The Good

Understanding how you make decisions about technology is critical to your success or failure using AI.

A good thing is using the current AI hype to incentivize your people to articulate where they fall on the spectrum of types of technology buyers.

• Fast Movers
• Fast Followers
• Majority Followers
• Laggard Followers

Where your company lands in these categories will in large part determine if not only your AI, but all future technology investments will bear fruit. Fast Movers and Fast Followers will champion trying out new, unproven technology. They put up with delays and disruptions. Majority and Laggard followers will not put up with either delays or disruptions and need time and social proof before they get on board.

The Bad

“The hype surrounding generative AI can lead to use of the technology where it is not a good fit, increasing the risk of higher complexity and failure of projects.”

“Overfocusing on GenAI can lead to ignoring the broader set of alternative and more established AI techniques, which are a better fit for the majority of potential AI use cases.”

(Gartner, Emerging Tech Impact Radar: 2024, Nguyen, Casey)

For most of my clients, their most important area of work still not done is organizing the thousands of files, and millions of data points. Add to that our stubborn tendency to use a 40-year-old file storage technology means that generative AI will only make things worse, faster.

For this reason, many of the ‘cool’ new tools like Data Bricks and Data Lakehouse that could make AI work magic for a large company don’t work yet for small and medium sized companies.

Artificial Intelligence

The Ugly

Yikes! What do you mean my proprietary information is on the public web!

Check with your people to see how many of them are already using ChatGPT, Bing Search, Co-Pilot, Bard, etc. (You’ll be shocked!) And remember that anything typed into these tools instantly becomes searchable and usable by anyone else! When your salespeople want to ‘improve’ a proposal and type in your company and customer names, addresses, etc. – into ChatGPT, or your engineer types in your proprietary formulas to see if there are any errors that CoPilot can find – this is now in the public domain!