The CTO's Roadmap to Generative AI Success
Generative AI (GenAI) is a powerful platform of capabilities with the potential to transform your organization's operations in unprecedented ways. As the CTO, you have a unique opportunity to drive that success.
The ideal approach to GenAI
Practical AI is about aligning AI and data strategies to business objectives and balancing the adoption of third-party and custom-built AI solutions. GenAI, which generates new content from existing data and models, requires different technologies, knowledge and techniques than predictive AI, which has been around for decades. By taking a Practical AI approach to GenAI, leaders can embed GenAI capabilities into their applications and workflows, create differentiated content and experiences, and gain a competitive advantage.
Accelerating GenAI adoption
While GenAI offers many benefits today, most organizations are still in the experimentation phase. It's time to level up, which means moving from proofs of concept (POCs) to production. To do so, you need to:
- Select and prioritize GenAI use cases that address specific business needs and that provide measurable value
- Align stakeholders behind your overall GenAI vision and the outcomes, roles, timelines, metrics and feedback related to your chosen use cases
- Iterate and experiment with RAG (retrieval-augmented generation) and fine-tuning techniques to incorporate proprietary data into your GenAI models
- Develop a robust evaluation framework to measure and monitor the quality and impact of GenAI outputs across your organization
- Consider existing and emerging regulatory, governance and risk policies and frameworks
- Monitor and optimize the costs of maintaining, optimizing and scaling GenAI outputs
The importance of governance and ethics for GenAI
AI governance is the set of policies, procedures and standards that ensure GenAI is used ethically and effectively within an organization. It involves creating guidelines, integrating security and data governance, and fostering a culture of awareness and accountability. To achieve this, we recommend forming an AI Center of Excellence (CoE) — a dedicated team that drives the adoption, implementation and governance of AI initiatives.
Agentic AI
Agentic AI is an important concept to understand. Agentic AI enhances the power and adaptability of GenAI systems by enabling them to interact with various external systems and data sources through a standardized agent-based interface. This approach allows GenAI models to query and retrieve information from different agents, providing flexibility and modularity without direct integration with underlying systems. By leveraging a broader range of data and capabilities, agentic AI can significantly expand the utility of GenAI solutions. Although still in its early stages, CTOs should closely monitor its development, as agentic AI promises to become a highly impactful capability once fully realized.
Build or buy?
CTOs need to weigh the benefits and challenges of building custom AI solutions versus buying commercially available AI solutions. The decision depends on your organization's needs, resources, AI maturity and strategic goals. Custom AI solutions offer more control, security and innovation, but require more time, cost and expertise. Commercially available AI solutions may provide faster and cheaper implementation but with fewer opportunities for customization.
The role of high-performance architecture
High-performance architecture (HPA) is a conceptual framework that integrates artificial intelligence and machine learning (AI/ML) workflows with secure and sustainable IT infrastructure. HPA includes components such as GPU systems, high-performance storage, memory, high-performance networking, automation, orchestration, and AI security. HPA also requires data and AI strategy, governance and expertise. Without the right foundation, your enterprise will struggle to realize the full value of investments in AI applications.
Prepare your data center for GenAI
AI workloads require significantly higher power and cooling capacities than traditional IT workloads, which poses challenges and opportunities for data center infrastructure and modernization efforts. To support GenAI, most data centers will need upgraded power distribution systems, advanced cooling solutions, and flexible and scalable designs to meet future demand. Your infrastructure modernization efforts should align with your sustainability goals and relevant industry regulations.
The AI Proving Ground
WWT's AI Proving Ground (AIPG) is a unique lab environment where your organization can test, implement and integrate AI infrastructure and AI solutions tailored to your specific needs and business goals. The dedicated lab environment facilitates hands-on training, real-world testing and access to a team of technical experts across various domains, including AI, ML, storage, networking, high-performance computing (HPC), and much more. By leveraging the AIPG, you can accelerate AI education and adoption across your organization, ensuring the seamless integration of AI systems into existing IT environments in a way that accelerates your ability to build and deploy your own AI solutions.
AI talent development and retention
AI solutions require skilled talent to build, implement, operate and scale. However, there is a shortage of AI expertise and intense competition for that talent. Your organization should invest in upskilling existing employees and strategically recruiting external AI talent to stay competitive and innovative.
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