- Why has brand positioning become a matter of digital survival?
- What is really happening to organic traffic with the arrival of AI Overviews?
- How does brand positioning really work in generative engines?
- What is tone of voice, and why do generative engines use it to recognize you?
- How do you build an AI-compatible Brand Voice Guideline document?
- What is semantic authority, and how do you build it in practice?
- Why everyone is talking about it now: AI market numbers in Italy for 2025-2026
- Which AI tools can produce visual content that stays on brand?
- 1. Midjourney
- 2. Adobe Firefly
- 3. Canva AI
- 4. Runway
- 5. Leonardo.ai
- How do you integrate AI into creative production without losing your brand identity?
- Where do you start to build solid brand positioning in the AI era?
- What are the most common mistakes that make a brand invisible to AI systems?
- How do you measure whether brand positioning in generative engines is working?
- Is it worth investing in brand positioning for generative engines now?
- How we see it at SHM
- Frequently Asked Questions
- Generative engines choose sources based on a brand's semantic consistency: a fragmented identity means invisibility in AI answers.
- Traffic from ChatGPT and Perplexity converts much better than traditional organic traffic: losing these citations has a real cost.
- Tone of voice, semantic authority and AI tools for visual content are the three pillars to work on right away.
- 76% of Italian SMEs have not yet invested in AI: those who move now build an advantage that will be hard to close.
Updated on October 18, 2026
When a user asks ChatGPT to solve a problem your company knows how to solve, does your name appear in the answer? If you haven't worked on your brand positioning from a semantic standpoint, the answer is almost certainly no.
Why has brand positioning become a matter of digital survival?
Generative engines don't return a list of sites: they pick a source and cite it. If your brand isn't recognized as a reference on a topic, it simply doesn't exist in that answer.
User search behavior has changed deeply. Traditional queries, built to get a list of results to browse, are giving way to systems that can analyze information and return concise, ready-organized answers. This shift also changes how a company must work on its brand positioning: while SEO remains important, businesses now need to be recognized by AI systems as relevant, reliable and consistent on specific topics.
When a user asks a chatbot about a product or how to solve a problem, the generative model selects information based on the connections it can build between brand, content and digital reputation. A company without a clear semantic presence risks being left out of answers or tied to categories that are too generic, losing visibility at a decisive stage of the buying journey.
Brand positioning thus takes on a technical dimension that involves brand strategy, semantic SEO, digital content and the management of corporate identity.
| Your situation | Urgency | why |
|---|---|---|
| Your brand doesn't appear in ChatGPT or Gemini answers | Yes High | You're losing qualified traffic that converts better than organic traffic |
| You publish content without a consistent editorial line | Yes High | AI systems can't classify you as an industry reference |
| You have a defined tone of voice, but it isn't applied to AI tools | Medium | You risk generic content that dilutes your brand identity |
| You already have a traditional SEO strategy in place | Medium | It needs to be integrated with semantic logic for generative engines |
| You produce in-depth, vertical content on your industry | Not for now Low | You're on the right track: you need to optimize your structure and external signals |
What is really happening to organic traffic with the arrival of AI Overviews?
The data is clear: when an AI Overview appears, clicks on traditional results collapse. But companies cited in AI answers get traffic that converts far better.
The impact of AI Overviews, the answers Google generates automatically above traditional results, on organic traffic is measurable and significant. According to a Pew Research Center study of 68,879 real searches, when an AI Overview is present only 8% of users click a traditional organic result, versus 15% when there is no AI Overview: a relative drop of 47%.
An analysis by Define Media Group of 64 sites via Google Search Console, reported by Search Engine Land, shows organic clicks fell 42% compared with the pre-AI Overviews period in Q4 2025. The decline was gradual: -16% right after launch, then -42% after the May 2025 expansion.
At the same time, Google has officially stated that AI Overviews drive growth of more than 10% in total query volume for the types of searches where they appear. Traffic doesn't disappear: it shifts toward those who get cited.
The flip side is just as important. According to Seer Interactive data (June 2025), visitors from ChatGPT convert at 15.9%, those from Perplexity at 10.5%, and those from Claude at 5%. Google organic traffic converts at 1.76%. Brands cited by generative engines get fewer visits, but of far higher quality.
