Archilochus, the ancient Greek poet, wrote a line that has traveled through 28 centuries and now belongs to every Navy SEAL training manual and leadership keynote: We don’t rise to the level of our expectations. We fall to the level of our training.
That is exactly where most modern marketers find themselves with artificial intelligence today.
The expectations surrounding artificial intelligence are staggering. Every technology vendor promises a revolutionary new AI feature, every industry conference features a keynote on generative algorithms, and every analyst firm publishes complex frameworks on cognitive enterprise transformation. Meanwhile, marketing teams are under constant pressure to deliver greater growth, deeper personalization, and higher operational efficiency—all while working with flat or shrinking headcounts.
The practical reality of this landscape is far less polished. According to a research study by Gartner, From Efficiency to Impact: How CMOs Can Achieve Real AI Value, Chief Marketing Officers (CMOs) are now dedicating an average of 15.3% of their total marketing budgets to AI initiatives. Yet, despite this significant financial commitment, only 30% of marketing organizations report having a mature or fully developed state of AI readiness. The budget is actively being deployed, but the organizational capability is lagging far behind.
This discrepancy has created a widespread sense of “AI overwhelm” across the industry. The critical challenge facing marketing leaders is no longer determining which new AI tool to purchase. Instead, the real challenge is figuring out how to capture actual, measurable value from the systems and technologies they have already integrated into their stacks.
A study commissioned by Optimove, the “Forrester Opportunity Snapshot AI: Accelerating Marketing Impact Through AI And Agile Workflows,” confirms this gap between ambition and execution. The research reveals that while interest in AI is high, actual execution remains limited to a few specific areas. For example, only 39% of surveyed marketers utilize AI for content creation, 37% leverage it to streamline campaign workflows, and a mere 14% use it to build sophisticated audience segments. This data highlights a clear paradox: the highest-impact capabilities, such as automated segmentation and precision targeting, currently have some of the lowest adoption rates in the industry.
The McKinsey Diagnosis: Why AI Pilots Stall
In their book, “Rewired: How Leading Companies Win with Technology and AI,” the authors from McKinsey & Company argue that many enterprises approach AI through isolated pilots. They often mistake basic experimentation for genuine digital transformation. Without a fundamental rewiring of how an organization operates, these initiatives struggle to deliver measurable financial value.
To help companies evaluate their progress, McKinsey outlines six core capabilities that separate organizations achieving concrete value from those merely spending budget on experimental technology:
1. A Value-Driven Transformation Roadmap
Successful organizations move beyond disconnected pilot programs. They align every digital and AI initiative directly with clear financial outcomes and strategic business objectives. If a marketing team cannot link a specific AI tool directly to a profit-and-loss (P&L) result, that tool is not proving its worth to the business.
2. An Internal Talent Bench
Winning enterprises focus on upskilling their existing business leaders in technology and AI, rather than constantly outsourcing core capabilities. Relying entirely on external consultants or third-party agencies prevents an organization from building the institutional knowledge required for long-term innovation.
3. A Cross-Functional Operating Model
Legacy, waterfall-style workflows often struggle to keep pace with modern technology. High-performing organizations shift to product- and platform-based operating models. In this setup, multidisciplinary teams containing developers, data scientists, and marketers work closely together as a single, agile unit rather than passing work off across departmental silos.
4. A Distributed, API-First Technology Environment
Rigid, monolithic IT architectures frequently create operational bottlenecks. Modern organizations decompose these older systems into modular, API-enabled components. This modularity allows individual business units to experiment and deploy new capabilities quickly without waiting on central IT approvals.
5. Governed Data Democratization
AI models require consistent, high-quality data to function effectively. Leading companies build robust, federally governed data products that give distributed teams direct access to clean information. Organizations that struggle with AI adoption are often still manually emailing CSV files between teams, while leaders have already automated data accessibility.
6. Active Change Management and User Adoption
This is where many corporate AI initiatives fail. Overcoming adoption hurdles requires more than just launching a training video or making a company-wide announcement. It demands deep process transformation and ongoing support to change how day-to-day work is actually performed across the enterprise.
Most marketing departments will recognize gaps in several of these six areas. Identifying these gaps is a necessary first step toward building a more effective operational structure.
