The burgeoning landscape of global commerce, characterized by hyper-competition and an unprecedented deluge of data, places an ever-increasing premium on profound customer understanding. In this environment, generative artificial intelligence (GenAI) tools, particularly when integrated with large language models (LLMs) through techniques like retrieval-augmented generation (RAG), are rapidly transforming how enterprises unearth, analyze, and disseminate customer insights. This hybrid approach allows companies to infuse their proprietary, context-specific customer data with the expansive general knowledge base of LLMs, unlocking new efficiencies and deeper understandings. Yet, while GenAI offers revolutionary capabilities, its ultimate success hinges on overcoming long-standing organizational and cultural impediments that echo challenges from previous eras of knowledge management.
The strategic imperative to capture nuanced customer preferences, purchasing behaviors, and service expectations has never been greater. Insights, traditionally gleaned from market research agencies, sales interactions, social media monitoring, customer service logs, and direct feedback channels, often manifest as a vast, heterogeneous mix of structured and unstructured information. Historically, managing this information presented a formidable challenge, leading to siloed data, redundant efforts, and underutilized intelligence. GenAI-powered systems promise to transcend these limitations by offering intuitive, natural language access and sophisticated summarization capabilities, particularly vital within sprawling multinational corporations where relevant insights might be buried across diverse departments and geographic regions. The value proposition extends beyond mere data storage to encompass the entire knowledge flow—from creation and analysis to curation and dissemination.
GenAI’s Transformative Potential in Insight Generation
Companies are adopting GenAI in varied configurations, from proprietary in-house solutions to vendor-supplied platforms or hybrid models. Procter & Gamble (P&G), a consumer goods titan with a century-long commitment to market research, exemplifies a hybrid approach. While leveraging external software for knowledge storage, P&G developed its own GenAI system for deep analysis and categorization, enabling employees to receive "sharp, pointed answers" rather than just document links, according to Kirti Singh, P&G’s chief analytics, insights, and media officer. This strategic layering allows for tailored intelligence extraction from a colossal data repository.
The vendor landscape itself is evolving, with specialized tools emerging to address different facets of the insights lifecycle. Some focus on sophisticated storage and access, offering automated curation, integration of disparate content types, and on-demand analysis that synthesizes answers from existing insights. Others concentrate on the rigorous analysis of qualitative data, while a third category emphasizes rapid consumer response testing for marketing campaigns. The market is trending towards comprehensive "customer insights platforms" that unify these functions, employing GenAI and other advanced analytics to manage the entire process, from data acquisition to actionable intelligence delivery. Regardless of the vendor’s primary focus, a foundational principle remains: "AI is only as useful as the data it learns from." This necessitates meticulous data curation, categorization, summarization, and intelligent tagging to ensure the quality and discoverability of insights.
Novartis’s "Sherlock" system stands as a compelling case study for GenAI-enhanced insight storage and access within its consumer business. Developed with an external vendor, Sherlock provides precise answers to user queries, pinpointing specific text lines or video timestamps. It integrates expert-curated "Knowledge Zones" on topics like packaging, and even allows direct upload of project deliverables from research vendors. Critically, the system incorporates governance guidelines for document formats and quality, and features like "WatchOut" flag context-specific data, such as results based solely on European patient data, preventing overgeneralization. This strategic implementation saved Novartis over $29 million in primary market research costs in a single year, demonstrating the tangible economic benefits of democratizing information and reducing redundant research efforts.
Revolutionizing Qualitative Data Analysis
Qualitative data analysis, traditionally a labor-intensive and often subjective endeavor, represents another area ripe for GenAI disruption. Historically, market researchers have relied on semi-manual processes or academic-grade specialized software, often involving extensive spreadsheet work and cut-and-paste coding. Generic AI chatbots, as some academic critiques suggest, may fall short in the nuanced interpretation required for qualitative insights. However, specialized GenAI tools are proving adept.
Tracy Tuten, who leads qualitative research at Illuminas (now part of Radius Insights), pioneered what she terms "conversational qualitative data analysis" using GenAI software. This approach allows for natural language prompts to analyze rich qualitative data from interviews and focus groups. The system automates transcription of audio and video, generates summaries, surfaces emergent themes, and facilitates cross-segment comparisons. What once took six weeks for a large global qualitative study can now be synthesized in a single day, dramatically enhancing efficiency and enabling researchers to uncover secondary insights that might have been overlooked. Tuten often uses the software collaboratively in client workshops, fostering faster, more participatory insight discovery. While GenAI significantly augments researcher capabilities by handling the heavy lifting of data processing, the critical human element of interpretation, contextualization, and strategic framing remains indispensable.
