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How AI can help companies improve efficiency

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

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.

In addition, organizations can reform their products and data security by leveraging AI to detect anomalous behaviors in their systems.

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.

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.

How-to advice on using artificial intelligence to improve efficiency

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. 

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.

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。

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