Navigating the AI Landscape

Matt Seaman, Executive Director of the Consortium, presented on the evolution and current state of AI in business, highlighting its rapid advancement and integration into various industries.

Folks are eager to leverage AI to improve efficiency and customer experience, but should be wary of the “trough of disillusionment.” Consortium Members are exploring AI via the lens of valuable use cases, especially for knowledge management and how it fuels positive outcomes for the business.

Watch the recorded presentation or keep scrolling to read through the highlights.

Where We’ve Been So Far

This is part 1 of 2. Explore part 2 – The AI Landscape: Where We Are Going

Evolution of AI in the Service Industry

From the centralization of support in call centers in the 1980s and 90s to the explosion of digital transformation and self-service communities in the 2000s, the service industry has been at the forefront of technological change for over 30 years. With rapid advancements in AI technology, many organizations are grappling with the challenges of adopting and integrating new technology and models effectively.

The last 12 to 18 months have seen a surge in AI adoption, driven by advancements in computing power, data availability, and new machine learning algorithms. The democratization of AI by companies like Amazon, Google, and OpenAI has made it easier than ever for organizations to integrate AI into their operations, often without users even realizing they are interacting with AI-powered systems.

This shift has transformed AI from steady to rapid evolution, with businesses now expected to quickly implement AI solutions that can drive significant improvements in efficiency and customer service.

Consortium History with AI-related Models

The Consortium for Service Innovation has been exploring the potential of AI in the service industry for over a decade. As early as 2012, Consortium Members began discussing the use of machine learning automation to enhance support services. This led to the development of Predictive Customer Engagement, which aims to address customer issues before they happen.

Over the years, these concepts have continued to evolve, with Consortium Members identifying 36 specific use cases for AI in knowledge and service environments.

Beware of the Hype Cycle

Despite the excitement surrounding AI, it is important to recognize that the technology is still evolving. Many organizations are currently navigating the “AI hype cycle,” a concept that describes the phases of excitement, disillusionment, and eventual productivity that often accompany new technologies.

We may be near the “peak of inflated expectations,” where the potential of AI is often overstated, leading to unrealistic expectations. However, as organizations begin to encounter the challenges of implementing AI, they may experience a “trough of disillusionment” before ultimately reaching the “plateau of productivity,” where AI delivers tangible benefits.

To minimize the impact of the trough of disillusionment, businesses must set realistic expectations, prioritize strategic implementation, and focus on achieving sustainable, long-term results with AI.

See three examples of Members setting the standard for thoughtful and strategic AI in Part 2: The AI Landscape – Where We Are Going

Resources Shared

Consortium Resources

• Getting Started with AI
• Strategic Automation: AI Needs Knowledge Management
• 2024 Member Summit Presentations (Consortium Member access only)
• Upcoming events
• Join the mailing list
• Become a Consortium Member
• Get notified when registration opens for Consortium Summit 2025

Additional Articles

• The 1955 academic paper which coined the phrase “artificial intelligence”
• Study: AI Integration Strategy for Learning and Knowledge Management Solutions by Rachad Najjar, Ph.D on Medium
• The Rise of AI as a Buzzword by Alexander S. on LinkedIn

Chat Highlights

• Sara Feldman | Consortium: Investment is so much more than the tools, too! The time/focus on THINKING and PLANNING the right approach has been emphasized by our Members.
• Kelly Murray | Consortium: “If you want the R, you have to make the I.” -@Jacob Watts | Guidewire | Sacto, CA
• Libby Healy | Waters Corp: We have launched an enterprise AI tool for our Global Support team after a pilot with a clear business case that resulted in measurable margin and revenue impact. Happy to chat with anyone thinking about doing something similar!
• Arash Kaur | Geotab: This is great session, in knowledge area alot is changing and to say that it has been established- then NO. as we are not there yet.
• Daniela Montero | ShareFile: Could AI be used to help automate case audits by checking the relevance of attached articles? Is there a use case for this already?
  • Kelly Murray | Consortium: Yes! We have high hopes for this – and Oracle was playing with it years ago – but we haven’t heard stories of success yet.
  • Dave Stewart | Akamai: From a KCS perspective, that would represent the evolution of PAR and give us back more time to work FROM those insights instead of just generating them
• Maddie Hoffman | Zendesk: Thanks for building my reading list for the rest of this week!