Can artificial intelligence (AI) help companies improve efficiency? The simple answer is yes. However, incorporating AI into business processes and workflows is not a simple process, though it is both an achievable and in many cases, a necessary one. As a leader of AI teams at Intuit®, I have learned a few lessons about the process, and I’ll share them with you today as we look at AI’s ability to improve efficiency in businesses in every industry.
人工智慧（AI）能否幫助企業提高效率？簡單的答案是肯定的。然而，將人工智慧融入業務流程和工作流程並不是一個簡單的過程，儘管它既是一個可以實現的過程，而且在許多情況下也是一個必要的過程。身為 Intuit® 人工智慧團隊的領導者，我在這個過程中學到了一些經驗教訓，今天在我們研究人工智慧提高各行業企業效率的能力時，我將與您分享這些經驗教訓。
How artificial intelligence improves efficiency
人工智慧如何提高效率
First, what is AI? Brian Gorbett has a great definition. He writes in Demystifying artificial intelligence and machine learning that AI is “taking data, learning from it, and redeploying outputs that help your customers.”
首先，什麼是人工智慧？布萊恩·戈貝特（Brian Gorbett）有一個很好的定義。他在《揭秘人工智慧和機器學習》一書中寫道，人工智慧正在“獲取數據，從中學習，並重新部署可以幫助客戶的輸出。”
According to IDC’s 2019 Spending Guide, AI system spending will reach $97.9 billion in 2023. Why? Because businesses are finding that AI technology provides a myriad of benefits for their customers as well as to the business itself, including improving their efficiency by performing data-driven tasks faster and better than humans.
根據 IDC 2019 年支出指南，2023 年人工智慧系統支出將達到 979 億美元。因為企業發現人工智慧技術為其客戶以及企業本身提供了無數的好處，包括透過比人類更快更好地執行數據驅動的任務來提高效率。
This doesn’t mean the human element is extinguished by AI; in fact, the human element is enhanced and supported it. For example, AI can help accelerate customer support processes by generating automated case note summaries to help agents catch up with previous calls content. It can also help coach agents on providing better support for their customers by leveraging explainable AI tools.
這並不意味著人工智慧消除了人為因素；事實上，人的因素得到了增強和支持。例如，人工智慧可以透過產生自動案例說明摘要來幫助加快客戶支援流程，以幫助代理商趕上先前的通話內容。它還可以幫助指導代理商利用可解釋的人工智慧工具為客戶提供更好的支援。
In addition, organizations can reform their products and data security by leveraging AI to detect anomalous behaviors in their systems.
此外，組織可以利用人工智慧檢測系統中的異常行為來改革其產品和資料安全。
One
of the main benefits of leveraging AI for such tasks is the ability to
automatically learn and update the models based on changing patterns in the
data. With traditional business rules, human interaction is required to modify
the logic in order to address changes over time.
利用人工智慧執行此類任務的主要好處之一是能夠根據資料變化的模式自動學習和更新模型。對於傳統的業務規則，需要人工互動來修改邏輯，以應對隨著時間的推移而發生的變化。
Without a doubt, AI is becoming a
necessity for businesses wanting to improve their efficiency and remain
competitive in a dynamic marketplace. However, it may be intimidating to those
who are new to AI, so I have some advice.
毫無疑問，人工智慧正在成為想要提高效率並在動態市場中保持競爭力的企業的必需品。然而，對於那些剛接觸人工智慧的人來說，這可能會令人生畏，所以我有一些建議。
How-to advice on using artificial intelligence to improve efficiency
使用人工智慧提高效率的操作建議
As I mentioned earlier, I lead AI teams at Intuit. We have found that using AI to improve efficiency should first start with the gathering of efficiency problems, and then ranking them by impact.
正如我之前提到的，我領導 Intuit 的人工智慧團隊。我們發現，利用人工智慧提高效率首先應該從效率問題的聚集開始，然後再按照影響力進行排序。
Once you’ve done that, try to understand
whether a simple rule-based solution based on domain expert heuristics could
solve this problem. If you find there’s still room for improvement, then you
should pair the domain and data experts with an AI expert to see if relevant
labeled data could be gathered to solve the problem using AI.
完成此操作後，請嘗試了解基於領域專家啟發法的簡單的基於規則的解決方案是否可以解決此問題。如果您發現仍有改進的空間，那麼您應該將領域和數據專家與人工智慧專家配對，看看是否可以收集相關的標記數據來使用人工智慧解決問題。
Note that AI is not always the best solution. Sometimes, there is just not enough relevant data to generate an efficient AI solution, and sometimes simple rule-based logic would be enough.
請注意，人工智慧並不總是最好的解決方案。有時，沒有足夠的相關數據來產生有效的人工智慧解決方案，有時簡單的基於規則的邏輯就足夠了。
It is important to understand that developing AI models should always start with a problem and a hypothesis that a solution can provide a certain benefit. It is recommended to test the hypothesis with a simple solution first, and then go on with researching AI techniques to solve the problem. This process exemplifies Intuit’s Design for Delight.
重要的是要理解，開發人工智慧模型應該始終從問題和解決方案可以提供一定好處的假設開始。建議先用一個簡單的解決方案來檢驗假設，然後繼續研究人工智慧技術來解決問題。這個過程體現了 Intuit 的「愉悅設計」。
Adopting AI into your process and workflows does pose some challenges, including prioritizing the integration by the product developer (PD) teams, which are needed to get AI integrated into existing products along with other business initiatives. Working closely with the PD and project manager (PM) as a mission-based team during the model development process, explaining to them how the AI works, and showing the potential business impact of the service. will help to build trust with the PD teams and accelerate the integration.
將人工智慧融入您的流程和工作流程確實會帶來一些挑戰，包括優先考慮產品開發人員 (PD) 團隊的集成，這是將人工智慧與其他業務計劃一起整合到現有產品中所必需的。在模型開發過程中，作為一個基於任務的團隊與 PD 和專案經理 (PM) 密切合作，向他們解釋人工智慧的工作原理，並展示該服務的潛在業務影響。將有助於與 PD 團隊建立信任並加速整合。
I would also recommend leadership invest in AI education for the PD and PM communities. Education, combined with specific goals and metrics around AI adoption, can really help the teams communicate and work better together. For more information on AI, check out How artificial intelligence is redefining apps and Forecasting and predictive modeling for marketing analytics.
我還建議領導層投資於 PD 和 PM 社群的人工智慧教育。教育與人工智慧採用的具體目標和指標相結合，可以真正幫助團隊更好地溝通和合作。有關人工智慧的更多信息，請查看人工智慧如何重新定義應用程式以及行銷分析的預測和預測建模。