Costas Tsougarakis

Laying the Groundwork for an AI Breakthrough

By Costas Tsougarakis
Costas Tsougarakis

Digital transformation requires a clear vision, buy-in at every level, and significant investment. Here are three steps to streamline the process.

Big data and artificial intelligence (AI) have been billed as “the next big thing” for more than a decade. Theoretically, these approaches present R&D groups in the medical device industry with enormous prospects. Realistically, many organizations have trouble realizing their full potential. Some businesses have resisted or hesitated to adopt these strategies. Others made an early attempt to adopt them and are now starting their second or third iterations of “digital transformation,” perhaps with some pauses along the way.

As early adopters have learned, digital transformation doesn’t happen overnight. It requires a clear vision, buy-in at every level, and significant investment—both in finances and organizational resources. As part of their digital transformations, many organizations have taken the head-first dive into AI, expecting to reinvent their business operations quickly, only to become disillusioned when progress stalls. As these misstarts demonstrate, digital transformation efforts are most successful when the teams implementing them remain focused on practical, targeted applications that can realize business value. Here are three steps to follow to streamline the process.

Reconsider Legacy Data Management Practices

Digital transformation doesn’t happen without a concerted effort toward improving data management. It takes time and focused strategy to unwind the legacy systems that organizations have relied on for data management. While these systems may be functional for limited applications and simple data storage, they typically lack the data standardization and discoverability tools needed to collect and curate the high volume of data necessary for AI.

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As part of their digital transformations, MedTech companies must look holistically at their data management systems and standards, and create strategies to de-silo data, improve discoverability, and better facilitate the sophisticated level of data management that is necessary for successful AI. It’s very common for legacy systems to be a big part of the data siloing issue, as individual groups may have adopted their own distinct platforms and practices, without having to consider how these platforms can integrate at the enterprise level.

Siloing, and a “my data” versus “your data” mentality can be especially prevalent in global organizations. But when data is stored locally or otherwise made inaccessible, it cannot be used to derive deeper insights. Enterprises must work to foster an enterprise-wide culture of data sharing, which can better drive business success.

This is made easier by data management tools that address the common objections to data sharing, with features that log changes and allow for data processing without changing the underlying data.

Focus on Opportunities to Create Business Value

Many organizations are hesitant to invest in AI, thinking that it hasn’t proved its value in the medical device industry yet. But the real issue is that most organizations haven’t used AI in the right applications. Instead of trying to use AI to solve everything all at once, stakeholders should identify select use cases where AI can be used to address a defined problem.

Under this approach, a medical device manufacturer might define a specific business need or clinical use case and then work backward to determine if AI is a viable solution. These use cases might be in areas like image analysis or patient outcomes, with scopes narrow enough to pinpoint the types of data necessary to build and train a successful AI model.

Once the problem is defined, the research team must determine if it has sufficient volume and diversity of data to fuel AI training efforts. This strategy goes hand-in-hand with the data management overhaul described above. Without the ability to survey data at an enterprise-level, teams won’t have the information they need to evaluate the feasibility of potential AI projects. But given a clear picture of the organization’s data, researchers can better identify projects where they have the tools to succeed.

Gain Executive Buy-in Early

Executive sponsorship is key to success for digital transformation and AI projects, as they typically require sustained investments before starting to show financial returns. AI initiatives often demand collaboration and data sharing across multiple divisions and business units—and friction points are reduced when an executive is backing the effort.

Executives won’t be wooed by tech talk, but rather dollars and cents (i.e., a compelling business case). Taking proactive steps to identify a starting point with AI and connecting it to a measurable business outcome will make it easier to persuade business leaders that the project is worth the investment. Clear communication about project timelines—and identifying how long it may take to realize ROI—will help keep the project a priority. Once again, having a clear picture of the organization’s data can help research teams make better, more accurate forecasts of the metrics leaders need.

Get Started with Digital Transformation

The ability to leverage big data and AI is a true differentiator for medical device manufacturers. But to maximize the odds of success, organizations must begin by leveling up their data management. With the right tools and strategies, R&D teams can identify practical use cases that will show measurable ROI, and then begin their research efforts with data that is centralized, uniformly curated, and ready for AI training.

Although digital transformation takes a clear vision, the elements can build on each other. By laying the groundwork with the right data management framework, a compelling business case, and executive buy-in, research teams can help their organizations move to the next level of R&D.

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Costas Tsougarakis