We know that the goal of operational AI is to scale human potential. We do this by leveraging Robotic Process Automation (RPA) to handle repetitive tasks so teams are empowered to take on more to scale human potential and productivity. This leads to streamlined operations, improved productivity and increased contentment in overall workforce.
I’ve talked a lot about what the path to implementation of an AI solution looks like in previous posts, so for this one I’d like to spend some time on the steps that follow a successful launch. Once your AI is up and running, you may find you have so many opportunities for your AI that you’re not sure where to begin. This is normal and also a good problem to have.
For now, I’m going to cover a few crucial steps at a high-level and assume that you’ve already identified the problem you want to solve with an AI solution. With that in mind, here’s what the roadmap looks like:
To explain these steps in a bit more detail, here’s a short explanation for each one. Keep in mind though that your own roadmap may be different depending on the need.
Map the Workflow
This is all about understanding what you want your AI solution to do—think of this step as the training phase of employee onboarding. This can include:
- List the steps, beginning to end, to complete this action
- Outline all potential obstacles with their respective solutions
- Identify all systems and access needed to complete the action
Code the AI
This is the phase when your AI solution is being defined and built by software developers. This can include:
- Define the various use cases for the development team
- Ensure the business needs align with the technical requirements
- Complete an informal review of the initial code
Test, Review, and Quality Assurance
This step makes sure your AI knows what it’s supposed to do and gives you the chance to verify everything is correct before going live. This can include:
- Let your AI complete the action in a safe environment to make sure it’s right
- Create multiple test scenarios to find for any potential errors or defects
- Allow your human staff to interact with your AI to check and verify the process
You’ve now launched your AI solution and are beginning to see the results come in. It’s time to make sure you got exactly what you wanted.
- Verify your AI is performing the action(s) you want correctly
- Communicate the launch out to your organization to build excitement and momentum
- Get feedback from anyone within your organization that interacts with your AI solution
No one plans for things to go wrong but it’s always a good idea to prepare in advance. You’ll also want an AI partner that’s invested in your success.
- List the employees and resources dedicated to the maintenance of your AI solution
- Create processes for troubleshooting and resolving any potential issues
- Make sure your team is seamlessly connected to your AI implementation partner
You’ll most likely have a lot of stakeholders invested in the success of your AI solution. Now’s the time to let them know just how successful you are.
- Identify the metrics you need to measure success and how to view them
- Overlay your expected results with the pilot with the actual results
- Look for additional benefits such as volume of work, cost savings, 24/7 operations, etc.
Continue to Iterate
This step can be a pitfall for some organizations where AI progress stalls so it’s important to have your list of priorities ready to go.
- Leverage your AI partner so can answer questions and understand your needs
- Improve the performance of your AI by closing gaps or adding additional actions
- Create the business case and project the ROI for your next AI phase
You might think that implementing an AI solution is daunting, but it doesn’t have to be. In fact, that’s one reason why we outline the steps above so everyone is on the same page. From here, it’s just moving from one point to the next in the roadmap. And once you get to your first launch, you’ll see just how easy it is to benefit from your own AI solution.