Editorial
Research Report
Building the Case for Modern Public Sector Messaging
The Gist
- Why isn't AI readiness improving faster? — Only 30% of CMOs have mature AI readiness capabilities despite 70% naming it a critical goal, per Gartner.
- Who should own AI readiness at the executive level? — The article argues C-suite ownership is essential, since 76.6% of AI decisions already involve 8+ different C-level roles with no single accountable owner.
- What's the biggest hidden risk in a company's digital estate? — Orphaned or forgotten sites and outdated PDFs can be mistakenly treated as authoritative sources by AI, as in a case where a five-year-old planning department site caused a chatbot to give incorrect information.
- How much of what AI cites is outside a brand's control? — Up to 90% of what AI search references sits on third-party sites, per McKinsey, with AirOps finding 85% of brand mentions come from third-party content.
- Why are PDFs a special risk for AI misinformation? — One regulator's poorly structured PDFs led to 84% of AI-generated summaries containing inaccuracies, including broken links to core legal basis.
Many marketing teams simply aren’t ready for AI. Gartner reports that 70% of CMOs describe becoming an AI leader as a “critical goal," yet less than a third (30%) have mature or fully developed AI readiness capabilities.
One aspect of AI readiness where there is work to do is in ensuring that your digital estate and footprint is in the best state possible so that AI tools and LLMs successfully representing your brand, establishing AI visibility while avoiding misinformation and misrepresentation.
In the first part article in this series I explored research that shows only 3% of the world’s top brands have leading maturity in this area of AI readiness, and also why there is an urgent need for action due to commercial, reputational and compliance risk exposure. In the second article I proposed an AI readiness maturity model that provides both a starting point and a structured approach to reducing that risk over time.
In this third and final article, I explore four critical success factors that need to be in place to mature AI readiness. Let’s dive into the first.
Why AI Readiness Needs Clear C-Suite Ownership
Improving AI readiness starts with the issue being understood, owned and prioritized at the C-suite level. To date the composition of the digital estate and how it is managed have often been regarded as tactical issues and largely something for the digital team to worry about.
However, the heightened risks around AI misrepresentation and misinformation that can lead to reputational damage and compliance issues, combined with the urgent need to control future AI visibility, creates exposure that must be taken seriously at board level.
It’s not that the C-suite is not taking a keen interest in AI, but ownership patterns are not necessarily mature. A survey from Futurum reports that 76.6% of AI decisions are actually being taken by C-suite members; however, the survey also shows this collectively involves at least eight different C-level roles involved. Further research from Larridin, indicates that 58.2% of organizations believe “unclear and fragmented ownership” is the main barrier to measuring AI performance.
Executive ownership and accountability are essential to make sure AI readiness is actually prioritized. Giving a member of the C-suite explicit responsibility is the starting point of action, so that digital teams are given the tools, budget and mandate to actively improve AI readiness maturity.
What Matters Here: Which C-suite role should own AI readiness accountability?
Ownership is fragmented across 8+ C-level roles today, which the article ties directly to the 58.2% of organizations citing unclear ownership as their top AI measurement barrier.
Related Article: A 10-Principle Maturity Model for AI-Ready Brand Content
How to Measure and Report AI Readiness Maturity
Improvement starts with measurement and tracking progress, as well as effective and targeted reporting on top of that. It is essential to have an independent and accurate picture of where you are in terms of AI readiness. When you have this, you can then use the comprehensive maturity model I outlined in my last article then provides leaders with an effective framework to consider their starting position, where they need to get to, and how to get there.
Generally, tracking AI visibility is still quite nascent, with surveys suggesting that only 14% of marketers actually track AI visibility. Despite this, each of the 10 principles or foundations within my AI readiness maturity model are measurable and trackable, from “performance” through to “machine structure” and “integrity and consistency.”
There are a range of automated diagnostic tools, some free, some paid for, that can run remotely over your digital estate and give you an accurate position of where you are across each of these fundamental areas, highlighting your level of maturity and what needs to be fixed.
The good news is that increasingly these web diagnostic tools are using AI, and the improvement has been striking. I’ve worked in the web diagnostic field for decades, and the solutions really are now in place to drive real AI readiness, and they’re only going to get better.
Effective and independent measurement must also be accompanied by effective reporting to senior leadership so that progress and risk can be tracked. This reporting must be appropriate to the C-suite — overwhelming them with technical detail means they lose interest. A scorecard approach based on a maturity model that provides an easy way to visualise where the organization is and the level of exposure is the way to go.
What Matters Here: What percentage of marketers currently track AI visibility?
Only 14% of marketers track AI visibility today, meaning most organizations have no way of knowing whether their AI readiness gaps are improving or getting worse.
How Digital Estate Sprawl Creates AI Misinformation Risk
Digital estate sprawl is the enemy of effective AI visibility and representation. Large and complex organizations – including global superbrands – start to build up what is effectively a hidden digital footprint over time. This consists of campaign sites, microsites, online newsletters and flip books, local country-level or brand-level sites, e-syndicated content, customer support channels, partner content, joint ventures and more that get created and then left and forgotten about.
