The Reality of AI Adoption in Traditional Industry

Kevin Coleman discusses practical perspectives on implementing AI across a $1.2 billion logistics operation. Rather than focusing on hype, he addresses real challenges traditional industries face: data quality inconsistencies, security concerns, and avoiding technology adoption without scalability plans.

About the Guest

Kevin Coleman is the CEO of CJ Logistics America, overseeing the Americas division with $1.2 billion in revenue and over 3,500 employees. The parent company generates $9+ billion in total revenue globally.

Key Topics Discussed

Data Quality Foundation

Kevin emphasizes the "garbage in, garbage out" principle—the importance of quality data over sophisticated models. Consistent data structures must be established before implementing AI solutions effectively.

Technology Evaluation Framework

CJ Logistics uses Centers of Excellence (COE) to assess and scale technologies across their network. The focus is on multi-site scalability rather than isolated implementations, evaluating additional use cases beyond initial deployment.

AI Integration in Operations

The conversation covers how CJ Logistics shifted from a safety focus to broader operational intelligence, including productivity optimization through warehouse waste reduction analysis and emerging quality and compliance applications.

Organizational Change Management

Kevin discusses embedding AI tools into leader standard work rather than overwhelming teams with data. Training and development must be tied to specific processes, balancing innovation with security constraints.

Multi-Client Facility Challenges

Operating multi-client facilities means managing multiple WMS systems, complex labor and volume management, and exploring shared services model potential with AI enhancement.

Key Quotes

"Supply chain or logistics has a ton of data. It's limited on their information and it's harder to get to the intelligence piece."

"How do you set boundaries but not stifle creativity?"

"It's not the technology's fault. It's the intersection of people, process, and technology."

Notable Statistics

• CJ Logistics America: $1.2 billion in Americas division revenue
• Parent company: $9+ billion total revenue
• Employee base: 3,500+ workers
• Safety metric: 1.60 (described as "phenomenal" for the industry)

Key Takeaways

1. Start with data quality - No AI solution can overcome poor foundational data
2. Scale thoughtfully - Use Centers of Excellence to evaluate technologies before broad rollout
3. Balance innovation and security - Traditional industries must navigate constraints while staying competitive
4. Integrate into workflows - Embed AI into existing processes rather than adding new complexity