Enterprises face a critical decision point in their AI journey. While many business leaders acknowledge AI’s transformative potential, many struggle with a fundamental question: Should they focus on quick productivity gains through generative AI, or pursue a more comprehensive strategy that fundamentally reimagines their operations?
A recent industry study of 500+ global decision-makers reveals a compelling insight: successful AI implementation isn’t about choosing between different types of AI – it’s about understanding how they complement each other to drive meaningful business transformation.
The Three Faces of Enterprise AI
The study identifies three distinct approaches to AI deployment, each serving unique business objectives:
1. Decisioning AI: The Analytical Powerhouse
Traditional “left-brain” AI excels at processing vast amounts of data to make rapid, intelligent decisions. Decisioning AI includes machine learning, as well as predictive and adaptive analytics. This form of AI has been quietly transforming businesses for decades, powering everything from fraud detection to supply chain optimization. For example, HCA Healthcare leveraged AI-powered workflow automation to standardize data entry for over 12,000 users and improve care coordination, demonstrating the power of decisioning AI in streamlining healthcare operations
2. Productivity AI: The Creative Assistant
Generative AI, often called “right-brain” AI, has captured headlines with its ability to enhance human productivity. According to the research, 52% of organizations primarily use it as a productivity tool, leveraging its capabilities to automate manual tasks, create content, and augment human work across the enterprise.
3. Transformational AI: The Strategic Catalyst
The most forward-thinking organizations are moving beyond isolated AI implementations to embrace what the study calls “transformational AI.” This approach combines both left-brain and right-brain AI capabilities to reimagine entire business processes. Leaders are identifying practical approaches to incorporating AI in key operations areas such as workflow design and process automation.
Breaking Down Implementation Barriers
Despite the clear benefits, the study reveals several common challenges organizations face when implementing AI:
45% of respondents cite security and privacy concerns as their primary challenge
38% express concerns about AI’s impact on jobs
31% point to inexperience or lack of knowledge in using AI
These challenges are particularly relevant in healthcare settings, where organizations must carefully balance AI implementation with regulatory compliance.
Charting a Path Forward
The research suggests a definitive set of best practices for organizations looking to advance their AI initiatives:
Start with Clear Business Outcomes: Successful organizations begin with specific business problems rather than technology solutions. This approach ensures AI investments directly contribute to business value.
Build an Integrated Foundation: Rather than treating different types of AI as separate initiatives, leading organizations create a unified AI strategy that leverages both decisioning and generative capabilities.
Focus on Responsible Implementation: With 77% of decision-makers expressing concerns about AI ethics and governance, successful organizations are establishing clear frameworks for responsible AI deployment.
Looking Ahead
As we move further into 2025 and beyond, the distinction between different types of AI will likely become less relevant. What will matter is how healthcare organizations combine these capabilities to create value for their customers and stakeholders. The most successful enterprises will be those that view AI not as a collection of tools, but as a fundamental capability that transforms how they operate and deliver value.
The choice facing enterprises today isn’t whether to embrace AI, but how to implement it in a way that drives sustainable business value. As one technology leader quoted in the study notes, “ In enterprise AI, the best results are achieved by using both left-brain and right-brain AI in a symbiotic manner, for both tactical and operational uses, but also for more strategic and transformative purposes.”
