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 precisely where most marketers find themselves with artificial intelligence right now.
The expectations surrounding AI are enormous. Every marketing software vendor has launched an AI feature, every industry conference has an AI-themed keynote, and every analyst firm has published a new framework. At the same time, CMOs and marketing teams are being asked to deliver more growth, more precise personalization, and greater operational efficiency—all while keeping headcounts flat.
Yet, there is a stark divide between the promise of AI and its actual implementation. According to a Gartner report, From Efficiency to Impact: How CMOs Can Achieve Real AI Value, CMOs are now allocating an average of 15.3% of their total marketing budgets to AI initiatives. Despite this massive financial commitment, only 30% of marketing organizations report having a mature or fully developed state of AI readiness. The budget is there, but the operational maturity is not.
This imbalance has created a state of “AI overwhelm.” Marketing leaders find themselves asking the wrong questions. Instead of focusing on which new AI tools to purchase, leaders must evaluate whether they are capturing the actual business value of the technology they have already deployed.
A study commissioned by Optimove, “Forrester Opportunity Snapshot AI: Accelerating Marketing Impact Through AI And Agile Workflows,” confirms this gap between ambition and daily execution. The study found that while marketers have high aspirations for AI, their practical adoption remains highly fragmented. Only 39% of marketers currently use AI for content creation, 37% utilize it for campaign workflows, and a mere 14% leverage AI for building complex audience segments. In other words, the highest-impact marketing functions are currently seeing the lowest rates of AI adoption.
The McKinsey Diagnosis: Why Organizations Struggle to Scale AI
In the book, “Rewired: How Leading Companies Win with Technology and AI,” McKinsey & Company authors outline why corporate digital transformations frequently fail. They argue that most enterprises pursue isolated pilots, confusing technology experimentation with actual organizational transformation. Without rewiring how the business operates, these investments fail to deliver measurable financial value.
McKinsey identifies six core capabilities that distinguish companies that successfully capture AI value from those that merely spend money on tools:
1. Transformation Roadmap
Organizations must move beyond isolated pilots. Every digital and AI initiative should be directly tied to concrete financial value and strategic business goals. If a marketing team cannot draw a clear line from an AI capability to a specific profit-and-loss (P&L) outcome, that tool is not earning its place in the technology stack.
2. Talent Bench
Rather than relying on outsourced agencies or external consultants to handle core technological capabilities, successful companies train the business leaders they already have. Building internal talent who understand both the business context and the application of AI is a primary driver of long-term success.
3. Operating Model
Legacy waterfall processes must be dismantled. Modern marketing organizations require product- and platform-based operating models where multidisciplinary teams—comprising data scientists, creative professionals, and campaign managers—work as a single unit rather than passing tasks down a slow corporate relay race.
4. Distributed Technology Environment
Monolithic IT systems must be broken down into modular, API-enabled architectures. The primary benefit of this shift is speed: individual business and marketing units gain the ability to build and deploy solutions independently without waiting on a centralized IT department to clear its backlog.
5. Data Everywhere
For AI to be effective, high-quality, governed data must be readily accessible across the organization. High-performing companies treat data as an internal product, making it easy for non-technical teams to access. Organizations struggling with AI adoption are often still stuck manually emailing CSV files between departments.
6. User Adoption and Enterprise Scaling
The majority of enterprise AI initiatives fail at the adoption phase. True transformation requires active change management and structural process redesign. Simply filming a training video and sending a Slack announcement is not enough to change how employees complete their daily work.
Evaluating a marketing organization against these six capabilities often reveals significant gaps. Acknowledging these operational gaps is the first step toward building a mature AI strategy.
The Evolution from AI 1.0 to AI 2.0
To understand how to close these gaps, it is necessary to recognize that we are transitioning between two distinct eras of artificial intelligence.
AI 1.0 was the productivity era. The focus was on speed and efficiency: tools designed to write copy faster, generate images quickly, summarize reports, and automate manual administrative tasks. For marketing teams that executed this well, AI 1.0 successfully accelerated production times, allowing messages to reach customers more quickly.
AI 2.0 is the business outcomes era. This next phase of technology builds on the efficiency gains of the first era but measures success through hard business metrics. AI 2.0 is not measured by hours saved; it is evaluated based on incremental revenue generated, conversion rate uplifts, customer retention improvements, and long-term customer lifetime value.
