Practical Strategies for Identifying and Prioritizing AI Use Cases in Your Enterprise

Headshots of Rupert Colbourne, CTO at Orbus Software, and Vikas Goel, AI Advisor and Director at Shiker Consulting, displayed in circular frames with a dotted background

Artificial intelligence (AI) has the potential to revolutionize businesses across all industries. However, with so much hype surrounding AI, it can be overwhelming to know where to begin. Our Orbinar, ‘Practical Strategies for AI Use Case Identification and Prioritization,’ tackled this very issue. Led by Rupert Colbourne, CTO of Orbus Software, and Vikas Goel, AI Advisor and Director at Shiker Consulting, the Orbinar provided a roadmap for identifying and prioritizing AI use cases within your organization. 

Understanding your AI readiness 

Before diving into specific use cases, Vikas emphasized the importance of assessing your organization's readiness for AI adoption. His framework, the "4Cs," acts as a compass. It comprises:  

  • Constituents: Do your stakeholders and executive team have a clear understanding of AI's potential benefits and limitations? Is there buy-in for the transformation journey? 
  • Capabilities: Does your organization possess the necessary data, technological infrastructure, and skilled personnel to support AI initiatives? 
  • Culture: Does your organization culture embrace experimentation and innovation? Are employees comfortable with the potential changes AI might bring? 
  • Compliance: Is your organization aware of the ethical considerations and data governance regulations surrounding AI? Do you have the necessary frameworks in place to ensure responsible AI implementation? 

By evaluating these four critical factors, you can establish a solid foundation for a successful AI transformation journey. 

The role of OrbusInfinity in enterprise transformation 

While Vikas's framework establishes your AI readiness, OrbusInfinity empowers you to leverage AI effectively throughout your transformation journey. Rupert introduced Orbus' approach to enterprise transformation which included a three-phased strategy directed at organizations wanting to optimize their AI adoption: 

  • Automate: Reduce manual work by automating data collection, integration, and other routine tasks using OrbusInfinity's advanced capabilities. This ensures high-quality data is readily available for AI initiatives. 
  • Accelerate: Gain deeper insights into your enterprise architecture landscape with OrbusInfinity's data visualization tools. This allows you to identify trends, discover opportunities, and make data-driven decisions for AI implementation. 
  • Augment: Empower your team to proactively manage your enterprise architecture by enabling them to plan, mitigate risks, and make informed decisions throughout the AI transformation process. 

The role of enterprise architects in AI transformation 

Enterprise architects (EAs) play a critical role in guiding your organization's AI journey. They act as strategic advisors, ensuring that AI initiatives align with your overall business goals. Here are some key responsibilities of EAs in AI transformation: 

  • Feasibility assessment: EAs evaluate the technical feasibility of proposed AI projects, considering both the capabilities of the technology and the business fit of the solution. 
  • Architectural schema and roadmap: They define the architectural framework and roadmap that will support your AI initiatives, ensuring seamless integration with existing systems and future-proofing your technology stack. 
  • Data governance: EAs establish robust data governance practices to ensure the security and compliance of AI solutions.  

Practical guide to AI use case identification 

Just like any powerful tool, AI needs the right application to be truly effective. Focusing on the right use cases from the beginning ensures your AI journey is efficient and delivers real value. Vikas categorizes use cases based on their goals and impact: 

  • Everyday wins: Focus on improving efficiency and productivity by automating tasks. 
  • Big changes: Transform internal operations with AI. 
  • Game changers: Create entirely new products or businesses with cutting-edge AI. 

Here are some key questions to consider when choosing an AI use case: 

  • Does it align with your overall AI goals? 
  • Will it make things faster or smoother? Can it free up your team for more strategic tasks? 
  • Can it turn data into better decisions? 
  • Will it improve how you connect with customers or employees? 
  • Can it spark innovation and create something new? 
  • Will it build trust with your customers? 

Once you've identified potential use cases, prioritizing them becomes crucial. Vikas introduced a framework with four quadrants to guide this process: 

  • Commit: These are your highest-priority use cases with the most significant potential impact. Focus your resources on implementing and scaling these use cases first. 
  • Observe: These use cases may not be feasible yet due to technological limitations or other factors. However, they hold high potential value and should be monitored for future development. 
  • Backlog: These are promising use cases that are relatively easy to implement due to existing data and technology. Consider piloting these use cases in a controlled environment to assess their value before scaling. 
  • Discard: While it's important to be comprehensive, having a few use cases in this category demonstrates a critical evaluation process. These use cases may not be the best fit for your current priorities. 

Key takeaways for leaders 

By implementing these strategies, you can enable your organization to identify and prioritize high-value AI use cases. Here are some key takeaways for leaders embarking on this journey: 

  • Define a clear strategy: Develop a clear vision for how AI will be used to achieve your business goals. Understand the specific use cases you want to target. 
  • Identify risks and governance: Establish a data governance framework to mitigate potential risks associated with AI, such as data privacy concerns and ethical biases. 
  • Invest in training and talent: Provide your team with the necessary training and resources to develop their AI skills and feel comfortable working with this technology. 

Identifying and prioritizing the right AI use cases is essential for a successful AI transformation journey. By assessing your organization's readiness, understanding the role of EAs, and following a structured approach to use case identification and prioritization, you can leverage the power of AI to achieve significant business benefits. 

If you weren’t able to attend or would like to rewatch the full session, you can access the recording here.     

Want more? You can also access our Generative AI Adoption Reference Model for further information on leveraging enterprise architecture to get more from your AI deployment.