The Provectus machine learning and data team has developed an approach to help enterprises quickly adopt the most impactful Generative AI use cases, this process will include business teams who will use the applications to align to their requirements.

This offer is broken up into 2 discrete phases. Phase I is a data readiness and use case prioritization workshop that includes IT and business teams to help identify the best place to start for the highest return. Phase II is building the proof of concept in your organization’s AWS account to test the use case value and determine how impactful Generative AI can be.

Phase I is the data readiness assessment workshop (1-2 week) that will include data and infrastructure discovery, AI use case prioritization and adoption roadmap, the expected business value, success criteria, and complexity/risks estimation along with a proposed use case candidate.

Phase 2 is where we build the proof of concept (3-4 weeks). Phase II will include a proof of concept deployed in your AWS development environment, solution architecture documentation, a plan to deploy an at-scale production-ready solution, and a TCO estimate for AWS and foundational model services.

Use cases to explore include:

• Generative AI Assistant: Interrogate external and private data sources like contracts, support documents, collaboration platforms data, messaging apps, and task management systems to accelerate answers to important questions.

• Customer Service: Virtual assistants to help support representatives answer customer questions and resolve common issues faster and more accurately. Customer support representatives can be an expert from day 1 by leveraging AI, allowing people to focus on more complex issues, and improving customer experience.

• Sales: Build a custom sales playbook with contextual information for customer meetings, suggest the next best action, and a expert virtual assistant in every customer meeting.

• Structured Finance: Extract insights from historical data, reports, and news on current market conditions, identify potential risks, and make better investment decisions.

We encourage cross-functional participation in this project, from C-suite Executives, Business Unit Leaders, Data, AI/ML Leaders, and IT professionals to increase adoption and capabilities to measure sucess criteria.