insights1 Concrete AI use cases are crucial to gain profitability for AI



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Concrete AI use cases are crucial to gain profitability for AI Featured

Many CIOs are unsure whether investing in artificial intelligence is worthwhile. This is shown by a new study by Gartner. According to the study, companies should first identify concrete use cases before experimenting with AI.

Artificial intelligence has experienced a real boom in recent times. Whether private individuals or companies - everyone seems to be using AI in some form. But many may have implemented the technology prematurely without having clarified its actual benefits. At least, this is what a Gartner study conducted in 2023 among almost 700 IT managers in the USA, Germany and Great Britain suggests. According to Gartner, CIOs are unsure what value AI really brings to their company.

  • 95 percent of IT managers said they had already introduced AI in at least one business process. However, the topic still leaves many uncertain.
  • For 49 percent of the CIOs surveyed, the difficulty of estimating the value of AI projects was the biggest obstacle to implementation.
  • Other factors cited by respondents include a lack of employee skills, a lack of trust in technology, a lack of data, or a general lack of trust in AI.
  • 39 percent were hesitant to introduce AI because they could not find any use cases or the implementation did not align with business goals.

Mere experimentation is not enough

Concrete use cases and expertise are also central to the implementation of AI. It is highly recommended that CIOs first conduct pilot programs and establish criteria for measuring value before starting large-scale projects, as AI projects involve high ongoing costs.

Companies prefer to use GenAI

The study also found that generative artificial intelligence ( GenAI ) is the AI solution most commonly used in companies. In any case, the pressure on CIOs will continue to increase. The implementation of AI recently rose to fourth place among CEOs' top priorities.

Business benefits and challenges

AI/ML is increasingly being used to simplify, improve and scale various business functions, including

  • Data and analytics: AI/ML can automate the input, storage and security of data and also gather predictive business analyticsCustomer support: Chatbots and call classification systems use natural language processing (NLP) to serve customers quickly and route complex requests to the right channels.
  • Operations: Robotic Process Automation (RPA) uses software robots to perform repetitive tasks that were previously done by humans. In combination with AI, this allows unstructured data sets to be analyzed with a speed and accuracy that is not possible with human processes.
  • Marketing and sales: DL algorithms can help marketing teams gather analytics about customers to develop information-based strategies and personalized marketing campaigns. Sales teams can use AI to process information and develop leads quickly.
  • Human resources: Bots trained with AI base models can come in handy when evaluating applicant profiles in the hiring process. Employee satisfaction surveys can also be conducted and analyzed using artificial neural networks, allowing positive changes to be implemented quickly.

Companies prefer to use GenAI

The study also found that generative artificial intelligence ( GenAI ) is the AI solution most commonly used in companies. In any case, the pressure on CIOs will continue to increase. The implementation of AI recently rose to fourth place among CEOs' top priorities.

Nine use cases of artificial intelligence in industry

1. AI for customer experience, service and support: Chatbots, for example, use both machine learning algorithms and NLP to understand and respond appropriately to customer queries. And they do this faster than human employees and at a lower cost.

2. AI for targeted marketing: AI is also helping companies to carry out targeted marketing in the real world. Some companies have started to combine smart technologies such as facial recognition and geospatial software with analytics to first identify

3. smarter supply chains: Companies across all industries are using AI to improve the management of their supply chains. They are using machine learning algorithms to predict what is needed when and when is the optimal time to move deliveries. In this use case, AI helps companies create more efficient and cost-effective supply chains by minimizing and possibly even eliminating overstocking and the risk of running out of in-demand products.

4. smarter processes: As business process application developers build AI-enabled capabilities into their software products, AI is being integrated throughout the organization. There's AI in all the functions that support the business, like HR, finance and legal. The [software] itself uses AI, and team members are using the tool and may not even know that AI is being used in a way that supports their function. For example, AI can handle many customer queries; it can route customer calls not just to available staff, but to those best suited to fulfill the specific requirements. Meanwhile, retailers are using AI for smart store design, optimized product selection and monitoring in-store activity. Some are using AI to monitor stock on shelves in various ways, including the freshness of perishable goods. AI also has an impact on IT operations. For example, some intelligent software applications detect anomalies that indicate hacker activity and ransomware attacks, while other AI solutions provide self-healing capabilities for infrastructure problems.

5. safe operation: AI is being used by a variety of industries to improve safety. Construction companies, utility companies, agricultural operations, mining companies and other firms working in outdoor or wide geographic areas collect data from end devices such as cameras, thermometers, motion detectors and deep learning sensors. Companies can then feed this data into intelligent systems that identify problematic behaviors, dangerous conditions or business opportunities and then make recommendations or even take preventative or corrective action.

6. AI-supported quality control and quality assurance: Manufacturers have been using machine vision, a form of AI, for decades. But they are now expanding these applications by integrating quality control software with deep learning capabilities to improve the speed and accuracy of their quality control functions while keeping costs under control. These systems are providing more precise and ever-improving quality assurance as deep learning models create their own rules to determine what characterizes quality.

7. AI for contextual understanding: Companies are also using AI for contextual understanding, e.g. the use of surveillance technologies in the insurance industry to offer discounts for safe driving. AI is used in processing data about driving behavior to predict whether it is low or high risk. AI is used in a similar way for usage-based billing.

8. AI for optimization: Optimization is another use case for AI that spans all industries and business functions. AI business applications can use algorithms and models to turn data into actionable insights on how companies can optimize a range of functions and business processes - from work schedules to pricing for production solutions.

9. AI and more effective learning: The potential impact of AI on education is significant, with many organizations already using or exploring AI software to improve learning methods. There are so many ways in which AI can be used to improve learning.  Intelligent tools can be used to customize educational plans to meet the individual learning needs and level of understanding of each student. Companies can also benefit from AI-powered training software to upskill their employees. Overall, the use of AI in this area is still in its infancy, but will definitely continue to develop significantly over the next few years.

Further information on this topic can be found here: Gartner



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