Harnessing Proprietary Data for Competitive Advantage in Generative AI Adoption

According to the IBM Institute for Business Value, 72 percent of top-performing CEOs believe that possessing the most advanced generative AI tools provides a significant competitive advantage. However, the true value of these tools depends on grounding them in an organisations unique context and specific data sources.

While general-purpose models like ChatGPT and Google Gemini exhibit remarkable capabilities, they are not tailored to individual organisational data sets. When integrated into business processes, these models may lack vital organisation-specific information, leading to less accurate results or confusion. Adapting AI models with proprietary data enables organisations to enhance relevance and precision in outputs.

Michael Choie, Senior Managing Consultant at IBM, highlights that every organisation has its own language. For instance, the term dressing could mean salad dressing in a grocery store or wound dressing in a hospital. This emphasises the importance of customisation for AI tools to understand context and terminology.

Research from AI in Action 2024 indicates that 15 percent of surveyed organisations, labelled as AI Leaders, achieve measurable success with AI. A key differentiator for them is confidence in their ability to customise AI by leveraging their proprietary data. Unlike vendors who access public or platform data, these organisations gain a competitive edge by feeding their exclusive data into AI systems.

There are three main methods to incorporate proprietary data into AI models: prompt engineering, retrieval augmented generation (RAG), and fine-tuning. Prompt engineering involves including organisation-specific data within the prompt, suitable for low-volume tasks. RAG connects AI systems to proprietary databases, allowing retrieval of relevant information during responses, thus improving accuracy. Fine-tuning modifies specific model parameters by training on organisation-specific data, creating specialised AI applications tailored to particular tasks such as processing insurance claims.

Shobhit Varshney from IBM suggests that fine-tuning a model, comparable to training a new employee, can significantly improve performance in specific domains while keeping costs lower than retraining from scratch. When organisations fine-tune models on their data and run them on their infrastructure, they retain full ownership of the intellectual property and enhance output accuracy.

Open-source models such as IBM Granite offer additional benefits. They allow easier and faster fine-tuning, which is less resource-intensive than building models from scratch. Plus, utilising open-source models promotes experimentation and supports multi-model strategies—using many models tuned for different tasks—which is considered a best practice. AI in Action 2024 reports that 62 percent of AI Leaders employ multiple models versus 32 percent of AI Learners.

Effective data management is fundamental for maximising AI potential. Organisations success depends on breaking down data silos, ensuring interoperability, and orchestrating fluid data movement through integrated data fabrics. Data pipelines, hybrid cloud infrastructures, and automation tools help retrieve, prepare and validate high-quality data for AI applications.

Data quality directly impacts AI output reliability. Organisations must clean, curate and augment datasets with tools like synthetic data generators, data validation systems, and observability platforms. Investing in robust data workflows ensures that AI models are fed with accurate inputs, leading to optimised results.

The strategic deployment of AI should consider the entire organisational workflow. Organisations need to adapt existing processes to fit the capabilities of AI models, much like redesigning dishwasher processes for efficiency. Achieving this synthesis of traditional and advanced AI techniques is key to unlocking lasting value and maintaining competitive advantage in the AI age.