Data, AI, & Machine Learning

Understanding large language models’ limitations can help users discern which tasks they are and are not well suited for.

Employees in connector roles can bridge the gaps between departments that often thwart data science project success.

The public availability of generative AI models, particularly large language models (LLMs), has led many employees to experiment with new use cases, but it also put some organizational data at risk in the process. The authors explain how the burgeoning open-source AI movement is providing alternatives for companies that want to pursue applications of LLMs but maintain control of their data assets. They also suggest resources for managers developing guardrails for safe and responsible AI development.

Effective governance enables KPIs to evolve, remain aligned with strategic goals, and gain workers’ and managers’ trust.

Research finds that managers who want to create better job roles can use an effective new assistant: ChatGPT.

Emerging machine learning technology could enhance sound creation and the detection and analysis of acoustic signals.

A data monetization matrix can help leaders assess opportunities and approaches for converting data into revenue.

With a modest amount of training, nontechnical employees can automate complex processes.

A clear, engaging, evidence-based guide to monetizing data, for everyone from employee to board member.

When organizations create forward-looking smart KPIs with AI, they see increased strategic alignment.