Whether or not your organization has waded into AI or is still just tipping a toe into the edge of the pool, you’ve probably concluded AI is here to stay.  So what should you be thinking about, in infrastructural terms, as you consider how best to incorporate AI into your operations?

Tech writer Anhil Prabha takes up that question in a recent review of a survey by PwC that looked at the range and extents of planning businesses have done to prepare for AI programs.  To be sure, the survey wasn’t specific to healthcare, but when it comes to IT infrastructure there’s often no small amount of crossover and, thus, plenty of lessons to be learned from other sectors.

For example, when considering structural changes to accommodate new AI, “the emphasis should be on organizing the enterprise for Return on Investment (ROI) measures and momentum. Scaling up by moving AI models into production will ensure operations enhance decision-making and provide forward-looking intelligence. Businesses should set up a diverse team that has business, IT and specialized AI skills in an organizational structure that crosses functions, and be responsible for identifying use cases, and how to develop accountability and governance.”

Again, the language is obviously pointing toward organizations whose primary focus is the bottom line, but it’s not hard to see how guidance can be applied to healthcare organizations, too.

Similarly, workforce considerations loom large regardless of whether the organization is focused on healthcare or not.  For example, the article notes, “workplace culture is also seen as a big pull-factor for job seekers who crave organizational excellence, resources, definition of roles, exciting research and individual empowerment.  Currently, the challenge is to fill jobs. Upskilling can create citizen users and developers, but you’ll likely need to hire highly-trained programmers and data scientists. Forming partnerships with colleges or apprenticeships is a suitable place to start.”

Next, healthcare organizations have long recognized the importance of trust, particularly when it comes to patient relationships.  Consequently, they should be able to manage AI-related issues rather easily.  “The key tenets of responsible AI revolve around fairness, interpretability, robustness and security, governance, and finally ethics.  . . . Job roles which combine technical expertise with an understanding of regulatory, ethical and reputational concerns can only be a good thing.”

There is one area in which Prabha points to healthcare, and that concerns what he calls the opportunities for “reinvention” that AI can offer. “AI in healthcare,” he notes, “could enable new business models based on monitoring patient lifestyle data; quicker and more accurate diagnoses of cancer and other diseases; and personalized and adaptive health insurance.”

Ultimately, Prabha says, “every company considering AI implementation should establish a strategy with their own distinctive organizational structure and workforce plans, trustworthy algorithms based on the right data and a dedication towards reinvention— converging existing and emerging technologies— with the ultimate aim of growing revenue and profit.”

Or, put in healthcare terms, of improving care and outcomes for your patients.