TLDR: A recent global study by Qualtrics reveals that despite significant investments in business intelligence and advanced technologies like AI, nearly two-thirds of senior marketing leaders still rely on gut instinct for critical decisions. The study, which surveyed over 700 executives, highlights data overload, ROI measurement difficulties, data reliability, stakeholder skepticism, and skills gaps as primary barriers. While adoption of generative AI and synthetic data is rapidly increasing, concerns about bias and data security persist, underscoring a transitional period where balancing human intuition with data-driven insights is crucial for future success.
A new global study conducted by Qualtrics has unveiled a striking paradox within the marketing industry: despite substantial investments in business intelligence tools and technologies, a significant majority of senior marketing leaders continue to lean on intuition for key decision-making. The research, which surveyed over 700 senior marketing and insights executives from organizations with more than 1,000 employees and marketing departments of at least 50 people, points to a clear disconnect between the availability of data and its effective utilization in strategic processes.
Nearly half of the surveyed leaders reported allocating over 15% of their budgets to business intelligence. Yet, a staggering two-thirds indicated that ‘gut instinct,’ rather than comprehensive data-backed analysis, remains central to their decision-making framework.
Persistent Barriers to Data-Driven Strategies
The Qualtrics study identified five core obstacles that are hindering the effective adoption of data-driven strategies:
1. Data Overload: The most prominent challenge, with 56% of respondents feeling overwhelmed by the sheer volume and fragmentation of data sources. This abundance often leads to confusion rather than clarity.
2. Measuring Return on Investment (ROI): This is a primary business concern for 30% of leaders. Approximately half (51%) cited the absence of clear ROI from current business intelligence solutions as a significant barrier to further investment in these tools, making it difficult to secure approval for necessary technologies.
3. Data Reliability: An ongoing issue, with 29% of Chief Marketing Officers globally citing poor data quality and challenges in accurately forecasting customer behavior. This concern is particularly pronounced among leaders in the United States.
4. Stakeholder Skepticism: A notable 51% of executives identified skepticism about new methodologies and tools, including synthetic data and artificial intelligence, as the main reason for not increasing investment in business intelligence capabilities.
5. Skills Shortages: Around 49% of respondents believe their teams lack the internal expertise required to effectively implement and benefit from advanced AI-powered solutions.
Lynn Girotto, Chief Marketing Officer at Qualtrics, emphasized the financial implications of this reliance on intuition, stating, “Guesswork is one of the most expensive strategies in business. As spending slows, companies that listen to customers and respond to real needs are better positioned to win. This is where marketing and insights leaders will demonstrate true value and they must not shy away from the tools that allow them to do this well.”
Rising Adoption of Generative AI and Synthetic Research
Despite these hurdles, there is a strong appetite for advanced data solutions. The research indicates that 74% of executives expect their business intelligence budgets to increase by 5% to 20% in 2026. Almost all respondents (96%) acknowledged that generative AI and synthetic research technologies positively impact their marketing intelligence capabilities.
Executives are prioritizing three main objectives: acquiring fast and reliable insights, enhancing customer loyalty and conversion rates, and developing skills in AI and related technologies within their teams.
The adoption of synthetic research is particularly rapid, with 95% of executives either currently using or planning to use synthetic data within the next year. Many perceive significant advantages over traditional survey methods: 92% believe synthetic data provides more accurate and useful insights, 84% report improved speed in generating market insights, and 79% noted a deeper quality of insights generated.
Synthetic research is being applied in various ways, including generating new customer and market insights (68%), bridging gaps where conventional data is insufficient (63%), replacing or supplementing traditional survey methods (62%), simulating customer personas and segments (56%), and supporting competitive analysis and market simulation (54%).
Ongoing Concerns and the Path Forward
While the rapid adoption of generative AI and synthetic research is evident, concerns about responsible use and output accuracy persist. Nearly three-quarters of respondents expressed worries about bias in AI-generated insights, which could potentially impact the reliability of decision-making. Over 90% underscored the critical importance of mitigating both demographic and ideological bias to maintain trust in AI-driven results.
Additional executive concerns include data security and privacy risks (49%), the complexity of integrating new systems with existing infrastructure (41%), and the possibility of inaccuracies in AI-generated data (40%).
Nevertheless, a significant 90% of leaders agree that, if carefully managed with attention to issues such as bias, representativeness, and the validity of new experimental methods, AI will fundamentally enhance their organizations’ comprehension of customers and markets.
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The study concludes by highlighting a period of significant transition for marketing and consumer insights leaders. Their responsibilities and strategic importance are expanding, yet many still predominantly rely on intuition due to the challenges in obtaining timely and relevant data. The data suggests that organizations capable of demonstrating clear ROI, fostering AI competency within their teams, and effectively managing the increasing volume of data will be best positioned to navigate future challenges successfully. Many leaders are opting for hybrid models that blend human expertise with AI analytics to achieve this balance.
