Cole Witten’s Post

“If all you have is a GenAI hammer, everything looks like a GenAI use-case nail.” This quote from the Gartner article by Leinar Ramos is by far the best in this GEN AI hype ecosystem. Not everything can be a GEN AI Project and a project's true nature can only be understood if it can be categorized under Client usefulness and value-driven. Various AI Models (Non Gen AI) have long gone under the radar and can help enhance business efficiency and productivity equal to Gen AI projects. Read more through this Gartner Article.

Pragmatic advice worth reading to avoid being thrown under the “genai will solve ALL our business challenges” bus Damra Muminovic Vivek Chaudhuri Christiaan Van Eeden

Great insights from #Gartner, certainly need to analyze what use cases you need and which one of them will deliver real business outcomes with greater ROI and reduced risks!

Thats generally a point I try to make Gen AI is not equivalent to Ai - Ai has been around much longer - Gen Ai has a lot of potential but also has a lot of reliance training data issues - when tackling a problem you first have to decide what form of Ai makes s bae to use #genai #ai #ml

I did a post on this topic from Gartner already last week. Now this article from Leinar Ramos from Gartner gives more insights regarding when GenAI is not effective. He states that Generative AI is only one piece of the much broader AI landscape, and most business problems require a combination of different AI techniques. I couldn't more agree with this statement! Because we exactly do this in our use cases at Empolis Information Management GmbH and we see that the combination of GenAI models with other AI techniques (e.g. rule-based systems, decision tress, knowledge graphs etc.) can create more robust systems that mitigate some of the limitations of GenAI (such as inaccuracies, hallucinations etc.). If you want to learn more about how Empolis combines the different AI methods to create out-of-the-shelf business cases for the manufacturing industry, then register for Empolis Exchange at June, 05 and join our keynote from Andreas Klüter and me or my presentation together with Kai Kramer about Trustworthy AI for manufacturing industry. https://lnkd.in/d7PtVCWt

The ability of GenAI to understand language, generate new images from existing images based on text instructions and the like is amazing. But, personally agree with this article that it is not a one size fits all solution. For me it is like having someone that is really good with understanding language, but maybe not great with mathematics. So, if the language person could get a clear understanding of the problem statement, and to solve it, a strong mathematical person is needed, it would be wise for them to work together to come up with a solution. For me, GenAI is like the language person that is not great with math. It needs to understand when and which other solutions to interact with to be able to solve complex problems.

We understand that companies are going through the peak of great expectations regarding Generative Artificial Intelligence, however, its use must be carefully evaluated 🔎 , avoiding implementing it only due to expectations of quick gains in productivity. ⚡ It is vital that organizations establish clear principles, consider long-term risks and benefits, and balance automation with human involvement to ensure the success, safety and sustainability of their projects. This 👇🏼 Gartner publication about it is very interesting:

AI is not just GenAI....

Important fact, as per the Gartner article: Generative AI is only one piece of the much broader AI landscape, which has been around for decades, and solving most business problems requires a combination of different AI techniques. Ignore this fact, and you risk overestimating the impacts of GenAI and implementing the technology for use cases where it will not deliver the intended results. So.... as always.... start with a (small or big) business or consumer/customer problem to be solved, clearly define the use case and figure out which AI technique, or combination, is best to solve the problem. Sounds super logical, right? If you struggle to get everyone internally on the same page, possibly due to some knowledge gaps, it can be useful to put together a deck with potential problems to be solved / use cases, requirements & considerations - and workshop this with key stakeholders. Figure out who is interested in solving which use case (and why), plot short term gains vs long term wins & workshop this with key decision makers. It's a great (and fun!) way to fast track plotting a tangible AI road map.

Sometimes a single diagram captures it all. Yes GenAI is incredible, BUT there is way more to AI/ML. We haven't yet seen the scale and impact of other less-showy but in some ways more significant AI/ML techniques. With less consumer-focus, their reach will be deep and industry changing. Exciting days ahead and enjoying being part of this new Third Wave of AI after what we did in the first two.