That's why brand positioning in the AI era isn't an abstract topic: it's a revenue issue. To dive deeper into strategies for getting cited on ChatGPT, Gemini and AI Overviews, the SEO and AI visibility cluster collects all our up-to-date practical guides.
To understand how AI Overviews are reshaping brand visibility, also read what AI Overviews change for your visibility: a practical look at what algorithms examine when they build their answers.
How does brand positioning really work in generative engines?
AI models interpret a brand by analyzing thousands of signals spread across all digital touchpoints. The consistency of content, language and available information determines whether a brand is cited or ignored.
AI-based search works through different mechanisms than traditional search. Generative models analyze the overall meaning of the available information to work out which sources can offer the best answer, not just which pages contain a given keyword.
For a company, this means every element of its digital presence shapes the perception algorithms build: its website, published articles, service pages, social content and information found on external sources.
If a brand communicates in a fragmented way or uses messages that are too generic, the AI system struggles to identify the company's real area of expertise. The concrete risk is that the brand gets lumped in with competitors with similar traits, losing any chance to stand out.
| Element | What it does for AI brand positioning | Priority |
|---|---|---|
| Systematic publishing of vertical content | Builds a body of information that AI systems use to classify the brand | High |
| Consistent language and terminology | Makes it easier to recognize your industry and specific expertise | High |
| Site architecture (topic clusters) | Lets AI systems understand the relationships between the topics covered | High |
| External signals (citations, collaborations) | They strengthen your digital reputation in the eyes of algorithms | Medium |
| Social profiles consistent with your website | They maintain a single, unified identity across all channels | Medium |
| Structured, in-depth service pages | They clearly communicate which problems the company solves | High |
Effective brand positioning works like a concept map: it connects the user's need to the solution the company offers and makes it easier for AI systems to understand the brand's role. In 2026, digital reputation is built on strong semantic connections, not on technical page optimization alone.
A related and often underestimated topic is conflicting brand information in AI Search systems: when online sources describe your company inconsistently, generative models struggle to build a stable identity and may return inaccurate or misleading descriptions.
What is tone of voice, and why do generative engines use it to recognize you?
Tone of voice — the way a company speaks, the words it chooses, the rhythm of its sentences — becomes a technical signal that AI systems use to identify and present a brand consistently.
Tone of voice is one of the most important elements in building a digital identity. In the generative environment it also takes on a technical function: it helps determine how a brand is perceived and presented by AI systems.
When a company uses generative tools without clear guidance, the risk is content that is accurate but lacks distinctive traits. Your communication starts to sound like that of other players in your industry and loses its recognizability.
An effective tone of voice shouldn't be left entirely to AI. It needs to be designed around concrete parameters that guide both internal teams and automated tools.
| Parameter | What it defines | Practical example |
|---|---|---|
| Vocabulary | Which terms to use and which to avoid | A B2B manufacturing company says 'precision components,' not 'parts' |
| Sentence structure | Rhythm and average sentence length | Technical brand: longer, more precise sentences; consumer brand: short, direct sentences |
| Technical level | Depth of information relative to the audience | A guide for engineers vs. a guide for purchasing managers: same topic, different register |
| Priority topics | Subjects the brand wants to be known for | A digital agency owns AI, SEO and brand identity — not marketing in general |
| Emotional tone | Formal, authoritative, empathetic or direct register | Professional firm: authoritative and reassuring; tech startup: direct and innovative |
Consistent communication becomes a company's digital signature. When an AI system generates an answer about your brand, its tone should reflect the personality you've built through years of consistent communication. That's why tone of voice is a core part of brand positioning, and SHM Studio works on it through industry analysis and a strategy tailored to each brand.
How do you build an AI-compatible Brand Voice Guideline document?
A structured Brand Voice Guideline document lets AI tools produce content consistent with your company identity, without every output needing a full review from scratch.
Leaving communication style entirely to the AI model's interpretation means handing a big part of your brand identity over to uncontrolled criteria. An advanced brand positioning strategy includes precise guidelines that internal teams and AI-based workflows can both use.
- 1 · StrategyGlossary of proprietary termsDefine a proprietary vocabulary: which concepts should be associated with the brand, which recurring terms, which distinctive expressions. In technical or highly competitive sectors, precise terminology is essential for generative systems to recognize you.
