AI investments in marketing are misaligned
AI is a tool, and like any tool, what matters is where and how it’s used. In marketing, businesses are spending a lot on AI, tools for personalization, content creation, customer segmentation. But many of these investments are being made based on a flawed internal understanding of how the marketing function actually works day to day.
Here’s the core issue: executives think marketing cycles are fast and responsive. They see conversion dashboards and customer KPIs, and they assume the engine is running smoothly. But inside marketing teams, campaign delays, fragmented data access, and manual processes tell a different story. According to GrowthLoop’s survey, 51% of executives say their marketing cycles are “fairly fast” or “extremely fast.” Only 28% of operational marketers agree. That’s not a minor gap. That’s a massive disconnect between those making the decisions and those closest to the workflows.
This leads directly to wasted potential. Executives naturally invest in AI tools that are visible and strategic, things like personalization engines, because they align with what they think is needed. But these tools are layered on a system that’s already clogged. You can have the most advanced personalization system, but if your campaign takes three weeks to ship because of internal delays, then what’s the point?
For executives, this is about recalibrating your view. Before backing the next big AI tool, you need real insight into what’s happening in operations. What slows the marketing engine down? Where does the data break? Without this clarity, the AI execution won’t deliver, and the ROI won’t materialize.
The information asymmetry loop reinforces poor AI ROI
This gap in perspective creates what we can describe as an information loop, a feedback cycle where executives and marketing teams operate fundamentally out of sync. It reinforces wrong assumptions, and the results are clear: even with millions poured into AI platforms, ROI underperforms. That’s because the core friction points were never touched.
Here’s how the loop works. Executives look at top-line KPIs, revenue, customer engagement, click-through rates. These numbers look good, so leadership assumes marketing is executing efficiently and scaling through AI. Based on that, companies double down on more visible AI tools. Tools that boards understand. Tools that look good in meetings.
But operational friction doesn’t show up on high-level dashboards. Marketing teams are still dealing with legacy processes, delays in data access, and broken campaign workflows. When the AI tools don’t deliver the expected results, measurement systems are too blunt to explain why. They can’t isolate if the issue was a weak personalization model or the bottleneck in getting campaigns live. The AI investment is seen as underperforming, so the response is to buy smarter tools, further reinforcing the loop.
Marketing teams, meanwhile, face pressure to use solutions that don’t fit into their existing processes. This widens the gap between planning and execution. According to GrowthLoop, many marketers report that personalization initiatives pushed by leadership are not supported in day-to-day operations. That’s a red flag, one that shouldn’t be ignored.
If you’re in a leadership position, recognize that visibility is not the same as insight. Data tells a story, but only if you’re looking at the right chapter. Break the cycle. Get into the workflow. Audit where things slow down. Your AI strategy should be built on how work really gets done, not how it appears from a distance.
Operational efficiency is key to achieving higher marketing ROI
When AI doesn’t perform in marketing, the tools usually take the blame. But the issue often runs deeper, it’s not the AI itself, but the environment it’s dropped into.
Operational inefficiency is a quiet threat to effective AI. If your marketing cycle grinds through manual processes, outdated systems, delayed approvals, and fragmented data, then adding intelligent software won’t fix the baseline problem, it compounds it. Tools built to accelerate performance aren’t going to deliver if they’re trapped inside an inefficient structure. That structure has to change first.
GrowthLoop’s research makes this clear. Companies with faster, more refined marketing cycles consistently report stronger AI ROI. It’s not that they bought different tools. They structured their workflows and systems in a way that those tools could actually deliver. If you’re trying to understand why an AI investment isn’t showing impact in your organization, start here.
Executives already see AI as a strategic investment. PwC’s 2025 research shows that 49% of tech leaders say AI is fully integrated into their company’s business strategy. But only 33% say it’s fully integrated operationally. That gap is the problem. Decision-makers are thinking of AI as a front-facing innovation story, but the internal systems behind it don’t reflect that intent.
