AI 2.0: McKinsey framework and positionless marketing

The ancient poet Archilochus wrote a line that still appears in leadership literature and training programs today: we do not rise to the level of our expectations; we fall to the level of our training. That is where many marketing teams stand with artificial intelligence: expectations for growth, personalization, and efficiency are high, while organizational maturity often lags. Every vendor promotes AI features, every conference has keynotes – yet the question is no longer mainly which tool to buy, but whether you are capturing value from what you already invested in.

According to Gartner, an average of 15.3 percent of marketing budgets now flows into AI, while only about 30 percent of organizations report mature or fully developed AI readiness. Budget is in place; execution trails behind. This gap defines CMO overload in 2026 and sharpens the strategic focus: from tool purchases to measurable value creation.

A Forrester study commissioned by Optimove and published in May 2025 ("Accelerating Marketing Impact Through AI And Agile Workflows") shows the same pattern. Only 39 percent of respondents use AI for content creation, 37 percent for campaign workflows, and just 14 percent for building audience segments. Adoption is lowest where business impact would be greatest – a classic sign of isolated pilots instead of end-to-end transformation.

The McKinsey diagnosis: six capabilities instead of one-off projects

In the revised book "Rewired: How Leading Companies Win with Technology and AI," McKinsey authors argue that most companies approach AI incorrectly: they confuse experiments with transformation, chase isolated initiatives, and fail to deliver measurable value because they do not rewire operating models, data, and adoption. For marketing leaders, the thesis translates directly: without organizational rewiring, every AI feature remains an island.

McKinsey names six traits that distinguish winners:

• Transformation roadmap: Every digital and AI initiative must tie back to financial goals and the P&L.
• Talent bench: Upskill leaders internally in tech and AI instead of permanently outsourcing core capabilities.
• Operating model: Move from waterfall to product- and platform-based teams blending tech and business.
• Distributed technology: Modular, API-enabled architectures instead of monolithic bottlenecks.
• Data everywhere: Governed, high-quality data products for many teams – not email with CSV attachments.
• Adoption and scaling: Change how work happens through change management, not training videos alone.

Honest marketing organizations will find gaps in at least three of these six areas – that is not failure, but the starting point for AI 2.0.

From AI 1.0 to AI 2.0: productivity versus outcomes

AI 1.0 was the productivity era: write, generate, summarize, and execute faster. Teams that did this well accelerated campaigns and reached customers at the right moment. AI 2.0 measures success differently – not mainly time saved, but revenue, conversion, retention, and deeper customer relationships.

Gartner confirms the gap: only about one in three CMOs sees the returns they expect from AI investments. Many still measure efficiency and speed; high-performing CMOs prioritize business outcomes and KPIs such as conversion rates or customer satisfaction alongside pure time gains.

Overload shows up in daily work: teams test dozens of tools in parallel without a shared data foundation. Campaigns launch faster, yet lessons from experiments rarely land in a central playbook. Organizations repeat mistakes – segments that nobody maintains, or content automation without a quality gate. AI 2.0 therefore requires governance: who may feed models, which KPIs apply, and how results become visible on the P&L.

For SEO and growth teams, that means a perspective shift: visibility in search and AI surfaces still matters, but success is measured on revenue and retention metrics. Personalization without clean data and adoption fizzles out – whether the channel is organic, paid, or CRM-driven. Positionless marketing makes this linkage explicit: disciplines work toward shared outcomes, not isolated efficiency metrics.

Positionless marketing: the next step

The line "AI 1.0 saved time. AI 2.0 makes money" captures the shift. Winners in the AI 2.0 era are "positionless": they no longer think in rigid role or channel silos but orchestrate customer journeys, data, and automation across touchpoints. A positionless marketer connects strategy, activation, and measurement – supported by platforms that use AI not only as a writing assistant but as an engine for segmentation, journey orchestration, and outcome reporting.

Optimove positions itself in the gap between the McKinsey framework and day-to-day team reality: away from point AI features toward continuous workflows where agile marketing processes and AI-supported decisions run together. Teams that only report efficiency risk seeing AI 2.0 budget move elsewhere. Those who align roadmap, data, operating model, and adoption can turn the 15.3 percent budget share into provable impact.

Checklist for marketing leadership

Check whether every active AI initiative has a clear outcome path. Raise adoption in segmentation and campaign workflows – Forrester points to the biggest lever there. Build internal AI capability instead of only renting tools. And define what "positionless" means in your organization: shared data, shared goals, shared measurement – regardless of channel.