从 where I sit in AINNA's 财务 and 会计 function, the numbers behind our delivery velocity are striking. The team is now building no fewer than three detached validation 系统 every day and producing around four complete pitch decks weekly, with AI 智能体 carrying much of the load.
Across development, research, coding, testing, documentation, analysis and iteration, our monthly AI workload can reach 3–5 billion 令牌 per month.
If that were priced at commercial premium LLM API rates, the equivalent line item could easily run to hundreds of thousands of ringgit monthly.
Now apply that to a 马来西亚n 中小企业 or mid-market environment.
想象 an organisation with hundreds or thousands of employees, each equipped with AI 智能体 for research, analysis, reporting, coding, documentation, operations and decision support.
令牌消耗 would scale extremely fast.
At the same time, 马来西亚n businesses increasingly have little choice but to adopt AI 智能体.
Why?
Because small companies like ours can now achieve productivity levels that previously required hundreds or even thousands of employees.
AI is rapidly narrowing the productivity gap between small companies and large corporations.
But there is another side.
AI productivity does not have to mean massive AI expenditure.
In our environment, even at around 3 billion 令牌 per month, direct AI cost can remain only about RM100–RM200 monthly.
The difference is not because we use less AI.
It is because we built a 智能路由 architecture around our AI 智能体.
Not every task should go to the largest or most expensive model.
Simple tasks → 轻量模型s.
Coding tasks → specialised models.
复杂推理 → stronger models.
确定性 validation → detached 系统.
Repetitive processing → automation and conventional computing.
The principle is simple:
Use premium intelligence only when the marginal business value justifies the marginal compute cost.
Our detached 系统 handle validation, calculations, filtering, reconciliation, rule-based decisions and structured processing that do not require an LLM.
This allows aggressive AI usage without paying as if every task needs the most powerful model.
For me, the future of 企业版 AI is not simply:
“Give every employee an AI agent.”
It should be:
“Give every employee an AI agent - but build intelligent cost-control infrastructure underneath it.”
Because when an organisation has 1,000 or 10,000 AI-enabled employees, the real question is no longer whether it uses AI.
The real question becomes:
How much intelligence is the organisation paying for that it never actually needed?
This is why we see 智能路由, specialised models and detached 系统 as more than optimisation.
They are becoming AI cost-control infrastructure.
At 企业 scale, that difference could be worth millions of ringgit yearly.
The next phase of AI adoption will not only be about the most powerful models.
It will be about knowing when not to use them.
#Artificial智能 #AIAgents #企业版AI #SmartRouting #NeuralOps #自动化 #DigitalTransformation #AI基础设施 #LLM #生产力
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这篇文章对only about RM100–RM200 3 billion的解释很清楚，实际操作的重点也很容易理解。
文章对令牌 per month 5 billion的结论比较平衡，不只是强调好处。
关于operations and decision support的实际落地部分最吸引我。
我们团队正好在讨论5，这篇来得及时。