Often this content is created and managed locally, and the central digital team may not even know that it even exists. Consider an organization built up through acquisition; it is very hard to keep track of sites and content that was created way back when.
Comprehensive research that I carried out back in 2023 found that some organizations have a digital estate where as much as 41% of it is unknown to the central web team.
When AI Sees Data as Data, Brands Push Outdated Content
A hidden digital estate can lead to specific issues. For example, a local authority deployed an AI-powered chatbot on its official website to answer questions from citizens and improve digital services to the public.
Unfortunately, in the past an unconnected planning department had built a separate website but failed to tell the digital team. This site was still live and had not been updated for five years. Despite its content being out of date, it contained focused content with named authors and version numbers, which the AI treated as the authoritative source.
The chatbot subsequently provided incorrect planning information and flawed initial assessments of planning applications, with the root cause being the lack of governance across the digital estate, rather than necessarily the AI itself.
In large, complex, global and multi-brand organizations, widespread decentralized publishing is likely inevitable, but central digital teams must wrestle back some kind of control over their digital estate to be able to install some governance. At a minimum that should at least involve:
- Having an asset register of all digital properties and who is responsible for each, so the estate is at least not hidden.
- Having approval workflow in place for any additions to the digital estate, without creating bottlenecks.
One of the most challenging aspects of managing this digital estate is that content might be hosted on third-party sites which makes it harder to change or remove it. McKinsey research suggests that up to 90% of what AI search references sites outside the brand’s control. AirOps research draws similar conclusions, finding 85% of brand mentions come from third-party content. Of course, some of this content will be beyond the control of the digital team, but it should be possible to delete that flip book from four years ago that has old information in it.
What Matters Here: How did an orphaned planning department site mislead an AI chatbot?
A planning department site that hadn't been updated in five years was still treated as authoritative by an AI chatbot, showing how governance gaps — not the AI itself — cause real misinformation.
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FAQ: AI Readiness Maturity for Digital Teams
Editor's note: The following FAQ addresses common questions about building AI readiness maturity across governance, measurement and document management, based on the trends and case examples explored in this article.
AI Readiness Maturity: Key Factors for Digital Teams
The following table highlights the most important lessons, actions and strategic considerations emerging from this article's four AI readiness success factors.
| Key Area | What Happened | Why It Matters | Recommended Action |
|---|---|---|---|
| C-suite ownership | 76.6% of AI decisions involve C-suite members, but span 8+ roles with no clear owner | Fragmented ownership is cited by 58.2% of orgs as the top barrier to measuring AI performance | Assign explicit AI readiness accountability to a single C-suite role |
| Measurement and reporting | Only 14% of marketers track AI visibility | Without measurement, maturity gaps go undetected until they cause reputational or compliance harm | Adopt a maturity model scorecard for board-level reporting |
| Digital estate governance | Up to 41% of one organization's digital estate was unknown to its central team | Orphaned sites can be mistakenly treated as authoritative by AI, causing factual errors | Build an asset register and approval workflow for all digital properties |
| Document management | 84% of AI summaries of one regulator's PDFs contained inaccuracies | Broken links and poor structure in legacy PDFs actively mislead AI-generated summaries | Move from unstructured to structured document management |
Why PDF and Document Management Is an AI Readiness Risk
Documents, particularly PDFs, are a problem area and present some of the greatest risks associated with AI misinformation. For multiple reasons, some purely logistical but also sometimes legal, website documents are often not updated, but are kept online. When this happens it increases risk exposure, with old documents not updated to reflect brand position, links to other sources, or critical regulatory detail.
For example, a national financial regulator published key regulatory and legal documents as PDFs which contained links to other web content. However, around 20% of links within the documents were broken, including the core legal basis for some of the documents. Additionally, documents were poorly named and pages within each PDF had the same title, effectively making them unreadable to AI tools.
I found that 84% of AI-generated summaries produced from these documents contained inaccuracies, with the potential to provide erroneous positions on legal and regulatory matters, which is not a great position for a regulator within a strictly regulated sector.
Digital teams wanting to mature AI readiness need a special focus on document management that requires taking far more control to enable retrospective changes. The best way to do this is to move from living with unstructured documents to managing structured documents which not only enables teams to make necessary updates to archived documents, but is also good for GEO, accessibility and more.
What Matters Here: Why did 84% of AI summaries of a regulator's PDFs contain inaccuracies?
Broken links and duplicate titling in unstructured PDFs were enough to make the majority of AI-generated summaries inaccurate, underscoring why document structure is a distinct risk from general web content.
How to Start Building AI Readiness Maturity Today
AI readiness maturity is arguably one of the key challenges of today for digital marketing teams. Yes, that involves GEO and AEO, but it also means actively managing the entirety of your digital estate.
Brands need to take it seriously and act soon to limit risk exposure. Using a maturity model and building the right foundations are important steps to getting ready for AI.
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