Gartner Forecasts Worldwide GenAI Spending to Reach $644 Billion in 2025 Worldwide generative AI (GenAI) spending is expected to total $644 billion in 2025, an increase of 76 4% from 2024, according to a forecast by Gartner, Inc “Expectations for GenAI's capabilities are declining due to high failure rates in initial proof-of-concept (POC) work and dissatisfaction with current GenAI results,” said John-David Lovelock, Distinguished VP Analyst at Gartner
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Hype Cycle for Artificial Intelligence 2024 | Gartner Generative AI (GenAI) receives much of the hype when it comes to artificial intelligence However, the technology has yet to deliver on its anticipated business value for most organizations The hype surrounding GenAI can cause AI leaders to struggle to identify strong use cases, unnecessarily increasing complexity and the potential for failure
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Gartner Top 10 Strategic Technology Trends for 2025 Trend 4: Post-quantum cryptography (PQC): Data protection that is resistant to quantum computing (QC) decryption risks Business benefits: Protects data from the security risks that will come with the advent of quantum computing Challenges: PQC algorithms are not drop-in replacements for existing asymmetric algorithms Current applications may have performance issues, will require testing and
Gartner 2024 Hype Cycle for Emerging Technologies Highlights Developer . . . The 25 disruptive technologies to watch on the Gartner, Inc Hype Cycle for Emerging Technologies, 2024 fall into four key areas: autonomous AI, developer productivity, total experience, and human-centric security and privacy programs “Generative AI (GenAI) is over the Peak of Inflated Expectations as business focus continues to shift from excitement around foundation models to use cases
Gartner Predicts by 2027, Organizations Will Use Small, Task-Specific . . . Gartner, Inc predicts that by 2027, organizations will implement small, task-specific AI models, with usage volume at least three times more than those of general-purpose large language models (LLMs) While general-purpose LLMs provide robust language capabilities, their response accuracy declines for tasks requiring specific business domain context