Intelligent automation has become an essential tool for driving operational efficiency, and organizational leaders are struggling to identify which processes are prime candidates for intelligent automation. Leaders should balance long-term strategy with realizing quick wins to begin their intelligent automation journey, laying a strong foundation to enable enterprise roll out for intelligent automation and artificial intelligence more broadly.

Crafting an "A" team is a blend of art and science, demanding a thoughtful talent assembly and a culture that learns from mistakes. Strategic planning and steadfast commitment lead to innovation and optimized performance. Establish clear metrics and accountability within a well-structured hierarchy to navigate challenges and drive your organization to its full potential.

AI in healthcare offers transformative solutions for health equity, enhancing diagnostic accuracy, administrative efficiency, and personalized care. Leveraging AI can address disparities, improve access, and deliver tailored treatments, making it a crucial tool for achieving equitable healthcare outcomes across all demographics, fostering a fair and just healthcare system.

Effective strategy links business goals and objectives to the actions and resources required to achieve them. Use a systematic process to describe an organization’s vision and mission, evaluate strengths and opportunities, and develop strategies to achieve its goals. Understanding the why of what you’re doing, where you’re going (vision), and how you’re going to get there.

Change management requires substantial resources, dedication, and persistence. They cannot simply be put on executives’ “to do” list. The line organization should be supported with systematic planning and a programmatic organization. These measures require investment and there are calculated trade-offs required. Yet if organizations neglect to take these steps, their digital investments will fail to yield a payoff, and competitors and incumbents continue to siphon away customers, and start to extend their competitive advantages at accelerating rates.

Classical Machine Learning techniques have achieved incremental improvements over time and are fundamentally limited in certain areas. In contrast, neural network-based Deep Learning techniques like CNNs, RNNs are unique in that their accuracy scales almost infinitely with the amount of available data. One of the most well-known applications of Deep Learning, autonomous vehicles, would not be practically feasible without modern Deep Learning techniques.

Effective decision making is part art, part science. In the rapidly changing times of AI and Intelligent Automation, thoughtful disagreement and conflict in the pursuit of truth should be encouraged (and not avoided). Leaders should be willing to share perspectives openly and shift opinions as they learn. A hierarchy should not exist as decisions should be rooted in underlying reasoning, supported by data, not the position or title.