For three decades, search has operated on a simple premise: match queries to pages and rank them.  But the way that matching happens has changed drastically over the past three decades and that evolution is now reshaping how brands are discovered, summarized, and represented.  History of Search Search didn’t become intelligent overnight. It evolved in stages – each building on the last moving from simple word matching to contextual understanding and now generative synthesis. 

• 1990 – Keyword Matching
  Search engines crawled the web and ranked pages based on exact keyword overlap and word proximity. If the keywords appeared, the page ranked higher.
  
  Search was literal. It matched text, not meaning.
  
  
• Early 2000s – Statistical Relevance (TF-IDF)
  Algorithms began weighting terms by importance, measuring how frequently words appeared in a document relative to the broader web.
  
  Search became smarter about importance — but still didn’t understand intent.
  

• Late 1990s–2000s — Authority Signals (PageRank)
  PageRank introduced link analysis. Instead of evaluating just the words on a page, search engines measured how many other credible sites linked to it. Links acted as votes and authority became quantifiable.
  
  Search evolved from “find the words” to “find the most authoritative page containing the words.”
  
  
• 2010s — Context and Personalization
  Search engines incorporated user signals and contextual understanding to better interpret intent and refine results. A query like “dog park” automatically returned nearby parks — even without specifying a city.
  
  Search started interpreting intent — not just matching text.
  
  
• 2019 and Beyond — Transformer Models (BERT, MUM)
  Language models enabled deeper semantic understanding of natural language queries and relationships between words, improving intent detection and relevance — especially for conversational queries.
  
  Still, the interface remained the same: a ranked list of links.
  
  
• 2023–Present — Generative AI and LLM-Based Search
  Instead of returning links, AI-powered search systems now generate answers. Using Retrieval-Augmented Generation (RAG), they interpret the query, retrieve semantically relevant content, and synthesize a response grounded in that material.
  
  Search moved from retrieval to representation.

Writing Content for LLMs
Content quality has become increasingly important – both in the words themselves and how stories are structured.  AI systems don’t “read” content the way humans do;  they scan structure and relationships within the underlying HTML to understand hierarchy and meaning. Clear H1/H2/H3 headers, strong header-body relationships, and self-contained sections aren’t just good editorial practice – they’re signals that help machines correctly interpret what content is about and increase the likelihood it’s retrieved in the first place. 

LLMs process text by turning words into numerical representations (vectors) and mapping relationships between them using mechanisms like “attention.” The closer the wording mirrors how people naturally ask questions, the more likely the content is to align with the prompt and be incorporated in the generated answer. The goal is to anticipate not just the first question a user might ask, but also the likely follow-ups. These systems are conversational by nature, and content that addresses second- and third- order questions has a stronger chance of appearing multiple times within a single thread.  Authority is another major factor. Over time, AI models build an understanding of organizations based on consistent patterns across the web. When a company’s name, leadership, and initiatives are accurately reinforced across multiple credible sources, the model forms stronger associations and deeper trust – and is more likely to surface that organization in relevant responses.

Distribution and Signals of Visibility
This is where 3BL’s role becomes especially relevant. Distribution through 3BL places an organization’s content on a trusted third-party platform, creating the cross-source reinforcement that strengthens an AI model’s association between a brand and its key narratives – a strategic middle ground between a traditional press release and owned channels.  While direct attribution data in generative environments is still emerging industry-wide, there are measurable signals that this approach is working. 3BL tracks when major AI crawlers access client content, and that activity is consistent – an early but meaningful indicator that distributed content is entering the knowledge system these models draw from. 

Looking ahead, 3BL’s research team is actively developing analytics built for the generative era: visibility scoring, mention rate, share of voice within AI responses, and query intent attribution. The organizations investing in structured, well-distributed content now are the ones most likely to show up when it matters.  In a world where AI synthesizes the answer before a user ever clicks a link, visibility belongs to the organizations that built their presence before the question was asked.

(Image: Solen Feyissa/Unsplash)

Generative artificial intelligence and large language models (LLMs) are transforming how people access information and changing which sources are viewed as trusted enough to be shown. Traditional SEO, built by page rankings and keyword optimization, has expanded into AIO (AI Optimization) and GEO (Generative Engine Optimization), where the goal isn’t just to rank but to be retrieved, understood and cited by AI systems in their chats with human users. Where people once searched their questions in Google and clicked on a handful of websites to find the best answer, they’re now asking AI chatbots to do the searching for them and deploying AI agents to complete increasingly complicated tasks. Beyond simply finding answers, people are using agents to build decks, write code, automate customer experiences and organize data, with little to no human involvement. 

