Understanding the Business Case and Requirements Before Building Generative AI Applications
Generative AI is revolutionizing industries by offering unprecedented humain-like capabilities in content creation, such as text, music, image and video generation, and decision-making. However, the successful deployment of Generative AI applications hinges on a clear understanding of the business case and requirements.
This foundational step in the overall Gen AI System development life cycle ensures that the AI solution aligns with organizational goals, delivers measurable value, and operates within set parameters. Let's do a deep dive into why identifying the business case and requirements are crucial and how it shapes the AI development process.
Identifying the business goal is the #1 step
Identifying the business goal is the most critical phase of the Generative AI lifecycle. Organizations must have a clear idea of the problem they intend to solve and the business value they aim to gain from the AI system. This involves setting specific business objectives and defining success criteria. Given the constantly evolving nature of Generative AI (sometimes month to month), this step can be particularly challenging but is essential for aligning the AI solution with strategic business outcomes.
To begin, businesses should determine their criteria for success and evaluate their capability to achieve these targets. These targets should be realistic and provide a clear path to production. Early involvement of relevant stakeholders is crucial to align everyone with the project’s goals and any new business processes that may arise from the initiative.
Is Generative AI Even The Right Tool?
Before committing to a Generative AI approach, it's important to evaluate whether it is the most suitable solution for the business goal. This evaluation involves considering various alternatives and weighing their potential outcomes against the cost and scalability of the AI solution. Keep in mind that the cost of building a complex AI system, training the data, hiring the staff, and operating the systems after deployment may not be worth it when simple business rules might suffice for certain problems. Generative AI systems can sometimes take tens of millions of dollars to build... sometimes a lot more, and companies are betting the bank on it.
Align All Affected Stakeholders
Aligning all affected stakeholders from the start ensures that everyone understands and supports the business goals, fostering a unified approach to the Generativen AI project. Stakeholders should be engaged in defining the core business requirements and identifying critical features that the AI solution must have. Additionally, it's important to consider any new business processes that might emerge as a result of implementing Generative AI for their use case.
Defining the Data Requirements
The success of a Generative AI project relies heavily on understanding its specific data requirements. This involves identifying the types of data needed, assessing the quality and availability of this data, and ensuring compliance with data security, bias mitigation and governance standards.
Organizations must determine the types and sources of data required for the AI model, whether it be documents, images, sensor data, relational data, etc. The quality and completeness of this data must be evaluated to address any gaps or limitations. Understand if there is any erroneous data going into the system, the Gen AI will have be extremely expensive to operate and also produce bad outputs. Bad data in, Bad data out.
Most organizations do not have mature data management practices, so this should be addressed before building the AI system also.
Consider Security In Every Step
Security considerations include protecting data, systems, and assets, leveraging appropriate technologies to improve the security posture, and ensuring data permissions, privacy, and compliance with software license terms.
Robust data security measures must be implemented to protect sensitive information and ensure compliance with relevant regulations and standards such as GDPR and HIPAA.
Establishing a process to review privacy and license agreements for all software and AI components throughout the Gen AI lifecycle is essential. This ensures that these agreements comply with the organization’s legal, privacy, and security terms and conditions.
Performance and Cost Optimization
To maximize the benefits of Generative AI, it is essential to focus on performance and cost optimization. This includes determining key performance indicators (KPIs) relevant to the business use case and evaluating the cost implications of data acquisition, training, inference, and potential errors. By optimizing performance and managing costs effectively, organizations can ensure a higher return on investment (ROI).
KPIs should be directly linked to business value. This involves identifying metrics that can measure the effectiveness of the AI solution, such as accuracy, response time, and throughput. It is also important to establish a minimum acceptable accuracy and maximum acceptable error for these KPIs to manage the risk of variable results.
Assessing the costs associated with data acquisition, model training, and inferencing is crucial. This includes considering the cost of wrong outputs and the impact of these errors on business objectives. Evaluating whether external data sources could improve model performance and whether managed services from cloud providers could reduce the total cost of ownership is also essential.
Consider how the Generative AI system will scale
Ensuring that the Gen AI system can scale seamlessly as the user base grows and maintaining efficiency in resource usage is important for long-term success.
The AI system must be designed to handle increased demand without compromising performance. This involves planning for future data growth, increasing computational resources, and implementing architectures that can scale horizontally or vertically.
Determine Environmental Impact
Organizations should measure their Gen AI workload’s impact on the environment. This includes assessing the data storage and processing requirements, the impact of training the model, and the frequency of retraining. By understanding these factors, businesses can establish sustainability objectives and success criteria.
Consider the talent needed to operate the Gen AI system.
Building an enterprise Generative AI solution requires hiring top talent to manage the system. Defining roles and responsibilities, such as domain experts, data engineers, data scientists, and MLOps engineers, at the beginning ensures that all aspects of the AI lifecycle are managed effectively.
Implement Feedback Loops
Establishing feedback mechanisms to share successful experiments, analyze failures, and communicate operational activities is crucial for continuous improvement.
Conclusion
Understanding the business case and requirements before building a Generative AI application is crucial for its success. By focusing on the business goal, stakeholder alignment, technical and data requirements, performance and cost optimization, sustainability, scalability, and security, organizations can ensure that their AI solutions deliver significant business value and align with strategic objectives. This comprehensive approach not only reduces risks and optimizes resources but also enhances the overall effectiveness and efficiency of the AI solution.
Investing time and effort in the initial phases of defining and understanding the business case and requirements will pay off in the long run, leading to successful Generative AI implementations that drive innovation and growth.
References:
- AWS. Machine Learning Lens - AWS Well-Architected Framework. Retrieved from https://docs.aws.amazon.com/pdfs/wellarchitected/latest/machine-learning-lens/wellarchitected-machine-learning-lens.pdf#well-architected-machine-learning-design-principles
- Go Cloud Careers. Generative AI Architect Development Program. Retrieved from https://gocloudcareers.com/generative-ai-architect-development-program/