16 Keys to Succeed with AI Operations and Automation Implementation in Medical Device

AI Operations can be a game changer in medical device sales if implemented well

AI operations and automation can be game-changing tools for organizations looking to accelerate innovation, reduce administrative overhead, improve team efficiency, and positively impact revenue and cashflow. While powerful, these emerging capabilities require careful planning and thoughtful consideration to get the most value out of your implementation efforts. Use this Dos and Don’ts list as part of your ideation process when exploring these initiatives. 

 TO DO … 

Define clear objectives and strategy 

Every project should start with a clear definition of objectives and the strategy for achieving them. Why are you doing this? What outcomes do you expect? What does success look like for your organization? How will you get there? Who needs to be involved and why? Be very specific in your documentation. 

 Identify specific problems 

Most implementations are designed to help you solve problems. But these tools, no matter how sophisticated, can’t do that on their own. You need to clearly define the specific problems you intend to solve by building out AI operations, the outcomes you anticipate, and your baseline for success. 

Conduct a thorough data audit 

AI operations are driven by data. Whether you are training your own models or leveraging canned tools, you need to ask a variety of questions like: do you have the data you need for a meaningful implementation? If not, can you get it? Is the data in an appropriate format for use? If not, what extra effort is required, and by who? 

Identify internal capability gaps 

Success ultimately rests on the capabilities of your internal team, even if you engage external resources for the initial project. Take the time to identify any skill-gaps up front, and then build a plan to fill those gaps as part of the implementation project. 

Prioritize concrete value 

AI and automation should be about creating real, objective value for your business. Take time to clearly document the real-world outcomes AI and automation will help your team achieve. Be specific about how those outcomes help your business, how quickly the value will be realized, and how that value will be measured. 

Start with a small-scale pilot 

Define a small-scale pilot to introduce your initiative to a select group of stakeholders. Look for quick wins that demonstrate value to your pilot cohort to help with broader adoption. Most importantly, take pilot user feedback seriously, and appropriately address the issues raised. Be sure to leverage this stakeholder group during your production rollout.

“ Rather than wringing our hands about robots taking over the world, smart organizations will embrace strategic automation use cases. Strategic decisions will be based on how the technology will free up time to do the types of tasks that humans are uniquely positioned to perform. ” – Clara Shih

Invest in training for your team 

Both AI operations and automation tools may be brand new concepts for your team. Don’t assume they can figure this out on their own by watching YouTube videos or doing some Google searches. Plan to invest time and resources getting your team the training they need for these initiative to be successful. 

Ensure proper integration 

Integration is likely going to be a key component of AI operations or automation projects. Whether it is integration with core data sources, or with other tools and platforms that drive automated workflows, make it a top priority to get those integrations right the first time. Rework can undermine the whole initiative. 

Implement security measures 

Security is a primary concern with any new technology implementation. AI operations and automation introduce additional challenges. Make sure you understand the security policies controls for tools you select, and you ability to influence them with your own requirements. Don’t just assume third-parties care about your data. 

Plan for ongoing maintenance 

No technology is evergreen. There will alsways be maintenance and upkeep, including updates, security patches, and version releases/retirement. Include a formal maintenance plan, along with regular reviews of release notes, security updates, and your own internal knowledge base documents. 

DON’T DO:

Over-rely on AI without oversight 

AI is not infallible. In fact, it often gets things wrong. Why? Because it can only ever be as good as the data is uses to generate its recommendations, insights, or content. Resist the temptation to allow any AI tools full autonomy. Humans are required. Check the work. Correct as needed. Train as often as you can. 

Neglect change management 

Change is complex. The larger your organization, the more fraught with challenges it can be. Make sure you understand and then implement appropriate change management strategies when rolling out any new tool. And especially when implementing emerging technologies that have a higher risk of failure. 

Underestimate costs 

Just because “Google can do it” doesn’t mean it isn’t hard. It also doesn’t mean it won’t be expensive. Be realistic about the true cost of implementation and ongoing use. That includes both direct costs (what you pay for the tools) and indirect costs (the people, process, and infrastructure needed to make it work.) 

Rush deployment 

If it’s worth doing, it’s worth doing right. It can be tempting to quickly roll out new tools and resources. Resist that temptation. Instead, ensure your team has rigourously tested each tool or process, validated all implicated data, and thoroughly trained your early cohort of users. 

Treat AI as a one-time project 

The pace of technological change is speeding up, not slowing down. Any AI implementation should be viewed as an ongoing project. Both AI and automation tools will have a lifespan and will require periodic care and feeding. 

Try to replace all legacy processes 

Technology can solve many problems. It can’t solve all of them. Resist the urge to replace all legacy processes and workflows with AI or automations. In some cases, manual effort is the best solution. Make sure you are critically evaluating each process on its own and identifying the pros and cons of each and the risks associated with updating them using AI or automation.

“By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it. ” —Eliezer Yudkowsky

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