As amazing as that is, it’s a big change for communicators looking to maintain brand visibility online. Gartner forecasts up to a 25% drop in traditional web searches this year compared to two years ago, and up to a 50% hit in organic search traffic by 2028, as more people embrace chatbots over search engines. 

The challenge for communicators is ensuring your organization’s expertise is referenced within AI-generated answers, rather than being buried behind them. This is less about manipulating algorithms than about earning trust and building retrievability.  At 3BL, we’ve helped more than 1,500 companies and nonprofits tell their stories online — from the early days of Twitter chats and live Google Hangouts, to engineering for AIO and GEO today. While we do the work in the back-end to make sure our clients’ content is well-structured and readable to AI systems, here’s what we tell them about how to format their articles, press releases, and other content for AIO and GEO.

Inside the Machine: How LLMs actually find and use information 
To optimize for AI, we need to understand how AI systems “think,” or how they find and process information. At the heart of AI visibility is RAG: Retrieval-Augmented Generation. Here’s what that means, in plain English:

1. Retrieval: AI-powered search experiences pull relevant and recent information from trusted sources such as researchers, nonprofits, government websites, and news providers like 3BL.
2. Augmentation (Grounding): The system blends what it retrieved from the first step with its model training data so the response is based on real source material rather than memory alone.
3. Generation: The model writes an answer for the user based on a mix of retrieved content, pretrained knowledge and the user’s query.

In other words, visibility in AI comes down to:

Retrievability + Authority + Structure. 
This is what’s often referred to as “RAG-ready content.” It’s legible to both machines and people. The good news for communicators is that a lot of the things we already do to tell great stories are also good for AIO and GEO, and a few intentional tweaks can make a big difference. 

Writing for AIO and GEO: Designing content that works for both humans and machines
On today’s internet, you’re really writing for two audiences: the human user (i.e., your target audience) and the AI chatbot that is more and more likely to be assisting them. Think of your content as two-layered: It’s organized in a way that can be parsed and referenced by AI systems, but it still reads naturally to people.  

Writing for people is second nature to communicators, but what about writing for robots? It may sound foreign, but it’s not as different as you may think. Checking these six boxes can help. 

Structure your content with intention. 
AI systems see the headline, description snippet and headers as key pieces of information to determine how your content can be used to answer people’s questions and perform tasks.  Lead with the answer or most important point first, then add context, citations, examples and storytelling that connects with the reader while still being easy for AI systems to parse.

• Your headline should clearly communicate what the content is about in less than 60 characters. 
• Use the description to add context about why this topic matters and why your organization is positioned to speak about it.  
• Within the content, the first header (H1) should mirror your headline, using words that communicate authority or nod toward search intent where relevant (i.e., “how,” “why,” “answered,” “explained”). 
• Secondary headers (H2, H3) help break up your content and transition from one topic to the next, which is more readable to robots than a long block of text. 
• Keep each section modular, using short and self-contained sections with one clear idea to avoid long, blended paragraphs covering multiple topics. 
• Add descriptive alt text to images to help visually impaired readers and AI systems interpret the visuals you use. 

Formats like Q&As and lists are also more likely to rank in AI search because they speak directly to people’s questions and provide valuable information in response. For example, consider reformatting the next executive op/ed on your content calendar from a long thought piece into a Q&A that showcases their expertise in a digestible and discoverable way, or talk about your next big program launch in a list that’s oriented toward solving problems. 

Add substance to improve visibility.
Including quotes, statistics and links to sources not only makes your content more valuable and trustworthy to human readers, but it can also increase visibility in AI queries by up to 40%, according to an analysis of 10,000 real searches. When citing a source or mentioning an announcement, be clear about time orientation (i.e., “as of 2025”) to inform readers and signal recency to AI systems. 

Use a clear and authoritative tone. 
An eye toward clarity in your writing helps engage human audiences and showcases answer density to AI systems. 

• Clearly define the industry or topic-specific terms you use, and break down steps explicitly to help both humans and robots follow along. 
• Avoid the passive voice (subject, past participle, object, i.e., “mistakes were made”) in favor of declarative statements (subject-verb-object, i.e., “cats are mammals”).
• Where your organization has authority to speak on a topic, use an authoritative tone to communicate this to your audience as well as their AI assistants. 

Mention your brand and focus topic, but not too much. 
Just like with SEO, keyword optimization is helpful, but keyword stuffing is not. Including explicit details like company names, platforms, frameworks, products, people, and locations helps give valuable context to both human and machine, but mentioning keywords too much can be a red flag to AI systems. Aim to keep your core keywords below 3% density (about five mentions in a 600-word article). This goes for your brand name, too. Mention it once in key areas like your headline, description, H1 header and first paragraph where applicable, but avoid overuse by incorporating synonyms elsewhere (i.e., “our,” “us,” “we,” “our team”). 

