Enterprise AI

DeepSeek delivered higher benchmark performance than OpenAI o1 at lower cost, but what enterprises need isn't a score competition—it's trustworthy results that attach to real work. According to Dataiku and Deloitte data, only 20-30% of GenAI projects reach production.

Enterprises have tried to reduce uncertainty through the prompt and pray approach, or by bolting static, hardcoded chains and verification steps around LLMs. But this approach consumes significant time and resources without solving the core problem.

Because LLMs generate answers probabilistically, results vary even with the same prompt, and hallucinations accumulate across multi-step tasks. Beyond price guidance or simple summarization, in areas where outcomes matter—like financial forecasting or medical classification—this instability becomes a failure.

The alternative is AI systems. Systems must dynamically orchestrate when to look up factual data, when to hand off calculations or tasks to tools, and when to route through human review. For example, a SaaS company shouldn't just answer customer inquiries directly—it should connect real-time product information and CRM data to route to the appropriate team.

The four criteria enterprises should newly focus on are as follows.

• Controllable: Is the output controlled to meet requirements?
• Reliable: Does it consistently produce correct results?
• Integration: Does it connect with structured data, APIs, tools, and enterprise systems?
• Traceability: Can the path by which the AI reached its conclusion be audited?

Many current "agent" solutions still resemble uncertain LLM chatbots. AI21 Labs, which has been building enterprise AI for 8 years, emphasizes that enterprises must now center on systems, not models.