THE DIMENSIONS OF AI
Introduction
82% of companies are exploring AI for their businesses, half of those are actually using it in some capacity, and only 10% of businesses overall are leveraging AI to start to differentiate themselves.
Artificial intelligence (AI) is becoming an essential driver of innovation, yet most organization’s struggle to understand it’s applicability within their space. While it’s not a definitive evolution process, I think of AI in terms of dimensions—a framework that helps clarify where organizations stand today and how they can evolve to unlock greater potential.
These dimensions represent increasing levels of complexity, integration, and impact; starting from basic task automation and extending to future possibilities that challenge human comprehension.
Most organizations are currently just starting to advance the 1st or 2nd dimension, but those aiming to differentiate themselves need to consider how to move beyond.
Here’s a thought breakdown;
1st Dimension AI: Streamlining Single Actions or Processes
This dimension focuses on applying technology to simplify or automate a single task. It’s about producing a singular output from a finite request or input—simple and to the point. Examples include customer service chatbots, automated document drafting, or basic sentiment analysis based on trends or feedback.
While straightforward in concept, even 1D AI requires a foundation of well-organized data and technical infrastructure to execute effectively. For many organizations, this dimension represents the entry point into AI, offering quick wins with relatively low risk.
However, not every organization needs to start here. Depending on their strategy and capabilities, some may leap to more advanced applications.
2nd Dimension AI: Streamlining and Automating Entire Processes or workflows
This dimension elevates AI’s role from singular outputs to automating or optimizing entire workflows, similar to that of a line. Here, AI collects data from various inputs—customers, systems, or feedback loops—and generates outputs that extend beyond what an individual might request.
For example, an AI system might not just process customer feedback but analyze sentiment, identify attrition risks, and recommend targeted actions. This level of AI often spans multiple teams, roles, or systems, requiring collaboration and alignment across the organization.
The leap to 2D AI introduces more complexity. It’s not just about deploying a tool; it’s about rethinking processes, integrating siloed systems, and ensuring data is governed effectively. Organizations embracing this level understand its potential to deliver competitive advantage, but the operational lift should not be underestimated. Examples include automated marketing campaigns, intuitive fraud detection or predictive maintenance.
3rd Dimension AI: Creating an Integrated Ecosystem of Insights and Action
At this level, AI moves from optimizing processes to orchestrating an interconnected ecosystem. It synthesizes multiple inputs, roles, and systems to uncover patterns, relationships, and dependencies; driving strategic recommendations.
Imagine a multi-story apartment building: 3D AI doesn’t just manage individual units or floors; it understands how they all interact, pinpointing inefficiencies, dependencies, and opportunities. An example could be a global supply chain system integrating data from suppliers, market conditions, logistics, and customer demand to optimize operations across the board. Complex health systems are another good example with their multiple facilities, technology platforms, physicians, billing components, patients themselves and all the interdependencies that create complexities.
This dimension represents a major leap in complexity—not just technologically but organizationally. Success depends on breaking down silos, fostering cross-functional collaboration, and building robust data infrastructures capable of supporting real-time, multidimensional insights. Perhaps the largest variable to success for an organization, is ensuring there is a culture and leadership team that humbly embraces where there are opportunities for improvement.
Different industries approach 3D AI in unique ways. A retailer might focus on dynamic pricing and inventory optimization, while a healthcare provider might aim for patient care systems that integrate diagnostics, monitoring, and treatment planning. The key is aligning the application to specific goals and challenges.
4th Dimension AI: Exploring the Frontier of Predictive and Adaptive Intelligence
This dimension ventures into what’s hard to conceptualize today. Just as the fourth dimension in physics challenges our understanding, 4D AI combines ecosystem-wide integration with speculative, predictive, and adaptive capabilities.
At this level, AI might model "what if" scenarios, simulate domino effects of decisions, or balance competing priorities across economic, social, and environmental factors. Imagine an AI system that can anticipate global market shifts and recommend strategies in real time, factoring in thousands of interdependent variables. This is likely reaching into an area where we as humans can’t calculate or fathom the outputs ourselves, because we as humans couldn’t calculate these outputs ourselves.
Conclusion
Most organizations are only just now pursuing the 1st and 2nd dimensions, with the 3rd dimension representing the next frontier for those seeking true differentiation. The journey is not always linear, and industries will prioritize different dimensions based on their unique needs and opportunities.
AI is not just about technology. It’s about building a culture of innovation, establishing teams who evolve with the technology, aligning strategies with long-term goals, and investing in the infrastructure and processes to unlock its potential. The future of AI lies in the 3rd dimension and beyond, and those who make the leap will redefine how they operate and lead in their industries.