Few priorities feel more urgent to marketers right now than recognition from the world’s leading AI systems. The need to be noticed, understood, and, of course, recommended, is palpable in a way we've not seen since the Internet was all the rage.
But, the obnoxious truth is that AI cannot just go out and recognize expertise that a company has not made legible, consistent, and accountable, and companies aren't always great at making it easy for them.
I could publish years of research, executive commentary, product guidance, and customer education, but my content and brand may remain difficult for AI systems to interpret. The problem is often treated as a content volume issue or a customer data issue.
This is a problem I know first-hand from working to build Communication Orchestration into a category of its own.
But it is more fundamental than making more stuff: the organization’s source environment is often not built for machine discovery, interpretation, and reliable use.
Orgs must instead build a governed body of expertise that AI can actually find, interpret, compare, and, where applicable, cite with greater confidence.
For marketing leaders who desperately require the attention of LLMs on their products and services, the priority cannot remain simply producing more material endlessly until we've created a swamp of unmitigated AI slop.
For content strategists like myself, this governance is an essential system that connects editorial standards, metadata, permissions, compliance, publishing workflows, and system interoperability together into one big strategic basket.
And yes, I know that hurt to read, but it's important, I swear.
We must all instead build a governed body of expertise that AI can actually find, interpret, compare, and cite with greater confidence.
This is source governance.
AI systems encounter companies through distributed evidence: web pages, newsroom articles, documentation, executive posts, campaign assets, social content, email, partner sites, and structured data.
Each of these things is content, and each can provide evidence of the company’s expertise.
However, each can also introduce ambiguity to a system with the critical thinking skills of a pigeon.
Conflicting terminology, missing authorship, outdated claims, duplicate pages, and disconnected publishing systems make it harder to identify which source represents the company’s current position or up-to-date branding. More content can make this problem worse if every team creates its own version of the truth.
Source governance gives that evidence a better way to operate.
Leaders should be able to answer several practical questions:
These decisions can help AI systems interpret the relationship between a brand and a subject while reducing contradictory signals. They also help teams reuse expertise without stripping away its context. This is why source governance belongs in brand strategy and content operations, not only in IT or legal review.
Enterprise marketing almost never operates within in a singular tech system.
Planning, asset management, social publishing, campaign execution, analytics, customer data, and content production often sit in separate tools with little to no integration. Furthermore, walled-gardens touting all-in-one suites cause environments that are hostile to flexibility.
A composable model connects specialized products so teams can adapt the stack without depending on one monolithic suite.
That flexibility needs a governing layer.
Entirely’s view of composable marketing technology emphasizes interoperability, governance, visibility, and control rather than unmitigated tool bloat. This distinction matters for AI because an agent working across connected systems needs more than technical access. It needs usable context.
Source governance turns connection into dependable orchestration. It supplies common taxonomies, content states, ownership rules, retention policies, and usage rights.
It also identifies where human judgment remains mandatory.
In an AI-native operating model, those controls become part of the system rather than a review added at the end. For example, our vision for Entirely's AI frames governance by design, context, compliance, and human-agent collaboration as components of orchestration.
The leadership lesson is straightforward: composability expands what AI can access, while governance determines what AI should rely on.
Trust is not a badge that marketing can attach to a page (although every time G2 updates its rankings we certainly try).
It is something that develops from signals that make information easier to interpret and verify by both humans and machines.
Human signals still offer a lot of trust.
Clear authorship (ideally by a person who breathes), publication dates, descriptive headings, consistency across terminology and semantics, structured metadata, actual accessibility in UX/UI, and links to supporting material all provide useful context.
And this isn't new or unique to LLMs, either. Google has been pushing for these stronger, more human signals - and in particular accessibility - as indicators of quality and authority.
Governance makes those signals repeatable and easier to maintain.
A single well-structured article can certainly be helpful. A governed publishing standard applied across a body of expertise is more valuable because it creates recognizable patterns.
With better human signals, AI systems can more readily distinguish a company’s position, connect related concepts, and identify whether a statement is recent enough to be accurate and useful for a person, because the writer created it to be that way.
This principle extends beyond public web pages. Elaine’s discussion of AI readability in email marketing (page in German) points to semantic structure, metadata, authentication, and collaboration among marketing, IT, and data protection teams.
This prediction is specific to email, but the strategic point applies more broadly: content designed only for visual presentation may be poorly prepared for machine interpretation.
Marketing leaders do not need to turn every writer into a data architect, but they do need a shared standard that addresses each of the following:
This is not technical housekeeping. It is how editorial credibility becomes machine-readable.
Thankfully, a company’s authority is not confined to its own website.
Subject matter experts speak at events, employees share commentary, service teams answer questions, and regional groups adapt corporate messages. These activities create valuable evidence, but they also increase the chance of inconsistency.
But we don't want to just go out and centralize every sentence. We must govern the elements that need to remain stable while giving teams room to communicate in context.
Approved claims, core terminology, disclosure requirements, asset permissions, and escalation paths can be standardized (hello knowledge graph my old friend...). Tone, examples, and channel-specific expression can remain flexible.
Communication Orchestration shows how we can connect strategy with execution across entire organizations, rather than keeping content and comms all bundled up in the marketing silo. However, it must also connect source governance to execution by ensuring that authoritative knowledge retains its context, ownership, and constraints.
The broader lesson is that governed distribution is part of source credibility. When expertise moves through controlled workflows, the organization can preserve provenance and reduce unauthorized variations without silencing its experts or keeping impenetrable silos sectioning off its departments.
This also does more to clarify the role of human oversight.
AI can assist with classification, adaptation, retrieval, and reuse, but people must remain responsible for deciding which sources actually deserve authority, whether context has changed, and where a claim requires expert review.
Source governance becomes actionable when it has owners, measures, and a sequence of work.
I would start with a bounded domain tied to strategic visibility, such as a product category, executive point of view, or high-value customer problem.
1. Map the evidence. Identify the public and internal sources that define the company’s expertise in that domain. Who are these people or content sources and what are they doing?
2. Name the authority. Assign owners for claims, terminology, approval, updates, and retirement.
3. Remove contradictions. Resolve duplicate definitions, stale pages, unsupported statements, and unclear content status. It's content audit time!
4. Improve readability. Apply semantic structure, metadata, authorship, dates, internal relationships, and direct supporting links.
Fresh customer profiles still matter for relevance and personalization. They cannot compensate for weak source authority. If the underlying expertise is fragmented, poorly structured, or unsupported, better audience data only helps the organization deliver uncertain material more precisely.
The brands most prepared for AI discovery will treat their expertise as governed infrastructure. They will know where trusted knowledge lives, how it is expressed across systems, and who is accountable for its quality. That work gives AI a stronger basis for finding the brand, interpreting its position, and presenting its expertise with confidence.