Transitioning from AI 1.0 to AI 2.0
The evolution of artificial intelligence in business can be understood as two distinct phases: AI 1.0 and AI 2.0.
AI 1.0 focused primarily on productivity. This phase was characterized by tools designed to write copy, generate images, summarize documents, and execute repetitive tasks faster. For early-adopting marketing teams, these speed gains allowed them to ship campaigns quickly and react to customer behaviors with less delay.
AI 2.0 focuses on business outcomes. While built on the speed and productivity of the previous phase, AI 2.0 measures success through concrete financial and customer metrics. Instead of tracking time saved, organizations focus on incremental revenue, conversion rate uplifts, customer retention rates, and long-term customer lifetime value (LTV).
Gartner’s research indicates that only one in three CMOs are currently seeing their expected returns from AI investments. The majority remain focused on efficiency metrics like production speed. By contrast, high-performing CMOs prioritize bottom-line business outcomes. They focus their optimization efforts on conversion rates, overall customer satisfaction, and repeat purchase behavior.
Marketing teams that automate larger portions of their operational workflows are twice as likely to realize a measurable return on investment from AI. However, short-term productivity gains rarely translate into meaningful business results unless the organization deliberately designs its systems to optimize for business impact.
By 2028, Gartner projects that only 10% of CMOs who focus primarily on time-saving metrics over hard business outcomes will secure the strategic budgets required to meet their goals. This highlights a growing divide: marketing leaders who measure AI value through operational hours saved will struggle to justify budgets compared to those who tie AI directly to revenue growth.
This dynamic is reflected in how leading companies allocate their resources. Gartner’s data shows that the most AI-ready marketing organizations allocate 21.3% of their budgets to AI initiatives, compared to an average of 15.3% across the broader market. Financial investment scales naturally when organizations have the discipline and infrastructure to measure concrete outcomes.
What AI 2.0 Looks Like in Practice
When an organization successfully rewires its data and workflows, the operational changes are immediate. A clear example of this transition can be seen in the iGaming sector.
A leading iGaming operator managed to reduce its campaign execution cycle from five days down to just five minutes. They achieved this by combining a unified customer data platform with agentic AI for real-time decisioning and orchestration. While the reduction in production time was a major operational win, the true value came from the business impact: the marketing team could deliver highly relevant, context-aware offers to customers at the exact moment of highest engagement, significantly lifting conversion rates.
This case demonstrates how AI 1.0 and AI 2.0 work together. The initial efficiency gains created the operational capacity, while the outcome-focused application of real-time data delivered the business results.
The Shift Toward Positionless Marketing
Marketing teams that successfully navigate the transition to AI 2.0 often adopt a “Positionless” operational structure. In traditional marketing organizations, rigid roles create natural friction points: a data analyst must extract a list, a campaign manager must construct the journey, a creative specialist must design the assets, and an optimization analyst must run the post-campaign reporting.
A Positionless marketing model breaks down these operational silos. Supported by integrated AI systems, individual marketers can execute end-to-end workflows from a single interface, accessing advanced data insights and optimization tools without waiting on external department queues.
Optimove supports this shift by integrating AI across three distinct layers of its platform architecture:
- Native AI (Inside the Platform): Embedded algorithms that handle automated customer segmentation, predictive modeling, and journey optimization directly within the core campaign workflow.
- Model Context Protocols / MCPs (Outside the Platform): Integrations that extend the platform’s data and intelligence outward into the specialized, external toolsets that creative and operational teams already use daily.
- Custom Applications (On Top of the Platform): Tailored, API-driven applications built directly on the central data foundation to solve specific operational challenges unique to an organization’s business model.
By structuring AI across these three layers, organizations can ensure their technology works as a cohesive ecosystem rather than a collection of fragmented tools. Marketers can start where it makes the most sense for their current workflow, while the underlying platform maintains data integrity and campaign coordination.
Ultimately, the marketing teams that realize the greatest value from AI will not be those with the largest catalog of individual software tools. They will be the organizations that build a unified data foundation, align their operational workflows, and focus their teams on clear, measurable business outcomes.
As Archilochus observed centuries ago, success is determined by the quality of an organization’s underlying preparation and operational discipline. For modern marketing leaders, the priority is clear: build the foundational training, systems, and structures required to turn AI potential into business reality.
Written by:
By Pini Yakuel, CEO, Optimove