Information management leaders must determine how AI can be used to support their organizations:
- To improve operational efficiency in order to do more with less
- To scale information governance and data management practices to turn unstructured data into structured knowledge
- To ensure privacy, security, standards, and usage rights are understood and in practice
- To acquire deeper understanding of data-oriented copyright restrictions and contract terms amid the evolving digital era
- To transition from a cost center to a profit center by participating in the AI innovation value chain as a recognized source of quality data that can be used to train AI
Information management centers must be recognized as best-in-class knowledge centers:
- To demonstrate relevance and influence in their organizations
- To permit scaling to ensure that their resources and services are on-target to today’s users
- By applying AI to help educate the organization on new technologies and skills
- To illustrate a new class of information managers with modern digital services that provide more value than what end users can obtain online today in open platforms and interfaces
Practitioners must be closer to the end user
- AI technologies can be applied to scale the business to address a growing rate and diversity of client queries.
- Content must be delivered in new, modern, immersive formats (e.g., text, video, and audio) with interactive user experiences across platforms and screens.
- Quality content and contextually relevant knowledge continue to drive improved decision-making, increasing the importance of the use of advanced analytics and data visualization software by information management and corporate libraries to deliver value directly to the end user.
This study combines:
- Synthesis from Outsell’s daily contact and interviews with CEOs, CDOs, CPOs/CTOs and heads of analytics and decision-making data businesses.
- Deep knowledge of Outsell’s ongoing and unique 20+ years of market analysis
- Original analysis based on the deep industry experience of our analysts
- Assessment of technology vendors’ public disclosures and earnings
It also draws upon our unique discipline of studying the major information market components (data/content, technology, and workflow) that, when triangulated, add 360-degree context to our insights into how businesses view, capitalize, and evolve their data and product strategies with AI, while building on prior reports analyzing this topic.
Table of Contents
Why This topic IS IMPORTANT FOR Information Management and Corporate Libraries 3
OUR Methodology 4
AI definition and getting beyond the “Buzz” 5
The AI Market landscape has expanded 13
Core Foundation (AI technology and Data Services) 15
Example: Enabling Immersive Customer Experiences 17
Example: Extracting Meaning an New Insights from Content 18
Example: Leveraging AI an the Cloud to Scale the Business 19
Advancing Functional Applications for the Enterprise 20
Embedding AI in Legacy Portfolios 21
Example: Improving Behavioral Targeting 22
Example: Optimizing Situational Advertising 23
AI is an Enabler for IoT and Vice Versa 24
Advancing Industry solutions for the Enterprise 25
Industry Use Case Examples 26
AI having a Profound Effect on the Financial Sector 27
Example: Amassing New Insights and Predictions in Financial Services 28
Example: Advancing Scientific, Patient Care an Drug Discovery 29
Example: Innovations in Legal Solutions 30
Key Takeaways 31
Imperatives for IM 32
Related Research 33
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