My Saturday mornings used to be full of artificial intelligence. Thanks to the TV shows I watched and the comics and books I read, I grew up expecting to live in a world of robots that could think and talk, vehicles of all sizes that would whisk me off to far-away destinations with no need for drivers or pilots, and computers that responded to voice commands and knew the answer to just about everything.
I may not yet have that robot butler, and my first experience with a self-driving car left me more apprehensive than impressed, but in other ways artificial intelligence (AI) is now part of my everyday existence, and in ways that I don’t even think about.
One of the first things I do each morning is ask Siri for the day’s weather forecast, and then check to make sure that my Nest thermostat is reacting accordingly. During the day, Pandora’s predictive analytics choose my music, and in the evening Netflix serves up my favorite shows and movies. My books arrive courtesy of Amazon, and there’s a fair chance that some of those purchases were driven by recommendations generated via AI.
Technology Debt Hampers AI Advances
This is all great stuff, but it’s just a small representation of the promise of AI, and that promise has not yet been realized.
We are carrying a lot of technology debt that is hampering the truly seamless AI customer experience. When the systems we already have don’t interact, and companies continue to build point solution silos, duplicate process across business units, or fail to take a holistic view of their data, content and technology assets, then the existing AI systems will continue to pull from a restricted set of information.
Over the past several years, as I have talked and worked with companies that are pursuing AI initiatives, I have noticed that the majority of those projects fail for a common reason, it may not be the only reason, but it’s definitely a common denominator.
AI Needs Intelligent Content
No artificial intelligence proof of concept, pilot program or full implementation will scale without the fuel that connects systems to users — content. And not just any content, but the right content at the right time to answer a question or move through a process.
AI can help automate mundane tasks and free up the humans to be more creative, but it needs the underpinning of data in context — and that is content, specifically content that is intelligent.
The way we deliver and interact with content is changing. It used to be good enough to create large monolithic pieces of content: manuals, white papers, PDF brochures, etc. and follow the traditional broadcast model or publish in a passive mode in the hope that in the best case we could drive our customers to find it or, in the worst case, just hope that whoever needed it stumbled across it via search or navigation.
With the rise of new delivery channels and AI-driven algorithms, that has changed. We no longer just want to consume content, we want to have conversations with it. The broadcast model has changed to an invoke-and-respond model.
To meet the needs of the new delivery models like AI, our content needs to be active and delivered proactively. It should also be modular, coherent, self-aware and quantum. Here are definitions of those last four characteristics:
Modular: Existing in smaller, self-contained units of information that address single topics.
Coherent: Defined, described and managed through a common content model so that it can be moved across systems.
Self-Aware: Connected with semantics, taxonomy, structure and context.
Quantum: Content segments that can exist in multiple states and systems at the same time.
Intelligent content with a common content and semantics model that allows systems to talk the same language when moving content across silos may be the key to unlocking the technology disconnect that is holding AI back from even greater acceptance.
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