3 Simple Tips for AI transformation
A million pompous Tweets don’t lie : Artificial Intelligence (AI) is here to stay. Ok. Good. What now? Well, before AI can truly be called a democratised technology, we have to go beyond Silicon Valley startups and implement it within small/medium businesses and governments.
And so we must ask ourselves : how does a non-tech company go about this? What are the pitfalls to avoid? Where to begin? Below are a few lessons I’ve learned throughout my time as a technology consultant for some of Europe’s largest companies.
Identify and remove small(er) obstacles by answering the following questions
- How will we ensure user adoption both internally and externally?
- Is the quality of our data good enough for this project?
- Are our business and IT teams close enough?
- Do we have the relevant AI skills within our organisation?
- Does our company have a sufficient data culture within top management? (hint : no)
- Are our data management processes adapted?
Avoid the real pitfalls by tackling this challenges
- Prefer “Business Pull” to “Techno Push”
- Invest in the “boring” architecture and statistics capabilities
- Do not make false promises
- Be realistic on your skills
- Don’t copy Big Tech (you can’t)
Start with the beginning
- Define AI ambition (“Everyone else is doing it” is a terrible reason to get into the A.I game)
- Define priority use cases
- Gradually industrialize (“AI at scale”)
This is a very short summary of a longer article which you can find by clicking this link.