AI & ML SHOULD NOT BE A TECHNOLOGY LED INITIATIVE

With #chatgpt starting to gain huge interest, I thought it was important to take a step back for one moment. Let’s not forget why we may want to use #AI & #ML in the first place. The basic premise is that we can automate mundane repetitive tasks with high levels of accuracy, and gain data driven insights. Where humans are subject to fatigue, and quite frankly could be doing higher value work

It is useful for companies to look at AI & ML through the lens of business capabilities rather than technologies. Broadly speaking, AI & ML can support three important business needs:

1) Automating business processes

2) Finding insight through data analysis

3) Engaging with customers and employees.

One of the biggest reasons for AI project failure is that companies don’t justify the use of AI & ML from a return on investment (ROI) perspective. Many reports have shown that between 60% to 80% of AI & ML projects fail, depending on which report you read. Reports further identify that in companies who succeed, they were using the same technologies as those that failed. The main reason for their success was that they did not see AI & ML as a coding exercise. They approached it from a “which business problem required solving”.

AI & ML project ROI can prove more difficult than first expected. Far too often teams are getting pressure from upper management, colleagues, or external teams to just get started with their AI efforts, and projects move forward without a clear answer to the business problem they are actually trying to solve, or the ROI that’s going to be seen.

Start with the business user and requirements in mind. Figure out if you should even move forward with an AI & ML project.

If AI & ML is the right solution then you need to make sure that you answer “yes” to a variety of different questions to assess if you’re ready to embark on your AI & ML project. The set of questions you need to ask to determine whether to move forward with an AI & ML project is called the “Go No Go” analysis. Considerations for “Go No Go” are;

1) Business feasibility

2) Data Feasibility

3) Implementation Feasibility

Part of this assessment is ensuring the #datamaturity level of your #business is at at a sufficient level , to get maximum ROI from your AI & ML implementation.

Is your company offering any feasibility assessment services for what I laid out. If so I would be interested to have a conversation about a possible collaboration with AWS. Please inmail me.

Sources I used for my article

Forbes, Gartner and Ron Schmelzer Managing Partner & Principal Analyst at AI Focused Research and Advisory firm Cognilytica.

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