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Generative AI is revolutionizing business operations, yet AI software sales by major tech firms remain modest. The Economist delves into practical applications and the evolving landscape of AI in commerce. Read more for insights on this transformative tech wave. 🔗 https://lnkd.in/giSWVtu8 #GenerativeAI #AIinBusiness #TechTrends #Innovation #FutureOfWork #ArtificialIntelligence #BusinessStrategy #DigitalTransformation

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Did you know that most proof of concepts for #genai launched in 2023 won't make it to production in 2024? Despite this tech companies are investing heavily in Gen AI tools. Naturally the question arises: why the continued commitment? What has shifted in the landscape? How has it impacted enterprise spend for gen AI tools? Discover this and more at: https://okt.to/Cvl9zL

Let’s see what Qlik can offer for their AI ecosystem

The motives for genAI adoption as sometimes just as bad as the motives we were seeing for AI adoption 7-8 years ago in the enterprise AI wave. There's really only one good motive: -- AI is seen as the right tool for the job, but it's also seen as a part of a broader transformation vision. I.e. AI has near-term value but is also part of a capability-layer for the enterprise to derive lasting value from. Bad motives include: -- A VP wants to be seen as a maverick and convinces themselves to buy into a vendor's story to aggrandize themselves. -- A pressing near-term problem requires a band-aid temporary solution and #AI seemed like the right tool for the job (more technical debt, hurray!). -- The board is asking "what're we doing with #genAI?", and someone buys it just to have an answer. All in all the C-suite is more AI-fluent now than they were years ago, but it's surprising to see - every week - so many signs that a new buzzwork will bring back the same goofy, wasteful FOMO motives...

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OPTIMIZE YOUR AI AND SOFTWARE INVESTMENTS Evaluate the true cost and functionality of your technological solutions. Initially, building proof of concepts is crucial as it offers a clear perspective on functionality and cost-effectiveness. However, the real value emerges when transitioning from these proof of concepts to fully productive solutions. A key principle to adhere to is avoiding throwaway prototypes. Instead, focus on developing prototypes that are robust and scalable from the beginning. This strategy not only conserves resources but also ensures a seamless progression to implementation. What's your approach to bridging the gap between prototypes and production-grade solutions? #TechInnovation #AI #ProductDevelopment