AI search is changing how customers, business buyers, and other audiences discover brands, compare products, evaluate experts, and make decisions.
In a webinar on August 5, 2026, titled “Is Your Brand Optimized for AI Search?,” Greg Abel, CEO and founder of Abel Communications, and Brandi AI CEO and co-founder Leah Nurik explained how organizations can improve their visibility in AI-generated answers by strengthening the credible evidence available across company websites, earned media, reviews, social channels, industry publications, and original content.
Their central message for marketing, public relations, content, and digital teams was that AI visibility depends on more than traditional search rankings. Brands must build a consistent, authoritative public narrative that AI systems can understand, verify, summarize, and cite.
Key Takeaways
- AI search has shifted brand discovery from blue links to synthesized answers. Buyers can now compare companies, products, and experts within AI-generated responses before visiting a website or contacting a sales team.
- AI visibility depends on credible evidence across the public internet. Company websites, earned media, reviews, industry publications, social content, videos, podcasts, and user-generated content can all influence how AI systems describe and recommend a brand.
- Earned media plays a renewed role in brand visibility. Greg Abel emphasized that authoritative third-party coverage gives AI systems credible sources to cite and can strengthen a brand’s reputation, expertise, and market position.
- Marketing teams must collaborate across disciplines. Leah Nurik argued that public relations, content, digital marketing, product marketing, social media, and brand strategy must work together to create a consistent and well-supported narrative.
- Human expertise produces more distinctive and durable content. Abel and Nurik recommended using AI to support research, organization, proofreading, and optimization while keeping firsthand experience, original insights, customer stories, and final editorial judgment in human hands.
How Has AI Search Changed the Buyer Research Process?
AI answer engines have shifted online research from a link-based process to a conversational, synthesized experience. Instead of reviewing a page of search results and visiting several websites, users can ask detailed questions through platforms such as ChatGPT, Claude, Perplexity, and Google AI Overviews and receive a consolidated response.
Traditional search placed most of the research burden on the user. A potential customer entered keywords, reviewed several links, visited multiple websites, and formed an opinion based on the information collected.
AI answer engines now perform much of that synthesis before the buyer reaches a company website, contacts a sales representative, or enters a store.
Nurik described the change as a shift from syntax to semantics. Traditional search largely matched keywords with webpages. AI systems instead interpret meaning, context, intent, and narrative before presenting an answer.
“The algorithm is meaning—like meaning and narrative—and that’s coming from different places than what would return the blue link,” Nurik explained.
A business buyer may no longer search simply for the “best customer relationship management software.” The buyer can describe the company’s size, location, industry, budget, technical requirements, and business goals. An AI platform can then recommend products, compare available options, and explain why each one may or may not fit the buyer’s needs.
As Nurik put it, “It’s really like 1997 for the web.” Marketers are still near the beginning of a major shift in how information is discovered, synthesized, and used to guide decisions.
What Evidence Influences AI-Generated Brand Answers?
AI-generated answers are assembled from evidence found across the public internet. The sources influencing an answer may include company websites, earned media coverage, customer reviews, social media posts, videos, industry publications, forums, blogs, podcasts, and other user-generated content.
“Every AI answer is a story assembled from evidence,” Nurik said.
Brands therefore need to communicate their expertise, positioning, and value consistently across multiple credible channels. A claim that appears only on a company website may have less influence than a message independently supported by media coverage, customer reviews, industry commentary, original research, and third-party sources.
Consistency also affects the accuracy of AI-generated answers. When reliable sources repeatedly support the same facts and messages, AI systems have stronger evidence for understanding how a company should be described. Contradictory claims, weak proof, or negative customer experiences can create a different narrative.
“There are no tricks to this,” Nurik said. “You need to be a good, authentic, credible brand, and you need to fulfill your promise.”
Why Earned Media Matters More in AI Search
Earned media gives AI systems independent evidence that can validate a company’s expertise, leadership, products, and market position. Credible reporting, interviews, contributed articles, research coverage, and expert commentary can all influence whether a brand or individual appears in an AI-generated response.
Abel noted that this environment has renewed the importance of media relations and third-party validation. Search engine optimization previously gave digital marketers several ways to build links and improve webpage rankings. AI search places greater emphasis on the credibility, relevance, and authority of the sources supporting a brand’s story.
