1. Data Infrastructure

• Is your data centralized and accessible?

• Are there clear pipelines between systems?

• Is the data labeled, clean, and relevant?
  
  

🚩 Red flag: Teams still relying on siloed spreadsheets or manual exports from legacy systems.

✅ Action: Invest in data warehousing and ETL processes before diving into model development.

2. Organizational Alignment

• Do your leaders understand the value of AI?

• Is there cross-functional sponsorship?

• Are KPIs clearly defined for AI initiatives?

💡 Best Practice: Create a steering committee that includes tech, ops, and business stakeholders.

3. Technical Capability

• Do you have internal or partner talent for ML/AI engineering?

• Can your systems deploy models into production environments?

If not, don't worry — many companies start by partnering with hybrid firms like TripleBolt to fill the gap.

4. Workflow Integration

• Can you embed AI insights directly into employee workflows or customer-facing apps?

• Is there a clear path from model output to business decision?
  
  

Example:

One logistics company we worked with had a great demand forecasting model — but it was buried in an Excel report emailed weekly. We helped integrate it directly into their inventory system, saving $2.3M annually in overstock.

5. Culture and Change Management

• Are your teams prepared to adopt AI tools?

• Have you trained teams on how to interpret, trust, and use AI recommendations?

• Remember: Even the best models fail without adoption.