unbiased, and how can IT leaders address technologies' blind spots? Transparency may be key.

5 Powerful Strategies To Make Sure AI Doesn’t Steal Your Job (Towards Data Science)

This Spotify Data Scientist shares the practical steps they've taken to remove the constant stress and worry of impending AI replacement.

Uncovering the EU AI Act (Towards Data Science)

The European Parliament recently passed the EU AI Act, marking a significant step in regulating machine learning models affecting EU citizens. Focused on mitigating risks to health, safety, and fundamental rights, the Act imposes strict rules and penalties for violations. Check out the detailed breakdown of the act in the blog above