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Determining if businesses can use machine learning (ML) and artificial intelligence (AI) effectively requires careful evaluation and consideration. Organizations across the globe are seeking innovative, impactful ways to use this ever-developing technology. While the technology itself is a hot topic, that doesn’t necessarily mean it benefits every organization. You see it often with tools and platforms as well, where companies pay big bucks for products they only use 25% of. A car company focusing on accessibility and affordability has no need for an F1 vehicle’s engine. That same analogy applies to businesses and the implementation of these functionalities. Understanding feasible use cases is the best first step if your firm is looking to implement ML/AI. Here are a few tips a business can keep in mind when evaluating the feasibility and impact of AI/ML usage .
Organizations should evaluate their existing challenges and pain points to determine if AI or ML can address them effectively. That doesn’t mean doing a quick scan of the organization with rose-tinted glasses. A true evaluation involves analyzing the processes, tasks, or areas where AI or ML could bring improvements. Businesses can then assess the potential benefits and make an informed decision on whether to pursue implementation. Contextual understanding of the technology and its capability to address these pain points is key. You don’t want to end up in a situation where a biased approach impacts true comprehension of what needs to be addressed and how to address it.
The benefits, if the evaluation is done correctly, can be multifaceted. Businesses can identify areas where manual processes are time-consuming or error-prone or where data analysis is complex and time-consuming. By pinpointing these pain points, organizations can determine if AI or ML can offer solutions that streamline operations, improve efficiency, and drive better outcomes. Augmenting processes amplifies your team members’ talents and enables them to focus on high-level problems, which is really where they should spend the majority of their time anyway.
AI and ML heavily rely on data, so organizations need to assess the availability and quality of their data. This is a rather large misconception plaguing conversations with executives seeking to utilize this technology. If there’s no data, there’s no AI/ML. Simple as that. That initial building block must be constructed before any implementation can occur.
Organizations must then evaluate if they have sufficient data to train AI or ML models and if the data is reliable and representative of their business processes. This is vital. Why? Just because you have available data doesn’t mean it’s of high enough quality actually to be of use. That’s another key aspect that gets glossed over when discussing implementing AI/ML solutions.
Another key aspect to consider is the scalability of their data infrastructure to handle the increased data volume that AI or ML implementation may require. The processes you have in place currently might work for now, but if your organization and its requisite demands grow, then it’s imperative whatever infrastructure is in place can handle the increased workload.
The field of machine learning and AI is constantly evolving, with new technologies and techniques emerging regularly. The same can be said of all fields, to be honest, but the breakneck pace of development regarding AI and ML stands out. By staying informed, businesses can identify new opportunities and innovative ways to apply machine learning and AI in their operations.
It’s ok to borrow brilliance every now and then. This situation is no different. The knowledge will help businesses understand the potential benefits and limitations of these technologies, allowing them to make an informed decision on whether or not to pursue their implementation. It’s not all that glorious to say that listening to podcasts and reading articles can help, but it’s the truth. Everyone in the industry will have a different perspective and understanding of this technology and its capabilities. Everyone has something to say and something to sell. So, it is important to internalize information with a grain of salt at times. Nevertheless, consuming those pieces of information will only help your organization inform and refine how it utilizes AI and ML.
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