We like to start our work at a project with a top down analysis to find out if and how we can be of help. As a partner to you we thrive on the success of your organization. These steps help us understanding how to best serve you.
Is now the right time to look into Artificial Intelligence solutions for my organization?
With few exceptions the answer to this question is yes. Across all industries digitalization and implementation of artificial intelligence solutions are becoming competitive advantages. Falling behind in setting up effective critical business processes is increasingly risky.
Define what problem Artificial Intelligence is helping to solve?
Healthcare and medicine are riddled with many problems that with the right approach turn into data science opportunities. Here are a few examples.
Cost of care
Inability to meet patient expectations
Increasing demands for documentation
Complex and time-consuming workflows
Compound development and affordability
Customer retention and cost of acquisition
Analyzing and anchoring the key issues of an organization internally is time well spent. Once this step is taken it makes perfect sense to start looking into artificial intelligence as a potential solution.
Analyze the true costs of the problem?
Putting a dollar value to the cost of the problem is a very good exercise. In the end of the day all organizations need to justify an investment by a return on interest. Accenture has reported in some industries, AI investments are set to boost revenues by over 30% over the next four years.
State how AI can be used by your organization?
Start looking at data in the context of areas of pain. If you already have abundant data and preferably machine-collected data in an area of pain this is a good start. Next have a look at your workflows and talk to the organization that work in this particular area. Do they see a potential for a change? Invariably the result of introducing artificial intelligence solutions leads to a reengineering of business processes and a significant change.
When there is commitment it is time to dedicate staff to help build the artificial intelligence solutions. Someone with clout and access to decision makers facilitates change and someone accountable at the IT department is also critical.
Avoid getting lost in details or caught up in the maze of causality
Working with artificial intelligence brings pitfalls. One common pitfall is getting lost in details. Few people have a full comprehension of the math and logics behind artificial intelligence and spend an unnecessary amount of time getting into the nitty gritty. Put your focus on the needs of your organization and how the business process reengineering will make you more productive and you will be headed for success.
Another trap is the causality trap. Trying to understand every single step in value creation or putting an exact and constant value to the technology is often a tremendous time trap; especially so in healthcare. Breaking down value creation between humans and artificial intelligence in clinical decision support systems can lead to a philosophical challenge in establishing scientific theory.
Carefully select the key KPIs resonating with your organization and have faith they will guide you through the process.
Implement the AI solution
Obviously beating our own drum we recommend partnering with an AI consultancy company for the implementation. AI implementations are so complex by themselves and oftentimes pilot projects create domain specific challenges to solve along the way. In addition, there is the process change and the inherent organizational change that goes with it.
Going with a vendor that has made software installations including AI before will both bring most experience and seniority to your project and affect outcome. This allows your team to focus on the roll out and monetizing the change while you can hold the seasoned vendor accountable to the timeline and performance.
Encourage your change transformation team and celebrate wins
Business process reengineering can be both risky and difficult. There is the political risk by sticking one’s neck out and getting tied to a project that may not be favored or by default as successful as intended. Bear in mind, also AI projects carry an implementation risk.
So, make sure to adhere to fast prototyping and include reality checks. Prep your team and equip them to manage difficulties and empower them with relevant KPIs. Teams that can pull this off are worth its weight in gold so reward them generously and celebrate wins.