Gartner’s data highlights the risk of staying focused on productivity metrics alone. Currently, only one in three CMOs report seeing the business returns they expect from their AI investments. High-performing marketing leaders are moving past simple time-saving metrics to prioritize business impact, monitoring how AI investments influence customer satisfaction, loyalty, and revenue growth.
The correlation between automation and ROI is clear: organizations that automate a higher portion of their marketing workflows are twice as likely to report a positive ROI from their AI investments. However, short-term productivity improvements do not automatically translate into long-term profit unless the organization actively optimizes its workflows for conversion and retention.
Gartner predicts that by 2028, only 10% of CMOs who focus primarily on time savings over direct business outcomes will successfully secure the budgets needed to meet their strategic goals. Financial executives are increasingly demanding evidence of revenue generation, not just hours saved. Meanwhile, advanced marketing organizations are allocating 21.3% of their marketing budgets to AI, compared to the 15.3% industry average. Investment scales where readiness and clear measurement coexist.
Real-World Operational Impact: Five Days to Five Minutes
When an organization successfully rewires its data architecture and operations, the impact on efficiency and business outcomes is immediate.
For example, a leading brand in the competitive iGaming space sought to optimize how it delivered promotions and updates to its users. Historically, conceptualizing, targeting, validating, and executing a personalized multi-channel customer campaign took five working days. By integrating a unified customer data foundation with agentic AI built for real-time decisioning and orchestration, the brand reduced campaign execution time from five days to just five minutes.
This massive reduction in production time was a significant productivity win (the hallmark of AI 1.0). But more importantly, it enabled the brand to deliver highly context-aware offers to players at the exact moment they were most engaged. The shift in execution speed drove direct conversion and retention improvements, representing the exact transition from operational speed to business outcome that defines AI 2.0.
Positionless Marketing: The Strategy for AI 2.0
How do marketing teams structurally prepare for the AI 2.0 era? The answer lies in transitioning to a model of Positionless Marketing.
In traditional marketing structures, departments are heavily siloed. A data analyst builds a target list, hands it off to a campaign manager, who coordinates with a copywriter and a graphic designer, who then pass the assets to an operations specialist to schedule the campaign. This operational model is slow, prone to communication breakdowns, and highly resistant to real-time adjustments.
Positionless Marketing describes an organization where rigid operational silos are removed. Supported by advanced AI, any marketer can execute complex, end-to-end tasks without waiting on specialized bottlenecks. An analyst can easily generate creative variations, and a creative professional can build highly targeted segments using conversational data queries. AI acts as the connective tissue that empowers generalists to work across traditional operational boundaries.
To enable Positionless Marketing, Optimove has integrated AI across three distinct levels of its platform architecture:
- Native AI (Inside the Platform): Embedded intelligence that automatically optimizes campaign delivery times, matches audiences with relevant offers, and handles automatic multivariate testing directly within the core workflow.
- Model Context Protocols (MCPs – Outside the Platform): Seamless integrations that extend Optimove’s AI capabilities into the external creative, communication, and data tools that marketing teams already use on a daily basis.
- Custom AI Applications (On Top of the Platform): Specialized, client-specific AI applications tailored to the unique business rules, data structures, and industry requirements of individual brands.
This three-tiered approach ensures that marketing teams do not have to jump between disconnected point solutions. Instead, they can operate from a single, unified execution layer where the AI actively supports the user, regardless of their specific technical background or role.
Conclusion: The Path Forward
The organizations that will capture the true value of AI 2.0 are not those with the largest budget or the highest number of software subscriptions. They are the organizations that pair a robust data foundation with an agile operational model and a platform designed to democratize execution across the entire marketing team.
Every enterprise will eventually have to adapt its operations to the reality of intelligent, agentic systems. Marketing leaders can either lead this operational redesign deliberately or allow market pressures to force it upon them.
As Archilochus observed nearly three millennia ago, high expectations cannot replace rigorous operational preparation. Success is determined by the systems, data foundations, and operational models that teams fall back on when execution matters most.
It is time for marketing organizations to train for the future.
Written by:
By Pini Yakuel, CEO, Optimove