CIOs must shift from system-specific to persona-based AI strategies

CIOs must shift from system-specific AI implementations to persona-based strategies that tailor artificial intelligence capabilities to different employee roles and workflows. This approach promises to unlock AI’s full potential by delivering contextual intelligence that understands not just what tasks are being performed, but who is performing them and how they work best.

The big picture: Traditional enterprise AI initiatives often remain siloed within individual platforms like customer relationship management (CRM) or enterprise resource planning (ERP) systems, creating fragmented intelligence and duplicated efforts that fail to scale across the organization.

Why persona-based AI matters: Different employee archetypes require distinct AI capabilities to maximize productivity and adoption.

• Sales representatives benefit from AI-generated deal insights, intelligent forecasting, and next-best-action recommendations within Salesforce, a cloud-based customer management platform
• Customer support agents thrive with real-time generative AI summaries, sentiment analysis, and proactive resolution suggestions in service consoles
• Finance professionals gain value from AI-automated anomaly detection in expense reports, predictive revenue insights, and smart budget planning tools integrated into platforms like NetSuite, Oracle’s cloud-based business management software

The system-agnostic advantage: A centralized AI strategy that decouples intelligence from specific platforms enables seamless orchestration across business ecosystems.

• Core business processes typically span multiple systems—Salesforce for CRM, NetSuite for finance, Workday for HR, and SAP Ariba for procurement
• System-agnostic AI capabilities like forecasting, anomaly detection, and decision recommendations can be designed once and deployed universally
• This approach ensures resilience as enterprise systems evolve, accelerates time-to-value, and provides a scalable framework for innovation

Current AI integration examples: Major enterprise platforms are already embedding AI capabilities to enhance functionality and user experience.

• Oracle’s Fusion Cloud incorporates AI agents to automate business processes, reducing manual effort and improving efficiency
• ServiceNow’s Yokohama platform focuses on agentic AI, enabling more autonomous and intelligent workflows
• Salesforce Agentforce leverages AI across its ecosystem for intelligent customer service routing, predictive analytics, and sales data analysis

Edge computing convergence: The combination of AI with edge computing creates “edge intelligence” that enables real-time data processing and decision-making closer to the source.

• Smart city applications can process traffic camera data locally to dynamically adjust traffic light timings and optimize flow
• This approach reduces latency, bandwidth usage, and reliance on centralized servers while enabling faster responses to changing conditions

In plain English: Think of edge computing like having a smart traffic cop at every intersection instead of routing all decisions through city hall. The local “cop” (edge computer) can instantly analyze what’s happening and make adjustments without waiting for approval from headquarters.

Key challenges: Despite significant benefits, AI integration presents several considerations that CIOs must address.

• Data governance requires ensuring data quality, privacy, and regulatory compliance as AI systems rely heavily on data inputs
• Talent acquisition demands investment in training and development as the need for skilled AI professionals grows
• Ethical implications around transparency, fairness, and accountability become critical as AI systems make more autonomous decisions

Emerging technology intersections: Beyond traditional AI applications, new convergences are expanding enterprise possibilities.

• Artificial Intelligence of Things (AIoT) integrates AI with Internet of Things (IoT) devices to enhance data analysis and decision-making at the device level
• “Living intelligence” combines AI, biotechnology, and advanced sensors to create systems capable of real-time sensing, learning, and adaptation