A large global resources company wanted to reduce its exposure to counterparty credit risk (and reduce the time it takes to make decisions by making them more objectively). Finance deployed an AI predictive model that pulled information from a variety of internal and external sources. These included the company’s own database of customers’ historical payment patterns.

Everyone has their own ideas about how the project should go, but they don’t always share these thoughts during the planning process. You end up getting halfway through your original timeline when you discover that one of the stakeholders has made major changes that will require twice the resources to get back on track. Outlawing project changes may seem like a drastic move to battle scope creep, but the benefits far outweigh the pushback you’ll get from implementing this option.

Smart machines are here to stay. Not to replace finance executives, but to make their processes more intelligent.

Featuring The Hackett Group’s Bryan DeGraw. Amid a panoply of mobile apps and cloud-based tools for business travel, employees have more ways than ever to generate expenses, which means that finance leaders require more efficient ways to manage them. For example, the expense management workflow finance leaders have long relied on entails approving expenses only

A large global resources company wanted to reduce its exposure to counterparty credit risk (and reduce the time it takes to make decisions by making them more objectively). Finance deployed an AI predictive model that pulled information from a variety of internal and external sources. These included the company’s own database of customers’ historical payment patterns