The fix isn’t difficult, it just requires focus. Evaluate current workflows. Identify unnecessary manual tasks and remove friction. Once the process flow is clean, the AI tools will execute at a higher level, and the ROI will follow.
Closing the gap between executive perceptions and operational realities is critical
Executives and marketing teams often see different realities. That disconnect leads to wasted capital, slower innovation, and lower trust across functions. To solve the AI misalignment, that’s the point of failure that has to be addressed first.
Operations need to be visible, not just in metrics but in method. Executives must understand where campaigns stall, how long it takes to collect real-time data, and which tasks overload team capacity. Without that understanding, AI investments are built on assumption.
Operational audits are not just a compliance activity, they’re strategic. You want to know what part of the system can’t scale, what delays your time-to-market, and what still relies on manual input. That knowledge turns AI from an expensive guess into a high-leverage tool designed to fix problems that matter.
Current ROI metrics don’t fully account for operational improvement. Automation in campaign setup, more accurate segmentation from better data access, those outcomes typically aren’t quantified strongly enough. But they drive value. C-suite leaders need to demand clearer feedback loops between investment and actual performance gains.
GrowthLoop’s survey shows that when companies achieve faster marketing cycles, their AI returns improve. That suggests AI is highly effective, but only in systems where execution is fast and decisions move quickly. Clear visibility between teams makes that possible. Eliminate the blindspots, and the tools will deliver.
A strategic-operational realignment in AI spending is essential
Most companies are not against strategic AI investments, they just struggle to connect them to operational priorities. What’s missing is not ambition. What’s missing is alignment. Executives often prioritize high-visibility technologies intended to drive customer engagement or boardroom excitement. But these solutions frequently bypass the structural inefficiencies that stop teams from executing quickly and effectively.
AI has enormous potential to improve marketing performance. But that potential is realized only when execution is frictionless. Real ROI comes when AI is deployed to resolve upstream bottlenecks, data silos, slow approvals, fragmented tracking systems, not just to enhance customer experience at the end of the chain.
Strategic and operational teams must work from the same playbook. That means aligning timelines, priorities, and investment frameworks. Before onboarding personalization engines or AI-generated content tools, companies need to ensure that core systems, like campaign delivery, attribution tracking, and customer data infrastructure, can support and scale those capabilities.
GrowthLoop advocates for this shift clearly. Their data indicates that companies with more efficient marketing operations see better results from their AI deployments. That’s not about spending more; that’s about spending with greater precision.
Executives should mandate a change in how AI ROI is measured. Value must be tied not only to external outputs but also to internal improvements, faster data syncing, lower campaign lead times, reduced manual intervention. These are the indicators that determine whether your AI system is operating in the right environment.
The way forward isn’t about choosing between strategy and operations. It’s about making sure your AI strategy is grounded in operational capability. Once your systems are optimized, your smart tools work smarter, and the business impact becomes visible everywhere it matters, conversion rates, campaign velocity, and revenue growth.
Key executive takeaways
- Misaligned AI spending stems from perception gaps: Executives often believe marketing is running efficiently based on high-level KPIs, while operational teams face delays and inefficiencies. Leaders should align AI investments with actual workflow constraints to avoid wasted spend.
- Information asymmetry leads to repeated bad AI bets: When executives lack visibility into day-to-day marketing operations, they invest in highly visible tools that fail to address core execution problems. Fixing this feedback loop requires better attribution systems and clearer insight into workflow obstacles.
- Operational efficiency drives real AI ROI: Companies with faster marketing cycles consistently report stronger AI returns. Prioritize automating bottlenecks and streamlining internal processes before deploying strategic AI tools to maximize impact.
- Closing the visibility gap is critical to ROI improvement: Misaligned perceptions between leadership and execution teams prevent effective resource allocation. Executives should push for operational audits and transparent reporting to direct AI investments where they solve actual frictions.
- Strategy must be grounded in execution capability: AI only performs when it’s deployed into systems that can support it with speed and structure. Align strategic spending with operational readiness by measuring impact based on internal velocity, not just customer-facing results.
