AI Quickstart

AI Quickstart is a 4-week offering that combines strategy and implementation to deliver a minimal viable product (MVP) within your organization. Designed for speed and impact, it quickly demonstrates the value of AI and lays the foundation for scaling AI initiatives.

Week 1 - Define Objectives and Current State Assessment

Establish a shared understanding of AI (e.g. ML vs. AI vs. GenAI) while also align on common goals with respective to the AI initiative. Activities and deliverables include:

AI 101
Stakeholder interviews & workshops: Engaging business owners, process participants, and technical teams to capture how things currently work.
Document processes & workflows : Documenting existing processes, dependencies, and handoffs across teams and systems.
Technology & data review: Assessing applications, integrations, and data flows to identify gaps, redundancies, or inefficiencies.
Performance & pain point analysis: Reviewing KPIs, user feedback, and bottlenecks to surface challenges that need to be addressed.
Documentation of findings: A clear picture of the “as-is” environment through diagrams, reports, and summaries that highlight strengths, weaknesses, and opportunities.

Week 2 - Workflow Reengineering

Optimize and redesign key business processes to be AI-ready and deliver maximum impact. Activities and deliverables include:

AI use case identification: where AI can enhance workflows. Examples include automate approval routing, predict customer churn or prioritize tasks.
AI workflow identification: analyze mapped workflows
Technical readiness for AI integration: data inventory and assessment, system dependency analysis, recommend adjustments or infrastructure needed (e.g. ETL pipelines, storage, APIs)

Week 3 - Prototyping

Deliver a minimal viable AI solution to demonstrate value and optimize processes. Activities and deliverables include:

Data gathering: Pull data from internal systems, clean data and pattern recognition
Prototype development: Configure a prebuilt AI services or train a light AI model on the dataset (e.g. summarizing weekly updates, drafting reports, meeting recaps). Test initial accuracy and performance
Integration and workflow testing: Integrate model with existing workflow, testing to validate reliability

Week 4 - Evaluate Success

Reflect on the effectiveness of the AI implementation, capture key lessons learned, and align on a clear path forward. Activities and deliverables include:

Assess outcomes against the original goals and success metrics (ROI, efficiency gains, adoption, etc.)
Highlight organizational enablers and blockers (culture, processes, data readiness)
Recommend next steps for optimization and scaling (e.g., expand to other departments or use cases)