In the realm of artificial intelligence (AI), enterprises are at a crossroads, faced with a crucial decision point in their AI journey. While many business leaders are aware of AI’s potential to transform their operations, they grapple with a fundamental question: Should they opt for quick productivity gains through generative AI, or should they embrace a more holistic approach that redefines their entire business processes?
A recent industry study involving over 500 global decision-makers sheds light on a key insight: successful AI implementation isn’t about choosing between different types of AI, but rather understanding how these different types can complement each other to drive significant business transformation.
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 intelligent decisions swiftly. Decisioning AI encompasses machine learning, predictive analytics, and adaptive analytics. This form of AI has been silently revolutionizing businesses for decades, powering tasks like fraud detection and supply chain optimization. For example, HCA Healthcare utilized AI-powered workflow automation to standardize data entry for over 12,000 users, enhancing care coordination and showcasing the prowess of decisioning AI in streamlining healthcare operations.
2. Productivity AI: The Creative Assistant
Generative AI, often dubbed as “right-brain” AI, has garnered attention for its ability to boost human productivity. According to the research, 52% of organizations primarily leverage generative AI as a productivity tool, employing its capabilities to automate manual tasks, create content, and enhance human work across the enterprise.
3. Transformational AI: The Strategic Catalyst
Forward-thinking organizations are transcending isolated AI implementations to embrace what the study terms as “transformational AI.” This approach amalgamates both left-brain and right-brain AI capabilities to reimagine entire business processes. Leaders are exploring practical ways to integrate AI in crucial operational areas such as workflow design and process automation.
Despite the evident benefits, the study uncovers several common challenges that organizations encounter when implementing AI:
– 45% of respondents highlight security and privacy concerns as their primary challenge.
– 38% express apprehensions regarding AI’s impact on jobs.
– 31% point to inexperience or lack of knowledge in utilizing AI effectively.
These challenges are particularly pertinent in healthcare settings, where organizations must navigate AI implementation while adhering to regulatory compliance.
To propel their AI initiatives forward, the research advocates a definitive set of best practices for organizations:
1. Start with Clear Business Outcomes: Begin by identifying specific business problems rather than focusing solely on technology solutions to ensure that AI investments directly contribute to business value.
2. Build an Integrated Foundation: Develop a unified AI strategy that harnesses both decisioning and generative capabilities instead of treating them as disparate initiatives.
3. Focus on Responsible Implementation: Establish clear frameworks for responsible AI deployment, considering the ethical and governance aspects, as 77% of decision-makers express concerns in this area.
Looking towards the future, as we progress into 2025 and beyond, the distinction between different types of AI is likely to diminish in significance. What will be crucial is how organizations in the healthcare sector amalgamate these capabilities to generate value for their customers and stakeholders. The most successful enterprises will be those that view AI not as a mere set of tools, but as an intrinsic capability that revolutionizes their operations and value delivery.
The choice confronting enterprises today isn’t whether to adopt AI, but how to implement it in a manner that drives enduring business value. As echoed by a technology leader quoted in the study, in enterprise AI, optimal results are attained by leveraging both left-brain and right-brain AI symbiotically for tactical, operational, strategic, and transformative purposes.
