Advanced artificial intelligence (AI) techniques are improving business processes from the factory floor to the customer’s door.
Advanced artificial intelligence is used to its fullest advantage, AI has the potential to transform every aspect of business: from the supply chain and logistics to HR and marketing—even to customer care and beyond.
However, that potential often comes with roadblocks that could prevent a full realization of the intended benefits. Companies who implement AI solutions without first mapping out these possible barriers may not be allocating their resources in the best ways possible.
Each application has its own capabilities and its own inherent challenges. Let’s look at a few use cases to better understand what roadblocks might be present when using AI for business.
Artificial intelligence figures prominently in many of today’s sales and marketing platforms. AI-driven CRMs (customer relationship management programs) like SalesForce are invaluable for helping sales teams organize, nurture leads, and generally do more with the information they already have. However, if teams are not adequately trained in using a system to its fullest potential, a company runs the risk of wasting resources. Preparation for deployment should involve all stakeholders in an effort to bolster confidence in the AI’s ability to help move processes forward and improve efficiency and productivity.
AI has been instrumental in improving the function and economy of industrial systems. IIoT (industrial internet of things) sensors send data to the AI, which then analyzes that data—along with the data of all other machines connected to the network—and provides predictions as to when machines are bound to fail. In some cases, the AI can facilitate redundancy and repair before production feels the impact. By taking this task out of the hands of factory management, the human element is free to focus on higher-value tasks. AI intervention drastically reduces downtime and improves processes from end-to-end. With the help of AI, relevant data is delivered directly to the appropriate recipients—such as the ERP (enterprise resource planning system), the factory foreman, or personnel on the loading dock—instantly, accurately, and efficiently.
In this case, a possible roadblock can occur when changing systems over to take advantage of IIoT and AI technology. An established factory that has relied on manual systems in the past may be resistant to change because of the perceived impact on jobs and day-to-day processes in general. The investment could be significant, too; however, the cost impact of not making these upgrades could leave a factory eating dust from their competitors, including new startups eager to leverage the technology to their advantage.
We have all experienced AI in retail and customer service situations. It’s gotten to the point where the natural language-processing capabilities of some AIs are so advanced that they’re almost indistinguishable from humans. Not only has this served to improve customer satisfaction in industries ranging from retail to banking to technical support, but it has also established a minimum expectation among consumers everywhere.
In retail, as well as in many other marketing-centric businesses, personalization has proven its worth in improved customer loyalty and engagement—and, arguably, it has injected new life into brick-and-mortar retail that has seemed to adequately fend off the pervasive “Amazon effect.”
We can now receive recommendations for products based on past buying behaviors, or have Netflix choose programs that it thinks we will enjoy based on what we’ve watched before. However, to make these recommendations work, a lot of data needs to be collected, sorted, and processed far before any results can be seen.
Just as AI gathers data for customer service purposes, so too can it obtain data to gain business intelligence. This level of AI involvement requires the ability to collect and process data from many disparate sources. The question then becomes: what data is most useful? Ultimately, you want to make sure you aren’t creating more work for your business analysts, so you must determine exactly how your human analysts can get the most benefit from your artificial ones.
Used judiciously, AI can improve the ability to make important business decisions, automate repetitive processes, improve the accuracy of daily tasks, and support productivity, efficiency, and profitability from end-to-end.
Many companies still face an uphill battle when they make the decision to implement an AI strategy. It may be a financial issue, or it may be connected to finding a vendor that fits the company’s needs. Most often, however, it has to do with shifting gears from one workflow to a new, AI-powered one.
In the end, digital transformation is not an easy task any way you toss it up. The key is having the long-range vision to see what your company needs in order to grow, and then pairing those needs with the appropriate AI technology that can make it happen.
If you would like to learn more about artificial intelligence, machine learning, and what it can do for your Arkansas company, Business World can help. Call to set up a consultation today.
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