“What was old is new again,” Abel said. “Media relations matters. It matters even more because, as the AI engines get even better, what is going to be most important are credible sources to cite.”
A stronger media strategy begins with identifying the reporters, publications, podcasts, and industry sources that influence a specific market. Organizations can then develop relationships, offer useful expertise, release original research, contribute informed perspectives, and create news that authoritative sources have a reason to cover.
Media quality matters as much as media volume. Brands should evaluate whether the publications they pursue are accessible to AI systems, frequently cited in relevant answers, and trusted by the audiences they need to reach.
AI Visibility Requires Collaboration Across Marketing Teams
Improving AI visibility is not solely the responsibility of search engine optimization, public relations, content, social media, or product marketing. The strongest programs align these disciplines around a shared understanding of the questions buyers ask, the messages the organization wants to communicate, and the evidence needed to support those messages.
PR teams can generate authoritative third-party validation. Content teams can publish useful, clearly structured resources. Digital teams can make information technically accessible and easy to navigate. Product marketers can clarify differentiation, customer value, and competitive positioning.
“The brands that get the most success are the ones that have that collaboration,” Nurik said.
This collaboration becomes especially important because much of the customer journey may now occur inside an AI-generated conversation. Buyers can compare providers, investigate product capabilities, assess reputations, and narrow their options before visiting a company’s website.
Traffic from AI discovery may be smaller in volume than traditional search traffic, but those visitors may arrive with stronger intent and a clearer understanding of the available choices. Marketing teams must therefore measure more than clicks. Relevant indicators can include brand mentions, citation frequency, sentiment, competitive positioning, message accuracy, and the sources shaping AI-generated recommendations.
Human Expertise Creates More Distinctive, Citable Content
AI can support research, outlining, proofreading, analysis, and content optimization, but it should not replace the original knowledge and experiences that make content valuable. Firsthand expertise, customer stories, proprietary research, informed opinions, and lessons from real projects give an organization material that cannot be reproduced through generic AI synthesis.
“Our data shows that human-driven content lasts longer, and it is stickier, and it will get read at a much higher rate,” Nurik said.
AI-generated content typically synthesizes ideas that already exist. Without meaningful human direction, it may repeat common observations rather than contribute a new perspective, verifiable experience, or original finding.
Abel recommended beginning with stories that only the organization, its employees, or its customers can tell. Useful source material can include a problem solved for a client, an unexpected project result, an operational lesson, a customer outcome, or an observation developed through years of industry experience.
“The thing that an AI won’t be able to do is tell a story about something that you experienced or your customer experienced,” Abel said.
AI can then help organize, proofread, or improve the readability of that material while the human author remains responsible for the facts, meaning, voice, and final judgment.
Frequently Asked Questions
How can marketers identify the AI search prompts that are most important to their brand?
Marketers should begin with the real questions customers ask during research, comparison, and purchase decisions, then expand those questions to reflect audience roles, goals, industries, locations, budgets, and buying stages. Sales calls, customer interviews, support requests, search data, and competitive research can all reveal high-intent prompts worth monitoring. Brandi AI addresses this need by helping organizations track the questions buyers ask across AI answer engines and evaluate whether, where, and how the brand appears in the resulting answers.
How often should a company measure its visibility in AI-generated answers?
Companies should measure AI visibility on an ongoing basis because model updates, new content, media coverage, competitor activity, and shifts in cited sources can change how a brand appears over time. Reviews are especially important after product launches, messaging changes, major campaigns, reputation events, or significant AI platform updates. Brandi AI helps teams monitor these changes across prompts, platforms, competitors, audiences, and markets so they can see whether their visibility and positioning are improving.
Which metrics best show whether a brand is gaining visibility in AI search?
The most useful metrics include brand mention frequency, citation frequency, share of voice, recommendation rate, sentiment, message accuracy, source influence, and competitive positioning. These measures show not only whether a brand appears, but also how AI systems describe it and which sources shape that description. Brandi AI brings these signals together so marketing, public relations, content, and digital teams can measure performance beyond website traffic and traditional search rankings.
How can marketers connect AI visibility efforts to campaign performance and business outcomes?
Marketers should establish a baseline before launching a campaign, then track whether new content, earned media, reviews, or thought leadership lead to more mentions, stronger citations, improved sentiment, or more accurate positioning in relevant AI answers. Teams can then compare those changes with qualified traffic, lead quality, demo requests, and other business indicators. Brandi AI helps create that connection by showing which sources and marketing activities are influencing AI-generated answers and how brand perception changes over time.
