How ontology turns business information into AI Advantage

Is there a way to differentiate when everyone has access to the same tool? That's a relevant question in the context of AI, because one of its distinguishing features is that AI is available to everyone, and adoption statistics indicate that practically everyone is indeed on board. When everyone possesses it, does it not become a commodity? Not when ontology serves as the foundation for your company's AI strategy.

That's because, yes, commoditization allows everyone to use the same tool. However, ontology alters how the tool works for you. 

Data has long been referred to as the 'new oil'. As a result, many of us have meticulously collected various types of data, often aided and encouraged by the realities of retention laws and practices. Many have asked what we should do with so much data. As a raw resource, it's basically a choice between crude and petrol to fuel our enterprises. Raw data, like a puddle of oil, does not create value. 

The breakthrough occurs when data is translated into insight and then into action. 

That's where ontology comes in. It's the secret element that turns artificial intelligence clever by putting corporate data assets to use. 

What is ontology? 

Ontology has traditionally been defined as the study of existence; if this seems metaphysical, it is. However, in technology, ontology has a slightly different and more precise definition: a structured framework for describing domain knowledge. 

As AI's semantic backbone, it connects objects and concepts through established relationships, organizing data and, eventually, exposing it for effective AI use. 

AI becomes far more helpful when data assets are defined alongside consumers, goods, processes, and how these assets are related. Instead of only operating on 'public' data, as Grok, Claude, ChatGPT, and other AIs do, your AI now crunches and contextualizes internal data too. 

In practical terms, this translates to a more helpful and useful AI. With ontology, AI knows that 'profit' is more than just a number; it is linked to revenue, cost, and strategic objectives. It understands the distinction between a lead and a prospect, and why it matters to your pipeline. 

Why ontology is a differentiator 

Our experience with AI demonstrates that organisations face the same challenges: inconsistent outputs, governance issues, and solutions that do not scale. Or, more simply, an inability to use AI effectively to assist your employees with mundane chores they'd prefer not do, such as accessing information or assets, gaining performance insights, or obtaining answers to common questions. 

In a whitepaper I wrote earlier this year, my research suggests that some of the advantages you could look forward to include:

• Up to 1.5x sales growth for leaders who lead with AI. 
• Intelligent automation reducing regular work by up to 75 percent. 
• Joining the 58% of leaders who report measurable ROI from AI efforts. 
   

Sounds good, but how do you plan to join that club? 

Leading with ontology 

The best AI strategies are built around ontology. Start by developing your company's ontology, analysing and arranging your data repositories ('pointing the AI in the correct direction'), then quickly creating and delivering prototypes showcasing where and how AI can offer value. 

Specific areas where we've seen success include healthcare with AI assistance for nurses, financial services agents tackling 'busy work', and AI bots improving decision-making by converting data into information. 

What's most fascinating about this strategy is that custom AI isn't nearly as demanding a project as enterprise data initiatives are. Commoditised technology allows for quick wins at minimal costs, emphasising the importance of the craftsmen over the tool itself. 

Want to make AI work uniquely for you? Start with ontology.