Strategy and Alignment

Extent to which your AI efforts are grounded in business needs and opportunities

1. AI Vision
The presence of a clear, organization-wide vision for how AI supports marketing objectives
1No articulated vision for AI exists; efforts are fragmented and reactive.
2Discussions are underway to define a marketing-relevant AI strategy; initial frameworks are forming.
3A formal AI vision is approved, aligned to business and marketing strategies, and shared across teams.
4AI vision is a strategic pillar of innovation and competitive advantage; it is adaptive and continuously evolving based on real-time insights and organizational learning.
5AI vision becomes a living framework: co-created with stakeholders, guided by real-time data, and continuously evolving to reflect new customer behaviors, tech shifts, and ethical standards.
2. Use Case Alignment
The degree to which AI use cases are strategically aligned with marketing and customer engagement goals.
1AI initiatives happen as opportunistic trials, with minimal connection to marketing goals.
2Some use cases show alignment with goals, but there’s no structured method to prioritize them.
3AI use cases are vetted, prioritized, and regularly reviewed based on strategic impact and performance data.
4AI use cases are co-created by cross-functional teams and customers, fueling continuous innovation and measurable outcomes.
5Use cases emerge organically from embedded AI discovery processes across the organization, supported by simulations, impact forecasts, and collaborative innovation with customers and partners.
3. Change Management
The organization’s ability to manage the transition to AI-enabled marketing processes and tools, including culture and workflows.
1AI-related change is unmanaged; teams are unaware or resistant to new processes.
2Change efforts are informal and reactive; champions emerge to raise awareness.
3Structured change management programs support AI adoption through training, stakeholder engagement, and communications.
4An agile, AI-fluent culture embraces continuous transformation; employees, systems, and leadership evolve together with AI.
5AI transformation driven by adaptive leadership models; employees and systems co-learn, with cultural agility and AI readiness metrics informing organizational design.
4. Business Value Realization
How well the organization measures and realizes the business value from AI initiatives in marketing, such as improved ROI or customer metrics.
1AI impact is not tracked; success is anecdotal or assumed.
2Basic metrics or KPIs are introduced for early AI projects, but tracking remains limited.
3Marketing systematically tracks AI outcomes like ROI, engagement, and operational efficiency, informing investment decisions.
4AI-driven value is predictive and optimized continuously, feeding into enterprise strategy, budgeting, and business planning.
5AI investments are optimized through autonomous business impact analysis, where ROI is forecasted and adjusted dynamically based on market conditions, model performance, and evolving goals.
5. Executive Support
The level of prioritization and support AI initiatives receive from senior marketing and business leaders.
1AI is not on the leadership agenda; no formal sponsorship or prioritization is evident.
2Leadership expresses interest in AI but provides inconsistent resources or prioritization.
3Key executives actively sponsor AI initiatives and tie them to departmental and organizational outcomes.
4AI governance and strategic outcomes are embedded into board-level priorities; leadership champions AI internally and shapes external industry standards.
5Executive leaders are AI-fluent, shaping industry standards and regulatory frameworks while embedding AI governance into every facet of the enterprise operating model.
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