How Does Brandi AI Recommend Building an Evidence-Based AI Visibility Strategy?
Brandi AI recommends that brands begin by identifying the questions their audiences ask, evaluating how they currently appear in AI-generated answers, and determining which sources influence those responses.
According to Brandi AI, a practical strategy may include publishing original research, maintaining a useful company blog, strengthening media relationships, developing expert thought leadership, earning customer reviews, producing videos or podcasts, and communicating important facts consistently across owned and independent sources.
Brandi AI also advises organizations to examine whether AI answers mention the brand, cite its website or third-party coverage, describe its differentiators accurately, and position it positively against competitors. Those findings can help marketing, public relations, content, and digital teams determine where stronger content, clearer messaging, additional proof, or new media outreach is needed.
From Brandi AI’s perspective, AI search optimization is not a one-time technical tactic. It is a long-term strategy for building a coherent, credible, and well-supported public record.
Brands that consistently communicate authentic expertise across owned, earned, and third-party channels will be better positioned to appear in AI-generated answers and influence the stories those answers tell.
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Webinar Transcript: Is Your Brand Optimized for AI Search?
August 5, 2026
Presenters
Greg Abel: CEO and founder, Abel Communications
Leah Nurik: CEO and co-founder, Brandi AI
How Search Is Changing
Greg: Search has shifted from Google presenting a list of links to Google and other platforms providing AI-generated narrative responses. People are also searching directly through tools such as ChatGPT and Claude.
The question for marketers is how to ensure that their brands, organizations, and experts appear authoritatively within those narrative answers.
AI-generated answers are influenced by citations and third-party validation. Relevant sources can include a company’s website, social media, media coverage, reviews, and other public content.
In the past, someone searching for a thought leader might have seen a link to Wikipedia, a company website, or a Reddit thread. Today, an AI platform may provide a complete explanation of what a thought leader is and cite the sources it used.
The same applies to consumer searches. Someone looking for an oil change in Baltimore may receive a conversational recommendation based on the type of car, location, preferred travel radius, reviews, and other factors rather than simply receiving a list of nearby businesses.
Leah: Traditional search gave the buyer a blue link. The buyer remained in control of the research process and might click multiple links before deciding what college to attend, which software to purchase, or which consumer product to buy.
AI search now provides a synthesized answer. That fundamentally changes the algorithm and the customer experience.
I think about the difference as syntax versus semantics. Traditional search relied heavily on matching keywords. Marketers used keyword optimization to help a brand appear on the first page of Google.
AI systems focus more on meaning and narrative.
“The algorithm is meaning—like meaning and narrative—and that’s coming from different places than what would return the blue link.”
Our data shows that AI citations can come from earned media and other unpaid sources. They may come from review sites, major media outlets, independent blogs, or individuals whose content has become authoritative within a market.
The visibility and lead-generation landscape has completely shifted for marketers. Responding effectively requires a new way of thinking.
Why AI Visibility Requires Marketing Collaboration
Leah: Digital teams traditionally held primary responsibility for online visibility. They managed search engine optimization, website performance, keyword research, and content intended to place a brand on the first page of Google.
AI visibility requires close collaboration across multiple marketing disciplines.
Digital teams responsible for technical structure and traditional search engine optimization need to work with content teams to produce meaningful, well-constructed, narrative-driven material. Content teams must also work closely with public relations.
Public relations often operated independently under the brand function. Our data now shows that PR plays a leading role in determining whether a brand is mentioned in an AI response and what story the answer tells about that brand.
PR can influence brand visibility, mention rates, thought leadership, and the narrative presented in AI-generated answers.
Greg: That is especially exciting for people who work in media relations and communications.
PR campaigns and media coverage have always contributed to search visibility. However, digital marketers previously had several ways to create backlinks and improve search performance through the tactics available at the time.
That sometimes reduced the perceived importance of third-party validation through media coverage.
AI answer engines have renewed the importance of credible third-party sources. What was old is new again. Media relations matters, and it may matter even more as AI systems become better at distinguishing authoritative sources from low-value content.
Brands should examine their media strategies, the news they announce, their owned-content programs, and the journalists who cover their markets. Relationships with credible media sources will influence agentic research and AI-generated answers.
