AI Is Now Table Stakes. The Advantage Has Quietly Shifted.
For much of the past two years, the marketing industry has treated artificial intelligence as a competitive breakthrough. Early adopters gained speed, efficiency, and scale. Content could be produced faster, campaigns optimized more aggressively, and analysis completed in a fraction of the time.
That period is ending.
By late 2024, nearly 90 percent of marketers reported using generative AI tools at work, with the majority using them weekly and a significant minority daily. Writing marketing copy has become the most common application of these tools, with close to half of marketers now relying on AI to generate copy as part of their core workflow.
AI, in other words, is no longer scarce.
What once differentiated teams now defines the baseline. The strategic question is no longer whether AI should be used, but why some organizations continue to outperform while using the same tools as everyone else.
The answer has less to do with technology and far more to do with how organizations integrate AI into judgment, workflow, and value creation.
The Productivity Paradox of Generative AI
From a narrow efficiency perspective, generative AI has delivered on its promise. Surveys show that 85 percent of marketers who use AI report a slight or significant increase in productivity, and roughly half say it has improved both the quality and quantity of their creative output. Another study found that 86 percent of marketers save at least one hour per day on creative tasks through AI assistance, while 42 percent now outsource writing and content creation directly to AI systems.
These gains are real. They explain the speed and scale with which AI has been adopted across the industry.
They also explain the emerging problem.
When production becomes cheap and fast, volume stops functioning as a signal of quality or differentiation. As AI removes friction from creation, it compresses the distance between average and excellent output. What remains is content that is often competent, timely, and indistinguishable.
The effect is compounded by how AI is actually used in practice. One survey found that 14 percent of marketers do not edit AI-generated content at all before publishing. At scale, this produces a flood of output that is syntactically sound but strategically hollow—content that fills space without building trust.
AI did exactly what it promised: it made content cheaper and faster.
The unintended consequence is that it made much of it look the same.
If 40 to 50 percent of marketing copy is now AI-assisted, and a meaningful portion of that goes out unedited, differentiation cannot come from speed or volume alone.
Why AI Itself Is No Longer a Competitive Advantage
This dynamic reflects a broader economic shift. Generative AI is rapidly becoming commoditized.
Open-source models, declining infrastructure costs, and widespread platform integration mean that access to advanced AI capabilities is no longer a meaningful barrier. Analysts increasingly argue that simply “having AI” confers no durable advantage. Competitive separation now requires deeper organizational transformation.
A 2025 Fast Company analysis captured this reality succinctly: many organizations are mistaking adoption for differentiation. Having a large language model is not a moat; translating it into unique, context-specific customer value is.
As AI capabilities converge, advantage migrates away from the tools themselves and toward three less visible assets: proprietary context, institutional judgment, and system design.
AI as a Force Multiplier for Strategy, Not a Substitute for It
Research and executive guidance increasingly converge on a consistent conclusion: AI performs best when it supports strategic work rather than attempting to replace it.
Harvard-affiliated analyses of AI in marketing emphasize its role in reducing time spent on repetitive tasks, accelerating research, and synthesizing complex information across channels. These applications do not remove the need for strategic thinking; they expand the capacity for it.
This distinction matters because generative models are probabilistic systems. They are optimized to produce plausible outputs, not to determine relevance, intent, or priority. They generate options effectively, but they do not decide which options matter.
Organizations that treat AI output as authoritative rather than provisional often amplify noise rather than insight. Those that perform best design workflows where AI generates hypotheses and humans evaluate consequence, context, and fit.
In this configuration, AI becomes a force multiplier for judgment, not a replacement for it.
The Emerging Importance of Internal Data Readiness
As public AI models converge, internal data has become the primary source of differentiation.
Organizations that rely solely on generic prompts inevitably receive generic answers. Those that invest in structuring and curating proprietary knowledge—past campaign performance, customer conversations, research archives, product documentation—enable AI systems to operate with specificity.
This shift is subtle but decisive. When internal data is accessible and well-organized, AI stops behaving like a general assistant and begins functioning as an extension of institutional memory.
Analysts increasingly argue that this form of data readiness—not access to the latest model—is where long-term advantage will reside.
From Tool Accumulation to Resource Sprints
One reason many organizations fail to realize this advantage is structural. AI adoption often takes the form of tool accumulation rather than system design.
A more effective pattern is emerging among high-performing teams: short, outcome-oriented cycles of learning and application, sometimes described as resource sprints.
Rather than asking which tool to deploy next, these teams ask which resource—insight, dataset, model, or capability—would most meaningfully improve decision-making in the near term. AI is embedded within these sprints to accelerate synthesis and pattern recognition, but the sprint itself is anchored in value creation, not experimentation.
This approach allows organizations to move quickly without fragmenting workflows or diluting strategic focus.
Value Over Volume, Judgment Over Automation
As AI lowers the cost of execution, judgment becomes the scarce resource.
This is evident across channels. In SEO, AI accelerates keyword clustering and content production, but sustainable gains increasingly depend on demonstrated experience and authority. In paid media, automated bidding systems perform well within constraints, but positioning and risk tolerance remain human decisions. In lifecycle marketing, personalization tools abound, yet segmentation strategy and offer design still determine outcomes.
Across domains, AI improves efficiency, but it does not define value.
Organizations that perform best are not those that automate most aggressively, but those that are most disciplined about what they automate—and what they reserve for human judgment.
From Funnels to Connected Value Journeys
The most consequential shift enabled by AI may be conceptual rather than technical.
Linear funnels are increasingly poor representations of how customers learn, evaluate, and commit. Modern growth unfolds across connected value and need journeys, shaped by evolving context, intent, and trust over time.
AI can surface patterns across these journeys, but only if organizations resist the temptation to optimize isolated touchpoints. The strategic task is to connect channels, insights, and experiences into coherent trajectories of value.
In practice, this often means choosing to be deeply relevant to a specific audience rather than broadly visible to all. Focus, not ubiquity, becomes the organizing principle.
The Quiet Advantage
The early phase of AI in marketing rewarded speed and experimentation. The next phase will reward discipline, integration, and judgment.
AI is now table stakes. Nearly everyone has access to the same tools, the same models, and the same efficiencies. What differentiates organizations going forward is not technological sophistication, but strategic coherence.
The winners will be those who treat AI not as a shortcut to output, but as an instrument for clearer thinking, better decisions, and more connected value creation.
The technology has been democratized.
The thinking has not.
Ryan Edwards, CAMINO5 | Co-Founder
Ryan Edwards is the Co-Founder and Head of Strategy at CAMINO5, a consultancy focused on digital strategy and consumer journey design. With over 25 years of experience across brand, tech, and marketing innovation, he’s led initiatives for Fortune 500s including Oracle, NBCUniversal, Sony, Disney, and Kaiser Permanente.
Ryan’s work spans brand repositioning, AI-integrated workflows, and full-funnel strategy. He helps companies cut through complexity, regain clarity, and build for what’s next.
Connect on LinkedIn: ryanedwards2