市場調查報告書

新技術帶來豐富收穫:亞太地區金融服務產業中早期導入人工智慧 (AI) 的20家企業案例

Many Things from the Shiny New Thing: 20 Early Adopters of Artificial Intelligence in Asia/Pacific Financial Services

出版商 IDC 商品編碼 913848
出版日期 內容資訊 英文 24 Pages
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新技術帶來豐富收穫:亞太地區金融服務產業中早期導入人工智慧 (AI) 的20家企業案例 Many Things from the Shiny New Thing: 20 Early Adopters of Artificial Intelligence in Asia/Pacific Financial Services
出版日期: 2019年10月07日內容資訊: 英文 24 Pages
簡介

人工智慧 (AI) 並非能夠解決金融服務產業的所有問題。但透過適切的應用,將能大幅提升顧客體驗/參與、優化營運效率、創造新產品/服務。預估在2022年,亞太地區超過50%的金融服務業者將投資相關的人工智慧 (AI) 技術。

本報告分析亞太地區 (日本以外) 金融服務市場的人工智慧 (AI) 導入情況和運用動向,彙整主要早期導入企業之目前應用情況、未來具潛力應用領域、以及推動未來普及時需解決的課題等情報。

摘要整理

概況

  • 誰在做什麼?
  • 人工智慧 (AI) 正確定義
  • AI計畫為何?
  • AI市場機會評估
    • 主要趨勢
    • 益處
    • 課題
      • 識別適切案例
      • 數據呈現了最大難題
      • 核心策略中AI「缺少的那一片」
      • 變更管理的不適合嘗試
      • 缺乏技術和人才
      • 缺乏技術基礎建設
  • 最普遍的導入領域
  • AI領域早期導入者行動:代表案例 (20件)

技術買家建議

  • 需考慮的行動
    • 策略和贊助
      • 大膽思考,從小處開始
      • 管理代言問題
      • 建構多功能COE (Center of Excellence)
    • 流程識別和優化
      • 設定業務目標/問題
      • 流程的重新設計和重新構想
    • 人員和變更管理
      • 多功能合作:比想像還要重要
      • 專注主動變更管理
      • 思考新作用和技能
    • 可擴展的基礎建設
      • 需要可擴展且可適應的技術基礎建設
    • 數據和模式的生命週期管理
      • 注意數據各個面相
      • 思考AI生命週期管理模式
      • 強健安全性、合規性、治理能力可建立起信任

參考資料

  • 相關研究
  • 總結
目錄
Product Code: AP43052218

In this report, IDC Financial Insights discusses how Asia/Pacific (excluding Japan) (APEJ) is embracing artificial intelligence (AI) and the many technologies that come under its ambit. Several early adopters of AI have emerged from the region, with a wide range of objectives from the ability to offer superior customer and employee experience and the augmentation of operations to the design and launch of new products and services.In our opinion, AI is not the answer to every business goal and problem. However, for those in which AI is the answer, it can significantly transform customer experience and engagement, optimize operational efficiencies, and create new products and services. Regardless of its promises and emerging evidence of significant benefits, AI adoption has been low. Most of the FSIs in the region were not born digital, and they are still stuck with traditional ways of thinking and working. They still do not consider AI as part of their core, enterprisewide strategy, and as a result, it is being implemented as part of a fragmented, siloed approach without any long-term road map to achieve scale.

However, we are nearing a tipping point and expect this situation to change considerably within the next two to three years as there are more successful use cases and real evidence of unprecedented benefits available in the market. This will further change with a better understanding of the capabilities of AI and as more institutions invest in AI readiness. Sneha Kapoor, research manager, IDC Financial Insights, says, "By 2022, IDC Financial Insights expects more than 50% of Asia/Pacific FSIs to invest in one or more AI technologies. Majority of projects will focus on three objectives: transform the customer experience, optimize operational efficiencies, and create new revenue streams. We also believe that AI will be one of the key technologies to drive institutions through digital business transformation."

Executive Snapshot

Situation Overview

  • Who Is Doing What?
  • Defining What Exactly Is AI
  • What Is an AI Project?
  • The AI Opportunity Assessment
    • Key Trends
    • Benefits
    • Challenges
      • Identifying the Right Use Cases
      • Data Presenting the Biggest Conundrum
      • Missing AI Piece in the Core Strategy
      • Inadequate Attempts at Change Management
      • Lack of Skills and Talent
      • Lack of Technical Infrastructure
  • Most Common Areas of Implementation so Far
  • 20 of the Best, Early Adopter Initiatives in AI

Advice for the Technology Buyer

  • Actions to Consider
    • Strategy and Sponsorship
      • Think Big But Definitely Start Small or at Least Somewhere
      • Management Endorsement Matters
      • Build a Cross-Functional COE
    • Process Identification and Optimization
      • Identify a Business Goal/Problem
      • Redesign and Reimagine Processes
    • People and Change Management
      • Cross-Functional Collaboration Is More Important Than You Think
      • Focus Proactive Change Management
      • Think About New Roles and New Skills
    • Scalable Infrastructure
      • Scalable and Adaptable Technical Infrastructure Is Needed
    • Data and Model Life-Cycle Management
      • Pay Attention to All Aspects of Data
      • Think About AI Model Life-Cycle Management
      • Strong Security, Compliance, and Governance Will Create Trust

Learn More

  • Related Research
  • Synopsis
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