- 2 · ContentResponse guidelines and prompt engineeringDefine sample responses and binding communication patterns. These examples are built into prompt engineering processes — the instructions you give AI tools — to steer the AI toward results closer to the brand's personality. Also useful for customer care <a href="https://shm.studio/servizi/ai/sviluppo-chatbot-assistenti-virtuali/">AI chatbots</a>.
- 3 · EditorialPriority topics and topic ownershipIdentify the topics the company wants to own over time. The frequency and quality of in-depth content on specific subjects help build the digital authority that AI systems perceive.
- 4 · OversightHuman validation processesAutomating production doesn't remove the need for control. Review by professionals remains essential to verify that every piece of generated content respects your set goals, values and positioning. The human-in-the-loop model — AI produces, humans supervise — is the right standard.
- 5 · ScalabilityDigital brand guidelines for AI toolsYour guidelines should include instructions for generative tools: visual style, graphic criteria, aesthetic references and content generation instructions. This lets you increase output while keeping one recognizable identity.
SHM Studio supports companies in designing these systems through dedicated AI consulting, analysis of communication processes and integration of specific AI tools, so that AI boosts productivity while your company identity stays strong.
What is semantic authority, and how do you actually build it?
Semantic authority is a company's ability to be recognized by AI systems as a go-to reference on specific topics. It's built through vertical content, structured digital architecture and consistent external signals.
In generative systems, semantic authority plays a central role: AI has to decide which sources to use to build an answer, and it picks those that show specific expertise consistently over time.
This authority also matters for traditional search engines. The rollout of AI Overviews has reduced the clicks a website receives, because users often find the answer in AI boxes. Being part of those answers means continuing to generate qualified traffic.
To build semantic authority, a company must make three key elements clear to AI systems.
| Element | How you communicate to AI systems | Main tool |
|---|---|---|
| Operating sector | Precise description of your field of action across your site, service pages and editorial content | Site architecture and service pages |
| Technical expertise | Guides, analyses, deep dives and case studies that demonstrate real capabilities | Blog and educational content |
| Problems solved | Clear communication of how your solutions match real market needs | Service pages and case studies |
| Digital architecture | Topic clusters, internal links and in-depth sections that show how topics relate | Website structure |
| External signals | Mentions, partnerships, publications and presence in relevant digital ecosystems | Digital PR and link building |
Building semantic authority means turning your company website into a structured, organized source of information. Through advanced SEO and GEO strategies — where GEO stands for Generative Engine Optimization, meaning optimization for generative engines — SHM Studio helps companies stay competitive while search paradigms change fast.
An often overlooked aspect is brand context in AI systems: being cited isn't enough, you need to be cited in the right context. Generative models build associations between brands and topics, and if those associations are wrong or too generic, the citation adds no value.
An effective service page, for example, should clearly explain: which problem it addresses; which solution it offers; which benefits it delivers; and which expertise backs the service. Content that is too short or lacks depth rarely lets a brand stand out as a reference for generative systems.
Why everyone is talking about it now: AI market numbers in Italy for 2025-2026
The Italian AI market reached €1.8 billion in 2025, yet 76% of SMEs have not invested yet. Those who move now are building a competitive advantage that will be hard to close.
According to the Osservatorio Artificial Intelligence of the Politecnico di Milano, the Italian AI market reached €1.8 billion in 2025, growing 50% compared to 2024. Generative AI, the systems that generate text, images and answers like ChatGPT, accounts for 46% of the total.
The gap between large companies and SMEs is huge, though. According to data from the Osservatorio AI, 71% of large Italian companies have launched at least one AI project, compared to just 8% of SMEs in 2025. And the 2025-2026 Research of the Osservatorio Innovazione Digitale nelle PMI by the Politecnico di Milano shows that 76% of Italian SMEs have neither invested nor plan to invest in AI, and only 7% have launched structured training programs.
For Italian SMEs that haven't yet started a structured path, the risk isn't just losing visibility in generative engines: it's letting larger competitors build a semantic advantage that is hard to recover. Our article on mistakes to avoid in your first AI project for SMEs offers a practical checklist for anyone starting from scratch.
