Top 10 Core Concepts in Generative AI Transformation by

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This article was written by an organic human without any “artificial flavorings or additives.”

You can skip to the bottom for the article on jobs at risk, however, teachers, data researchers – scientists – analysts – anything where “big data” is used. This could include research for any kind of forecasts, decision-making, hiring, etc. Seriously, it the same ole story as humans will always be involved in decisions where humans are involved and AI will be used where data is processed such as transactions, gathering and presentations.

Generative AI will take not one but hundreds and thousands of forms.

There are already far too many to name here, AI apps like Chat GPT, Dall-E, Support-GBT and others.

Called large language models or LLM and now extremely large-scale language modeling is transforming the way people and organizations do many things.  Some say it will completely transform every business.

But like with any technology some will begin with generative AI or GAI.

While others may eventually transform and some never.  

As anyone who knows of, uses and implements technology, diffusion – the process of absorption will take a while and for some, never.  

For those who pursue this, here are the Top-10 core concepts to consider.

1 – Validate your AI Business/Product/Strategy now.

 – in order to make AI pay for itself, provide real ROI or sell by the thousands to customers looking for solutions you are looking for. 

2 – Build a long term view – figure out what you are really trying to do.

Also realize what AI is today will not be what it is tomorrow.  

3 – Focus on compelling business issues aka delivery logistics, complex customer order process.

Along with changing customer patterns and other complex issues that you don’t have an immediate solution.

or that your business is changing, and you want a new way to solve the problem.

4 – Explore both historical and new data – neither is important alone but both working together can find weaknesses and opportunities.  

History is certainly any guide for the future, however, at the very least you may find existing problems.

that will continue to exist unless something is done about them.

5 – Build for changing data analytics – more than just a better data “algorithm” model builds an ever evolving data modeling process.

6 – Tracking and testing – simulate and then test “live” across customer pockets aka customers who live in warmer climates wear, eat, watch, do things differently than just a few hundred miles north. 

7 – Build an “ever-evolving” AI business model – what is the goal and how does this AI system or approach fit.

– into an immediate and long-term business model as well is this an internal business or external business to be sold to others.

8 – Consider Ethics – issues and answers as the costs are real and your reputation is at risk.

It isn’t just about cheating on tests but any company culture of deception, deceit and dishonesty.

9 – Regulation, governance and compliance is coming along with industry if not international standards.

Technology can always be used for harm and data security should always be paramount.

This means AI needs to be real, not just the “wild west” anymore if ever it should have been.

And issues and answers presented and that always elusive issue of privacy.

10 – Anticipate the worst possible consequences to be ready for unanticipated consequences.  

In medical terms, it is called contraindications where one drug combined with others can have lethal consequences.

No one or “machine thing” can anticipate what will happen from the use of any technology other than to expect the worst and the best may emerge.