In Episode 3 of The AI Advantage, "Internal alignment: How the CIO can mobilize the entire IT organization for AI," host Barbara Call sat down with industry CIOs to map the operational blueprint for mobilizing internal technology teams.
Meet the Experts:
- Carmen Granto: CIO, Fortitude Re.
- Ganesh Subramaniam: Head of Strategic Initiatives, Guardian Life.
- Keith Schlosser: Multi-time CIO, and industry advisor for agentic AI and cybersecurity.
1. Closing the AI skills gap: beyond general training
Mobilization requires more than just new tools; it requires a radical shift in human capital. Skill sets identified in the early stages of AI are already outdated.
How to identify and bridge critical gaps:
- Perform a role-based gap analysis: Skills needed for business units differ from IT; customize training to specific roles to ensure everyone speaks the same "AI language".
- Target the "four priority gaps": Focus on Ops and Automation, Product Engineering, Secure AI, and AI-augmented workflow proficiency.
- Don't replace domain expertise: In sectors like insurance, the goal is to add AI capabilities to deep domain knowledge in underwriting or claims, not replace it.
- Leverage strategic partnerships: Use external partners to "jump-start" progress while internal training programs catch up.
2. Breaking down silos with a shared AI operating model
A synchronized AI engine cannot run on "skunkworks" or pet projects. To avoid fragmented efforts, leaders must establish a centralized framework.
Key elements of a unified platform:
- A shared lexicon: Define enterprise-wide standards for fairness, robustness, bias, and risk levels.
- Cross-functional AI pods: Create pods with shared accountability to break down communications barriers between data scientists and business analysts.
- Defined success metrics: Avoid the common pitfall of starting projects without defining ROI or how the outcome benefits the customer.
3. Selecting a platform vs. point solutions
One of the biggest hurdles to scale is the proliferation of "point solutions" with AI add-ons that don't talk to each other.
- Platform orchestration: The right AI platform joins traditionally disjointed processes into a cohesive value chain.
- Model agnosticism: Consider a universal solution that decouples the specific GenAI engine from the user, allowing the platform to route prompts to the model best suited for the task.
- Vendor strategy: Be discerning about vendor claims; distinguish between true AI innovation and products that are simply rebranded with "AI" labels.
The future: multi-step agentic systems
The next evolution of AI is moving beyond simple task automation. Experts predict a shift toward multi-step systems that orchestrate actions across various tools and data sets. This "agentic" future involves embedding AI agents into redesigned workflows to manage complex transactions and follow-up actions.
"We have to get used to the speed at which this is being introduced... a new model comes out on a very regular basis." — Carmen Granto, CIO, Fortitude Re
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Listen to the episode now! And stay tuned for Episode 4, where we’ll deep dive into securing AI, with perspectives from industry leading CISOs.