Which AI tools can produce visual content that stays on brand?
Five generative tools cover the main needs of visual production: images, working graphics, video and social content. The right choice depends on the type of company and the workflow already in place.
Artificial intelligence is also changing how companies produce visual content. Images, videos, social materials and advertising assets can be created faster thanks to ever more advanced generative tools.
Automated production can increase the amount of content available, but it needs a defined visual direction to avoid producing generic material. Brand positioning must guide the visual side too: colors, photographic style, graphic composition and aesthetic language must stay recognizably consistent on every channel.
1. Midjourney
Midjourney is one of the most widely used tools for generating images with artificial intelligence. The ability to create complex visual concepts lets companies quickly develop ideas for campaigns, presentations, editorial materials and social content. With features dedicated to stylistic consistency, you can keep recurring elements such as mood, composition and visual traits.
2. Adobe Firefly
Adobe Firefly integrates into the Adobe tools used by professionals and web designers. Its main strength is fitting into existing design processes, allowing edits, variations and generation of visual elements without interrupting the workflow. For companies with a strong need for visual consistency, Firefly lets you work on already defined materials while keeping precise design standards.
3. Canva AI
Canva AI is particularly useful for the daily content production aimed at social and digital channels. Thanks to its automation features, you can quickly create graphics, adaptations and different formats while keeping the elements defined in the brand kit. This makes the tool appealing for companies that must produce lots of content without losing consistency in their communication.
4. Runway
Runway is one of the most advanced tools for AI video generation and editing. It can be used to create dynamic content for advertising campaigns, social media and corporate storytelling. Video is becoming ever more important in digital communication, and Runway lets you experiment with new formats while keeping production more agile.
5. Leonardo.ai
Leonardo.ai offers advanced customization through dedicated models and settings. Working with custom styles lets companies create images that better match their identity. This approach is especially useful when a brand needs frequent visual production while keeping recognizable traits.
| AI tool | Main use | Ideal type of company |
|---|---|---|
| Midjourney | Creative image generation, visual concepts, moodboards, advertising campaigns, social visuals | Companies working on brand identity, creative agencies, fashion and design brands, high-impact visual campaigns |
| Adobe Firefly | Image creation and editing integrated into Adobe workflows, adapting existing content, materials for digital communication | Companies with in-house marketing or creative teams, corporate organizations, e-commerce with large volumes of graphic materials |
| Canva AI | Fast production of content for social media, presentations and sales materials through templates and brand kits | SMEs, companies with a strong social presence, marketing teams producing frequent content without a complex design setup |
| Runway | AI video generation and editing, animations, advanced editing, audiovisual storytelling for digital campaigns | Companies investing in video marketing, advertising, social communication, online training and multimedia content |
| Leonardo.ai | Custom images through trainable models, development of proprietary styles, visuals consistent with specific creative lines | Brands needing strong visual customization, companies developing products, creative businesses with a distinctive graphic identity |
How do you integrate AI into creative production without losing your brand identity?
AI brings speed and variability: without clear rules, the result isn't productivity but a gradual loss of consistency. You need a structured framework where brand positioning guides every creative decision.
Using artificial intelligence in creative production requires precise governance. The technology acts as a generative system that produces variants, interpretations and visual or textual solutions that can be very different from one another. Without clear rules, the result isn't greater productivity but a gradual loss of consistency.
That's why AI must be placed within a structured framework, where brand positioning becomes the reference for every creative decision. Every output, whether an image, a text or a video, must be assessed not only for its aesthetic quality but for how well it represents the brand identity.
| Governance element | What it ensures | Who manages it |
|---|---|---|
| Digital brand guidelines for AI | Visual style, design criteria, aesthetic references and instructions for generating content | Strategic team + external consulting |
| Validation processes | Every piece of AI-produced content is checked for consistency with values, audience and positioning | In-house team with strategic oversight |
| Smart scalability | Higher production capacity while keeping one recognizable identity | Structured AI workflow |
| Specialist consulting | Cross-disciplinary skills combining technology, marketing and communication | Specialized external partner |
Combining AI and creativity lets you build products with huge potential for digital marketing and integration, but it all has to be managed so the result feels personal and recognizable. Production speed without identity is a cost, not an advantage.
