Daniel Fallmann is Founder and CEO of Mindbreeze, a leader in enterprise search, applied artificial intelligence and knowledge management.
Artificial intelligence (AI) has long since evolved from mere hype into a major competitive differentiator. Given its vast potential, a growing number of companies are pressing ahead with their plans to implement AI-based systems. But what exactly does the adoption of AI mean in concrete terms — for the management team, for an employee in a call center, for a doctor or for a research assistant?
There are so many different AI applications that it can be difficult to find the right place to start. After all, AI technologies can be used in virtually every corporate division to make processes more efficient, to find new approaches and to master tasks and challenges.
Here is a brief overview of the steps to consider before embarking on an AI project.
1. Identify a specific use case.
With so many vendors, models and approaches, finding the right entry point for launching an AI solution can be daunting.
Before starting the project, you’ll need to take a critical look at your own processes so that you can identify your specific pain points and, based on those past experiences, find potential entry points. To avoid getting bogged down, however, your initial focus will need to be directed at a very specific use case in one given department.
2. Define the criteria for performance success and the ROI.
Once a suitable use case has been identified, define some tangible metrics. These primarily include the requirements placed on the solution, the data sources needed, the data quality and the performance evaluation. Here are three meaningful questions to ask to help find which metrics are most applicable:
• Business needs. What should be achieved by implementing the solution?
• Data sources and data quality. What data and which data sources need to be considered so that the previously defined requirements and targets can be achieved?
• Performance evaluation. How can the solution’s performance be measured? (Define meaningful KPIs.)
3. Test with your own company data.
A proof of concept (POC) is considered a key milestone in verifying that the chosen solution will actually meet the requirements. Running a test with your own data is advisable so that potential problems can be identified early. Once the test has been successfully completed, all of the settings can be applied to your live operation.
But don’t forget to focus on data quality. Only a good database with a low amount of duplicates, errors and other garbled data will provide the ideal basis for extracting good results (otherwise known as “the garbage in, garbage out principle”). To avoid the “garbage,” the existing data in all the various data sources need to undergo a more detailed examination beforehand:
View and understand the data. Think about all the different data sources your company draws from and whether all these sources are relevant for the planned POC.
Data cleansing. The existing data needs to be subjected to a critical examination to determine whether all available file shares are still needed.
Process and link the data. The better the chosen product is, the less manual effort this step will entail.
These steps can be completed in a very short time and are enormously important for ensuring the quality of the results.
4. Involve the users.
The sooner your staff is involved in the process, the greater your chance of success. They know their processes and are in the best position to judge whether there’s still room for optimization.
The users also supply valuable input, especially when it comes to training the AI solution. They test the solutions and provide feedback actively — which is the only way AI can keep learning and expand its knowledge, deliver more accurate results and, ultimately, support you in your everyday work.
5. Validate the ROI.
Once the practical test has been completed successfully, check the results against the previously defined success criteria. Implementing an AI solution for a specific use case is often what “gets the ball rolling” enough to allow other departments to recognize AI’s added value.
For business, using AI opens up wide-ranging possibilities that can have a positive impact on a company’s strategic and operational position while generating competitive advantages. With that said, implementing the right system for your business in a well-considered and intelligent way is the real game changer.