How Health System Leaders Invest for Enterprise Impact
AI and automation have drastically changed the world around us – today, industries large and small are turning to artificial intelligence to drive financial performance and efficiency, and healthcare is no exception. In fact, in the face of shrinking margins and unpredictable change, providers’ AI adoption more than doubled from 22% in 2018 to 51% in 2019. But even as healthcare AI investments continue to soar, many providers are struggling to achieve results.
RPA projects have a 50% failure rate1
The root cause? Healthcare leaders underestimate the resources needed to ensure AI technology can thrive in the industry’s dynamic environment and scale across their enterprise. Forrester recently reported that for every dollar spent on RPA software, $3.41 was spent on services to make it work. That’s why it’s important that health system leaders carefully consider the various purchasing models for healthcare AI – and the long-term cost of each – to ensure they’re investing in sustainable, scalable solutions.
Investing for impact: Cost vs. value
Implementing an AI solution means considering more than just the up-front licensing costs and the estimated people required to manage the project. It’s crucial that healthcare leaders understand their associated impact on cumulative business value to understand the total cost of ownership of AI. Choosing a model that accelerates results without straining your resources is key to reaping the benefits of the next wave of healthcare innovation.
AI can be transformational, but there are many critical services needed to drive AI productivity and payoff – establishing governance standards, ensuring ongoing maintenance, and analyzing system intelligence, to name a few. Health system leaders that understand the associated impact on their internal resources, required optimization costs, and perpetual impact over time will guarantee the impact and longevity of their investment.
Scaling healthcare AI & automation across your enterprise
Almost every major hospital is already using AI and automation, so why does scaling these technologies continue to be a hurdle for most health systems? A recent study by Accenture showed that 87% have failed to scale AI and RPA across their organization. That’s partly because deployment models to scale your AI workforce may carry similar costs, but time to value, rate of expansion, and internal resource requirements vary.
When considering whether to buy, build, or partner to implement your AI solution, ask yourself the following questions:
- Do you prefer one technology partner or several vendors?
- Do you have the talent & technology to gather and analyze intelligence from your automations?
- Will your platform equip you with intelligence & learning beyond your organization?
- Are you able to measure the impact of AI & automation across your entire enterprise?
Scaling AI successfully requires an implementation strategy that ensures your technology is built to lessen the burden on your internal resources, ensure ROI, and accelerate impact across your entire health system.
Comparing total cost of ownership for AI & automation models
Maximizing the long-term value of an AI workforce means choosing a deployment model that drives sustainable reinvention – want to avoid the common pitfalls in AI implementation? Read this white paper to better understand the total cost of ownership of various purchasing models for AI, so you can invest for enterprise-wide impact.
- The Cumulative Value & Cost Projection of Different Deployment Models
- How to Avoid Common Pitfalls of AI Adoption
- Key Considerations to Ensure Your AI Workforce is Primed for Success