If you want to see how B2B brands are using these tools to earn citations in AI systems, our analysis of LinkedIn and AI for B2B brands shows concrete strategies already adopted by Italian and international companies.
Where do you start when building solid brand positioning in the AI era?
It starts with an audit of what you already have: content, terminology, site architecture and external presence. Only then do you decide what to build, and in what order.
Three illustrative scenarios show how typical Italian companies can tackle the problem in practice. The numbers are there to show the reasoning, not real client data.
| Company type | Typical problem | First concrete step | Expected result |
|---|---|---|---|
| E-commerce store selling technical products | Doesn't appear in AI answers when a user searches for solutions to the problem it solves | Semantic audit of the site + restructuring of product pages with a problem-solution logic | More citations in generative engines for industry queries |
| Professional firm (accountant, lawyer) | Gets mentioned by name only, not for specific expertise | Creation of vertical content on 3-5 key industry topics, published monthly | AI systems recognize it as a go-to reference on specific topics |
| B2B manufacturing company | Competitors get cited instead of it in AI answers about industry products | Definition of a proprietary technical glossary + optimization of service pages | Generative models correctly link the brand to its specific expertise |
- 1 · Step 1Audit your current semantic presenceAsk the main generative engines (ChatGPT, Gemini, Perplexity) to solve problems your brand is an expert in. If you're not cited, or cited incorrectly, your semantic presence is lacking.
- 2 · Step 2Define your area of expertisePick 3-5 topics where you want to be recognized as the reference. Choose specific topics, not generic ones: 'industrial plumbing systems for the food sector' beats 'plumbing systems'.
- 3 · Step 3Build your glossary and Brand Voice GuidelinesDefine your proprietary terminology, tone of voice and communication parameters. This document guides both your internal teams and the AI tools you'll use to produce content.
- 4 · Step 4Restructure your site architectureOrganize your content into topic clusters: groups of linked pages that explore one subject from different angles. Each service page should clearly explain which problem it addresses, which solution it offers and which expertise backs it up.
- 5 · Step 5Publish vertical content on a regular scheduleTechnical articles, how-to guides and case studies build the body of information AI systems use to classify your brand. Frequency matters as much as quality.
- 6 · Step 6Monitor and adjust over timeRepeat the semantic audit every quarter. Check whether the associations AI systems build around your brand are accurate and consistent with the positioning you want.
What are the most common mistakes that make a brand invisible to AI systems?
Most mistakes come from fragmented or overly generic communication: the brand exists online, but AI systems can't classify it precisely.
When you work on brand positioning and AI visibility, the same mistake patterns keep showing up. Spotting them early saves you from wasting resources on activities that deliver no results in generative engines.
| Error | Why it's a problem for AI systems | How to fix it |
|---|---|---|
| Generic messaging ('we offer complete solutions for every need') | AI models can't place the brand in a specific category | Define 3-5 precise areas of expertise and communicate them consistently across all channels |
| Inconsistent terminology (the same service called by different names) | AI builds weak or contradictory associations between the brand and topics | Create a proprietary glossary and apply it systematically |
| Short, superficial content | Generative systems favor sources that offer complete, in-depth information | Invest in vertical content of at least 1,500-2,000 words on key topics |
| No external signals (no mentions, no partnerships) | AI finds no outside confirmation of the brand's authority | Digital PR, guest posts on industry sites, listings in specialized directories |
| Delegating everything to AI with no oversight | Content generated without control dilutes the brand identity over time | Adopt a human-in-the-loop model: AI produces, a professional supervises |
| Ignoring your presence in generative engines | The brand never gets cited in AI answers, losing high-converting traffic | Run periodic semantic audits and optimize based on the results |
How do you measure whether brand positioning in generative engines is working?
The metrics to track are different from traditional SEO: you're measuring the frequency and quality of citations in AI systems, not just SERP rankings.
