Generative AI: the 12-step blueprint for efficiency and innovation

Generative AI for business is a huge opportunity, but it comes with real risk. Imagine wasting months on an AI project that fails to perform, generates poor feedback, and leaves stakeholders frustrated. The waste of resources and missed opportunities can be devastating. Fortunately you can guarantee a better, more measurable outcome.

The key to success is following a proven framework that addresses potential challenges head-on. Based on Google's expert guidance, here is our 12-step plan for running a successful, ROI-driven, Generative AI project:

Step-by-step project success

1. Identify a specific problem: Focus on one challenge that will significantly impact your business, like automating data analysis or speeding up customer service response times. Use the "Five Whys" to get to the root of the issue. For more information the the ‘Five Whys’ see our article covering: How we find the root cause of your conversion-killing issues (and protect your budget).

2. Choose a target user or persona: Select a customer, employee, or department. This helps tailor the AI solution to meet their specific needs.

3. Define specific KPIs: Set measurable goals to track progress. For example, if you build a document-chat AI, a KPI could be how quickly users locate information compared to traditional methods.

4. Brainstorm potential solutions: Use the "How Could We" approach to encourage creative thinking. This fosters innovation and helps you identify ways to enhance the solution with additional features.

5. Identify and gather data: Quality data is key. Ensure your internal or third-party data is clean, relevant, and of high quality. This step also helps prioritise projects where you have the best data available.

6. Assemble a cross-functional team: Build a "tiger team" with diverse skills (engineers, data scientists, project managers). A well-rounded team significantly increases the likelihood of the project’s success.

7. Develop prompts: Quickly trial prompts in a development environment to validate the feasibility and potential business impact of your AI idea before committing significant resources.

8. Design a user-friendly interface: A great UI/UX can make or break your project. An intuitive interface ensures users can easily interact with and benefit from the AI solution.

9. Develop a plan for MLOps: Create a plan for managing and maintaining your language model (MLOps). This includes monitoring performance and retraining on new data and feedback to ensure long-term success.

10. Roll out the AI solution: Once your proof of concept (POC) is successful, scale up the infrastructure and provide training. Expanding users maximises impact and gathers data for continuous improvement.

11. Regularly assess KPIs: Use ongoing KPI assessments to track performance, identify areas for improvement, and make necessary adjustments to stay aligned with your goals.

12. Launch and maintain: Launch your AI project into production and establish a maintenance plan. This ensures your AI solution continues to run smoothly and deliver value over time.

Generative AI: the critical KPIs for business impact

KPIs are the most critical factor for proving project success. Focus on these metrics to justify investment and inspire future initiatives:

Accuracy and quality: Measure how accurately and reliably your model produces relevant outputs (e.g., using precision or recall). High accuracy builds user trust and ensures reliable results. Assess the overall quality of outputs against predefined standards.

Productivity and turnaround times: Evaluate the impact of AI on the productivity of your target persona (e.g., tasks completed per unit of time or reduction in manual effort). Also, track the time taken for the AI model to generate responses compared to traditional methods. Faster turnaround times are a significant competitive advantage.

Financial performance: Measure the cost savings achieved compared to traditional manual processes. Identify specific relevant business metrics directly impacted by the AI use case, such as increased sales or improved employee retention.

Satisfaction and error rate: Use customer satisfaction surveys to gauge how well the AI system meets customer needs. Conversely, quantify the error rate—minimising this is crucial for maintaining reliability and user trust.

Operational metrics: Track training time and cost (efficient processes lead to faster implementation). Assess scalability to ensure the solution grows with your business, and monitor regulatory compliance for sensitive domains.

With your 12-step project plan and KPIs in place, you can guarantee a better, more measurable outcome for your generative AI project. The key is to iterate and enhance your solution as you progress.

Secure your AI success with Firney's expertise

At Firney, we’re AI specialists with a proven track record of delivering measurable business impact. Our one-day Data & AI Discovery Workshop is designed for marketing leaders who want to identify high-value opportunities and build a clear, actionable roadmap to ROI with Generative AI.

With a wealth of successful case studies, we’re dedicated to ensuring your AI project not only meets but exceeds expectations. Whether you’re exploring AI for the first time or scaling existing initiatives, Firney will guide you through every step turning strategy into results.