Editorial
Research Report
Business Leaders CX Report: What’s Working and What’s Not in AI-Driven CX
The Gist
- AI mandates are outpacing real adoption. Executives want AI embedded across marketing, but many teams still see it as optional or disconnected from daily work.
- Data confusion and unclear rules are slowing progress. Marketers need stronger data literacy and clearer boundaries for where AI helps versus where human judgment leads.
- Managers will decide whether AI sticks. Adoption becomes real when middle managers tie AI to workflows, KPIs and measurable campaign outcomes.
Marketing leaders are under increasing pressure to incorporate AI into their teams' workflows. But in many organizations, that pressure is translating into mandates, not meaningful adoption.
As a result, AI is showing up everywhere from campaign strategy and audience targeting to forecasting and content development. The goal it may seem is to position AI as the newest "teammate" on the marketing staff. But, there's a disconnect between executives' enthusiasm towards AI and marketing teams' adoption of the tools.
According to the Digital Work Trends Report, 86% of C-suite executives believe AI usage is required in their company operations. Yet, fewer than half (49%) of middle managers are reinforcing that expectation with their teams. In marketing departments specifically, AI may be written into strategy documents, but for many marketers, it still feels optional or disconnected from how their performance is evaluated.
Leaders can't simply mandate adoption and expect results. Until marketers can clearly see how AI will be part of everyday workflows, not just part of executive messaging, it will be nothing more than an aspiration.
Here are three reasons AI adoption in marketing is lagging behind executive ambition—and how leaders can close the gap.
Table of Contents
- 1. Marketers Hear About AI — But Not Enough About the Data Behind It
- 2. Uncertainty and Perception Are Quietly Holding Marketing Teams Back
- 3. AI Vision May Start at the Top, But Marketing Adoption Is Manager-led
1. Marketers Hear About AI — But Not Enough About the Data Behind It
From segmentation and personalization to campaign optimization and attribution modeling, AI depends on clean, connected and accessible data. But there's a clear perception gap when it comes to how data is actually used.
Why Better Data Confidence Drives Better AI Outcomes
While 70% of executives believe employees are constantly relying on data to drive decisions, less than one-third (31%) of employees say they actually do. Many marketers still lean on instinct, past campaign performance or wait for a data analyst to surface insights before adjusting strategy. That delay can limit AI's impact by slowing decision-making and preventing teams from spotting opportunities in real time.
For example, instead of waiting for end-of-month reporting, AI can flag underperforming campaigns mid-flight and recommend budget shifts across channels—but only if teams trust and understand the data behind those recommendations. AI can surface patterns instantly but if marketers aren't comfortable working with the underlying data, those insights may go unused.
In some organizations, customer data is fragmented across platforms—CRM, marketing automation, analytics dashboards and ad platforms operating in silos. In others, teams aren't sure what data exists or how AI systems access and interpret it.
The solution is to focus on strengthening data literacy before adding more AI tools. Without a clear understanding of what data is available and how it connects across systems, marketers can't confidently use AI to inform decisions. Leaders need to clarify what customer, campaign and performance data exists, how platforms integrate and how AI translates that information into recommendations.
Plus, training should be tied directly to real marketing workflows. For example, showing how AI can analyze multi-channel performance data to reallocate budgets in real time. When marketers see how data fuels actionable outcomes, AI becomes less abstract and far more indispensable.
Related Article: 3 Ways Marketers Move Beyond AI Tools to AI Thinking
2. Uncertainty and Perception Are Quietly Holding Marketing Teams Back
Even among digitally native employees, AI adoption isn't automatic. Nearly one in five Gen Z employees (19%) and 17% of Millennials worry AI could replace them.
In marketing, where originality and creativity are core to employees' work, another concern is that using AI can feel like "cheating." Tasks like drafting copy or brainstorming marketing campaign ideas with AI can feel like cutting corners, especially if leadership hasn't clearly defined what responsible AI use looks like.
