Implementing Artificial intelligence Solutions: Are Your Operations Ready?
Artificial intelligence (AI) has certainly become a popular buzzword. But it’s more than that–it’s a critical new technology that too many companies have heard about yet aren’t certain how to use. Today, and every day for the foreseeable future, a multitude of industries and companies are contemplating how to incorporate AI into their operations. To implement such solutions into your company, though, answering a few questions about feasibility is essential.
Understanding the problems that AI can solve:
To understand if your company is ready to utilize artificial intelligence, you must first ask why you will be employing this technology. What problem are you trying to solve? Is there a simple solution before approaching something more complicated?
Without identifying a clear problem to solve, your company will not reap the desired benefits when utilizing machine learning. If you are looking for a solution to detect anomalies, predict an event or outcome, or optimize a procedure or practice, then you likely have a problem AI can address.
Once you have recognized a quantified problem for artificial intelligence to solve, you need to inquire about your company’s current collection of data. Data is not just a component of AI; rather, the two are intertwined. In order to employ AI, understanding the connection between the data you’re compiling and the problem you are striving to resolve is key. It needs to be data in which you can reasonably expect to find a pattern. Data — quality data — is imperative to the learning you seek from an artificial intelligence solution.
The fundamental aspects of AI solutions:
Data quality consists of three C’s: consistency, completeness, and compactness. Consistency refers to the importance of uniformity to prevent problems in analytics. Likewise, presenting a thorough, complete data set is essential to the learning an AI solution undergoes. Finally, compact data denotes the concision and singularity of the information to negate redundancy. With these three components, the data can be trusted for model building. Low-quality data can impact the performance of a machine learning model. An AI solution may not be able to deliver the results you need unless it has proper data to work with.
While data is an integral part of employing AI, the project can only be as successful as the team that works on it. Implementing artificial intelligence solutions within your company is not as simple as it may seem. A successful launch into the realm of machine learning requires a dedicated team with a clear understanding of the goal at hand. You need to get buy-in from within your company from the bottom up. The team will require individuals who are competent in working with and understanding the system. Likewise, there needs to be a cross-alignment between your business processes to ensure data connectivity.
An extensive IT infrastructure is necessary to administering an in-house AI model. A solutions model would include:
- A myriad of servers
- Method of data acquisition
- Storage of data
- A method to running code and algorithms
The greater the value of an AI solution, the more complex it may be to implement.
Other options to implement AI solutions:
The phrase “easy to say, hard to execute” certainly applies in the case of building up infrastructure to employ an artificial intelligence solutions model. Utilizing an in-house model can be a powerful tool for your company, but whether or not this can be successfully executed is dependent on your company. However, if you’re still interested in taking advantage of AI technology without confronting the challenges involved in using an in-house model, a handful of companies are willing to provide these solutions for you.
Outsourcing to a company specializing in AI models provides you with ready-made solutions to the challenges you are facing, without having to build the necessary infrastructure in-house. Companies working in the realm of AI solutions are willing and able to execute results, provided that the problems are measurable and trackable.
Answering these questions, and understanding how to implement artificial intelligence to provide a solution, is crucial to the preparation and success of the project. But if your company has an appropriate need for AI and an understanding of how to make it happen, AI is undeniably a powerful technology for your company.
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The article was written by Usman Shuja, General Manager Industrial IoT (IIoT) at SparkCognition. Prior to SparkCognition, Usman worked as a management consultant at the Boston Consulting Group (BCG) and in software development and sales roles at IBM. He holds a bachelor’s degree in Computer Science from the University of Texas at Austin and an MBA from the Kellogg School of Management at Northwestern University. Originally the article was published here.