Impactful use cases for AI in business

Zebra Technologies

By Darren Bretherton, Regional Sales Lead Software Solutions ANZ, Zebra Technologies
Friday, 05 January, 2024

Impactful use cases for AI in business

In the dynamic landscape of 2023, artificial intelligence (AI) has emerged as the focal point for businesses wanting to unlock greater efficiency, reduce costs and gain a competitive advantage. According to a 2023 Forbes article, AI “is the fastest-adopted technology in history”.

Notably, generative AI tool ChatGPT became the first tech application to reach 100 million unique users in its first two months. While it dominated much of 2023’s AI conversation, there are many other types of AI and use cases that deserve attention too. Many business leaders are only beginning to understand the power, reach and capabilities of this innovative technology.

That said, here are a few examples of immensely impactful — yet less talked about — AI use cases that businesses can use to save time, protect profits and get ahead of the competition.

AI for workforce optimisation

Preparing rosters and ensuring there are enough staff available to keep up with demand can be a time-consuming and error-prone task. Employees have diverse availability and skill sets, while variables such as the weather, promotional activities, standard workloads and seasonal events add to the complexity of the task. Unfortunately, manual attempts to forecast labour requirements can lead to overstaffing, understaffing, compliance issues, increased labour costs, disgruntled employees and dissatisfied customers. This is where an AI-powered workforce management solution can help.

Deployed in these types of solutions, AI can help analyse data from various sources to accurately predict labour requirements, thereby maintaining control over labour costs and streamlining labour scheduling. In addition, managers who have access to these tools spend less time in the back office considering staffing requirements and preparing rosters, and more time nurturing employees, serving customers and focusing on other value-added tasks.

Using AI to prevent inventory loss

Inventory loss is a common challenge, especially for retail businesses. Aside from making it difficult to maintain accurate and appropriate stock levels, inventory loss can impact the overall financial health of the company. In its recent 16th Annual Global Shopper Study, Zebra found that 87% of retailers in APAC believe they need better inventory management tools to improve accuracy and visibility.

There are several different root causes of inventory loss — mistakes in relation to point-of-sale (POS), unnecessary damages or waste, and online fraud, to name a few — which would traditionally be time consuming to identify. However, with the help of AI analytics, retailers can quickly be alerted to these incidents, and take corrective action to prevent recurrence or further losses.

Consider this scenario, for example: a fashion retailer is alerted by its inventory management system that a large number of damaged units have been returned and are slated for destruction. Following the alert, the retailer investigates the anomaly and discovers that the items were actually returns that were mistakenly scanned as “damages” by new employees. As a result of the investigation, the retailer successfully prevents the destruction of products and returns them to the store shelves, averting financial losses, out-of-stock issues and potential customer dissatisfaction.

AI for software testing and quality assurance

In today’s technology-driven world, the need for software applications to be reliable and functional cannot be understated. Software testing and quality assurance teams play a critical role in ensuring that new applications perform as expected before they are released.

Traditionally, software testing has been a laborious and time-consuming process, often prone to human errors. However, AI-powered testing tools can now automate repetitive test cases, allowing QA teams to focus on more complex scenarios and explore novel avenues for test case optimisation. Consequently, businesses that use AI for QA can deliver more reliable software releases.

In addition, AI can help QA testers create automation scripts that would usually demand a portion of software engineering expertise. Natural language processing (NLP) is an AI technology that allows testers to write test cases in plain language, which the AI-powered tools can then interpret and convert into executable scripts. This bridges the gap between technical and non-technical team members, making testing more accessible to all stakeholders and improving collaboration.

As AI technology continues to evolve, it is expected to play an even more significant role in the way that business functions are carried out. The challenge for leaders will be to determine which AI tools make the most sense for their business.

For some businesses, generative AI will be the go-to. For others, AI tools that simplify workforce management, prevent inventory loss, streamline software testing, or deliver something entirely different will deliver more return on investment in terms of efficiency, profitability and competitive advantage.

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