Leah: Our data supports that. Earned media gets cited and can influence brand mentions, although the impact depends on the publication and the market.
Product marketing also plays an important role in AI visibility and competitive differentiation.
Marketing leaders should structure their organizations around collaboration. They need people who can think critically, operate beyond the boundaries of one discipline, translate abstract concepts, and tell coherent stories.
Traditional disciplines that were sometimes measured through soft or indirect outcomes can now be connected to harder metrics. Marketers can begin measuring how media relations, content, and social activity influence AI citations, mentions, positioning, and sentiment.
That creates new key performance indicators for reporting to executive leadership. Marketers have always understood the value of earned media, content, and social engagement. AI visibility gives them additional ways to demonstrate that value.
Why Marketing Silos Limit AI Search Performance
Greg: Why has the structure of marketing organizations become such an important issue in your work?
Leah: The brands achieving the strongest results are the ones whose teams collaborate.
Traditionally, many organizations separated brand marketing from growth marketing. Growth teams might own digital, paid media, and affiliate marketing. Their priorities were driving website traffic and improving conversion rates.
AI has changed what happens before a buyer reaches a website or enters a store. Buyers can now conduct extensive research within an AI platform, which means they may be significantly more qualified when they finally contact a company.
In the software market, companies historically generated demo requests and then struggled to reach those prospects. Conversion from a demo request to an actual conversation often hovered around 15%.
Some software companies are now seeing more than 40% of demo requesters agree to a conversation. Buyers have already conducted more research before submitting the request.
Under the traditional model, a buyer might click 10 blue links and request 10 demonstrations, even though that buyer did not have time to attend all of them.
Now, much of the funnel takes place inside the AI chat experience or AI overview. A company may receive less traffic, but the visitors who arrive may be much more qualified and convert at a higher rate.
Narrative development, earned media, content, and other AI visibility activities help shape the answer before the buyer reaches the company.
Growth marketing, brand, PR, product marketing, and content teams need to communicate with one another. Marketing leaders must foster that collaboration if they want to succeed in the next phase of search and buyer discovery.
Greg: One of the strongest takeaways is that organizations should not operate in silos. Many factors influence the outcome, and no single team owns AI visibility.
Leah: “It’s really like 1997 for the web.”
Most people experience only one or two major paradigm shifts during a career. The rise of AI search is as significant for marketing, earned media, and buyer discovery as the early development of the commercial web.
We are still at the very beginning.
Content and Media Strategies for AI Search
Greg: Many of the tactics brands need today would also have been good advice 20 years ago.
Organizations should consider conducting proprietary research around an important industry issue. That research can generate media coverage, website content, social content, and ongoing commentary.
Brands should apply for relevant awards and publicize meaningful recognition. Awards can become another form of evidence supporting authority and credibility.
Organizations should also maintain useful blogs. A company expert should regularly share informed perspectives, particularly because buyers may conduct substantial research before visiting a website or contacting a representative.
A smarter media strategy is equally important.
A small organization can begin by identifying the three reporters most likely to cover its company or industry. It can then reach out, build relationships, and communicate useful ideas.
Larger organizations may need ongoing content distribution, regular announcements, expert commentary, and a properly maintained online newsroom.
How Prompts Differ From Traditional Search Keywords
Leah: AI research begins with prompts. Traditional search relied on short search terms, while people give AI systems much more detailed and directional guidance.
A traditional search might have been “top 10 places to visit in Ireland.”
An AI prompt could be: “I am planning a family vacation to Ireland with two children, ages 20 and 17. One enjoys physical activities, while the other loves history. Where should we go?”
That is a very different query.
Identifying the prompts most likely to be used within a company’s market is essential. Prompt research is not the same as traditional keyword research.
A company wants its brand, institution, or organization to be mentioned in the response. It may also want its website, earned media, affiliate content, influencer coverage, or other sources to be cited.
The answer itself is equally important. A blue link did not communicate much about positioning or sentiment. An AI-generated answer tells a story.
Marketers must ask:
- How does the answer position the brand?
- Is the description accurate?
- Is the sentiment positive or negative?
- Does the answer communicate the intended differentiators?
- Which sources influence the narrative?
Why Every AI Answer Is a Story Built From Evidence
Leah: “Every AI answer is a story assembled from evidence.”
