In an increasingly data-rich and competitive global economy, understanding the customer has become the cornerstone of sustainable business success. Companies across every sector are grappling with an explosion of information, ranging from traditional market research reports to real-time social media sentiment, transactional data, and direct customer service interactions. The promise of generative artificial intelligence (GenAI) and its underlying large language models (LLMs) offers a compelling solution to harness this deluge, transforming what was once a laborious "knowledge management" challenge into a dynamic, accessible intelligence ecosystem. Yet, as pioneering firms discover, while GenAI dramatically enhances capabilities, it simultaneously underscores the enduring human and organizational complexities that no technology, however advanced, can fully resolve.
A significant shift in how organizations access and analyze proprietary content is emerging through hybrid knowledge approaches, predominantly leveraging retrieval-augmented generation (RAG). This technique seamlessly integrates a company’s bespoke customer insights with the vast general knowledge embedded within LLMs, offering a powerful synergy. The benefits are multifold: employees can query vast repositories of information using natural language, receiving concise summaries and direct answers rather than sifting through countless documents. This capability is particularly impactful within sprawling multinational corporations, where insights are often scattered across departments, geographies, and historical projects, making traditional discovery a daunting task. The sheer volume of data, encompassing everything from structured quantitative surveys and sales figures to unstructured qualitative transcripts, customer emails, and social media chatter, makes advanced analytical tools not just beneficial but essential for competitive differentiation.
Early adopters, including industry giants like Procter & Gamble, PepsiCo, and Novartis, are at the forefront of this integration. P&G, for instance, augments vendor-supplied knowledge storage with its own GenAI system, designed for precise analysis and categorization. This allows their teams to receive "sharp, pointed answers" rather than generic document links, accelerating decision-making and innovation cycles. The market for GenAI tools in customer insights is rapidly diversifying, with vendors specializing in various aspects: from comprehensive insight storage and intelligent access platforms that offer automated curation and content integration, to those focusing on the nuanced analysis of qualitative data or rapid consumer response testing. The trend suggests an eventual convergence into broad customer insights platforms that orchestrate the entire lifecycle of insight generation, curation, and dissemination.
Central to GenAI’s effectiveness is the quality of the data it learns from. Organizations embarking on this journey must prioritize meticulous curation, consolidating customer and market content into centralized systems, eliminating redundancies, and ensuring relevance. This process often involves GenAI-powered categorization, summarization, and content tagging, which significantly improves retrievability and accuracy. Novartis’s "Sherlock" system exemplifies this, providing its consumer business with precise answers linked to specific text lines or video timestamps, alongside expert-curated "Knowledge Zones." By enforcing strict governance guidelines for content uploaded by internal teams and external research vendors, Novartis achieved over $29 million in primary market research cost savings in just one year, demonstrating the tangible economic impact of enhanced insight management. This democratization of information empowers employees to swiftly locate critical knowledge and connect with internal experts, fostering a more informed and agile organization.
One of GenAI’s most profound contributions lies in revolutionizing qualitative data analysis—a domain historically characterized by its "messiness" and labor-intensive, semi-manual processes. While traditional off-the-shelf analytical tools have largely overlooked qualitative data, and some academic debates question generic AI chatbots’ suitability, specialized GenAI applications are proving transformative. Tracy Tuten, a qualitative research leader at Illuminas (now Radius Insights), highlights "conversational qualitative data analysis." Using GenAI-based software, Tuten can upload audio and video files for automatic transcription, generate summaries, identify emergent themes, and compare them across audience segments with unprecedented speed. A large-scale global qualitative study that previously demanded six weeks of analysis can now be synthesized in a single day, while also uncovering secondary insights that might have been missed manually. This not only boosts efficiency but enhances the rigor and depth of qualitative research, fundamentally augmenting, rather than replacing, the human researcher’s role. Similarly, PepsiCo’s "Ask Ada" platform integrates structured and unstructured data, significantly reducing dependence on external agencies for campaign response analysis and overall market understanding, thereby fostering internal expertise and agility.
Despite these technological advancements, GenAI is not a panacea for all customer insight challenges. As Stephan Gans, former 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 reveals several entrenched organizational and cultural obstacles that persist, often predating GenAI and even earlier knowledge management initiatives.
Firstly, geographical and business unit fragmentation frequently undermines the utility of global insight platforms. A global consumer goods company, for example, struggled to implement a GenAI-based knowledge tool effectively across its 100+ country operations due to a lack of common terminology for brands, categories, and distribution strategies. This autonomy, while sometimes beneficial, resulted in "pockets of knowledge" that were incoherent and contradictory, preventing cross-learning and leading to redundant efforts. The economic cost of such fragmentation includes missed global market opportunities, inefficient resource allocation, and a slower pace of innovation. In contrast, PepsiCo, under Gans’s leadership, consciously built a "one nation" approach to market research through a Global Insights Council, integrating customer insights tightly into its innovation framework.
Secondly, the absence of a data-driven culture and strategic integration is a critical impediment. Even the most sophisticated technology will falter if decision-makers are not ardent consumers of insights. P&G, with a century-long tradition of customer-centricity, exemplifies a culture where passion for data runs high. Its strategy is rooted in providing superior product experiences, integrating experimental science, behavioral science, data science, and technology to understand consumers deeply. Without such organizational commitment and executive sponsorship, market research efforts risk becoming academic exercises with little tangible business impact, leading to costly missteps in product development, marketing, and customer engagement.
Thirdly, agency relationships often introduce complexities around data ownership and strategic learning. Many companies rely heavily on external advertising and market research agencies, which can blur the lines of data ownership and analysis interpretation. If agencies retain ownership or companies merely outsource the learning process, internal teams are hampered in their ability to build institutional knowledge and apply lessons to future campaigns. PepsiCo’s Gans strongly advocated for client companies to own all research results and insights generated on their behalf, emphasizing that true strategic value comes from internalizing and applying those learnings. This necessitates clear contractual agreements and a strategic approach to partnership that prioritizes internal capability building.
Finally, the perceived low status of analytics professionals can undermine even the best technological implementations. If insights and analytics functions are viewed as mere "order takers" or librarians, their strategic influence and ability to drive innovation are severely curtailed. In one consumer products company, despite democratizing access with a GenAI tool, the insights function saw budget cuts and limited user engagement, as users failed to provide high-quality prompts or contribute to the knowledge base. This contrasts sharply with PepsiCo’s transformation, where a conscious effort to elevate the insights and analytics organization, coupled with platforms like Ask Ada, led to it becoming a respected and well-funded strategic partner. The economic consequence of a marginalized insights function is a reduced capacity for informed strategic planning, leading to suboptimal investment decisions and a reactive rather than proactive market stance.
In conclusion, GenAI tools represent an undeniable leap forward in managing the complexity and scale of customer insights. They augment human capabilities by accelerating data aggregation, enhancing accessibility, and revolutionizing qualitative analysis, leading to significant cost savings and efficiency gains. However, their true potential can only be realized when coupled with robust human leadership, organizational transformation, and a culture that inherently values data-driven decision-making. Companies must address foundational issues such as data standardization across global units, foster a deep-seated passion for customer understanding, assert clear ownership over proprietary insights, and elevate the strategic role of analytics professionals. Ultimately, GenAI is a powerful co-pilot, not an autonomous driver. Its success hinges on humans’ ability to integrate, standardize, and strategically leverage insights, ensuring that technology serves to amplify, not replace, the essential human element in understanding and responding to the evolving demands of the global customer.
