9 minutes reading
Identifying and prioritising AI use cases for your business is important to effectively meet specific challenges. To succeed with AI, these technologies must align with your business goals and needs. This guide provides you with tips on how to select the AI use cases that will be most beneficial for your operations.
The article is based on a study conducted by Ida Holmberg and Julia Nordgren from Uppsala University, in collaboration with our team. We would also like to extend a big thank you to all the companies that participated and shared their thoughts and information – you are invaluable.
Before we begin, it's good to have some background. A survey from O'Reilly shows that the biggest barriers to AI are a lack of skilled personnel and issues with data quality. Finding the right AI use cases is also a significant challenge.
There is a clear difference in challenges between companies that are already using AI in their production and those that are still evaluating its potential. For companies that are still assessing potential, the major challenges often relate to cultural readiness and the complexity of identifying feasible AI use cases. For companies that are already using AI, the challenges are more about data quality and technical infrastructure.
Source: Oreilly – AI Adoption in the Enterprise 2022
The first step on this journey is to take a proper deep dive into your company's business landscape. This means you need to have a clear picture of your strategic goals, understand the details of your operational processes, and identify the issues that are hindering efficiency or growth. Whether you want to improve customer satisfaction, streamline production, or optimise logistics, a comprehensive overview of your business will lay the foundation for identifying relevant AI applications.
AI needs data to function. Ensure that you have access to qualitative data and that your technical infrastructure can support AI. Without the right tools and systems, it becomes difficult to succeed. This step is important to ensure that your business is not just ready in theory but can also practically use AI technologies effectively. By thoroughly reviewing your technical infrastructure, you ensure that your business can receive AI and truly use it to its full potential.
Once you have a solid understanding of your business goals and technical capabilities, it is time to explore the landscape of AI for potential opportunities.
Once you have a solid understanding of your business goals and technical capabilities, it's time to explore the landscape of AI for potential opportunities. This involves researching AI trends, solutions, and success stories both within and outside your industry. Look for AI use cases that address similar challenges or objectives, and gather insights on how these technologies have been used to create value.
Once you know what AI solutions are available, it’s time to choose the ones that best fit your company’s needs. This means reviewing the problems or goals you have and selecting AI tools that can assist with those specific areas.
For example, if you want to reduce your costs, AI that automates tasks or predicts when machinery needs maintenance could be beneficial to use.
Once you have identified various AI use cases that could benefit your business, it is important to determine which of them are the most important to implement first.
This means that you need to examine each potential AI project and assess them based on three main criteria:
Let’s say your company has identified three potential AI use cases:
If your main goal for the year is to reduce costs, then predictive maintenance might be prioritised highest as it directly addresses this goal by reducing maintenance costs and unexpected downtimes. AI-driven customer service may also have a high priority if customer satisfaction is a secondary goal, while automated document management may be less critical and can be implemented later.
By carefully assessing and prioritising your AI initiatives, you can ensure that you are investing your resources where they can make the most impact and best support your business objectives.
Before carrying out a full-scale implementation, it is wise to conduct pilot projects for the AI use cases that are of the highest priority. These pilots serve as test environments, where you can evaluate the effectiveness of the AI solutions in a controlled situation. Analyse the results of these tests against your goals and make adjustments based on the insights you gain.
When your test projects with AI prove to be successful, it's time to start using AI more and in larger parts of your business.
Scaling means using AI in more areas of your company. If you started with AI for a specific task, such as improving customer service, you can now expand this to other areas, such as marketing or inventory management.
Integration is about getting the AI technology to work together with your existing systems. Instead of AI functioning on its own, you connect it to your other systems so that everything can work together seamlessly.
The AI landscape is constantly changing, and technological advancements are happening rapidly. It is important to have an attitude that embraces continuous improvement and to be ready to adapt your strategies to leverage new opportunities and meet new challenges.
By following these steps, you can ensure that your AI efforts are well aligned with your business goals and capable of delivering tangible value. If you need guidance or assistance in navigating the complexities of AI integration, Elvenite offers a workshop designed to help companies discover and develop the most valuable AI use cases tailored to your unique operational needs and strategic goals.