Measuring the effectiveness of brand positioning in generative engines takes different tools and metrics than classic SEO. The starting point is always the direct semantic audit: query the AI systems and see what they return.
| Indicator | How it is measured | Recommended frequency |
|---|---|---|
| Citations in generative engines | Manual queries or specialized tools on ChatGPT, Gemini, Perplexity for industry queries | Monthly |
| Quality of semantic associations | Check that how AI systems describe the brand is accurate and consistent with the positioning you want | Quarterly |
| AI-referred traffic in Google Analytics | Tracking of sessions coming from chatgpt.com, perplexity.ai and other generative engines | Weekly |
| AI traffic conversion rate | Comparison of the conversion rate of AI traffic versus traditional organic traffic | Monthly |
| Topic cluster coverage | Check that all priority topics are covered with in-depth content | Quarterly |
| Terminology consistency | Audit of published content to check that the proprietary glossary is being applied | Every six months |
If you want to dig deeper into optimization strategies for generative engines, our complete guide to advanced SEO and GEO strategies explains how to structure content to get cited in Google AI Overviews and ChatGPT. Our analysis of local visibility in AI Search for Italian SMEs also offers practical guidance if you operate in specific geographic markets.
Is it worth investing in brand positioning for generative engines now?
Worth it if your brand operates in a competitive industry where customers search for solutions online, you already have a digital presence but don't show up in AI answers, or you want to build a semantic advantage before your competitors do.
Somewhere in between: you already have vertical content and consistent messaging, but haven't yet optimized your site structure and service pages for generative systems. You can start with an audit and make changes step by step.
Not yet if your business relies entirely on direct relationships and word of mouth, with no online search in your customers' buying journey. In that case, other digital investments take priority.
How we see it at SHM
We believe technology is the means and people are the end. In brand positioning projects in the AI era, that translates into a clear choice: we don't use generative tools to mass-produce content, but to free teams from routine work and give time back to strategy.
We've worked with manufacturing companies that had extraordinary technical know-how but a digital presence that didn't show it. The challenge wasn't producing more content: it was turning existing expertise into a structured information system, readable by both users and AI systems. This kind of work takes analysis, architecture and human oversight, not just automation.
For companies that want to build solid brand positioning in generative engines, we offer programs that start with a semantic audit and go all the way to implementing structured AI workflows: from strategic AI integration to semantic SEO for generative engines, through to developing chatbots and virtual assistants that speak in your brand's voice.
The AI chatbot that answers your customers, the content that shows up in Gemini's answers, the service pages that Perplexity cites: all of this is brand positioning. It's not a topic just for specialists. It's the new normal of digital marketing for companies. Contact us for a strategic digital consultation and find out where it makes sense to start for your business.
Frequently Asked Questions
Traditional SEO aims for organic rankings based on keywords and links. Semantic authority aims to make your brand a reference point for generative models, meaning AI systems like ChatGPT and Gemini. If these systems don't recognize your brand as an expert on a topic, they won't cite it in their synthesized answers, which makes every other visibility effort ineffective. The two strategies don't exclude each other: they complement each other.
Yes, as long as you implement the Human-in-the-loop model: AI handles the technical production, while human strategic oversight makes sure every asset follows your Brand Guidelines. AI works as the operational arm, while strategy stays in the hands of professionals. Without this control, automated production tends to dilute your brand identity over time.
You can run direct query tests on the main generative engines: ask ChatGPT, Gemini or Perplexity to solve problems your brand is an expert in. If you aren't cited, or if your company isn't mentioned among the solutions, your semantic presence is lacking and needs attention. Repeat this audit every quarter to track your progress.
Yes, the focus changes. In B2B, semantic authority is built on technical guides, white papers and in-depth case studies that demonstrate specific expertise. In B2C, emotional consistency and how quickly you solve the customer's problem matter more. Both, however, require a solid content structure so AI systems can associate your brand with the right value.
Timing varies based on how competitive your industry is and where you're starting from. Generally, the first citations in generative engines appear after 3-6 months of consistent work on vertical content and site architecture. Building solid semantic authority is a gradual process: results consolidate over time, they don't arrive in a few weeks.
For an SME with an in-house marketing team and limited resources, Canva AI is the most accessible starting point: it lets you produce social content and everyday graphics quickly, while keeping your brand kit as the reference. For more elaborate images or high-visual-impact campaigns, Midjourney delivers higher-quality results with a manageable learning curve. The key is to define your visual Brand Voice Guidelines first, then choose the tool.
In generative engines, the winner isn't whoever publishes the most, but whoever has built an identity clear enough to be recognized by a machine.
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