How to Identify AI Use Cases for Your Business [+Free Template]

Key Takeaways

• The three most important steps for choosing the right AI use case are identification, prioritization, and selection.
• Identifying and prioritizing AI use cases based on impact, feasibility, and alignment with strategic goals is essential for maximizing ROI.
• Start with small, high-visibility AI projects to demonstrate value quickly and gain support for broader initiatives.
• Ensure scalability of AI solutions to maximize long-term benefits and support future business growth.
• Executive sponsorship and cross-functional collaboration are key to overcoming challenges and driving AI projects to success.

What Are AI Use Cases?

AI use cases refer to specific applications of artificial intelligence technologies within a business context. These can range from automating customer service interactions with chatbots to leveraging machine learning (ML) algorithms for predictive maintenance in manufacturing. 

The right AI use cases can streamline business processes, reduce costs, enhance productivity, and ultimately improve organizational efficiency and decision-making capabilities.

Examples of AI Use Cases

AI use cases vary widely across enterprises, reflecting each company's unique business needs and goals. However, they often fall into common categories, such as creating smarter products and services, automating routine tasks, and optimizing manufacturing processes.

Here are some of the most common AI use case examples:

• Customer service: 

Automating customer service agents to answer common customer queries using conversational AI, improving response times and customer satisfaction.

• Predictive maintenance: 

Utilizing AI to predict manufacturing equipment failures reduces downtime and maintenance costs.

• Security & fraud detection: 

Applying machine learning to analyze vast amounts of transaction data to detect and prevent fraudulent activities.

• Marketing:

Using AI to create personalized marketing campaigns by analyzing customer data, identifying patterns, and predicting customer behavior. 

• Sales:

Leveraging AI to analyze sales data, optimize pricing strategies, and identify potential leads, increasing revenue and sales efficiency.

• Technology:

Implementing AI to enhance software development processes, automate testing, and streamline IT operations, improving productivity and reducing errors.

• Data:

Employing AI to manage and analyze large datasets, uncovering valuable insights, and supporting data-driven decision-making across the organization.

How to Identify Your Best AI Automation Use Cases?

Identifying the right AI use cases for your business involves a strategic approach that aligns with your overall business objectives. These are the steps we take with our clients, and that we recommend you take, to discover where AI can deliver the most value:

1. Define Clear Business Objectives

Start by outlining what you want to achieve with AI, such as cost reduction, efficiency improvement, or enhanced customer experience. Having clear goals will guide your decision-making process.

Andrew McKishnie, our Senior NLP Engineer always emphasizes:

“The most important thing is getting everyone to understand the purpose of the AI you’re building. We’ve had situations where someone from the client side comes in in the finishing stages of the projects and asks why the solution doesn’t do other things.“ 

This highlights the importance of clear communication from the outset. When business objectives are well-defined and communicated effectively, it ensures that the AI solution being developed remains aligned with your original goals. This avoids confusion and ensures that the project delivers the intended value.

2. Assess Available Data Quality and Quantity

AI models thrive on data, making it crucial to assess the quality and quantity of the data you have available. High-quality, well-structured data is essential for training AI systems effectively and ensuring accurate predictions and insights. 

Start by evaluating the sources of your data: 

• Is it reliable, 
• Is it up-to-date, 
• Is it relevant to the problem you're trying to solve? 

Additionally, consider the volume of data you have. AI models, particularly those based on machine learning, often require large datasets to perform well. Insufficient or inadequate data collection can limit the model's ability to learn and generalize, leading to suboptimal results.

3. Identify Pain Points in Existing Processes

Look for repetitive and time-consuming tasks in your operations. Processes prone to human error or requiring significant manual labor are prime candidates for AI automation.

Andrew recommends starting with menial tasks, upskilling your employees, and understanding that upfront costs will save you a lot of money in the long run.

“Such use cases are the easiest to automate and will generate an ROI faster. This means they’re also great for convincing on-the-fence stakeholders and team members. Fast ROI makes it easy to gain trust in AI.“ — Andrew McKishnie

4. Evaluate Potential Impact and ROI

Speaking of ROI, consider how AI can impact your business in terms of cost savings, revenue generation, and overall efficiency. Prioritize use cases that offer the highest return on your investment.

5. Consider Technical Feasibility

Not all AI projects are feasible with your current resources. Assess whether your team has the necessary skills and whether your infrastructure can support the implementation of AI tools.

If gaps exist in expertise or capacity, consider outsourcing certain tasks or partnering with a specialized vendor to ensure successful execution. This approach can help bridge resource gaps and bring in the necessary expertise to tackle more complex projects.

Also, consider evaluating the scalability of the AI use case. Will you be able to expand accordingly and maintain the solution as your business grows? Ensure your AI use case can adapt to increasing demands without significant overhauls.

6. Align with Regulatory and Ethical Considerations

Ensure that your chosen AI use cases comply with relevant regulations and ethical guidelines, especially in sensitive areas like data privacy and security.

7. Gather Input from Stakeholders Across Departments

AI implementation affects various parts of your organization. Involve stakeholders from different departments to identify use cases that align with the company's broader strategic goals.

Securing buy-in from stakeholders is crucial to ensure you have the necessary time, personnel, and financial resources for the project.

How to Prioritize AI Use Cases

Not every identified AI use case is suitable for development into a full project. Various challenges can obstruct progress, including technical difficulties, data limitations, organizational barriers, and staffing constraints.

