The new visibility game: why media relations matters in the age of AI

Stakeholder perception of your organisation is no longer shaped only by journalists, analysts, policymakers, search results and social conversations. Increasingly, it is shaped by algorithmic systems that decide what information is surfaced, ranked, summarised and trusted.

An investor may use ChatGPT to research a company’s ESG position. A policymaker may encounter a brand through Google’s AI-generated summaries. A potential employee may form a view from LinkedIn, Glassdoor and Reddit. A journalist may use AI tools to research an organisation’s background and controversies before making contact. A donor might find you through Perplexity.

In each case, your organisation’s story is being filtered through systems you do not directly control. That is not new. Communications has always operated in spaces where direct control is limited e.g media stories, broadcast interviews, social conversations, conference debates. As communications specialists, our job has always been to work hard enough that the message lands with the clarity and purpose needed to deliver impact.

But AI creates a specific new challenge: how do you ensure your organisation is represented accurately, consistently and compellingly across every touchpoint, including the large language models that now have real power to shape reputation?

The answer is not to abandon media relations, search strategy or stakeholder engagement. It is to connect them through a broader discipline: algorithmic visibility.

What is algorithmic visibility?

Algorithmic visibility is the strategic management of the information, signals, sources and digital evidence that algorithmic systems use to understand and represent your organisation.

Visibility now extends well beyond Google rankings. It includes the sources that large language models, AI search engines, recommendation systems, social feeds, knowledge panels and digital assistants draw on to retrieve, summarise and present information.

A simpler way to put it: Algorithmic visibility is the work of making sure both people and machines can understand who you are, what you do, why you matter and why you can be trusted.

For communications teams, this is a natural evolution. Media relations focused on influencing journalists and securing editorial coverage. SEO focused on discoverability in search. Social media strategy focused on platform reach, community conversation and engagement. Algorithmic visibility brings these together and asks a new question: what does the wider information ecosystem enable machines to understand, retrieve and repeat about your organisation?

Here is how the disciplines connect:

• Media relations: earning credibility through journalists, editors and broadcasters

• Search optimisation: ensuring discoverability when audiences are actively seeking something you can provide

• Cross-channel content: building visibility and adding value across social media feeds (especially LinkedIn for our audiences), online communities and video platforms

• Stakeholder engagement : informing and leading the networks that influence your target audiences

• Algorithmic visibility: creating the right source environment for algorithms, AI tools and LLMs to accurately understand, retrieve, summarise and recommend your organisation

Why algorithms now shape stakeholder perception and the risks this creates for communications leaders

Algorithms do not simply reflect reputation, they mediate it. Your organisation has far less perceived agency than you realise.

Most stakeholders no longer build an opinion from a single source. They encounter a layered information environment, with content algorithmically curated and delivered in seconds across multiple devices and platforms.

Search engines decide which sources are most visible. AI assistants generate summaries from available information. LinkedIn, Meta and TikTok recommend content based on relevance signals and engagement patterns. Reddit and forums surface community discussion. News aggregators rank stories. Each touchpoint can reinforce, distort or omit part of your organisation’s story.

This creates two connected risks for communications leaders.

Visibility risk: the organisation is absent or underrepresented in the sources that matter. If credible, current and detailed information is not available, AI systems may rely on incomplete, outdated or third-party material.

Interpretation risk: the organisation is visible, but the story is inconsistent, poorly evidenced or dominated by others. AI-generated answers then reflect confusion or outdated assumptions rather than the organisation’s actual position.

Algorithmic visibility is the discipline for managing both.

From media relations to algorithmic visibility and the new questions you should be asking

Media relations has always been about more than publicity. At its best, it helps organisations earn credibility, shape narratives, explain complexity, respond to scrutiny and build trust through independent voices.

Algorithmic visibility does not replace that work, it expands it. It also changes the questions communications teams need to ask:

• What do algorithms currently appear to know about us?
• Which sources are AI engines using when they answer questions about our organisation?
• Are those sources accurate, current and credible?
• Are we visible in the places AI systems retrieve from?
• Is our story consistent across owned, earned, shared and third-party channels?
• Are there gaps, contradictions or outdated claims in circulation?
• Are our experts, leaders and campaigns clearly connected to the topics we want to be known for?
• Are we building enough third-party validation to support our key claims?

Answering these questions reveals the strength, consistency and credibility of your entire information footprint. The organisations that succeed will be those that combine data-driven intelligence, strategic communications and creative execution to ensure their story is not only heard by people, but understood, trusted and surfaced by the algorithms shaping public perception.

Building a strong algorithmic visibility strategy

A strong algorithmic visibility strategy shapes the inputs that AI engines, search engines and recommendation systems draw from directly. 

At Greenhouse, we build all our work on our Impact Framework. Algorithmic visibility is an opportunity to deliver faster, better outcomes by connecting what we already do with a clearer understanding of how machines read and represent organisations.

Our core algorithmic visibility services

Narrative clarity and visibility: Define the narrative and key messages so you’re clear on what your organisation want to be known for, why your organisation matters to target audiences and where you need to show up?

AI audit: Benchmark the current algorithmic footprint of your organisation against the messaging work and identify visibility levels across key touchpoints so you can fill and control any gaps.

Optimisation roadmap: Use the messaging and audit work to develop a clear content strategy that builds visibility across AI’s trusted sources and answers the questions stakeholders are actually asking.

Third-party trust signal activation: Build credibility through earned media, expert commentary, owned reports, podcast appearances, first-party newsletters, paid media, employee advocacy and partner content.

Monitor, measure and iterate: Track change and measure impact over time so visibility improvements are evidenced, not assumed.

Employee training: Ensure everyone in your organisation has the understanding, knowledge and tools to work with AI intentionally and effectively so your message lands and you deliver the outcomes you seek.

If you’re not sure how AI systems and algorithms are representing your organisation right now, that’s the right place to start. 

Greenhouse works with communications teams to audit their current algorithmic visibility, sharpen their narrative and build a strategy that ensures their story is told accurately  by people and machines alike.