We engage executive decision makers and product team leaders to arrive at your AI strategy — without the hype.
Our process includes an assessment, workshops on current generative AI and traditional AI (machine learning) approaches that are relevant to your product and industry domain, and other topics to get everyone up to speed on AI/ML.
Understand the business problem to be solved with AI.
Define the business value for AI.
Review where generative AI and traditional AI could apply.
Lead workshops to explore highest value generative AI and traditional AI use cases.
Evaluate the availability and quality of data relevant to your use cases, and whether data is available internally or externally.
Pick the top projects to AI-enhance your current solution, or brainstorm new AI-first product concepts.
Scope AI Market Requirements Doc (AI MRD) with KPIs.
Review the latest best practices and platforms available for our chosen use cases.
Determine generative AI and traditional AI/ML tools and technology approaches to help select prototype.
Review computational resource requirements, expertise in AI/ML, and access to software frameworks.
Define KPIs to measure success of prototype, including content quality, efficiency gains and cost savings.
Scope AI Product Requirements Doc (AI PRD) for prototype which defines implementation roadmap.
Build user-ready prototypes.
Hand-off prototype for development into customer-ready solution, or iterate the approach based on results.
Validate potential and KPIs.
Continuously monitor the performance and gather feedback for improvement.
Scope AI responsibility and risk management requirements, including contingency planning to mitigate risks.
Determine your data governance strategy.
Build plan to ensure AI safety, accuracy, explainability and privacy.