Illuminating Your Healthcare AI Journey, Part 3: Build vs. Buy in Healthcare AI

AI is your power source for the future. As an organization dedicated to healthcare, we want you to make it to your best destination. In this series, Lumiata walks you through the AI journey so your organization can: 

  • Build an AI-enabled strategy (not just acquire new technology)
  • Focus your AI investments
  • Streamline decision-making processes
  • Plot a path forward into the future
  • Generate material impact on your business

This series will prepare you to mature through the AI Journey curve, understanding what’s happened with your data, why it happened, predicting what will happen in the future, and then optimizing your actions and decisions. Check back for more in the series.

Part 1: Assessing Your Starting Point 

Part 2: Sharpening the Tip of Your Strategy Spear

If you walked with us through Part 2 of your AI Journey, you’ve taken critical first steps in sharpening the tip of your AI spear — establishing an AI-enabled strategy and evaluating your tech tools. This is the foundation that gets you past AI hype and helps you make the acquisitions that will carry you along the rest of your path and beyond. 

In this part of the series, we want you to make even more progress. This means evaluating your options in bringing on new AI tools and answering the build vs. buy (BvB) question, specifically in an AI context. 

Establish and assess your options.

Using your short list from Part 2, we’re going to finalize your options by bumping them against the AI Journey Curve. So, for example, ask yourself — as an organization, how advanced are our needs for predictive power? How sophisticated are our analytics capabilities? Are we at a stage where we’re just figuring out basic reports, or are we ready for autonomous systems? 

Answering these questions will help you identify stage-appropriate tools and avoid committing to solutions that are either too remedial or advanced for your plans. 

Answer the build vs. buy question — for AI.

The build vs. buy question takes on a new face in an AI context. 

AI is a high-stakes game. Healthcare organizations need to be strategic about BvB decisions. This means your executives will want to be familiar with the potential, limitations, and capabilities of the fundamentals of AI. (They’re off to a strong start if they’re reading this series.) 

Remember, these executives are key to your AI success

“…at the most successful companies, business leaders oversee A.I. initiatives. These executives, who control budgeting and resources, then build a group of data scientists and key personnel from departments like sales or marketing to oversee the A.I. project to completion.” 


That said, many technology leaders know the story of an executive that was seduced by the control of a build scenario, only to later learn a similar or superior commercial solution already existed — or they invested extensive time and resources, drastically slowing time to market, and leading them to miss out on other opportunities. We’re willing to bet that these stories are especially common in healthcare, where high-performing, industry-specialized software options have evolved significantly in the last few years, but awareness is still catching up.

Here’s the thing. There is no “right” answer to the build vs. buy question, especially in the AI space, where the supply of experienced talent has yet to catch up with demand. Also, few vendors are selling pure plug-and-play applications, meaning that you will work closely with a partner during the training stages and after run-time deployment. 

Going 100% on building your AI solutions can drain resources and rob you of progress — AI is a fast-moving space that’s almost impossible for an internal shop to keep pace with. At the same time, you have to be selective on the buy side, making sure that any solutions you do acquire give you flexibility to build around them. 

Your goal should be striking a balance between build vs. buy that fits best with your needs and that aligns with your strategy.  

Answer BvB strategic questions.

At this stage, conversations at multiple levels in your organization will be critical. Your staff has insights, connections, and wisdom from their network that will be invaluable to your progress. Leverage these when addressing the following questions:

  • How critical is the process that we’re considering for AI enablement in meeting our business goals?
  • Is this process powering our core business? 
    • If not, consider buying. 
  • Are we aiming to accomplish a long-shot, transformative goal, or are we targeting low-hanging fruit and trying to establish immediate value? 
  • Is this supporting us beyond AI, i.e., managing data that provides a single source of truth?
    • If so, consider partnering with a vendor for support in advancing in-house capabilities.
  • How well do we work with our own data?
    • If you’re doing a significant amount of manipulation or cleansing, vendor-enabled automation might be your best bet.

So, for example, if you’re considering an AI solution to streamline the patient experience, improve clinical decision-making, or support a population health initiative, you should be able to answer all of the questions above. 

BvB considerations

By and large, healthcare organizations could benefit from an AI-specific approach to the build vs. buy question. This means focusing on core competencies and looking at build vs. buy as a spectrum where you’re always in search of your optimal position. No one benefits from a 100% buy scenario where they give up total control of their business, and the same is true on the build side — unless building technology is the heart of your business, outsourcing should be at play in your automation strategy.

Back to your tool list. For each option, consider the following points: 


How will your decision impact your product roadmap? While buying from the right vendor can quickly get you close to 100% of what you’re looking for, only build options will give you complete control over the product roadmap. 


Buy options will make it easier to evaluate factors such as return on investment (ROI), total cost of ownership (TCO), and time to value (TTV). Build considerations create complications and unpredictable spend over time in the form of maintenance and staffing expenses, not to mention the opportunity cost of dedicating internal resources to fast-changing AI initiatives. Internal AI teams can easily end up spending extensive amounts of time cleaning data, distracting them from advancing the techniques that undergird the rest of your business. 

Staffing Challenges 

Building with an internal shop takes time and is a multiphase process. First, you have to assemble or modernize your team, but an internal build takes more than hiring a few AI experts — it’s a commitment to building out a robust software engineering practice consisting of data and machine learning engineers in addition to data scientists and analysts. This is a complex undertaking of properly vetting, evaluating, and training these professionals for an indefinite period. It’s worth noting that, in healthcare-specific applications especially, academic institutions are still catching up on producing qualified AI talent, and few have a depth of experience. 

Buying where applicable on the other hand, allows you to vault ahead and speed time to market by tapping into the critical momentum and energy of a vendor organization that has made deep investments in experienced AI talent.  

Evaluate your starting lineup.

For your final list of options, both build and buy, you should be able to evaluate the following:

  • Cost benefit analysis
  • Predicted impact on existing network and organizational/software ecosystem 
  • Expected lifespan
  • Success metrics 

Assemble your implementation team.

Even if you’re leaning heavier toward the buy side of the spectrum, consider creating an AI implementation task force or other qualified ad hoc committee. The conversations you’ve been having along your AI Journey will be helpful in identifying the most effective members of your team. Some roles to consider include: 

  • Data management: data engineer 
  • Analytics: data analyst and BI pros
  • Machine Learning (ML): data scientists and ML engineers 
  • Dev Ops: ML engineers 
  • CFO or financial analysts to determine financial impact in terms of cost and revenue 

Ultimately, as you navigate the changing waters of AI, you want to be in control of your own destiny. For the vast majority of organizations, this means exercising a right-sized approach to build and buy options to ensure you have the right accelerators in the right strategic spots. 

Steering your AI vessel through these waters is a resource-intensive task, which is where platforms that simplify your AI advancement provide amazing value and improve your speed to market. If you want support in finding areas to simplify your approach to AI, we’d love to talk that over during a demo.  

Want to learn more about the AI journey? 

The Build or Buy Dilemma in AI

Should You Build Or Buy Your AI?

AI technology: When to build, when to buy

Why Most Companies Are Failing at Artificial Intelligence: Eye on A.I.