AI excels at creating polished, efficient, and frictionless outputs ("smooth surfaces"). However, humans provide the essential nuance, imperfection, and emotional depth ("texture") that audiences connect with. Effective AI integration requires balancing AI's smoothness with human-driven texture for authenticity.
Traditional ABM relies on static personas and idealized journey maps. AI tools can analyze vast datasets to identify real-time "human signals"—individual behaviors, interests, and needs. This allows for a more nuanced and dynamic approach that targets actual people, not abstract demographic buckets.
When introducing paradigm-shifting tech like AI, old KPIs don't fit. The term 'horsepower' was a psychological bridge to help people understand steam engines relative to horses. Similarly, leaders may need to create temporary, imperfect KPIs to help teams transition their thinking while new value metrics emerge.
Traditionally, marketing defines a budget then finds a scope, while technology defines a scope then finds a budget. AI initiatives live at the intersection of these disciplines, forcing a reconciliation of these fundamentally different operating models for successful implementation and measurement.
AI adoption acts as a catalyst, highlighting weaknesses in strategy, culture, and workflows that were already present. Leaders should view AI as a diagnostic tool for their organization's health, rather than the source of new problems, and focus on fixing these revealed, underlying issues.
Leaders who say "we need an AI strategy" often reveal a lack of a clear core business strategy. AI should be a component that enables the overall company vision, not a separate initiative. This mindset shift grounds AI efforts in tangible business value tied to your unique differentiators.
Calculating the ROI of an AI initiative as a simple tool investment is a mistake. The true ROI comes from transforming entire systems and processes. This requires measuring the impact on organizational change, new workflows, and strategic alignment, which is far more complex than a basic software cost-benefit analysis.