AI Use case

Selecting the Right AI Use Case for Your Business

In the dynamic world of AI, investing in the right technology and making informed decisions is crucial for business success.

This post will explore optimising the total cost of ownership and ROI for AI by selecting appropriate use cases using insights from IBM’s AI Academy.

This blog post is based on insights from a video shared by Nicholas Renotte, Chief Engineer at IBM Client Engineering, on the IBM website. You can connect with Nicholas Renotte on LinkedIn. The video is part of IBM’s AI Academy.

Understanding the AI Landscape

Implementing AI in business is like strategising in a team sport. As Nicholas Renotte, Chief Engineer at IBM Client Engineering, explains, just as a quarterback or a playmaker organises their team’s approach based on real-time circumstances, businesses need a similar strategy to succeed with AI.

The Importance of Choosing the Right AI Approach

AI is not a one-size-fits-all solution. Generative AI (GenAI) is currently the hottest topic in the tech world, but there are better choices for some business use cases.

Misapplying AI can lead to missed opportunities, financial losses, and brand damage. Therefore, aligning your business problem with the correct AI technique is essential.

Key Considerations for AI Implementation

When implementing AI, several factors must be considered to ensure you are making the right decisions:

  1. Needs: Assess whether GenAI is necessary for your project. Often, existing machine learning or AI technologies can achieve the desired outcome at a lower cost.
  2. Capabilities: Evaluate if you have the necessary capabilities in place for the technology you intend to use.
  3. Integration: Consider how the new technology will integrate with your existing IT infrastructure.
  4. Skills: Ensure your team possesses the required skills to effectively utilise GenAI or other AI tools.

Optimising Total Cost of Ownership and ROI

To maximise the benefits of AI, you must align your technology investments with suitable use cases.

This approach helps optimise the total cost of ownership and ROI. Here are some strategies to achieve this:

  1. Evaluate Existing Tools: Before jumping to GenAI, explore whether existing models or tools can effectively address your needs. For example, generating a financial forecast might be more efficiently handled by current models than GenAI.
  2. Focus on Business Needs: Always match the technology to the problem, not the other way around. Identify the business problem first and then find the right technology to solve it.
  3. Leverage Existing Investments: If you have already invested in AI, GenAI, or machine learning technologies, explore how these can be utilised for your current use cases.

Making the Right Call

Successfully implementing AI involves continuous assessment and adjustment—it’s akin to making the right call on the field.

Businesses must gather all possible information, assess real-time conditions, and be proactive to pivot as necessary.

Conclusion

Choosing the right AI use case is critical for business success. By focusing on specific needs, capabilities, integration, and skills, businesses can ensure they are using AI effectively. Technology should always be aligned with the business problem to optimise ROI and total cost of ownership.

At NexaQuanta, we help businesses navigate these decisions, ensuring that your AI investments align strategically with your business goals. Contact us to learn more about how our solutions can help you select the right AI use case for your business.

Watch NexaQuanta’s on-demand webinar to see how we demonstrate the IBM Watsonx AI use case for regulatory compliance.

You can also contact us through our email at [email protected] or visit this page to schedule a Free Generative AI Strategy Workshop with us.

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