An interview with Information Energy 2018 speaker Cruce Saunders, conducted by tcworld editor, Helena Maier
Who hasn't heard about bots, AI and intelligent content in the last two years? You can really feel a movement that wants to tell us: make your content smarter, now, for bots and for you. But how great is the benefit when we make our content smarter for humans and chatbots? And how do we start?
and founder of [A], Cruce Saunders says that intelligent content
has the ability to deliver far more than the investment you put into it. This is a great opportunity. Let's ask him how we approach intelligent content.
How can a company tell if their content is intelligent? Are there certain criteria?
Content intelligence emerges from content that is: Coherent, Self-Aware, and Quantum.
Coherent – Orchestrated against standard schemas, a content model, that unifies systems of record for content interoperability.
Self-Aware – Connected with semantics, taxonomy, structure, and customer context.
Quantum – Able to exist in multiple states and systems at one time, leveraging content assets for reach and impact.
Leaders can ask a number of questions to determine how content indexes against a measure for intelligent reuse. Here are a few:
Do we understand the value and actual impact of our content assets?
Does our content perform work outside of one system?
Can our content move from system to system and channel to channel in a way that streamlines reuse, adaptability, and reach?
Is our content domain connected semantically? Do we have a common reference for products, entities, and taxonomy associations?
When we publish content to the web, does it contain schema.org, Open Graph, or other semantic markup for robot consumption?
Do we have a way to expose our content domain expertise in an interactive or conversational way via chatbots or voice interfaces?
How close are we to having enough structure and (ethically-collected) customer context data to create realtime personalization?
What level of friction do we have in the content lifecycle, from authoring, to management, to publishing? How much copy and paste activities exist to move content between systems or channels?
Do we orchestrate content experiences across the whole customer lifecycle from pre-sales marketing through post-sales support?
Does our organization have practices in place to manage cross-functional content structure and semantics independent of a department silo?
How do we achieve intelligent content? How do we start?
Creating content intelligence
across a large content domain requires time, effort and executive mandate. True intelligence cannot be accomplished in an isolated context by itself, simply because knowledge needs connections, and connections do not stop at departmental walls. And yet, trying to roll out a new publishing lifecycle all at once across a sprawling enterprise is too broad and impractical. Life doesn't stop to allow for change.
However, content intelligence can and does have origins at a departmental or isolated portfolio level. Someone within technical or marketing communications decides that it is time to get on top of growing publishing headaches. Some leader, somewhere, decides, “enough is enough” and “it is time to start stitching content systems together.”
Usually the genesis of content intelligence comes not from any utopian desire for smarter content, but from a realization that doing things the old, slow, copy-and-paste way will eventually collapse under its own weight. It becomes obvious that the structure that the technical communications team has in place (DITA) is meaningful and useful, and that it extends beyond publishing PDFs or disconnected self-help customer web portals. From there a drive emerges to represent that structure through to more endpoints across customer experience management platforms.
The very first place to start is at the beginning, with a scope. Examples of places to start include:
Pick two existing authoring systems and two destination publishing channels.
Pick a single customer experience to enable, or a customer journey to facilitate, or a single personalization use case.
Pick two content domains with overlapping content types that can be unified against a single model.
Work with an existing governance team to streamline content workflows within their oversight mandate.
Pick two departments that author structured content against different schemas, and build a joint commercialization initiative between the two.
All systemic content intelligence
improvements need to start with a tangible focus on a clear and measurable value. As of 2018, [A] has not yet discovered an organization that, at the CEO level, understands the need to invest in content intelligence
holistically as a large enterprise initiative. It is all still costed out within groups that own and manage content. That will change. No one department level budget should have to carry the cost for building the company's brain.
However today, the value of content publishing innovation must be proven in slow and measured steps. So, start with a scope and establish a cross-functional team to drive towards that single scope, documenting the improvements and business value impact along the way.
You also said that to reach humans and robots, published content must be intelligent. Is it really necessary for us to reach robots? What would be the benefit?
Absolutely, we must target our content structure
and publishing activity very consciously at robots. Whether we structure content on purpose or not, robots consume and represent our content all the time.
Robots increasingly have become the most important mediators between our content and our customers. In many environments we see organic search driving 70%+ of an organization's overall new site entrances or exposures to it’s content assets. Many of those sessions are already voice driven originating on mobile devices. Voice driven assistance will broker more and more interactions with our content.
But it is not only external robots that matter. Our own chatbots, personalization engines, and marketing automation systems need to be able to work with our content in a discrete way. Content intelligence, not only unlocks external consumption, but also internally driven publishing and syndication. Robots use structure and semantics to drive those interactions.
On the other hand, there are also critical opinions on AI technologies as chatbots. The content strategist and blogger Mark Baker, for example, has said ‘chatbots are not the future of technical communication.’
Actually, I agree with Mark on this point. It’s true. Chatbots are not the future of technical communication. The future is omnichannel. Actually, one could make the argument that the PRESENT is already omnichannel, as evidenced by the proliferation of channel and function-specific authoring groups. Chatbots are just a channel. People still will want PDFs. And web interfaces. And search results. Conversational interaction with our content already happens every day, whether we structure for a clean experience or not. We already ask Google for answers, and Google crawls our sites to find the answer. As Mark puts it, “every page is page one.” True. Except it’s more than that. Every atomic fragment of content is a customer’s potential first interaction with us. We need to structure content for conversational variants within our CMS, along with the traditional article form content. If we care about meeting customers at the channels, we need to accommodate for channel interactions at the level of content structure. We design the model, then the modes. Structure precedes presentation.
Our last question – a fictional scenario: The time traveler Dr. Emmett Brown from the film series Back to the Future answers two questions from the future. What would you ask him?
Tough one ... here’s a couple:
Dr. Brown, please describe how knowledge moves in the future...how do we discover content, learn, and interact with knowledge?
- Dr. Brown, please describe the organizational structures of the future...how are the most successful corporations and governments organized?
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