The 2025 Writer AI Survey reveals a critical inflection point for enterprise-level market intelligence teams. While 97% of C-suite executives and 88% of employees report benefiting from generative AI tools, a concerning 72% of organisations face significant challenges implementing these technologies at scale. This disconnect presents a strategic opportunity for market intelligence teams to position themselves as transformation leaders.

This report analyses findings from a comprehensive study of 1,600 AI users, including 800 C-suite executives and 800 employees across finance, technology, healthcare, and retail sectors – to identify how market intelligence professionals can overcome AI adoption challenges.

Source: Writer AI

The data exposes five critical barriers to successful implementation: organisational silos (71% report AI applications created in isolation), power struggles between IT and business units (68% report tension), misalignment on strategic priorities, employee resistance (31% actively sabotaging AI initiatives), and inadequate vendor partnerships (94% of executives dissatisfied with current AI providers).

For Competitive Intelligence Analysts, Market Research Managers, and Strategic Planning Analysts, these findings offer a roadmap to deliver transformative value beyond traditional research.

Intelligence teams can elevate their organisational influence while accelerating decision-making capabilities by up to 50% by breaking down silos and implementing strategic frameworks for AI governance. This shift positions market intelligence not merely as information providers but as strategic partners driving enterprise-wide AI transformation and delivering measurable ROI on technology investments.

The market intelligence teams enjoying the greatest success are those moving beyond data collection to become cross-functional AI enablers, creating a 40 percentage-point performance gap between organisations investing strategically in AI capabilities versus those making minimal investments.

For intelligence professionals seeking to expand their strategic value, AI adoption represents the most significant opportunity in a generation to reshape their organisational impact.

The insights presented in this analysis are derived from the 2025 Writer AI Survey, a comprehensive examination of generative AI adoption in enterprise settings. Unlike broader AI studies, this research specifically targeted active AI users, creating a uniquely valuable dataset for market intelligence professionals seeking to understand adoption patterns and challenges.

The methodology involved surveying 1,600 knowledge workers across the United States, including 800 C-suite executives (CEOs, CFOs, CTOs, CIOs) and 800 employees from diverse functional areas, including finance, HR, legal, marketing, sales, and customer support.

All respondents were actively engaged with AI technology, employees were required to be using generative AI tools at work, while C-suite respondents worked at companies permitting such tools.

This approach provides market intelligence teams with rare visibility into the experiences of early AI adopters rather than theoretical perspectives.

The enterprise organisations represented span company sizes from 100 to over 10,000 employees across four key sectors: financial services (32%), technology (27%), healthcare/pharmaceuticals/life sciences (23%), and retail/consumer goods (18%).

The research was conducted in December 2024, offering the most current perspective on enterprise AI implementation challenges and opportunities.

For market intelligence professionals, this dataset represents a particularly valuable resource for benchmarking their own organisation’s AI readiness and identifying strategic priorities that can accelerate adoption while minimising common pitfalls documented by early implementers.

The research reveals a significant opportunity for market intelligence teams to expand their traditional role by addressing the five critical challenges hampering enterprise AI adoption. By leveraging their unique cross-functional visibility and analytical expertise, market intelligence professionals can position themselves as essential facilitators of organisational AI transformation.

The data shows that companies making strategic AI investments are significantly outperforming their peers, with a 40 percentage-point performance gap between top and bottom-tier investors. Organisations investing over $5 million annually report 77-94% success rates compared to just 54% for those investing under $100,000.

This presents an objective, data-driven case for market intelligence teams to advocate for increased AI investment while offering frameworks to measure potential ROI.

For Competitive Intelligence Analysts specifically, the findings highlight how AI tools can dramatically improve decision latency. Among companies effectively implementing AI, 50% of executives report faster data-driven decision-making capabilities, with 51% gaining more time for strategic activities, precisely the outcomes intelligence teams are tasked with enabling.

What makes this particularly relevant for market intelligence professionals is the finding that 71% of C-suite respondents report AI applications being developed in silos, creating a fragmented approach that undermines collective intelligence gathering.

Market research teams, with their enterprise-wide perspective and established information-sharing protocols, are ideally positioned to introduce governance frameworks that maintain the benefits of decentralised innovation while ensuring organisational alignment.

For intelligence functions that successfully position themselves as AI transformation partners, the potential organisational impact extends far beyond traditional research deliverables.

Rather than simply providing information, these teams become essential orchestrators of enterprise intelligence, directly influencing how strategic decisions are evaluated and executed across the organisation.

One of the most actionable insights from the survey is the untapped potential of AI champions within organisations.

The research found that 77% of employees using AI tools are either already acting as AI champions or have the potential to develop into this role.

These individuals are taking proactive steps that support broader AI adoption, including identifying new use cases (57%), demonstrating AI value (52%), and educating colleagues (48%).

For Market Research Managers, this finding presents a significant talent development opportunity. By identifying and nurturing team members with AI aptitude, research departments can establish themselves as centers of AI excellence that radiate expertise throughout the organisation.

The survey shows that nearly all AI champions (98%) want to participate in building AI tools for their companies, demonstrating a high level of engagement that managers can leverage to drive innovation.

