New GenAI foundation models are released every two and a half days, yet nearly half of CIOs report that AI hasn't met ROI expectations. Navigate this dichotomy by determining your organization’s AI pace — and taking the right next steps: https://gtnr.it/4eEgWp5 💡 Follow Gartner for IT for more actionable, objective insight. #GartnerIT #GenAI #CIO #Technology
This is the wrong visual metaphor for this information.
🙋♀️ 🙋♂️ Note the central human component - Central AI Committee; Communities of practice; Trust/Risk/Security Oversight Team <-- Now, how do you develop your employees to have the capabilities to run these committees, teams, etc.? The identification and development of key personnel will be crucial to a successful implementation.
顧能集團 Not surprised. Why don't we use AI? Because it can't solve our problem. Do you or any of your contacts need our expertise and intellectual property (IP), a copyrighted Chinese-English multilingual metadata, to do the data analysis that AI can't do? NO one in the world can change the fact that we need menu to order our food at a restaurant. A five-year-old kid knows this. Similarly, we need metadata to search/retrieve the right data for data analytics. Metadata is a copyrighted content, NOT technology. It is fundamental to enable data analytics. Without metadata, NO data can be found/retrieved, even by the most advanced technologies, like AI. https://lnkd.in/g-aJFnXR Let's try the go/no-go test here to see if AI works without metadata, and then, at what cost. 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 or 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 IP, we can answer, even by an ordinary laptop without AI.
There are lots of fantastic tools coming out of the AI space. They are tools, not solutions. They are brilliant at being part of a solution to a business need. There is no business need like "We must have a genAI tool; go and get it" Just having crap data is a business need for AI - a help me fix it tool might be a good place to start. genAI models make something out of data. They need training. Getting real value means leveraging your data and information. GiGo rules apply A T*rd is still a T*rd, even if built & polished by AI
The insights shared here underscore a pivotal reality for organizations: scaling AI is not just about adopting technology but also about aligning it with business objectives, governance, and employee adaptation. Whether pursuing an AI-steady or AI-accelerated pace, success depends on balancing innovation with practical outcomes like improved productivity, streamlined processes, and responsible governance.
The assumption in the "Build vs Buy" that most companies will be able to implement successful GenA platforms to meet their needs is not a strong one. The lack of in-house relevant expertise in the majority of companies, and the challenges of building usable and robust GenAI platforms means companies will have to look more at the "Buy" option from companies like Intelligent DataWorks to accelerate their adoption and benefits of GenAI.
Are you part of the AI risk and governance committee of your organization? How are you managing risks from your GenAI adoption? Cymetrics Vulcan, our automated GenAI Red Teaming tool, is here to help ensure AI systems are responsible and ready for real-world challenges. Contact us today: [email protected] https://cymetrics.io/en-us/products/ai-redteam
Our take on this would be slightly responsive, than reactive !! Though the rapid release of GenAI models is impressive, but it underscores a critical gap: adoption without alignment. Many organizations rush to implement AI without a clear strategy, leading to unmet ROI expectations. Success with AI isn't just about having the latest model—it's about integrating it into business processes, training teams to use it effectively, and ensuring it addresses real pain points. Tech leaders should focus on AI maturity rather than novelty. Customizing AI to specific business needs, optimizing workflows, and setting realistic KPIs can bridge the gap between innovation and value.