Artificial Intelligence (AI) and Machine Learning technologies have steadily increased in number and capability over the past 10 years. They are much more accessible to Government leaders, staff, and policy makers due to the popular “Large Language Models” such as ChatGPT, Bing, Bard, and others.
According to the Harvard Business Review, the average worker has attended 13.5% more meetings since the COVID-19 pandemic, with many held online. Given the Federal government’s partial shift to remote work when feasible, it is increasingly important to consider how teams can enhance the effectiveness and engagement of online meetings.
The adage of “don’t bring us problems, bring us solutions” no longer works in the complex and ever-changing world of the Government. Problems are not so simple and to solve them, stakeholders involved in problem-solving need to agree on the problem they intend to solve.
At FEMA’s International Assistance System Concept of Operations Tabletop Exercise (IAS CONOPS TTX), multiple government organizations solved a national disaster emergency scenario. This exercise is a prime example of a tool called joint training.
Citizen expectations, digital transformation, new missions and mandates, and budget constraints are just a few of the challenges that Government agencies face so, the need to strategically problem solve happens frequently. Developing effective solutions for the challenges agencies – from tax administration to transportation security to law enforcement – requires a thoughtful and structured approach. These challenges contain myriad independent issues, multiple stakeholders with unique needs and points of view, and (commonly) pre-conceived notions regarding solutions not grounded in fact.
