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Original article
2024: The State of Generative AI in the Enterprise - Menlo Ventures
Key takeaway
- For entrepreneurs: Focus on building vertical AI applications and autonomous agents as the market shifts from experimentation to production deployment, with enterprises willing to pay for value over price sensitivity
- For investors: The application layer is growing faster than infrastructure, with enterprise AI spending jumping 6x to $13.8B in 2024, signaling a transition from pilots to production-ready solutions
Summary
The 2024 State of Generative AI in the Enterprise report by Menlo Ventures reveals a dramatic increase in AI adoption and spending. Enterprises are moving from experimentation to implementation, with AI becoming a core part of business strategies. The application layer is growing rapidly, with specific use cases like code generation and support chatbots leading adoption. The modern AI stack has stabilized, with enterprises adopting multi-model strategies and favoring retrieval-augmented generation (RAG) architectures. Vertical-specific AI applications are on the rise, particularly in healthcare, legal, and financial services sectors.
2024 marked the year generative AI became mission-critical for enterprises, with spending surging to $13.8B from $2.3B in 2023. Organizations are moving from experimentation to execution, with clear ROI emerging in specific use cases like code generation, support chatbots, and enterprise search. While incumbents still dominate, startups are gaining ground, particularly in vertical applications across healthcare, legal, and financial services sectors.
Insights
- AI spending increased from $2.3 billion in 2023 to $13.8 billion in 2024
- 72% of decision-makers expect broader adoption of generative AI tools
- 60% of AI investments come from innovation budgets, but 40% now comes from permanent budgets
- 47% of AI solutions are developed in-house, while 53% are sourced from vendors
- Organizations typically deploy 3+ foundation models rather than relying on a single provider
- OpenAI's market share dropped from 50% to 34% while Anthropic doubled to 24%
- Top use cases include code copilots (51% adoption), support chatbots (31%), and enterprise search (28%)
- Enterprises prioritize ROI and industry-specific customization over price when selecting AI tools
- Technical departments command largest spending share (49%), but AI budgets now flow to all departments
- Top implementation challenges include costs (26%), data privacy (21%), and disappointing ROI (18%)