1. Anchor AI to Business Objectives

Start with clarity. What pain points are you solving? Is it fraud detection, customer churn, operational inefficiency, or new product innovation?

💡 Tip: Avoid starting with “we want to use ChatGPT.” Instead, say, “We want to reduce average claim processing time by 40%.”

2. Prioritize Use Cases with ROI Potential

Rank potential AI use cases by:

• Data availability

• Business impact

• Ease of implementation

• Time to ROI

You don’t need to boil the ocean — one well-executed initiative can fund the next.

3. Conduct an AI Readiness Assessment

Evaluate whether your infrastructure, data quality, team, and workflows can support AI. If not, lay the groundwork first.

4. Design for a Proof-of-Concept First

Before building enterprise-wide systems, test your ideas. Create a lean, 4–6 week POC and measure outcomes against KPIs.

Example:

A health insurer wanted to automate prior authorization. TripleBolt helped them build a POC using a fine-tuned LLM that cut manual review time by 60%. That success earned internal buy-in for full rollout.

5. Build the Right Cross-Functional Team

Include business owners, data scientists, software engineers, and DevOps from Day 1. AI is not a siloed initiative.

6. Plan for Deployment and Change Management

You don’t just need models — you need usable tools. That means:

• APIs or UI integration

• Monitoring and retraining pipelines

• Training and adoption programs