PepsiCo further illustrates the power of GenAI in customer knowledge creation, particularly in understanding consumer responses to advertising and brand messaging. Their "Ask Ada" platform integrates various GenAI and analytical capabilities to rapidly test concepts, validate marketing strategies, and provide comprehensive market sensing. This ecosystem of tools has empowered PepsiCo to significantly reduce its dependence on external agencies and consultants, fostering a more agile and internally driven insights function.
The Enduring Human and Organizational Imperatives
Despite the undeniable technological advancements, GenAI tools are not a panacea. Strategic leadership, organizational alignment, and a deeply embedded insights culture remain critical for maximizing their value. As Stephan Gans, former senior vice president and chief customer insights and analytics officer at PepsiCo, aptly noted, "Leading the understanding of consumer demand is much more strategic and still requires humans." Our research highlights four persistent challenges that GenAI alone cannot resolve:
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Fragmented Global and Business Unit Approaches: Many multinational corporations struggle with decentralized operations, where country-specific units maintain significant autonomy. This often leads to inconsistent taxonomies, brand definitions, and data collection methodologies across geographies, creating "pockets of knowledge" that are incoherent and contradictory. An unnamed global consumer goods company we studied invested in a GenAI-based knowledge tool but saw limited global impact because of this fragmentation. Without a senior executive mandate to standardize information formats and foster cross-unit learning, the tool’s utility remained confined, leading to missed opportunities and redundant investments. In stark contrast, PepsiCo, under Gans’s leadership, consciously forged a "one nation" approach to market research, establishing a Global Insights Council to ensure commonality and facilitate shared learning across its diverse markets. This strategic alignment, supported by the CMO, was fundamental to integrating customer insights into the company’s innovation pipeline.
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Lack of Insight Integration into Strategy and Culture: Even the most sophisticated technology will flounder if an organization’s decision-makers are not fervent consumers of data-driven insights. A corporate culture that prioritizes intuition over evidence or views market research as a peripheral function will undermine any investment in GenAI. P&G’s enduring success in consumer markets is deeply rooted in its "consumer-centric" philosophy, a tradition dating back to 1924 when its CEO commissioned research to understand Ivory soap purchases. This century-long commitment to understanding consumers through "experimental science, human and behavioral science, data science, and technology platform knowledge" creates fertile ground for GenAI adoption, ensuring that insights are not just gathered but actively consumed and acted upon at every strategic level.
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Complexities of Agency Relationships and Data Ownership: Many companies rely heavily on external advertising and market research agencies, which can introduce ambiguities regarding data ownership and analysis strategies. If agencies retain ownership or exclusive access to critical customer insights, the client company risks becoming overly dependent, hindering its ability to develop internal expertise and respond nimbly to market shifts. Gans at PepsiCo strongly advocated for client ownership of all research results and insights generated on their behalf, emphasizing that outsourcing the "learning" from data prevents employees from applying lessons to future campaigns. Best practices involve clear contractual agreements that ensure the client retains full rights and access to all collected data, fostering a more collaborative and empowering client-agency dynamic.
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The Perceived Low Status of Analytics Professionals: Historically, insights and analytics functions have sometimes been relegated to a "library-like" role, with professionals seen as "order-takers" rather than strategic partners. In one consumer products company, a GenAI tool democratized access to insights, yet users continued to treat it as a passive repository, failing to contribute new insights or formulate high-quality prompts. This perpetuates a cycle of underutilization and can lead to budget cuts and a lack of respect for the function. PepsiCo’s transformation under Gans provides a powerful counter-narrative. By repositioning the insights and analytics organization as a strategic driver of commercial excellence, investing in advanced platforms like Ask Ada, and fostering a culture of curiosity, the function evolved from being an "order-taker" to a respected, well-funded, and indispensable component of the company’s success.
In conclusion, GenAI tools offer a formidable new frontier for customer insight management, promising unparalleled speed, scale, and depth of analysis. From streamlining data synthesis to revolutionizing qualitative research, these technologies can significantly augment human capabilities. However, their true potential remains tethered to the foundational strength of an organization’s culture, leadership, and operational frameworks. Companies must proactively address challenges such as data standardization across disparate units, embed insights deeply into their strategic decision-making processes, ensure clear data ownership, and elevate the role of insights professionals. GenAI is not a replacement for human ingenuity, strategic thinking, or a genuine organizational appetite for customer understanding; rather, it is a powerful amplifier. Its successful deployment demands a symbiotic relationship between cutting-edge technology and astute human leadership, integrating and standardizing data, and fostering a pervasive culture where customer insights are not just gathered, but passionately pursued and strategically acted upon.