AI systems evaluate whether the available information is credible, authentic, and supported in multiple places.
Brands must tell their stories consistently across multiple channels to improve the accuracy of AI-generated answers and strengthen the narrative associated with the company.
There are no tricks to this. Any vendor that presents AI visibility optimization as a short-term tactic rather than a long-term strategy will not future-proof a brand’s narrative.
Throughout my career, I have advised brands to be mission-driven and authentic. They should bring products to market that genuinely affect people’s lives, fulfill their promises, and communicate their stories consistently.
AI rewards those qualities.
A company cannot make an unsupported claim in one corner of the internet and expect reviews, customer experiences, or other public evidence not to contradict it.
Brands must be authentic, credible, and able to fulfill their promises.
Greg: Authenticity is important, as is telling stories that support the messages a brand wants to be known for.
Those have always been core marketing principles. AI search may reduce the ability of a company to dominate a response simply by spending more money on paid placement.
In that sense, the environment may become more merit-based.
Leah: Paid media will still be involved in various ways. Earned media will remain important because it provides credible sources.
Publishers may develop new ways to monetize their content, and AI platforms will continue experimenting with advertising. The important distinction is whether advertising remains clearly labeled or begins influencing the substance of generated answers.
AI platforms currently benefit from a high level of consumer trust. They will need to decide whether to preserve that trust by separating advertising from organic answers.
Marketers will also need to monitor how AI models treat paid content, publisher partnerships, affiliate material, and earned media.
Model behavior will continue changing. Brands need access to data showing which sources models consider credible, how those sources are weighted, and how updates affect visibility.
How Brands Can Identify Influential Sources
Greg: Our firm works with thought leaders and subject-matter experts. These campaigns focus on elevating individuals as authoritative voices within their industries.
The expert might be a patent attorney, engineer, executive, or specialist in another field. The goal is to create an ecosystem of content that helps the person appear when audiences search for ideas, references, services, or experts.
Those appearances need to come from authoritative and citable sources.
How can technology help brands determine where to focus?
Leah: It requires a coordinated effort across owned content, earned media, and other public sources. The story must be coherent, credible, authentic, and consistent.
Brands should examine where AI systems obtain answers for the questions buyers ask within their market.
For example, a customer relationship management company could analyze hundreds of buyer-discovery prompts and identify the domains most frequently cited in the resulting answers.
The sources might include:
- Competitor websites
- Social media
- User-generated content
- YouTube
- Podcasts
- Product-review platforms
- Industry media
- Independent publications
That information helps marketers decide where to focus.
A company may discover that it needs a stronger podcast or YouTube strategy. It may need to participate more effectively in relevant online communities, develop an influencer strategy, or strengthen media outreach.
PR teams can also use source data when deciding where to place contributed articles. A traditional media list may include several respected publications, but some may be more influential in AI-generated answers than others.
Product-review data may show that one review platform is cited more often than another. Marketers can use that information to prioritize their programs.
Measuring Brand Sentiment in AI Answers
Leah: Marketers have used sentiment tools for years, but many tools provide a single score without clearly explaining what drives it.
AI visibility analysis can help brands examine what they are known for positively and negatively within specific areas, such as customer service, value, reliability, or product performance.
Teams can review the underlying answers and compare their positioning directly with competitors.
That is valuable for product marketers, communications teams, brand strategists, and anyone working on messaging or competitive intelligence.
Organizations can also track whether content and communications activities influence sentiment over time. Those activities might include a blog post, press release, earned media coverage, executive commentary, or a reputation campaign.
A company facing a crisis or broader reputation challenge can evaluate whether public perception improves and which cited sources are influencing that change.
Why Human-Led Content Performs Better
Leah: Marketers are receiving an overwhelming amount of advice about AI visibility. They are told that most citations come from earned media, that they need schema, or that they must structure content in a particular way.
The volume of conflicting advice can make it difficult to know what to do.
“I want to say this very clearly, like without any hesitation, that generating content with AI is not the way to future-proof your brand.”
“Our data shows that human-driven content lasts longer, and it is stickier, and it will get read at a much higher rate.”
Greg: You have created a software platform that helps brands improve their performance in AI search, yet you also advise companies not to use ChatGPT or Claude to write all their content.
You might use AI to develop an outline, but a human should write the final piece.
