AI 企业落地的四大障碍：技术理解、安全、法律与采购

In short : Explore the four main barriers to AI adoption in enterprises, including technology understanding and legal challenges.

AI adoption is slower than expected in many spaces. Some of the reasons are straightforward, but others are more subtle.

Most leaders wants to inject AI into their business to develop a competitive advantage. There are four challenges.

1. The first challenge is understanding the technology’s ability. Because the capabilities evolve so quickly, it’s hard to keep up. If PhDs in the domain are rushing to understand the capabilities reading papers every week, how are business leaders meant to grok the state of the art?

Also, because the systems are non-deterministic, they are unpredictable. The pace of innovation, the early understanding of AI internals, & the non-determinism compound to create doubt.

1. Security is the second challenge. Because of their unpredictable nature & because few have expertise launching these systems, product managers, engineering leaders, & security teams are hesitant to launch both internal & external systems until they develop confidence in data security.

AI security has at least four dimensions : model security, prompt injection, RAG authentication/authorization, & data loss prevention.

1. Legal is the next barrier to entry. Master service agreements (MSAs) are the contracts that dictate terms of service, data privacy, & service levels between a buyer & a vendor. These agreements’ clauses are well-trodden & known.

AI is new. Should a company allow a vendor to train a model using their data? Whose intellectual property is a fine-tuned model? What happens if a vendor violates the data privacy law? What training data is used that might subject the software buyer to future legal action?

Many legal teams are working to understand those questions.

1. Procurement is yet another barrier. SOC2, GDPR, ISO27001 & other certifications provide industry standards for security & compliance. But no such standard exists for AI - yet. Bias, fairness, & explainability are all important factors in AI : some are important for public relations, others for compliance.

Selling AI is not just selling software. Many of the processes are new & these barriers introduce friction into the sales process, extending sales cycles.

Over time, these rough edges will be worn smooth through practice. But the first companies selling today will need to persist through these challenges.

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GP at Theory Ventures. Former Google PM. Sharing data-driven insights on AI, web3, & venture capital.

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