3 Simple Tips for AI transformation

So simple, yet so hard

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.