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Schedule 2024

Session 305: AI Promise vs. Reality of Contact Centers

Christa Heibel  (CEO/Founder, CH Consulting Group)

Date: Wednesday, October 23

Time: 4:00 pm - 5:00 pm

Pass Type: Premium Conference Pass, Standard Conference Pass, Training + Standard Conference Pass - Get your pass now!

Track: Maximize Productivity and Operations

Session Type: Session

Vault Recording: TBD

In the realm of contact centers, where Artificial Intelligence (AI) holds significant transformative
potential, the success of AI initiatives hinges on the strength of existing foundations. Following
the labor disruption caused by the COVID-19 pandemic in 2020, “the Great Resignation” was
ushered into 2022. The market’s answer in the past few years to this labor shortage has been
AI... but is it the cure-all solution that it promises to be? Christa's presentation, titled "AI
Promise vs. Reality in Contact Centers," explores this pivotal aspect, contrasting the lofty
expectations of AI with the practical outcomes, highlighting the need for a robust foundational
base.
The envisioned future promised by AI entails swift resolution of customer queries, minimal wait
times, and human agents focusing on complex tasks while AI efficiently manages routine
interactions. However, in reality, many contact centers struggle to achieve the anticipated
efficiency gains post-AI implementation. A significant number of projects fall short of
expectations, and many never progress beyond the planning stage due to a lack of solid
foundational elements essential for AI success.
Dissecting the core reasons behind this disparity, the presentation underscores the critical
importance of strong foundational practices: realistic expectations, high-quality data, seamless
integration with existing systems, a solid knowledge management system (KMS) and
comprehensive training for human agents. Each element serves as a cornerstone for building a
resilient structure conducive to AI's success. Neglecting these foundational aspects leads to
diminished returns, reduced customer satisfaction, and lower employee morale, highlighting
the imperative need for robust groundwork before embarking on the AI journey. This
presentation serves not only as an analysis but also as a roadmap for successful AI integration
in contact centers. It emphasizes the importance of establishing a sturdy foundation before
building the AI superstructure, guiding attendees through the intricate yet rewarding path of AI
adoption in contact centers, where AI's promise can only be realized on the bedrock of solid,
well-established practices.

Takeaway

1. Foundation is Key: The success of AI in contact centers is contingent on the strength of the
existing operational and technological foundations. Without robust infrastructure, quality data,
and well-established processes, AI initiatives are likely to falter.
2. Realistic Expectations: It's crucial to approach AI with realistic expectations that are aligned
with your business needs and growth forecast. AI is not a panacea for all operational challenges
but a tool that, when used correctly, can significantly enhance efficiency and customer
satisfaction.
3.Quality of Data Matters: AI systems are only as good as the data they are fed. Prioritizing the
collection, cleansing, and structuring of high-quality data is essential for the effective
functioning of AI solutions in contact centers.
4. Integration and Adaptation: Successful AI implementation requires seamless integration with
existing legacy systems and adaptation by human agents. The synergy between AI tools and
human expertise is vital for delivering optimal results.
5. Continuous Learning and Improvement: AI in contact centers is an ongoing journey, not a
one-time implementation. It demands continuous learning, adaptation, and improvement
based on feedback and changing business needs. This approach ensures that AI remains a
relevant and powerful asset in the dynamic environment of contact centers.
6. Knowledge Management System (KMS): Knowledge management systems have a strong ROI
based argument attached to them and should be in the core CC technology stack, but is often
missing or ineffective. It is key to ensure you have an effective KMS in place prior to AI
implementation. There must be content available to allow AI tools (based on the tool you are
looking to deploy) to access the right, smart information and serve it up correctly.