Artificial intelligence (AI), robotics, automation – as well as less technologically-driven disruptions such as pandemics – mean the way our children and grandchildren are working will look very different to how we work today. We don't even have to look that far ahead to see change on a dramatic scale. It's been predicted that 85% of the jobs that will be available in 2030 don't yet exist! Factors such as the widespread shift to remote working, the emergence of the gig economy, and employees' increasing expectations of flexibility in their relationship with their employers will also play their part. Adding seismic shifts such as the great resignation into the mix means companies are frantically searching for new strategies when it comes to hiring and retaining talent.

RPA and cognitive automation are sometimes used interchangeably. While they are both important technologies, there are some fundamental differences in how they work, what they can do and how CIOs need to plan for their implementation within their organization. Key distinctions between robotic process automation (RPA) vs. cognitive automation include how they complement human workers, the types of data they work with, the timeline for projects and how they are programmed. CIOs also need to address different considerations when working with each of the technologies. RPA is typically programmed upfront but can break when the applications it works with change.

The work of the future is no longer about enhancing physical capabilities. Instead, it's based on enhancing intellectual work, complex cognitive decision-making and human insights. According to a 2019 Accenture study, 75% of global executives believe AI is critical to their company's future in the next five years. This is a phenomenal statement, yet effectively implementing AI is not an easy task. There are challenges to scaling an AI strategy, and I'll explore a few approaches to this in a future article.

The integration of artificial intelligence (AI) into acute care brings a new source of intellectual thought to the bedside. This much needed help should be embraced, if proven effective. However, there is a risk that the present role of physicians and nurses as the primary arbiters of acute care in hospitals may be overtaken by computers. While many argue that this transition is inevitable, the process of developing a formal plan to prevent the need to pass control of patient care to computers should not be further delayed. The first step in the interdiction process is to recognize; the limitations of existing hospital protocols, why we need AI in acute care, and finally how the focus of medical decision making will change with the integration of AI based analysis.

Given the state of existing systems, CRE management teams struggle to make educated decisions quickly. They lack real-time analytics on their performance, industry benchmarks, valuation and cash flow analysis, investment and debt management. We have already seen that a lot of sensitive information (such as lease rates, sales comparables, rental values, etc.) is kept secret by CRE brokerages. This makes it more difficult to compile reliable forecasts and budgets and to project future market trends. Additionally, data analysis is predominantly time-consuming and manual.