Foster content hubs to build credibility and brand association. 
Create “hubs” for the topics you’d most like to be known for, such as a page on your website, a category of your blog or a campaign in your 3BL newsroom.  Update these pages as you publish relevant content, and link back to them often so crawlers associate your brand with these topics and begin to see you as a trusted source for information about them. 

Publish to trusted domains.
As well as on your own website, publishing to trusted domains and syndicating to distribution networks like 3BL’s is shown to improve AI visibility. AI systems read laterally across the internet, meaning the more your content is out there and in trusted places, the more likely it is to be cited in chats. 

The bottom line
Writing for AIO and GEO isn’t all that much different from writing good content for human audiences. But it does call for communicators to be intentional about clarity and structure in their writing and strategic in how and where they publish.  You can’t buy placement in AI answers (yet), but you can earn it by providing valuable information often and proving your organization is a source AI systems can depend upon for answers.  Still have questions about writing for AIO or how 3BL can help? Get in touch below. 

(Image credit: Mariia Shalabaieva/Unsplash)

The “summer slowdown” can be a challenging time for organisations, whilst staff are on vacation, customers shift buying habits and attention spans can be limited. It can also be a time to reassess strategies, reflect on the first half of the year and plan for the next half.  But you still want to communicate your stories and company initiatives to maintain visibility and engagement through the slower months. We have a list of tips to help reach audiences who seek interesting and relevant content.

Maintain a consistent online presence by giving your initiative a permanent home on your website and announcing it on your blog. Also, post it on your socials and other owned properties. This helps keep your messaging clear and consistent whilst reaching different audiences.

Summer-friendly content is ideal for social media, such as creating short, engaging posts with eye-catching visuals or videos under 30 seconds to capture attention. Attention spans tend to be shorter in the summer, and also, people tend to be on the move, so you can use polls or ask a question to encourage quick responses that require little time commitment. Use a dedicated hashtag to create ongoing engagement and encourage employees to share posts – “The click-through rate on a piece of content is 2x higher when shared by an employee versus when shared by the company itself.”

What are your audience’s summer priorities? Try and match these to your “call-to-action.” For example, water availability could be a topic that’s important to your audience this time of year, so invite readers to join a water-saving challenge. Or, with temperatures rising, ask audiences for their tips to keep cool. 

Schedule posts ahead of time if you can, but also allow for spontaneity. This can maintain steady engagement whilst also allowing for tasks to be completed when teams are on vacation. Utilize newsletters and partner networks. Amplify your message and invite subscribers and collaborators to share your initiatives within their communities. Storytelling with testimonials, infographics, user spotlights, and case studies can emotionally connect your audience to the impact you’re making. Repurpose content. If you have longer articles, research, reports, video interviews, etc., break them down into smaller chunks to talk about a new or different angle. This also allows you to provide additional context or updates.

Host interactive experiences and user-generated content (UGC) campaigns. Things like photo contests, challenges, or quizzes related to your social impact cause encourage audience participation and creativity. Host live Q&A sessions, virtual events, or webinars. You may have more time or flexibility in the summer months to connect with your audience in real time, answering questions and sparking dialogue around your social impact goals.

Run summer-themed challenges or giveaways. This can encourage immediate participation and boost engagement

As AI tools become mainstream in both personal and professional use, large language models (LLMs) like ChatGPT, Gemini, and Claude are becoming go-to resources for learning, ideation, and even ESG strategy. But what exactly are people asking about sustainability?

Here’s a breakdown of the most-searched sustainability-related prompts, and what they reveal about public and corporate priorities.

1. “What is ESG and why does it matter?”

Why it’s trending: As new regulations like the EU CSRD and SEC climate disclosure rules go into effect, professionals across sectors are seeking quick, clear explanations of ESG — environmental, social, and governance factors—and how they relate to business risk and reputation.

✅ LLM tip: Most LLMs now deliver tailored ESG definitions by sector (e.g., ESG for finance vs. manufacturing), helping non-experts get up to speed fast.

2. “How can I make my business more sustainable?”

Why it’s trending: SMBs and internal departments want actionable ideas that go beyond plastic straws or Earth Day campaigns. They’re looking for help with energy efficiency, sustainable sourcing, and waste audits.

✅ LLM tip: LLMs are being used to generate checklists, policy templates, and tailored roadmaps to reduce carbon footprints or improve supply chain transparency.

3. “Examples of successful corporate sustainability initiatives.”

Why it’s trending: Professionals are researching case studies to benchmark their own programs or pitch ideas to executives. Interest is highest in net-zero strategies, circular business models, and DEI-linked community investments.