Can AI systems identify content produced by AI, and does that affect performance?
Leah: There are recognizable patterns in AI-generated content.
More importantly, AI synthesizes what already exists. It cannot independently develop a genuinely original perspective or idea.
If AI can only synthesize existing material, how does a brand stand out?
The marketer, product expert, communications professional, or brand strategist brings the unique perspective. The competitive advantage comes from that person’s ideas and the quality of the story used to communicate them.
Brandi AI came from brand storytelling, earned media, communications, and content rather than from a traditional search engine optimization discipline.
Our data shows that human-generated content remains influential longer and produces stronger results.
“There are no tricks to this.”
Brands should be honest, authentic, and forthcoming in the content they produce and the earned media they pursue. That approach helps protect the brand against future model and algorithm changes.
Greg: AI cannot independently tell a story about something a person or customer experienced unless someone provides that experience.
Brands should lean into anecdotes, human impact, customer feedback, project outcomes, and lessons learned from trying something in the real world.
As more AI-generated content appears online, many companies will be tempted to use AI simply because they do not want to write the blog post themselves.
What those companies will lose are the personal and human details that make content meaningful.
AI can help inform or improve the presentation of a story, but organizations should not turn content creation over to it completely.
Using AI to Proofread and Improve Human Writing
Greg: Can marketers use AI to proofread or improve writing without the content being treated as AI-generated?
Leah: Yes. The appropriate workflow begins with original, human-created content.
An optimization tool can review that material and suggest ways to improve readability while preserving the author’s perspective and ideas.
Problems arise when AI begins changing data, introducing unsupported claims, hallucinating information, or replacing the writer’s voice with recognizable patterns and disconnected thoughts.
My father was a college professor, and he often said, “Unclear writing is a function of unclear thinking.”
AI can support clearer presentation, but it should not replace the thinking behind the writing.
The human author must remain in control.
Paywalls and AI Citations
Greg: Earned media is valuable, but many articles sit behind paywalls. Can AI systems read and cite that coverage?
In many cases, paywalled content cannot be fully read by the models. Headlines and limited information may still be visible, but access depends on the publication and its agreements with AI companies.
Leah: Some publishers are forming partnerships that allow AI companies to access portions of their content.
That can affect media strategy. A publication may carry significant prestige but have limited visibility within AI-generated answers because its content is inaccessible to the models.
Another publication may be more frequently cited because its content can be read through a licensing or distribution agreement.
PR professionals should continue pursuing prestigious media coverage, but they should also evaluate which publications appear in AI citations for their markets.
Source visibility can help inform decisions about exclusives, contributed articles, media targets, and content partnerships.
Balancing Authenticity With Optimization
Greg: Some marketers see optimization and authenticity as conflicting goals. Should someone write a LinkedIn post to generate more clicks and shares or tell a real story that matters to that person and the work?
The process should begin with authenticity.
Start with a problem solved for a client, an insight developed through experience, an important conversation, or an outcome from a project. Those are stories an AI system could not independently know.
AI may then help improve the structure or presentation of the personal story.
Leah: Authenticity and optimization are not inherently contradictory.
The problem arises when someone gives a generic tool a piece of content and asks it to “optimize this for GEO” without protecting the original vision, facts, and voice. The tool may substantially change the material or introduce unsupported information.
A better approach is to tell the authentic story first and then optimize it for readability using clear principles.
The human remains in control and decides which suggestions to accept.
There is a significant difference between asking AI to draft a post from nothing and using a tool to improve the readability of an authentic, human-created story.
Final Recommendations for Brands
Greg: Blogs are becoming more important again. They never truly disappeared, but many organizations reduced their investment in them.
Leah: Blogs went away from some companies’ budgets, but they did not lose their underlying value. Now they are returning with force.
As organizations plan their marketing budgets, they should invest in owned content production as well as earned media.
Brands should identify the prompts that matter within their markets, determine how they appear in AI-generated answers, examine which sources influence those answers, and understand the sentiment and positioning associated with their names.
AI visibility is not the responsibility of one team or one channel.
It requires coordinated work across public relations, content, digital marketing, product marketing, brand, social media, customer experience, and executive thought leadership.
The strongest strategy is not based on tricks. It is based on creating valuable products, fulfilling the brand promise, communicating authentic expertise, and building a consistent body of credible evidence across the public internet.