For traditional companies—manufacturing, logistics, retail—the pressure to "adopt AI" is reaching fever pitch. Boards are asking questions, competitors are making press releases, and FOMO is setting in.
But for a non-tech company, blindly rushing into AI is a recipe for disaster. The wrong model can hallucinate, leak data, or alienate customers.
Here is a playbook for safe, high-leverage adoption.
Rule 1: Don't Start with the Customer
The highest risk area for AI is the customer interface. A chatbot that insults a customer goes viral for the wrong reasons. A recommendation engine that suggests insensitive products causes brand damage.
Start internally. Use LLMs to:
- Summarize legal contracts.
- Draft internal HR policies.
- Query your own inventory databases.
If an internal tool hallucinates, an employee catches it. The blast radius is small.
Rule 2: Buy vs. Lease vs. Fine-Tune
Most non-tech companies should never train their own models. The cost and talent requirements are prohibitive.
- Lease (API): Use GPT-4 or Claude via API. Fast, cheap, but you share data (check privacy agreements).
- Host (Open Source): Run Llama 3 or Mistral on your own private cloud (AWS/Azure). This keeps your data within your perimeter. This is the sweet spot for enterprise data privacy.
Rule 3: The "Human in the Loop" is Mandatory
Treat AI as a first draft engine, not a final decision maker.
- Bad: AI automatically approves loan applications.
- Good: AI scores the application and highlights risk factors for a human underwriter to review.
The "Boring" AI is the Best AI
The media loves stories about AI writing poetry or creating art. But for a business, the value is in the boring stuff.
Automating data entry from PDF invoices. Classifying customer support tickets. Transcribing and summarizing meetings.
These aren't sexy moonshots. They are efficiency engines. And they are the safest, fastest way to get ROI from AI today.