Technology Adoption Lessons from the Past
If you were part of the Cloud boom in the early 2000s, you saw massive changes in digital technology. Services like Amazon Web Services, Microsoft Azure, and Google Cloud revolutionized the tech world. Today, many of us cannot imagine plugging in a hard drive from a local technology store to save photos, documents and work materials.
We are once again at the crux of a significant transformation in technology. The rise of artificial intelligence (AI) has arrived–and it is expanding rapidly. Today, AI is at the forefront of technology conversations, even though it was an aspirational innovation not too long ago. We know that maintaining technological relevancy and driving industry innovation requires swift adaptation, as illustrated by the transition to cloud-based tech.
Harmonizing essential strategies is crucial for any enterprise to thoughtfully adopt new tools while maintaining a focus on sustainability and security. Furthermore, one individual implementation won’t change everything, and continued experimentation is needed to develop insight-driven processes that significantly impact how we deliver efficient software, increase business value, and enhance productivity.
MRO’s Enterprise Approach to AI in Healthcare Technology
Just as many technology-driven organizations do, MRO has been leveraging AI long before it became a worldwide buzzword. We began our artificial intelligence journey in 2021, experimenting across the organization with the goal of increasing business productivity and enhancing products and services.
MRO thoughtfully took lessons learned from the enterprise-level approach of the Cloud days, to find great value in innovative AI, proving great results. Our approach is simple: ideate, experiment, evaluate, adopt and scale.
Our technology team has a robust pipeline of ideas for the AI enhancement of products, allowing us to operate more efficiently. We even have a library of ideas that have the potential to completely disrupt the way we do business as a leader in healthcare technology.
We pull ideas from the pipeline to do targeted experiments, evaluate the results – and if successful, shift our focus to scalability – rinse and repeat. It’s easy to get lost in the hype of AI, but using this pipeline-driven approach helps us stay grounded and focused on the most valuable opportunities. When our experiments succeed, we scale them enterprise-wide, and if they do not, we quickly pivot and move on.
Throughout the entire process, protecting clinical data is our top priority. We activated several working groups to maintain discipline and ensure the security of the data we are entrusted with, while also leveraging AI efficiencies. These groups focus on AI guidelines, policies and procedures, along with validation and assurance. This means that any AI technology we introduce into our architecture and products must first be vetted at the highest level of scrutiny to protect clients, their patients and our ecosystem. Safeguarding the privacy and security of all data is the most important work we do here at MRO. Our team inspects every tool and ensures that the policies on using those tools are put in place before development begins. At MRO, we are proud to say that we have the right practices in place to experiment with artificial intelligence in a safe and secure environment, uniquely mindful of our clients’ needs, as well as their patients.
A Simple Example of AI in Software Engineering
One of the foundational pieces of delivering working software is quality. For a software engineer, creating a quality solution is like cooking a 5-star dinner for hours and being met with the less-enjoyable job of washing the dishes. For an end user, having software delivered to them with bugs feels like they are the ones stuck doing the dishes.
Delivering quality software that works, builds trust with end users without needing multiple iterations. We strive to build once and deliver quality the first time, so that our end users trust us to provide a product that works as expected and have a seamless user experience. AI has become a great multiplier, helping our teams to continue to meet this expectation.
Building quality software requires a developer to think about every possible scenario that could cause their software to break. This is where AI has become essential to delivering quality, working software. Using AI to help create test cases and the software to run them speeds up our development and reduces some tasks that many developers do not enjoy.
Wondering how it happens? Here is some ‘back of the napkin math’ to give you perspective on the impact to productivity:
- Let’s assume that 1 unit of work needs 5 lines of code and requires 10 quality tests.
- 10 automated quality tests could take a software engineer 1 hour to build.
- However, utilizing AI allows our teams to build those 10 quality tests in a couple of minutes – including validation.
Now let’s scale this across the organization:
- Our team delivers thousands of units of work a year.
- Keeping it simple, if we have 1,000 units of work, and look at the math above, we can put 1,000 hours back into a software engineer’s hands – which is half of an engineer’s annual working time!
At MRO, our momentum in driving AI into our software engineering lifecycle has created an opportunity to deliver even more to our clients. Imagine getting 1,000 hours back for your team to optimize…what would you do with it? This is just one illustration of the benefits of AI in technology operations.
What AI Technology Adoption Has Taught Us
We have learned many lessons from experimenting with artificial intelligence at MRO, but one vital takeaway is ensuring we have the proper people in place to drive adoption and scale. Establishing a thorough and clear implementation plan is the key to software engineering AI productivity.
MRO utilizes highly skilled software engineers to build the correct algorithm modeling foundations to ensure our solutions will scale. These engineers have extensive experience beyond coding – they have deep knowledge in architecture practices, technical principles, testing validation and driving quality across complex solutions. Through this foundational expertise, MRO is able to maintain the AI quality we want from the systems we build, while also giving our more junior engineers the confidence to adapt with speed and precision.
We expect AI to help us continually maintain our already low change failure rate, increase our test coverage and provide more value to our clients. As we implement artificial intelligence tools, a single implementation won’t change everything – it will take several experiments targeted in the right areas that add up to create significant change in how we deliver efficient software, increase business value and grow productivity.
Today, MRO is experimenting with AI via machine learning, robotic automation, and much more. By weaving artificial intelligence technology into operations beyond just internal productivity, MRO strives to improve and elevate our clients’ experience and enhance compliance with technologically advanced products.
To hear more about how MRO is utilizing AI, listen to our recent podcast here.
