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 an MIT article on measuring AI project value, a piece on why your organization might not be deriving value from GenAI, and an article consolidating and summarizing all of the on-going trends in the data revolution. Follow us on Twitter and LinkedIn to receive daily updates on IIA content and curated content as it becomes available.
Featured Articles on AI and Machine Learning
What Leaders Should Know About Measuring AI Project Value (MIT Sloan Review)
Most AI/machine learning projects report only on technical metrics that don’t tell leaders how much business value could be delivered. To prevent project failures, leaders need to press for business metrics instead. This article explores how to extract such metrics to ensure project success.
Enterprise large language models — small and nimble is smart (VentureBeat)
For enterprises, foundational LLMs offer broad applications, but specialized, smaller models trained on domain-specific data prove more effective while ensuring accuracy and privacy. The shift towards open-source, fine-tuned LLMs enables cost-effective deployment, empowering businesses to develop customized AI models efficiently. Ultimately, the future of AI integration lies in tailored, nimble models that align with specific organizational needs and objectives.
External Data and AI Are Making Each Other More Valuable (Harvard Business Review)
In private equity and venture capital, firms have invested in using external data sources often referred to as “alternative data,” a broad term used to describe information sourced from outside a company’s internal systems, including social media chatter, news feeds, government reports, industry databases, anonymized credit card transactions, and satellite imagery. For private investors eager to stay on the cutting edge, there are significant opportunities: from identifying potential investment opportunities and conducting due diligence, to adding value post-investment. While these approaches have been honed by investors, they also offer models for how companies across industries can use alternative data.
Featured Articles on Analytics Strategy and Culture
Your Organization Isn’t Designed to Work with GenAI (Harvard Business Review)
Many companies are struggling to derive value from GenAI because of a fundamental flaw in their approach: They think of GenAI as a traditional form of automation rather than as an assistive agent that gets smarter — and makes humans smarter — over time. The authors suggest a framework, Design for Dialogue, for reimagining their processes to mirror the back-and-forth collaboration of human dynamics to create an effective and adaptable human–AI workflow. At the heart of the framework are three primary components: task analysis, interaction protocols, and feedback loops.
Navigating the Data Revolution: Exploring the Booming Trends in Data Science and Machine Learning (KD Nuggets)
This article recaps many ongoing trends in the ever-expanding data revolution like this AI-driven task automation, NLP's exponential growth, ethical considerations in AI, decentralized machine learning at the edge, and interdisciplinary collaboration. The article also covers use cases across finance, healthcare, content generation, and smart cities which underscore the practical applications driving this revolution.
Featured Articles on Use Cases
The Future of Strategic Measurement: Enhancing KPIs With AI (MIT Sloan Review)
Traditional Key Performance Indicators (KPIs) are increasingly inadequate in today's rapidly evolving business landscape. To address this, organizations are turning to artificial intelligence (AI) to create smarter, more adaptive, and predictive KPIs. This long-form article serves as an introductory guide for how to improve your KPIs with various AI tools