Identify AI Use Cases for Support

Member company: PTC

Approach: Selected key processes for evaluation against AI, brought process owners and subject matter experts together, and held workshops to identify potential use cases.

Strategy: Focused on creating themes for Gen AI impact, such as helping humans author content and improving conversational engagement with the knowledge base.

PTC considered the ease of implementation against the value delivered, focusing on content creation and leveraging their mature KCS implementation.

Result: Launched an interactive article authoring assistant that automates the first draft to be reviewed by the Knowledge Worker, increasing content creation efficiency and accuracy.

Consortium Members: access PTC’s slides

Manage Expectations and Evaluate Build-versus-Buy Options

Member company: Akamai

Approach: Established company-wide protocols for AI deployments to thoughtfully move from experimentation to production while mitigating risk and prioritizing investments.

Strategy: Socialized an AI Adoption curve to standardize milestones and timeline expectations plus consistent evaluation dimensions for determining technology choices.

• Akamai’s approach highlights the importance of a structured process for managing both stakeholder expectations and the outputs of an AI implementation.
• A leadership committee helps prioritize use cases, moving them into proof of concept and testing phases.

Result: Leadership buy-in to this timeline provides the required space to be thoughtful and systematic about implementing AI at scale, while offering a structured approach to experimentation for all parts of the business as new tools and capabilities become available.

Consortium Members: learn more about Akamai’s strategy

Automate Knowledge Enhancements

Member company: Red Hat

Approach: Leveraged their strong KCS implementation and business intelligence systems to improve the employee and customer experience with AI.

Strategy: Used an ecosystem approach to understand relationships between interconnected elements, mitigate blindspots, and drive continuous improvement.

Result: Improved relevant content matching, AI-generated snippets for organic search, and KCS copilot for Knowledge Worker workflow while providing actionable feedback to all parts of the ecosystem.

Consortium Members: see Red Hat’s presentation