Why Your AI Strategy Misses Linguistics?
I was fascinated by Oliver Shoulson’s talk at PolyAI's Fluent conference today. His analysis hits a crucial gap in enterprise AI: we are focused intensely on the computational power of Large Language Models (LLMs), yet are strategically ignoring the linguistic foundation that drives true Customer Experience (CX).
As a research director and a multilingual person (fluent in six languages with passing knowledge of three others), I see the core problem clearly:
LLMs are trained on vast, static datasets like books and academic articles. They are not trained on the nuanced, cooperative, short-form flow of human dialogue. The key takeaway for CX executives, CIOs, and B2B SaaS marketers is this: Stop designing for mere comprehension and start designing for Cooperative Dialogue.
The Business Value of Linguistic Design
This is not a theoretical discussion; it directly impacts your bottom line by optimizing three critical areas of customer interaction:
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Reduce Cognitive Load:
An agent that overexplains every step—asking for an account number and justifying the reason for the request—wastes the customer's time. This shifts the focus from problem-solving to system-using. The linguistic design must anticipate and compress the conversational exchange.
Business Value: Faster task resolution and reduced handle time.
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Increase Trust and Adherence:
Research consistently ties a higher sense of social presence—that feeling of mutual engagement—to increased user trust and greater adherence to the agent’s advice. This is a subtle linguistic victory achieved by modeling the unspoken rules of conversation, rather than just the words themselves.
Business Value: Fewer repeat calls and higher customer satisfaction (CSAT) scores.
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Ensure Contextual Fluency:
The AI must sound spontaneous and unscripted, adapting instantly to tone and sequence. This requires applying principles from pragmatics—the study of language use in context—to ensure the system works across diverse scenarios and doesn't sound robotic.
Business Value: Future-proofed CX investment that handles nuance and new edge cases effectively.
Conclusion: The Next Step is Rigor, Not Size
The next step in AI-driven CX is not a larger model; it is an investment in the linguistic rigor that makes every customer interaction feel efficient and understood. The most powerful AI conversation is the one that follows the human script, not the model's training data.
What is the most linguistically complex customer scenario your organization handles today? Share your experience.
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Disclaimer: This blog post reflects my personal views only. AI tools may have been used for brevity, structure, or research support. Please independently verify any information before relying on it. This content does not represent the views of my employer, Infotech.com.
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