Gartner’s Post

Representing each of these use cases as monolithic entities is misleading. Every business process has subprocesses that contribute to the whole in different ways that require their own tools and solutions. A prediction and forecasting use case might require the structuring of unstructured content to populate inputs to a traditional ML model. Saying "don't use Generative AI" in these use cases misses the point that it can help solve the larger problem without being solely responsible for it. Imagine someone saying "You can't use databases to solve document automation because they can't read documents," instead of explaining specifically what problem they solve in the context of the larger problem. That's what this graphic is doing.

顧能集團 Yes, AI can't do everything. What we are doing is just what AI can't do, i.e., the "decision intelligence" in your table. Let's try a go/no-go test to see if AI works. Is there any data solution, AI or NOT, that can answer the following questions of business intelligence? "How many entities, in the Ontario province of Canada, have new US patents granted on the nearest Tuesday (Eastern Time), when the USPTO releases the newly granted US patents on a weekly basis?" "How many entities, in the "江蘇" province of China, have new US patents granted on the nearest Tuesday (Eastern Time), when the USPTO releases the newly granted US patents on a weekly basis?" With our intellectual property (IP), a Chinese-English multilingual metadata, we can answer. Do you or any of your contacts need our expertise/IP to do the data analysis that AI can't do? Metadata is an enabler. It is like a treasure map for treasure hunting. Without metadata, like a treasure map, NO data can be found/retrieved, even by the most advanced technologies, like AI, high-end chips, supercomputers, etc. https://lnkd.in/g-aJFnXR

We should put date and time stamp on such information now , given the pace of advancement in this area.

Not completely accurate. Certain prediction use case capabilities are good with proper data and instruction. You can’t expect an AI chatbot to help with this but an AI team can create a solid pipeline to handle predictive capabilities.

Fascinating insights! The limited effectiveness in prediction and decision intelligence highlights an important distinction: while GenAI excels at pattern recognition in linguistic data, it still struggles with complex causal reasoning and handling dynamic real-world variables that prediction tasks demand. Perhaps the future lies in hybrid approaches—combining GenAI's communication capabilities with traditional ML/statistical methods for predictive analytics. 

Great insights, Gartner! This breakdown really highlights where generative AI can be a game-changer and where it might not yet be the best fit. Understanding these distinctions can help businesses focus their AI efforts where they’re most impactful, like content generation and conversational interfaces. It’s a reminder that AI isn’t a one-size-fits-all solution, and mapping the right tool to the right task is essential for maximizing ROI. Thanks for sharing!

Aligning #GenAI use cases with strategic business goals is key to unlocking real value. In #supplychain and #manufacturing, the right applications—like #demandforecasting, #predictivemaintenance, and #processoptimization—can drive efficiency and resilience, while the wrong ones can divert resources. Excited to leverage these insights as we continue empowering businesses with AI-driven supply chain and manufacturing transformation solutions at Stellium Inc. #DigitalTransformation #SupplyChain #Manufacturing #Innovation

Great insights on determining the use cases for generative AI! Mapping the use case against the relevant use-case family is a smart approach to ensure that we are leveraging the technology in the right way to achieve the desired business results.

Insightful breakdown, Gartner! In the nonprofit world, the value of AI lies in its potential to reduce administrative burdens, allowing organizations to dedicate more resources directly to their missions. Content generation and conversational AI can be game-changers, helping with everything from donor engagement to streamlining volunteer coordination. One exciting area is grant writing—AI-powered tools can assist nonprofits in drafting grant proposals more efficiently, ensuring they meet funder requirements and communicate impact effectively. By automating portions of the grant-writing process, nonprofits can save valuable time and focus on creating compelling narratives to secure funding. At Hofbauer Consulting, we see generative AI as a key enabler for nonprofits aiming to scale impact without scaling overhead. By implementing AI-driven tools, nonprofits can improve efficiency, automate repetitive tasks, and enhance personalized donor outreach. As the technology evolves, we anticipate more tailored solutions that align with nonprofit needs for transparency, security, and affordability. #NonprofitCTO #AIforGood #DigitalTransformation #Nonprofit

Solid breakdown! When you need to drive a nail, a hammer works much better than GenAI.