While everyone seems to leverage the power Artificial Intelligence (AI) and Machine Learning (ML),but only few can define their objective. Besides leaders are often disappointed when AI and ML do not yield the results as expected by them, even after months of experiments, and proof-of-concepts.
Setting the Right Approach
One of the factors that must be kept in mind with regard to AI and ML that they are not about solving all business challenges immediately. AI and ML present evolutions in technology processes. Success comes from quality data, managed by highly qualified data scientists.
To reap the benefits of AI and ML, businesses need to set expectations. They must match what organizations hope to achieve by applying AI and ML, and what is currently realistic. Businesses must also line up the hurdles that must be leapt over to get to the best results.
It is all about Experience
Having spent several years working on different proof of concepts, with AI and ML technologies, we at NGA Human Resources have learnt important fundamental lessons. There are three key areas of capability that businesses need to be successful with AI and ML. Firstly, it is the skills and experience of people . Error! Hyperlink reference not valid.Machine Learning is dependent on complex mathematical calculations, wherein the role of data scientists become crucial who understand statistics and the maths that underpin both AI and ML. The areas of maths include linear and logistic regression. Both statistical models are used to describe data and to identify the relationships between variables. To apply these statistical models to supervised learning, support vector machines are used. While the process is difficult to do manually, there are various other methods, such as linear discriminant analysis.
However, many organisations start by experimenting with AI and ML tools. Most often, these fail to deliver the expected results...
AI and ML technology can seem like a bewildering Swiss army knife— lots of different tools, each with very specific purpose. Each tool requires an expert who can use it for its purpose, and in the right sequence.
There are many who claim to have experience in AI and ML technology
These vendors are often in a neckand-neck race to constantly improve their toolset. The rate with which AI and ML technology is developing is extraordinary. This makes it almost too easy for such vendors to start a project using these tools. As such, to leverage the real benefits of AI and ML, businesses must consult real expertson the toolsets they need. People with domain knowledge can explain the meaning of the datausing machine learning techniques. Such experts understand the specific data fields.
AI and ML need Good Quality Data,
Many AI and ML initiatives are working on gigabytes of data. To get good results, terabytes or even petabytes are needed. Huge volumes of data are essential for the statistical models to produce good results.
To the contrary, enterprise data often have empty fields) or fields with incorrect data whichmakes for a poor set of training data. If stakeholders cannot train their machines well by using high volumes of complete data, they can never get great results.
Experience shows that the preparation of a good set of training data is key to good outcomes from machine learning experiments.. It is resource intensive to clean and improve the data. Without this investment, businesses are unlikely to reap the full potential of machine learning.
Best Results Come from Hard Work and Scientific Approach
While everyone wants a piece of AI and ML, it is not a silver bullet. It requires hard work and a highly sophisticated scientific approach. There are no shortcuts.