Implementing this approach delivers both immediate benefits and long-term advantages. In the near term, intelligence teams can utilise AI champions to optimise existing workflows, reducing time spent on administrative tasks (39% of employees report this benefit) and freeing analysts to focus on higher-value strategic activities.

Longer-term, these champions become innovation catalysts who identify novel applications of AI specifically tailored to market intelligence functions.

The business case for nurturing AI champions is compelling: 94% of champions report career benefits from their AI advocacy, including increased job stability (42%), enhanced workplace respect (37%), and accelerated promotions (21%).

This creates a virtuous cycle where individual career advancement aligns with organisational transformation goals.

Market intelligence teams that successfully cultivate AI champions also gain a significant competitive talent advantage. With 59% of executives actively seeking new roles at more AI-innovative organisations, departments that establish reputations for AI excellence become magnets for top talent.

Given that 97% of C-suite respondents consider a company’s AI capabilities important when evaluating new opportunities, intelligence functions leading AI transformation significantly enhance their ability to attract and retain premier analytical talent.

The research highlights a significant disconnect between executive expectations and vendor performance in the AI space. While 98% of C-suite respondents believe vendors should play a role in shaping their AI vision, with 70% saying that role should be “significant”, 94% report they are not completely satisfied with their current providers.

This dissatisfaction creates a strategic opportunity for Strategic Planning Analysts to apply their vendor evaluation expertise to AI partnership decisions.

The data reveals five specific areas where executives believe vendors are underperforming: customising tools to business needs (53%), helping prepare organisations for implementation (52%), supporting cultural acceptance (50%), measuring impact (48%), and enabling scale (45%).

For market intelligence teams, this finding underscores the importance of applying rigorous evaluation frameworks to AI vendor selection. The research highlights security/data governance as the single most important factor for executives (80% rate it as “very important”), yet only 37% of employees believe their organisation’s AI tools are very secure.

This security gap presents intelligence professionals with an opportunity to establish evaluation criteria that prioritise both security and usability.

The findings also reveal concerning quality issues with current AI implementations. More than half of employees report regularly receiving information from AI tools that is inaccurate (59%), confusing (56%), or biased (52%).

For intelligence teams, whose credibility depends on delivering accurate, clear insights, these quality concerns emphasise the need for careful vendor evaluation to ensure AI outputs meet professional standards.

The most successful market intelligence functions are approaching AI vendor selection as a strategic intelligence exercise. Rather than evaluating vendors based solely on technical capabilities, these teams are assessing potential partners against organisational needs, cultural fit, and long-term transformation goals.

By applying familiar competitive intelligence methodologies to AI vendor selection, market intelligence professionals can significantly reduce implementation risks while positioning themselves as essential stakeholders in technology decisions.

• 71% of C-suite respondents report generative AI applications are being developed in silos, creating a strategic opportunity for market intelligence teams to introduce cross-functional governance frameworks.
• 40 percentage-point performance gap exists between organisations making strategic AI investments (>$5M annually) versus those making minimal investments (<$100K), demonstrating the direct correlation between investment and success.
• 77% of employees using AI tools are either already functioning as AI champions or have the potential to develop into this role, representing an untapped resource for market intelligence teams to accelerate adoption.
• 50% of executives report faster data-driven decision making capabilities when AI is successfully implemented, directly supporting the core mission of market intelligence functions.
• 94% of C-suite executives are not completely satisfied with their generative AI vendors, highlighting vendor selection as a critical intelligence activity.
• 98% of AI champions report interest in helping build AI tools for their organisations, creating an engagement opportunity for market intelligence leaders to direct this enthusiasm toward intelligence-specific applications.
• 59% of executives are actively seeking new roles at more AI-innovative companies, underscoring how market intelligence teams that lead AI transformation gain significant talent acquisition advantages.

AI Champions: Individuals who have fully embraced generative AI and actively advocate for its adoption within their organisation, often taking initiative to identify use cases, educate colleagues, and participate in development activities.

Enterprise AI Adoption: The systematic implementation of generative AI tools and processes across an organisation beyond experimentation, characterised by cross-functional governance, formalised strategies, and measurable ROI expectations.

Generative AI: Artificial intelligence systems capable of creating new content, insights, and analyses from existing datasets, notably including large language models that synthesise information for specific market intelligence applications.

Implementation Silos: The tendency for AI applications to be developed in isolation by different organisational units without coordination, creating inefficiencies, duplicated efforts, and inconsistent approaches.

Market Intelligence Platform: A technology solution that aggregates, processes, and delivers critical market insights to support strategic decision-making, increasingly enhanced with AI capabilities to accelerate information processing and pattern recognition.

Market Intelligence Transformation: The evolution of traditional market intelligence functions from information providers to strategic partners through the application of advanced technologies and cross-functional engagement methodologies.

ROI Latency: The gap between AI investment and measurable returns, with 62% of organisations expecting at least three years before realising substantial benefits from their AI implementations.

Strategic Planning Framework: A structured approach to identifying, prioritising, and implementing AI use cases based on organisational impact, resource requirements, and alignment with business objectives.

Vendor Evaluation Matrix: A systematic methodology for assessing AI technology providers against predefined criteria including security, integration capabilities, customisation options, and support quality.