Artificial intelligence (AI) may be one of the most impressive human achievements and offers endless opportunities for companies willing to foster this technology. As the benefits of main AI elements such as machine learning, data analysis and predictive analysis are undeniable, what are the few biggest challenges that companies may face while introducing AI to their day-to-day operations?
In the AI project’s initial stages, the key project stakeholders need to inform the business that the technology is not perfect and that its introduction might create some temporary inconveniences. Once the AI application gets deployed, it needs to be used and trusted to be continually improved. Unfortunately, learning and developing new skills and breaking up with old habits don’t come easily for some employees. During the project initiation phase, the company must provide lots of guidance and training to its employees on the benefits and opportunities that AI can deliver. That will help ensure that the employees understand the need and see how they can personally benefit from AI.
Fragmented systems are always an issue in any company. Systems may vary locally and globally within the same company and may not always cooperate in one eco-system. Lack of system interoperability may be an obstacle when deploying AI, as these systems generate data that is an essential component of any AI solution. It is vital to know or predict system standards, frameworks and possibilities. Using this information, a company should define how these systems can supply the required data and communicate with the AI framework.
Over the past few years, companies have generated more data than ever before. Data is the food that fuels AI, and manufacturing companies need to access this data efficiently. Before introducing AI in your company, the data access constraints should be minimized, ensuring that the relevant data sources and databases are easily accessible. Once you have access to the appropriate and comprehensive data lake, meaningful analysis and actionable insights can be derived. The proper data use can become an excellent opportunity for the company to win the race against its competitors. It is also imperative to remember that having access to the most massive data quantities is not the deciding factor for a successful AI project. It is more about selecting relevant data for the respective AI application, cleaning it up and applying the right analytical methods against that data.
Need for real-time response
Even with sufficient and complete AI data, you may face some technological constraints. Many applications can be significantly sensitive to latencies; for instance, predictive maintenance applications will only work when auto alarm mechanisms and rapid response are built into the overall process of handling predictive maintenance issues. That is especially true in high-volume, fast-moving production. Decisions need to be made in seconds, and this is where ultra-fast computing, together with the proper response process, can make a difference.