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 1. Home/
 2. Generative AI Customer Engagement


MISSING INGREDIENT IN GENERATIVE AI TRANSFORMATION OF CUSTOMER ENGAGEMENT






INTRODUCTION



According to McKinsey, generative AI (GenAI) can deliver up to 45% productivity
improvement in customer engagement, when effectively deployed. However, most
early GenAI projects have hit operational and compliance roadblocks that point
to a pattern of missing capabilities that underpin safe, agile deployment of
GenAI at scale. This article examines the foundational role of knowledge
management in GenAI transformation of customer engagement.


GENAI CAN TRANSFORM CUSTOMER ENGAGEMENT, WITH HELP FROM KNOWLEDGE MANAGEMENT



In an insightful Forbes article, Morgan Stanley’s CTO talked about the
“rejuvenation of knowledge management” as a key capability they have prioritized
to develop GenAI tools to assist financial advisors. In simple terms, GenAI
generates conversational responses based on natural language context, prompt and
training. One way to think about GenAI impact on business processes is to treat
GenAI like a “new college hire” in your team. The recruit is bright and quick
but unaware of your business-specific requirements, process, and know-how.



To onboard this “new hire,” you can assign it appropriate tasks while supporting
it with relevant know-how and quality assurance at all times. Here is how a
modern knowledge platform can support GenAI. Note that ALL capabilities outlined
below must be orchestrated in the knowledge platform to enable easy, effective
use of Gen AI for customer engagement.

 1. Best-Practice Prompts: Optimized prompts are at the core of getting good
    output from GenAI. But if your GenAI project needs raw prompt engineering
    talent to lift off, you will struggle. A business-friendly prompt management
    service can offer a library of configurable, best-practice prompts for your
    use cases. Think of prompt management service as a supervisor who needs to
    personalize instructions for the “new hire” to effectively execute routine
    tasks.
 2. Relevant Content: Without trusted content input, GenAI output cannot be
    trusted. Content creation and curation at scale are critical to feeding a
    GenAI tool. Further, this content must be attributable (who is responsible
    for what content), permissioned (who is allowed to use what content), and
    personalized (who should see what content elements). This “new hire” must be
    fed the right source material to be useful.
 3. Business Controls: GenAI must be used only at appropriate points and with
    suitable constraints for customer engagement. For instance, compliance-heavy
    content may not be subject to interpretation, summarization, or restatement
    by GenAI during customer interaction. Being able to easily configure and
    control knowledge content for GenAI access, therefore, is crucial. Your “new
    hire” should not be allowed to restate a required legal disclaimer paragraph
    while responding to a customer.
 4. Quality Assurance: GenAI can occasionally hallucinate, so its output must be
    checked in real-time for accuracy. Modern knowledge platforms can assess
    GenAI output against input context and content used to generate the prompt,
    ensuring relevance and accuracy. The Knowledge platform must allow for a
    step down to semantic search as part of the customer engagement workflow, so
    customer experience is not compromised in the event of GenAI hallucination.
    When you “new hire” delivers sub-par work product, you should be able to
    reject it entirely and resort to alternatives.
 5. Closed Loop Analytics: GenAI in customer engagement must be continuously
    measured and managed. Each iteration of prompt improvement or output
    feedback should be A/B tested to drive continuous improvement. Adoption of
    GenAI in the flow of work should be closely monitored to maximize adoption
    and impact. Measuring and managing your “new hire” closely is key to
    onboarding a confident, competent resource.
 6. Composable Architecture: As GenAI technology develops, one supplier may
    provide better tools than the next, and there are likely to be leapfrogging
    advances along the way. Situating GenAI within a composable knowledge
    platform minimizes switching cost and vendor lock-in for GenAI services.
    Occasionally, your “new hire” may not work out as expected, so you will need
    to easily replace it with another hire.

Orchestrated in a modern knowledge platform, these capabilities de-risk the
introduction and operationalization of GenAI to automate customer engagement.


BEST PRACTICES AND SUCCESS STORIES



Innovative organizations are recognizing knowledge management as a key missing
ingredient in GenAI transformation. Here are some common challenges our clients
faced, solved, and lessons learned.



 1. Connect process and content silos to empower AI: Using GenAI as a
    stand-alone tool with limited access to enterprise data and content is a
    common starting point for GenAI projects in customer engagement. A utility
    client struggled to build an effective knowledge base using GenAI to improve
    an existing knowledge base that did not have access to customer interaction
    store nor the right content sources. They recognized that the know how had
    to be assembled by using a connected capability to extract top customer
    questions from contact recordings and marrying those questions with
    consumable answers generated using correct, long-form documents sitting in
    other enterprise silos. With these connections in place, the client was able
    to build a new knowledge base in less than two weeks!
 2. Orchestrating AI and humans in the flow of work is key: Often AI can do a
    step or a task within a process well but to be operationally effective and
    scalable, it must work in concert with human experts and editors who can
    review AI output to build trust in this automation. An airline client used
    our platform to implement a knowledge base redesign project by invoking
    GenAI to propose a taxonomy coordinating a team of experts reviewing the
    draft results. Streamlined orchestration of AI and humans is vital to
    maximizing operational impact.
 3. Centralize and leverage knowledge everywhere: Some clients build GenAI
    capability focused on their digital channels first. In that process, they
    ignore the value of the knowledge assets they already have in their voice
    channels. One of our clients who has been using our AI guidance for their
    call center agents learnt the hard way that building new capability silos is
    a recipe to build fast and scale slow. When we got to helping them, we
    recommended leveraging the deep knowledge asset they successfully use for
    call center agent guidance. By using our platform to automatically repurpose
    call center knowledge into digital channels, they saw much bigger business
    impact, thanks to omnichannel experience consistency and high marginal
    return on centralized knowledge build.
 4. Crawl, walk, run quickly and keep a narrow focus: Given the potential of
    GenAI, there is a tendency to try to bite more than one can chew. Some of
    our clients went after the big prize of extreme contact deflection, using
    GenAI to automate customer self-service. The capability worked but it had
    occasional failures that became public customer satisfaction flash points.
    Others have been thoughtful, starting with supervised GenAI use cases, such
    as quickly building knowledge bases. Then they moved quickly to agent
    guidance use cases, gradually increasing the risk/reward aperture, learning
    along the way.


CONCLUSION



Knowledge management is the key missing ingredient in successful GenAI
transformation of customer engagement. By leveraging a modern knowledge platform
that integrates GenAI, organizations can harness the true potential of GenAI to
automate customer engagement – safely and easily.



To be published on Forbes.com

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