Enterprise AI: Lessons from Social Media

AI risks are not to be ignored, but Enterprise AI can mitigate the risks of consumer AI, much as enterprise social computing avoided the pitfalls of consumer social media.

In this edition, we examine the polarised debate around AI’s potential, fueled by claims that AGI might soon become a reality. While the rapid pace of advancements in AI—like LLM benchmarks, agents, and visual GenAI—creates opportunities, the adoption of such technologies in work, education, and daily life remains slow and complex. The article emphasises that even if AGI were here today, its integration would take time, as societal systems struggle to adapt quickly.

Enterprise AI use cases are beginning to show tangible benefits. Legal AI tools streamline contract drafting by analysing extensive document repositories, flagging clauses for human review, and ensuring compliance. Financial services are also benefiting, with AI automating up to 95% of IPO prospectus creation, transforming previously labour-intensive tasks. AI’s potential extends to compliance and regulation, where real-time analysis could replace slow, manual reviews, offering proactive risk management. However, these applications highlight the need for domain-specific models that minimise risks like hallucination, making tailored solutions crucial for enterprise contexts.

The article contrasts enterprise AI with consumer-facing technologies like social media. Social media platforms have often prioritised clickbait-driven incentives, leading to disinformation and a degraded public discourse. While consumer AI risks amplifying these harms, enterprise AI operates in bounded domains with clearer incentives and smaller, more focused groups. This creates opportunities for businesses to build tailored systems that improve collaboration and outcomes without the toxicity seen in public forums.

The rise of enterprise AI also offers a chance to rethink organisational design. By creating internal systems with their own rules and cultures, companies can mitigate risks like surveillance or worker replacement and attract top talent. The emphasis on meaningful use cases over hype ensures that AI contributes to long-term productivity gains.

With insights from Ethan Mollick , John Gapper , and others.

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