Your Enterprise Data is a Mountain, a Context Engine is the Sherpa
In conversation with customers, as well as in interaction with executives when I travel, there’s one common theme: they want to reach the high camp summit of artificial intelligence — yesterday.
There’s no shortage of reasons for the sentiment. A powerful new tool for programmers. A steroid shot to marketing. A universe of knowledge for the research team. A force-multiplier for the sales funnel. A productivity boost as never before. But the overarching motivation is even broader: They yearn to move their enterprises from a perception of AI as a threat, to a lean-forward embrace of this profound technology as THE chief competitive advantage.
You wouldn’t embark on a high-altitude climb without a “sherpa”. Similarly, your guide for the ascent to the summit of AI application should be a context engine, a tool that immediately opens up vast stores of your own data, hitherto locked away.
Let me share why the anecdotes I hear from corporate leaders are solidly supported by empirical studies. Earlier this year, Forrester Research surveyed 750 corporate decision-makers charged with upping their companies’ AI game. Their report, Where is Generative AI’s Transformation Value Hiding?, is filled with insight.
“Most respondents have genAI tech and infrastructure in place but struggle with data, governance, and skill development,” the study found. “Just 42% said they can train genAI models, and 89% struggle to prepare business data. A mere 24% have rolled out a governance policy, and 75% or more face challenges around genAI understanding.”
Another new survey conducted by Dataiku and Cognizant, polling 200 senior analytics and IT leaders globally, found similar results:
“Most of the respondents in the survey reported having infrastructure barriers in using LLMs in the way that they would like,” wrote VentureBeat, which first published the survey. “On top of that, they face other challenges, including regulatory compliance with regional legislation such as the EU AI Act and internal policy challenges.”
But the most fascinating and useful examination of this conundrum comes from Harvard Business Review. In Turn Generative AI from an Existential Threat in a Competitive Advantage, the authors explore the three levels of this “ascent to the summit”. They are:
- Level 1: Teams master general Large Language Models (LLMs) like ChatGPT, or industry-specific models like Alexi for law firms.
- Level 2: Teams customize LLMs using their firms’ own data and expertise.
- Level 3: The “holy grail” of AI usage in an enterprise, when data travels through a perpetual feedback loop, creating an ever stronger suite of powerful tools. Most critically, Level 3 is the point at which AI improves autonomously over time — the summit.
“Creating a feedback loop that is unique to each firm’s product or service is the holy grail,” write the authors. “The more customers use the offering, the more feedback signals they generate, which allows the generative AI tool to further improve itself, leading to more users, more usage, more feedback, and so on. The result is a powerful form of compounding competitive advantage.”
This Level 3 functionality is exactly what we deliver with our own AI Context Engine. Our tool builds on our nearly decade-old data catalog and knowledge graph architecture, which transform scattered data into actionable insights. These foundational tools create what I call the data-driven “nervous systems” and “brains” for our client enterprises.
Carrying the analogy, the AI Context Engine is an expanded “frontal lobe”. LLM models are great at reading and synthesizing emails, documents, and textual material, so-called “unstructured data”. But they fail when navigating “structured” data, from spreadsheets to massive databases. Why? Because structured data resources demand an understanding of the relationships between data points. They demand an understanding of the context. Traditional data catalogs may have AI-powered features, but they can’t chat with structured data, effectively stranding even the most data-savvy enterprises at Level 2 on the climb to the AI summit.
Until now.
With contextual intelligence, businesses move beyond surface-level AI applications and unlock deeper, more transformative uses of the technology. In essence, context engines can elevate AI from a tool that simply processes information, to one that truly comprehends the unique world of a particular business, driving competitive advantage in ways that were previously unattainable.
Read more on context engines here, and demo the AI Context Engine here.
