Conference Presentation, Fireside Chat, Interview
Neo4j, Eurazeo Growth: Connected Intelligence Why Graph Technology Is the Missing Link in GenAI
- Graph technology is projected to become essential for predictive analytics by 2030, with expectations that it will supplant AI methods prevalent in 2020.
- Knowledge graphs are anticipated to enhance the probability of Generative AI applications reaching production by 80% by providing a foundational basis for memory, knowledge, and human-machine interaction.
- Enterprise systems integrating data into knowledge graphs are expected to solve previously unsolvable problems through structures deemed accurate, secure, and useful enough for complex decision-making.
- Specific operational efficiencies include processing tasks up to one thousand times faster using one-tenth of the hardware compared to traditional database approaches like Oracle.
- The emergence of the ISO-standard GQL language in the previous year is expected to act as a successor to SQL, enabling models to communicate with property graph models via training on Cypher examples.
- Data and use case network effects, such as improvements in recommendations, fraud detection, and supply chain optimization, are predicted to emerge once data is connected.
- The adoption cycle for AI infrastructure is forecast to progress slowly through stages of ruggedization, fine-tuning, and agentic capabilities.
- Builders may face risks regarding simple SaaS applications acting solely as CRUD layers, which are expected to be easily replicable by Large Language Models.
- While economy may be realized from replacing simple SaaS, the primary financial benefits are expected to derive from new capabilities unlocked by graph technology.
- Confidence in the technology is expected to stem from its status as a rare component of the AI stack with an established international standard.
- Individual behaviors are predicted to be analyzed based on connections, unlocking possibilities for decision systems that incorporate all company knowledge within privacy and access constraints.