Artificial intelligence (AI) is becoming more commonplace, with applications in fields as varied as banking and finance, healthcare, and the media.

In a McKinsey 2020 worldwide poll, 50% of respondents said that their organizations had embraced AI in at least one business function. This percentage is expected to rise in the future years, leading to a 100% increase in AI-related income. 

AI has a huge potential, but it also presents significant challenges in terms of both research and application.

Managing AI Projects: The Challenges

The effects of AI on people and the economy are mind-boggling. By 2030, AI is expected to boost global GDP by $15.8 trillion. 

Assuming you’re intent on implementing AI projects at your organization, you’ll inevitably face 

certain challenges. Here are some of the most typical issues that arise during AI project implementation and management, as well as potential solutions.

Choosing the Right Data Set

The successful integration of AI in business depends on the availability and quality of the data used in training. To guarantee the most effective and timely AI capabilities, your business must employ appropriate data sets and have a reliable supply of pertinent data that is clean, easily available, well-governed, and securely stored. 

You can overcome the difficulties of applying AI tools by contacting specialists and working with the owners of various data sources.

Integrating AI into Current Systems

The difficulty of integrating AI into preexisting systems is the primary challenge when introducing AI in a corporate setting. It necessitates the assistance of experts in AI solutions who have substantial relevant experience and knowledge in the field. 

Making the transformation to AI frameworks requires more work than just installing some new plugins. It’s important to think about infrastructure, data storage, and intake, and to take precautions against any potential risks. The present systems and the future needs of AI must be compatible, and the former must run without a hitch. 

Once the switch is complete, staff members also need comprehensive training on how to use the new system. 

Data Security and Storage

AI development services need a constant supply of data to train algorithms. Big data helps streamline this process, but it also generates data storage and security challenges. More data and users increase the risk of dark web data leaks. 

Since millions of people create and use this data, security and storage challenges are apparent worldwide. Therefore, you must employ the best data management environment and AI application training methods to accommodate your business needs.

The Skills Gap

Finding and educating workers with the right mix of skills and expertise to implement and manage AI systems is often cited as a major challenge. A skill gap impedes an organization’s capacity to integrate AI technologies smoothly and slows down its progress toward full transformation. 

Because of the gravity of this problem, you should carefully consider allocating more resources toward AI project education, talent acquisition in the area of AI app development, or capability acquisition through licensing agreements with big IT firms.

A Lack of Computational Speed

The field of information technology faces a great deal of competition and has an ongoing need for further education and training. 

However, obtaining the processing capacity to handle the enormous amounts of data that are required for the development of AI systems is the most challenging obstacle that the industry has ever had to overcome. 

This is particularly difficult for newly established businesses and those with limited financial resources, especially in terms of achieving and funding the desired degree of computation.

Managing AI Projects: Solutions

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It’s easy to lose sight of the core challenges when executing a complex AI project and its subsequent management. The complexities described above might be difficult to handle, but taking the right steps early goes a long way to a successful outcome.

Identify the Problem

To have a better grasp of the data you already have at your disposal and to spot issues early on, such as incomplete, outdated, obsolete, or erroneous data. You should examine the data you already have on hand before moving forward 

Experiment

Your company may not have access to all of the data it needs to adequately address certain concerns or find solutions to certain problems. Find the means to get the necessary data within your organization, or get it from other sources if you don’t have the resources. 

Prepare and Manage Data

Get your information organized and ready to go. Data may need to be loaded into your data warehouse or data lake, cleaned, and aggregated before you can utilize it for further analysis.

Choose the Right Algorithm

Unfortunately, AI algorithms cannot be configured to regulate the flow of low-quality and erroneous data. So, determine the most effective strategy by trying out many different algorithms or iterations for it.

Leverage the Power of AI Project Management

Keep in mind the technical hitches that you may encounter when you bring data science, AI, and machine learning to your business, and don’t get entangled in the specifics of the technologies and toolkits. 

Give your attention to the things that matter more, like coming up with creative solutions to issues and asking intriguing questions. AI project management is one of the most effective technological advancements available today. In other words, learn to control your tools rather than the other way around.

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Author Bio:
Guido Voigt

Guido Voigt is the Director of Engineering, at Lantronix, a global provider of turnkey solutions and engineering services for the internet of things (IoT). Guido’s and Lantronix’s goal is to enable their customers to provide intelligent, reliable, and secure IoT and OOBM solutions while accelerating time to market.