Clear AI Boundaries Reduce Fear and Hesitation
When executives describe AI as a "teammate" but don't establish boundaries, marketers are left to interpret expectations themselves. Should AI generate first drafts? Can it inform campaign strategy? Where does human judgment take precedence?
Without clarity into those expectations and frameworks, the adoption of AI tools can stall. Some marketers may experiment under the radar while others avoid it altogether, even when leadership encourages it. In many teams, this creates a hidden divide—where some marketers are already using marketing analytics tools to move faster and make better decisions, while others are still relying on manual processes. That inconsistency quietly impacts performance across the team.
Leaders need to define the division of labor. For example, AI can support data analysis, trend identification, performance reporting and content drafting. Humans, on the other hand, should remain accountable for brand voice, strategic direction, storytelling and all final approval.
So, when AI is positioned as a tool that removes repetitive or administrative tasks, it frees up time for marketers to focus on higher-level strategy and creativity.
Where Marketing AI Adoption Breaks Down
Executive enthusiasm alone will not embed AI into marketing workflows. Adoption depends on stronger data practices, clearer expectations and manager-led execution.
| Barrier | What It Looks Like | Why It Slows AI | Leadership Response |
|---|---|---|---|
| Weak data confidence | Marketers rely on instinct, past campaign performance or wait for analysts to surface insights. | AI recommendations go unused when teams do not trust or understand the data behind them. | Improve data literacy, explain how systems connect and train teams on real workflow use cases. |
| Fragmented systems | Customer data sits across CRM, automation platforms, analytics tools and ad platforms in silos. | Disconnected inputs limit AI’s ability to generate timely, accurate recommendations. | Clarify available data sources, integrations and how AI accesses shared information. |
| Fear and uncertainty | Employees worry AI may replace jobs or feel that using it is “cheating.” | Some employees avoid AI while others use it quietly, creating uneven performance. | Define responsible use cases and position AI as support for repetitive tasks, not replacement. |
| No clear division of labor | Teams do not know whether AI should draft content, guide strategy or make decisions. | Confusion stalls adoption and increases hesitation. | Assign AI to analysis, reporting and first drafts; keep humans accountable for strategy, brand voice and approvals. |
| Manager resistance or overload | Middle managers prioritize quarterly goals and hesitate to test tools without clear ROI. | AI remains a side project instead of becoming part of daily execution. | Provide role-specific training, usage expectations and measurable success metrics. |
| Low operational dependence | Employees see AI as helpful but not critical to doing their jobs. | Without necessity, adoption remains inconsistent and easy to deprioritize. | Embed AI into planning, execution and measurement workflows where it saves time or improves outcomes. |
3. AI Vision May Start at the Top, But Marketing Adoption Is Manager-led
Middle managers are responsible for everything from overall campaign performance to individual employees' responsibilities. If AI adoption isn't directly tied to KPIs, it quickly becomes deprioritized. For a marketing manager juggling quarterly targets, a new tool without clear ROI feels like a risk they may not be willing to take.
Managers Make AI Adoption Real or Optional
At the same time, employees aren't yet dependent on AI. The report found that only 2% of employees believe they can't do their job without AI, and only about half (54%) say it's helpful but not critical to doing their jobs. In marketing terms, that means AI may speed up copywriting or reporting, but it hasn't yet been embedded deeply enough to feel indispensable.
Marketing managers need role-specific training on how AI can streamline their (and their employees') workflows. They also need clarity on expectations such as how AI usage factors into performance metrics and what success looks like. When managers model AI usage in real campaign workflows, its adoption becomes practical rather than theoretical.
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AI won't transform marketing just because executives mandate it. It will transform marketing when it becomes part of how teams actually work—how they plan, execute and measure. Staying current on marketing trends can help leaders set realistic expectations and build the case internally for deeper AI integration.
That shift doesn't come from more tools. It comes from better data, clearer expectations and managers who can bring AI into everyday workflows.
The teams that get this right won't just adopt AI faster. They'll outperform those still treating it like an experiment. Data is accessible, expectations are clearly defined and managers can integrate AI into daily campaign execution. When that happens, it will start delivering measurable impact across marketing teams.
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