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Hello readers,
Welcome to another edition of This Week in the Future! The latest reports on generative AI in the enterprise are here, and for anyone concerned about the AI hype cycle, there’s good and bad news. Is it now or never for Gen AI? Let’s find out!
Now or Never
The latest reports on generative AI are here, starting with The State of Generative AI in the Enterprise from Deloitte. In the report, Deloitte states that “the clock is ticking for organizations to create significant and sustained value through their Generative AI initiatives […] There is a chance that their interest in Generative AI could wane if initiatives don’t pay off as much, or as soon, as expected.” Of note:
55% of organizations avoid certain Gen AI use cases over data concerns
23% feel prepared for Gen AI risks and governance
40% are struggling to define and measure impacts of Gen AI
70% moved 30% or fewer of their Gen AI experiments into production
The report also showed that 42% of organizations achieved initial success with Gen AI through “improved efficiency and productivity and cost reduction.” The takeaway for organizations seems to be that “embedding Generative AI deeply into critical business functions and processes is the top way to drive the most value.”
However, this could simply be because business processes are so bad to begin with that anything is an improvement. A new study by IBM shows that C-suite confidence in basic IT services has waned significantly. “Less than half (47%) of those surveyed think their IT organization is effective in basic services compared to 69% surveyed in 2013.”
According to Lakera’s 2024 GenAI Security Readiness Report, only 5% of organizations feel highly confident in their AI security preparedness. Software engineers seem to be the most confident in Gen AI. A new survey by GitHub shows that 98% of respondents have used AI coding tools, though it’s not clear to what extent. Software engineers report a “perceived increase in code quality when using AI coding tools.”
Our Take
Is it now or never for generative AI? It feels like something has to give, but if there were a killer app that immediately justified all the investment in generative AI, wouldn’t it have presented itself by now? Based on these reports, it’s clear generative AI has its uses, but the pressure may be on to find new approaches to AI that can deliver more value. It will actually be exciting to see what innovations emerge as a result (if any).
🔥 Rapid Fire
OpenAI makes GPT-4o fine-tuning available to developers
OpenAI bans ChatGPT accounts in Iranian influence operation
OpenAI partners with Condé Nast for chat and search integration
Google enters America’s first newsroom funding deal to push AI
McAfee introduces the world’s first automatic deepfake detector
AI21 Labs introduces Jamba 1.5 long context open model family
Microsoft releases Phi-3.5 mixture of experts model to developers
NVIDIA and Mistral release Mistral-NeMo-Minitron 8B for laptops
Salesforce releases xGen-MM open source multimodal AI models
Salesforce announces two new autonomous AI sales agents
AMD acquires ZT Systems to expand AI data center capabilities
Appian expands AI features in latest Appian Platform version
Air Force eyes AI and data analytics to improve readiness levels
Researchers compute quantum excited states with neural networks
Generative AI not a substitute for teachers new study finds
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📖 What We’re Reading
“Service organizations that are early generative AI adopters are finding that to capture more value, they need to get more disciplined. Gen AI could provide the productivity boost operations leaders have hoped for, as well as a means to fight cost pressures—if only leaders could get going. McKinsey’s latest research finds that only 11% of companies worldwide are using gen AI at scale.”