A CTO's Guide to Practical GenAI Implementation

Generative AI (GenAI) is transforming how businesses create, innovate and operate. Unlike traditional AI, which focuses on prediction, GenAI generates new content and insights, opening doors to new revenue streams, efficiencies and user experiences. For CTOs, the challenge isn't in understanding the hype — it's in turning GenAI into sustainable value.

What is practical AI?

Practical AI means aligning AI initiatives with business outcomes. It favors actionable use cases over experimentation and emphasizes scalability. It also helps decide when to build custom solutions versus when to integrate third-party tools. Adopting this mindset is critical to moving GenAI from pilot to production.

Explore more on Practical AI at WWT.

Key building blocks

1. Data readiness

Data quality is the foundation of effective GenAI. Without trustworthy data, AI models produce flawed outputs. A mature data strategy includes knowing how data is collected, stored and applied. WWT's Data Maturity Model can help assess your data readiness.

2. Use case selection

Success starts with identifying GenAI use cases that offer high impact and are feasible to implement. Prioritize those that solve real business problems and can deliver measurable value quickly. Establish cross-functional alignment and create an AI Center of Excellence (CoE) to drive governance and collaboration.

3. Build vs. buy

CTOs must weigh the pros and cons of building custom LLMs versus adopting off-the-shelf tools like Microsoft Copilot or GitHub's Codeium. Building offers control but requires significant time, talent and infrastructure investment. Buying accelerates time to value and reduces complexity.

4. Deployment and infrastructure

Whether deploying workloads on-premises, in the cloud or using hybrid models, GenAI requires scalable, high-performance architecture (HPA). This includes GPU acceleration, advanced storage and modern networking. WWT's AI Proving Ground enables safe, real-time testing to guide infrastructure decisions.

5. Methodologies: Fine-tuning vs. RAG

Organizations can integrate proprietary data through fine-tuning or retrieval-augmented generation (RAG). Fine-tuning adjusts model behavior using internal data, while RAG provides context dynamically without changing the model. Both approaches have unique strengths based on use case complexity.

6. Governance and Responsible AI

Bias, transparency and security are top concerns in GenAI deployment. An AI governance framework should guide development, monitoring and ethical use. Incorporating UNESCO's policy recommendations and establishing a Responsible AI practice will build trust and reduce risk.

7. Cost optimization

GenAI costs fall into development (training, testing) and production (token usage, scaling). Use open-source models when possible and match model complexity to task needs. Monitor token usage closely, as it directly impacts billing and performance.

Organizational transformation

Effective GenAI adoption isn't just about tech. It requires a cultural shift, leadership support, education and continuous iteration. AI must be embedded into workflows, not bolted on. Starting with simple solutions like chatbots can help build confidence, competency and scalable capability.

Read about Atom AI and RFP Assistant for examples of this evolution.

Looking ahead: Agentic AI and DeepSeek

Emerging technologies like agentic AI and models such as DeepSeek R1 will redefine enterprise AI. These systems enable autonomous decision-making and API-based integrations but carry new security and governance challenges. Staying informed with a healthy dose of caution is key.

AI factories, studios and foundries

These development environments can help streamline AI implementation:

AI Studios help plan and test.
AI Foundries accelerate development.
AI Factories scale operations.

WWT uses all three to help our clients navigate the entire AI lifecycle. Learn more about WWT's AI capabilities.

Final thoughts

CTOs have a unique opportunity to drive competitive advantage through GenAI. Success demands a practical approach that aligns initiatives with business goals, focuses on scalable use cases, establishes strong data foundations and invests in the right infrastructure. By doing so, organizations can unlock transformative AI outcomes that deliver long-term value.