How to start implementing artificial intelligence
Artificial intelligence-led services, among others, are already permeating our lives, with many more business use cases being analyzed and new technologies developed.
As rapid advances begin to change industries, markets and the competitive landscape, how can a healthcare organization explore whether AI—and its branches of machine learning and deep learning—makes sense for implementation?
There’s a lot of buzz, there are plenty of gray areas. Many executives are under the impression they’ll have to invest in AI to stay competitive, but they don’t yet know how AI would fit into their organization’s business model. At the same time, there’s a plethora of companies, both established (Google, Microsoft, Amazon) and entrepreneurial (H2O.ai, DataRobot, Skytree) ready to help organizations attack problems with open-source and proprietary tools and methods and arrive at an informed recommendation for investment.
Where do you begin? First, you don’t need to understand everything about concepts like neural networks, Bayesian network inference or regression to get going. Start with an exploratory perspective and open mind.
Here are five recommendations for preparing to test drive AI in your organization:
• Consider AI when building your strategic goals. Look at AI as a means to an end—not the end in itself. AI should advance a strategy, not dictate it.
• Align projects with tangible business goals. Does an organization you aim to improve call center service? To reduce employee time spent on repetitive, manual processes? Create a list of applicable use cases with clearly defined success criteria.
• Gain the agility to pivot as needed. Having a centralized analytics team or innovation hub can help an organization hone valuable skills and stay abreast of advances in AI technology. Consider the balance of analytical versus domain expertise and understand the limits of an organization’s technological computing power.
• In this explorative phase, get to know AI players in the marketplace. What do they bring to the table? Attend a conference or symposium to see who’s out there. Connect with other groups that are open to exploring these emerging technologies and share lessons learned.
• Choose a focused pilot project and the right approach for the situation (i.e., proof-of concept). For example, one healthcare organization might look into predictive outcomes of a care practice within a therapeutic area. Another organization might pick an operational function and look at current work processes with high, manual touchpoints to see where there are opportunities to gain efficiencies.