✅ LLM tip: When prompted well, LLMs can surface concise summaries of sustainability programs from companies like Patagonia, IKEA, Unilever, or Microsoft — without the greenwashing.

4. “How do I measure sustainability impact?”

Why it’s trending: Measurement is a sticking point for many organizations. Users are asking about carbon accounting tools, materiality assessments, and ESG KPIs.

LLM tip: Advanced prompts can now generate sample metrics, GRI-aligned reports, or comparisons of frameworks like SASB vs. CDPgreat for sustainability analysts and CSOs.

5. “AI for climate solutions”

Why it’s trending: There’s a growing hunger for insight into how AI can accelerate climate action — whether through smart grid optimization, emissions modeling, or predictive analytics for climate risk.

LLM tip: Prompts about “AI for good” are popular in both the nonprofit and tech sectors. LLMs are helping users explore tools like satellite-based monitoring, regenerative farming algorithms, and carbon trading platforms.

LLMs are rapidly becoming sustainability co-pilots that are helping businesses translate big ESG goals into operational plans. But the quality of the output depends on the quality of the questions.

For CEOs, CSOs, and sustainability communicators, the rise of large language models (LLMs) like ChatGPT and Claude isn’t just a tech story — it’s a leadership tool. But knowing how to ask the right questions is where the magic begins.

Here are the top keywords and prompt strategies that sustainability leaders can use to turn LLMs into powerful, everyday partners:

✅ “Summarize this ESG report…”
Use LLMs to cut through dense reports or filings. Try:
“Summarize this 50-page ESG report into 5 key points for an executive audience.”

✅ “Generate social copy on…”
Speed up stakeholder communication with:
“Generate a LinkedIn post announcing our new emissions reduction target.”

✅ “Compare our sustainability goals to peers…”
Stay competitive with:
“Compare Unilever’s sustainability KPIs to ours using latest public data.”

✅ “Draft messaging for…”
Need consistent tone across formats? Prompt:
“Write a press release and a CEO quote for our biodiversity investment.”

✅ “Create a content calendar for…”
Let AI build structure for campaigns:
“Make a quarterly content plan for employee sustainability engagement.”

✅ “Explain this regulation like I’m 5…”
For decoding policy:
“Explain the new SEC climate disclosure rule for a boardroom audience.”

LLMs are most effective when used as co-pilots — not final decision-makers. The trick is giving clear, context-rich instructions. And as always, human review ensures tone, accuracy, and alignment.

Whether you’re optimizing ESG disclosures, planning content, or just brainstorming faster, prompt literacy is the new leadership skill.

Artificial intelligence is no longer just the domain of IT teams — it’s quickly becoming a must-have for forward-thinking sustainability leaders and CEOs. Large language models (LLMs) like ChatGPT can help organizations move faster, communicate more effectively, and stay ahead of evolving ESG demands.

Here’s how:

✅ Streamline ESG Reporting: LLMs can support sustainability teams by generating first-draft disclosures, summarizing regulatory guidance, or benchmarking peer reports — saving time while improving clarity.

✅ Accelerate Internal Comms & Training: From onboarding new employees on sustainability policies to answering everyday compliance questions, LLMs can act as internal knowledge assistants when fine-tuned with your company’s own data.

✅ Enhance Stakeholder Engagement: Whether crafting emails, talking points, or social media posts, AI tools can help your comms team tailor sustainability messaging across channels, keeping language consistent and impact-focused.

✅ Surface Strategic Trends: With the ability to analyze large volumes of ESG news, scientific literature, and regulatory updates, LLMs can flag risks and opportunities early — a critical advantage in a rapidly changing landscape.

Of course, these tools are only as good as the human leadership guiding them. CEOs and CSOs should think of LLMs not as replacements, but as scalable support for your mission — helping you spend less time chasing data and more time driving transformation.

Brands frequently grapple with identifying the right sources for content and understanding the preferences of their audience. 

Here are 3 tips on how you can source the best sustainability and social impact content: 

Look internally. 

Engage with different departments, from sustainability teams to community outreach programs, to uncover compelling stories and data that resonate with your audience. Additionally, tap into your company’s internal research and reports to highlight your progress and future goals in these areas.

Turn that LinkedIn post into a story.

LinkedIn serves as a place for executive leadership and employees to showcase thought leadership, share their accomplishments, and often boast about their company. It is also a goldmine for short-form content that can be turned into a larger piece of communication. Check out how Kimberly-Clark turned their LinkedIn post into a Flexible Media Release (FMR). 

Third-party media coverage. 

Keep track of articles, interviews, and reports from reputable sources highlighting your brand’s initiatives and achievements. By leveraging external media coverage, you can enhance the credibility of your message and showcase your contributions to a wider audience, strengthening your brand’s reputation.