Are Organizations Succeeding at AI and ML? Why organizations still make it wrong?
AI services on public clouds is available today cheap and easy to perform. AI and ML services started being used with business applications where the process of learning or finding patterns is not a requirement.
When AI is the shiny new hammer, every application looks like a nail. Most applications do not leverage in the right way without any business meaning. The organization CTO and CIO are all looking to present the shining tools to the management.
The larger issue is the misapplication of AI and ML for applications where these particular technologies don’t match and conflict with each other. This has been a problem since the advent of neural networks and AI, which is much longer than you think.
Applications that are good candidates for AI or ML are those that need to determine and assign meaning to patterns.
Think of the systems employed now on factory floors to determine product quality using image recognition and automation, or fraud detection programs in banking that look at transaction data.
Lack of training data to support the use of AI and ML
Data teaches the AI engine to assign meaning to patterns, and your AI engine is only as good as the training data available.
These days enterprises often don’t understand where the training data is located, what the single source of truth is, or what the data means. Data is everything in the world of AI. Knowledge is derived from data. If you don’t have a solid data source, and you don’t have an excellent understanding of the meaning of the data, AI won’t work for you. It is simple as it is.
AI/ML implementation fails often due to lack of internal resources
The failures underscore the complexities of building and running a productive AI and ML program. Lack of expertise within the organization is the main issue.
many enterprises don’t have the skills to select the right tools, build the right applications, and deploy AI and ML systems effectively. To get that talent is tough to find. It’s actually a pretty involved skillset of Cloud services, Cloud databases, Cloud AI and ML systems, and most importantly, the ability to configure the right technology to meet the needs of the business applications. Building the infra foundation and connect your data sources is essential for your organization.
This technology is powerful, a game-changer for many businesses, considering its potential. However, organizations need to focus on the proper purpose, understand their own data, and go after the right skills.
Organizations turn to trusted partners
Due to the high risk of implementation failure, the majority of organizations are to some degree, working with an experienced provider to navigate the complexities of AI and ML development.
IT decision-makers turn to artificial intelligence and machine learning to improve efficiency and customer satisfaction is the first priority and main goal of the organizations.
In conclusion before the start and diving headfirst into an AI/ML initiative, the best advice to any organization is to clean their data and data processes. In other words, get the right data into the right systems in a reliable and cost-effective manner.
In 2021, we believe we’ll see more organizations put this research and work into practice. There’s no longer a need to convince people that this is the way to go, as they’ve already gotten there. Now, it’s going to be a matter of bringing organizations the expertise to implement the responsible use of AI across their existing and future use cases.
