Strategies for AI Enterprise Deployment and ROI
I recently took a class about AI Strategy. What surprised me is how upper management struggle with AI investment for their enterprise. I come to realize that there are several aspects to it:
- How much is it going to cost?
- Where do we start?
- What do we use?
How much will it cost?
As you all know, AI Cloud services (Claude, ChatG, Gemini ...) are not free. Some cost more than others, and to deploy AI in a company, it's important to figure out what the cost will be.
Some of the tactics i've read about are:
- Use the proper AI model for the proper tasks. Not all AI tasks are similar. Using Opus to summarize an email is a waste. Sonnet will do well, Haiku might as well.
- Some companies are looking into local LLM. (Gemini4, Ollama).
It's important to plan ahead and decide which models will be used and for what.
Where do you start?
With AI moving so fast, it's difficult to keep track. I think of it like investing in a house you want to remodel and make it your own and efficient. Where do you start?
Is the foundation solid? Isolation? Quality material. Same for an enterprise.
Do you have a good data foundation? What is your data quality? Data gaps?
What are your top 5 business pain points?
Which LLM solution(s) to use?
This is tied to question 1 and 2. Depending on what you want to accomplish, different models will be needed. Nothing prevent you to use different solutions. Local LLM combined with commercial solutions + Automation tools like N8N.
I'm interested to hear from you about your professional experience in this new technology revolution. Are you cruising? Are you stuck? What have you learnt? What can you share for the community ; the dos and don'ts.