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Executive viewpoint: Lessons learned with AI in health care

When thinking about using AI for your business, it’s essential you don’t start with the technology and then force it into a solution. The key is to partner with the business leaders to understand their most important needs, and then develop the technology strategy to address those needs.

Once you have those conversations and understand the business needs, ask a few more questions:

  • What are the AI technologies that can enable your business solution? 
  • What is the right talent and skill set that you need? (And should you try to hire, train or partner?) 
  • How do you access and curate the data you need? 

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Expert opinion: AI talent shortage

One of the main issues with AI is that it requires a lot of data. And that requires data scientists who know how to work with all of the data. The problem is data scientists are in very high demand. There’s an incredible amount of competition for their skills (and not just within health care).

AI is not a single algorithm or technology, and it’s more than just math or computer programming. You need people who can work with the tools and the data, but there’s more to it than technical skill. 

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Expert opinion: Secrets to implementing AI

When it comes to AI, you can ask all the right questions, hire all the right data scientists, train all the right machine-learning models, and still end up with challenges.

Before you launch any data science project, we recommend using a simple four-step outline. This is adopted from years of working with technology and data projects.

If you can answer these four questions, you’re going to have a fairly successful project. With every project that goes off the rails, we ask, “Where’s the four-step outline?”

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Q&A on artificial intelligence

  • What are some top AI myths?

    The reality behind a few common misconceptions is:

    • AI won’t replace everyone’s jobs 
    • AI algorithms won’t make accurate predictions with messy data 
    • AI can’t remove human bias in decision-making 

    Read more about this in our executive viewpoint.

    OR
    1. Educate yourself and become your organization’s thought leader. 
    2. Create a clear vision of what you want to do. 
    3. Think carefully about how to get the right talent.

     Read more about this in our executive viewpoint.

    OR
  • To do AI well, you need people who not only have technical skills (math, programming, data science). They also need to understand the business context and the right AI methodology. And they should be humble enough to know they don’t know everything.

    Read more about this in our expert opinion article on AI talent.

    OR
  • Your options are to hire, train or collaborate. Most organizations won’t have enough money to hire all the data scientists they need. So the best answer really is “all of the above.”

    Read more about how you can solve this in our expert opinion article on AI talent.

    OR
  • It’s relatively easy to work with test data because test data is static, historical and safe. But live data is constantly morphing, changing and fluid. Working in the wild brings a whole new level of uncertainty.

    Read more in our expert opinion article on AI implementation.

    OR
  • Before you launch any data-science project, we recommend using a simple four-step outline. It’s adopted from years of working with technology and data projects. If you can answer these four questions, you’re going to have a fairly successful project.

    Read more in our expert opinion article on AI implementation.

    OR
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Tech talks: Listen to the conversation

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AI advice for CIOs and IT leaders

Listen as our experts share practical advice from their own experiences.

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Hiring vs. training the talent you need

Hear how to fix your AI talent shortage and what has worked well for us.

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Challenges of implementing AI

Our experts talk about the challenges that always come when releasing AI into the wild.

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How to solve problems by collaborating

Sound advice to help you succeed with AI and data science in your organization.

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This unique combination of data, analytics and expertise goes into every Optum solution.

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