Taking a few preparatory steps can greatly contribute to AI's smooth and successful implementation. 

Once you've identified potential AI use cases, it's important to prioritize them to maximize business value. Here's how:

Create a Scoring Matrix

Develop a scoring system that evaluates each use case based on criteria such as:

• Potential business impact
• Implementation complexity
• Data readiness
• Strategic alignment
• Resource availability

Let’s explore each criterion a bit further:

Potential business impact

Evaluate the potential for each AI use case to generate significant value for the business. This could involve improving operational efficiency, reducing costs, increasing revenue, or enhancing customer experience. 

The greater the potential impact, the higher the score this use case should receive.

Implementation complexity

Assess how challenging it would be to implement the AI use case and create an implementation roadmap. Consider factors such as the complexity of the underlying technology, the need for integration with existing systems, and the level of change management required. 

For example, here at Multimodal, we try to find the best way for you to interact with the model based on your existing processes and desired end state.

Prioritize use cases with lower complexity for quicker implementation.

Data readiness

Consider the availability and data quality needed for the AI use case. As we have mentioned, high-quality, well-structured, and sufficient data is crucial for AI model accuracy and effectiveness. 

Use cases with readily available and high-quality data should score higher, as they are more likely to succeed.

Strategic alignment

Determine how well the AI use case aligns with the company's overall strategic goals. This includes whether the use case supports key business objectives, such as entering new markets, enhancing competitive advantage, or improving customer satisfaction. 

Give higher scores to use cases that closely align with strategic priorities.

Resource availability

Evaluate the resources required to implement each use case, including technical expertise, financial investment, and time. Consider whether you have the necessary resources or can acquire them within the project's timeframe. 

Prioritize use cases that require fewer resources or have available resources.

You must assess each use case against the scoring criteria. Use your scoring matrix to evaluate each use case objectively. Consider factors like data analysis and the potential for process automation.

Plot Use Cases on a Value vs. Effort Matrix

Visualize your use cases by plotting them on a matrix that compares their business value against the effort required to implement them. This will help you identify "quick wins" — high-value use cases that require minimal effort. 

Prioritize these "quick wins" for initial implementation. 

Focus on use cases that offer high business value with low implementation complexity. These are ideal for initial AI projects as they can demonstrate tangible results quickly.

You also have to consider long-term strategic importance. While quick wins are important, don't neglect use cases that align with your long-term business strategy. 

Balance your portfolio with a mix of short-term and long-term projects.

Ensure that your AI strategy includes both immediate wins and long-term initiatives that support your business goals.

How to Select the Right Use Case for an AI/ML Pilot?

Selecting the right use case for an AI/ML pilot is crucial for demonstrating the potential of AI within your organization. Given that machine learning (ML) is a core component of many AI solutions, it's important to choose a use case where the application of ML can clearly showcase its benefits.

Some of these steps overlap with steps for identifying the best AI use cases, but there are a few more you need to be mindful of to ensure success:

1. Start Small

Choose a narrowly defined problem with clear success metrics. This approach minimizes risks and allows you to make adjustments more easily as you gather insights from initial results.

2. Ensure Strong Data Availability and Quality

Select a use case for which you have access to high-quality data. High-quality data ensures that your models are trained effectively and can achieve meaningful results.

3. Select a Use Case with High Visibility

Choose a use case that will showcase AI's potential to critical stakeholders. High-visibility projects can help build traction and support for further AI initiatives.

Check out our customer story with Cactus, a prime example of a successful high-visibility project.

4. Choose an Area with Strong Domain Expertise 

Start with a use case where your team has strong expertise. Leveraging existing knowledge reduces the learning curve and increases the likelihood of delivering a successful project.

5. Prioritize Use Cases That Deliver Quick Wins

To build momentum, focus on use cases that can deliver tangible results within a short timeframe (3-6 months). Achieving quick wins can help gain buy-in from stakeholders and justify further investments in AI.

6. Consider the Scalability Potential Beyond the Initial Pilot

Ensure that the selected use case has the potential to scale across other areas of the business. 

Scalability is key to maximizing the ROI of AI initiatives.

7. Ensure Executive Sponsorship and Cross-Functional Support

Gain buy-in from senior leadership and ensure cross-functional collaboration. Successful AI projects require support from various parts of the organization and at different project stages.

8. Verify Alignment with Regulatory and Ethical Guidelines

Ensure that your AI use case complies with relevant regulations and ethical standards. Aligning with these guidelines helps build trust in AI and mitigates data privacy and security risks.

9. Assess Potential for Knowledge Transfer and Skill Development

Choose a use case that will allow your team to develop new skills and knowledge they can apply to future AI projects. This focus on skill-building ensures that your team is better prepared for more complex AI initiatives down the line.

Building a Roadmap for AI Use Case Excellence

Many businesses have already successfully implemented AI in their operations. By following these steps, you, too, can effectively identify, prioritize, and implement AI use cases that align with your business strategy, maximize ROI, and drive meaningful outcomes. 

This approach allows you to balance quick wins with more complex, high-impact initiatives, creating a roadmap that progressively builds your capabilities and continually enhances your operations. 

Ready To Discuss Your AI Use Case Over a Free Initial Call?

If you're considering implementing AI in your business and want to explore the best use cases, we're here to help. Schedule a free 30-minute call with our team! 

During this call, we can discuss your needs, explore how we can tailor our AI solutions to your specific requirements, and provide guidance for the engagement period. Let us help you unlock AI's full potential for your organization.