GenAI can reshape work across many industries, not just technology. Organizations in finance, healthcare, retail, manufacturing, and the public sector are already using GenAI for tasks such as:

• Content creation – marketing copy, documentation, knowledge base articles
• Code development – assisting developers, automating routine coding tasks
• Digital twins – simulating systems and processes to improve planning and operations

Because GenAI can be customized to your specific datasets and use cases, it’s not limited to one type of business. You can fine-tune existing models on your own data so they reflect your products, processes, and terminology. This makes GenAI a practical tool for organizations of any size that want to improve productivity, decision-making, and customer experiences.

You can start with GenAI in a focused and cost-conscious way. Several common assumptions are worth rethinking:

• “Larger language models are always better.”
  In practice, smaller, tailored models can be more efficient and cost-effective, especially when they’re optimized for specific tasks and augmented with your own datasets. The goal is to find the right balance between model size, accuracy, and resource requirements, not to chase the biggest model.
• “We must build our own models from scratch.”
  Most enterprises don’t need to do this—and often shouldn’t. There is an abundance of pre-trained models and model marketplaces you can use. You can fine-tune these models to your needs, which accelerates your AI journey, reduces cost, and lets your teams focus on business outcomes instead of low-level AI research.
• “We need a large team of data scientists.”
  Modern tools such as Retrieval-Augmented Generation (RAG) have democratized access to GenAI. They make it easier for existing teams—business analysts, domain experts, and IT—to integrate quality data with GenAI without deep AI expertise.

From an infrastructure standpoint, GenAI doesn’t always require massive new investments. You can:

• Run smaller models on PCs for targeted use cases
• Scale up to larger infrastructure only where needed

By understanding total cost of ownership (TCO) and expected ROI, you can right-size your approach and make GenAI both accessible and sustainable for your organization.

Two important decisions shape GenAI success: where you run it and who drives it.

1. Deployment: bring AI to your data

GenAI is not limited to the public cloud. You can:

• Host GenAI on-premises
• Use a hybrid environment
• Run inferencing at the edge

This “bring AI to the data” approach helps you:

• Fine-tune and run models close to your highest-quality data
• Improve performance and cost control
• Address data privacy and regulatory requirements more directly

Security also goes beyond data privacy. You need a strategy to:

• Protect AI models themselves from attacks
• Maintain control over inputs and outputs
• Ensure regulatory compliance

2. Ownership: it’s a cross-functional effort

While IT is a key enabler, GenAI success is not an IT-only project. It requires collaboration among:

• Business leaders – to set strategic direction and prioritize use cases
• Domain experts – to define requirements, validate outputs, and refine prompts
• IT and data teams – to select, integrate, and operate the technology

This cross-functional approach helps you align GenAI initiatives with business goals, drive adoption, and measure ROI. With the right mix of deployment choices and shared ownership, GenAI can reimagine how your organization operates while staying secure, compliant, and cost-aware.