Leveraging Artificial Intelligence (AI) To Scale AI Transformations Posted on : Sep 21 - 2020

According to a recent McKinsey survey, just under a third of organizations use artificial intelligence for multiple business functions. Scaling AI transformations appears to be the most important challenge in this arena, and based on my experience, the key limiting factor here is that AI projects are typically human-workload intensive, resulting in lengthy and expensive projects.

But what if AI could help organizations implementing AI?

New technologies and concepts have recently come to the market to help accelerate and improve the AI implementation process. While most of these technologies are still maturing, they have already delivered significant benefits to the organizations that have adopted them.

AI implementation projects typically include:

1. The identification and assessment of AI opportunities.

2. The design and implementation (including coding) of the AI programs.

3. The maintenance of these AI programs.

For each of these three steps, I will describe the new concepts available and their impacts.

1. Identification And Assessment Of AI Opportunities

AI opportunities can be identified at two levels: process or data. At the process level, two technologies are available: process discovery and process mining. At the data level, the technology is referred to as data discovery.

Selecting the appropriate AI opportunity to implement is critical. Nevertheless, process and data analysis, documentation, assessment and prioritization are workload-intensive. They consist of interviewing, observing, collecting and analyzing data. As a result, this phase often needs two to six months of work.

Process Discovery

The first process discovery technology was launched in June 2018 by Kryon Systems. Here are the key steps it uses:

1. Observation: A program is installed on the users' computers. While users are performing their day-to-day work, it seamlessly records their clicks, user interface objects and their process steps, and it takes screenshots. This data is sent to a machine learning application for analysis.

2. Process Assessment: After a few days of recording, you're left with a dashboard that presents a list of the processes that were observed. The system ranks them by their potential benefits of being automated, analyzing criteria such as the length of the process or the number of people performing the process.

3. Detailed Process Analysis: The dashboard presented should let you access documentation for each process, which comes in the form of flowcharts that show process variants.

In my experience, this type of solution can help accelerate AI implementations three to five times faster than normal while increasing the number of use cases discovered by about two. View More