AI/Machine Learning -What is missing?
Many organizations, both private and government, are talking about and some even start implementing solutions based on Machine Learning (ML) or Artificial Intelligence (AI). However, there seems to be some hindrances to the widespread and speedy implementation of solutions based on ML/AI to reap its benefits.
In this article, I will try to scratch the surface by touching on one area of the obstacles to ML/AI adoption.
Lack of clear horizontal & vertical communication
ML/AI is not something to be left to the IT department. Just like the introduction of the personal computer at the work place and home affected us in many ways, even so more may be with ML/AI. For the most part, ML/AI is not something that is going to replace humans at the work place. It is just the new powerful tool that is going to cause newer opportunities to sprout and existing ones transformed. However, this new toolset may appear and sound intimidating until it is widely used and become ubiquitous.
The main issue observed today appears to be lack of a clear understanding of what ML/AI is, what it is not, what it is capable of solving in the context of the works of an organization. Finding a common language that is not riddled with jargon for communication, presenting simplified and concise problem statements, formulating clear solution statements and preparing proof of concept demos can go a long way for the success of ML/AI in an organization.
There may be more than one way of addressing this problem of lack of clear communication among the different stakeholders. One of them could be for organizations to establish a new position called something like “AI Liaison” as shown in the below illustration.
The “AI Liaison” is an individual or a small team of talented communications and business savvy people with a good amount of exposure to cutting edge technologies, more specifically on the inner workings of ML/AI systems. This person or team interact with the following stakeholders:
- Internal users or employees of the organization whose day-to-day activities could be affected by the introduction of ML/AI applications.
- Data analysts and anyone involved in data acquisition, processing, and archival of the organization.
- ML/AI applications developers in the organization.
- External clients and vendors of ML/AI products and services.
This position can be the missing glue that can be used to bring the ML/AI community of an organization together and act like the spark needed to fire-up the process. It is customary that Internal IT services users interact with the IT department to produce requirements documents and get an application developed or enhanced for them. Since regular IT services are mature and processes to implement solutions are in place, it may not badly require a special go between team. However, because it is new and there is a paradigm shift when it comes to ML/AI, the “AI Liaison” is invaluable in making sure the internal users, data analysts and ML/AI developers communicate clearly.
ML/AI solutions start by first formulating the problem through a serious of questions asked about a data we already have or we can acquire in the future. While the core problems an organization try to solve may be obvious and ready for implementation, the vast majority of problems the organization could solve may need a few brainstorming sessions among stakeholders. These exploratory sessions to formulate problems can be made productive by a talented “AI Liaison” facilitating communication & concept translation among users, developers, and data analysts.
Even as important as the above, an organization’s ML/AI program goes nowhere or face a very slow adoption without the blessings of its leadership. Here is where the talents of the “AI Liaison” become crucial. This liaison should be able to speak the business language leadership mostly speaks and sell the promises of the ML/AI process being setup using the input he or she gets when working with the different teams involved.
Having this position should not be considered redundant or wasteful. In addition to facilitating the internal ML/AI development process, they can be used to interact properly with external product and service vendors as well as potential clients of the organization’s ML/AI products.
Thank you for reading!
