Is Your Content Ready for AI?

How do you know if it is?

Whether you’re a small business operating on just two of the Digital Content Supply Chain (DCSC) cylinders, or a large enterprise purring on all seven, the readiness of your content for AI is directly proportionate to your appetite for the PRECISION of your content.

Too many organizations, it seems, value speed over precision. They prioritize the speed of getting to market and delivery of services, products, and features over "bakedness" (the degree of completeness or how fully-baked something is). The common product delivery posture is, "Get it out. We'll fix the bugs later."

Low tolerance for error

This is risky business in the age of Artificial Intelligence (AI). Speed is a baked-in feature of AI. But what is speed without a calculated degree of accuracy? In the thin-margin-for-error world of customer support, there is little room for avoidable mistakes. Today's customers are demanding, impatient, and fickle with their forgiveness and loyalty.

We’re all familiar with the adage, “Garbage In, Garbage Out.” For most businesses—small, midsize, enterprise—this isn’t their problem. Most companies have decent content. Let’s zero in on support content for a moment. As every business knows, Impressive revenue from products and services can be quickly eroded by poor customer support experiences, never mind the hit to hard-earned brand and market cred.

The problem most companies face isn’t garbage content. It’s too much content in too many repositories with slightly different, often conflicting versions of the same content.

Here’s a common scenario

One customer grievance policy states one thing, while four other slightly variant versions tell the customer something else. In the bright, brave world of Artificial Intelligence, where your LLM is trained on your content, the quick AI response might not be exactly the response you want your customers receiving. And if the response from your Intelligent agent is a hallucination because your support content was sub-par, then you have a different customer- and brand experience problem.

The more accurate measure, in my opinion, isn’t GIGO, but MIBO—Mediocrity In, Business Out, as in Business-out-the-door.

Mediocre content comes in different flavors: Can’t Find It, Can’t Trust It, Can’t Re-use It, Sub-par Quality, and Failed Promise.

What's the solution?

First, fix the source content your LLM trains on. Second, implement SSOT (Single-Source-of-Truth) content. For every unique topic---policy, FAQ, procedure, definition or description---ruthlessly root out "nearly-identical" content competitors until your organization has achieved SSOT for its mission- and business-critical content that serves employees, customers, partners, investors, and other high-value constituents.

Third, convert your Digital Content Supply Chain (DCSC) into an AI Content Supply Chain. (More on that in another blog post.) But for now, consider that AI can both identify the gaps in your DCSC and help to build a fast, precise, and sustainable AI Content Supply Chain for a competitive edge.

AI flavors for the Digital Content Supply Chain

• Predictive AI (planning & forecasting)

• Generative AI (creation & sourcing)

• Responsive AI (chat assistants in content consumption)

• Responsible AI (governance)

• Operational AI (content management

• Assembly AI (packaging & delivery)