When AI systems like ChatGPT, Perplexity, or Gemini describe your company, are they getting it right? Or has your brand “drifted,” misattributed, conflated with competitors, or described with outdated information?
This phenomenon, known as entity drift, occurs when brand or product entities shift, blur, or distort as LLMs misinterpret data, combine conflicting information, or lack updated context. The result: potential customers receive inaccurate information about your company before they even visit your website.
According to Gartner, graph technologies, which underpin how AI systems understand entities, will be used in 80% of data and analytics innovations by 2025, up from just 10% in 2021. As these systems become the primary way people discover brands, entity optimization isn’t optional. It’s essential for maintaining brand identity in AI and preventing LLM misattribution from eroding your market position.
How Brand or Product Entities “Drift”
Understanding entity drift requires understanding how AI systems conceptualize brands. In knowledge graphs and embeddings, your brand is an entity with attributes, relationships, and contextual associations. When these become inconsistent, outdated, or conflated, drift occurs.
Training data inconsistencies create foundational drift.
LLMs are trained on vast datasets. If your brand is described differently across sources—varying company names, inconsistent product descriptions, conflicting dates, the model internalizes multiple versions. When generating responses, it might blend these versions into inaccurate frankenstein descriptions.
A company that rebranded from “TechSolutions Inc.” to “TechSolve” but didn’t systematically update this across Wikipedia, directories, and press releases creates confusion. An LLM might reference TechSolutions when discussing recent accomplishments, or blend both identities together.
Semantic ambiguity causes entity conflation.
When multiple entities share similar names or operate in overlapping markets, AI systems can conflate them. “Delta” could refer to Delta Airlines, Delta Dental, or Delta Faucet. This duplication directly contributes to entity drift.
Outdated information persists.
LLMs are trained on data with cutoff dates, meaning information from years ago might carry equal weight to recent updates. If your company pivoted from B2C to B2B three years ago but extensive B2C content remains online, AI might describe you as B2C or confusingly reference both models as current.
Hallucinations amplify inaccuracies.
When LLMs lack clear information, they fill gaps with plausible-sounding but incorrect details, inventing product features, fabricating partnerships, or misattributing competitor achievements to your brand.
Relationship drift distorts context.
Your brand’s relationships to executives, products, markets, and partners contribute to how AI understands you. When an executive leaves but is still listed as CEO, or a discontinued product is described as current, the entire entity representation becomes unreliable.
Examples of Companies Misattributed or Conflated
Entity drift creates real consequences.
Air Canada’s Chatbot Liability.
In February 2024, Air Canada’s chatbot provided false bereavement fare information. When a passenger tried to claim the policy, Air Canada refused, arguing the chatbot was a “separate legal entity.” A tribunal disagreed, forcing the airline to honor the hallucinated policy and compensate the customer, demonstrating how LLM misattribution creates legal liability.
DPD’s Brand-Damaging Bot.
Early in 2024, DPD’s AI chatbot was manipulated into calling DPD “the worst delivery firm in the world” and writing poems about the company being “shut down.” The semantic consistency breakdown forced DPD to shut down the chatbot entirely.
X’s Grok Falsely Accuses The NBA Star.
In April 2024, Grok falsely accused Klay Thompson of vandalism, misinterpreting basketball slang (“throwing bricks” meaning missing shots) as literal brick-throwing. This entity drift from metaphorical to literal interpretation created false accusations against Thompson’s brand identity in AI systems.
McDonald’s AI Ordering Fiasco.
After three years with IBM on AI drive-thru ordering, McDonald’s ended the partnership in June 2024 due to persistent entity recognition failures, including adding 260 Chicken McNuggets to orders as customers pleaded for it to stop.
NYC’s Lawbreaking Chatbot.
In March 2024, MyCity provided entrepreneurs with illegal advice, claiming business owners could take workers’ tips and fire sexual harassment complainants. The entity representing “legal business practices” had drifted so far from reality that AI confidently dispensed illegal advice.
These cases share common threads: AI systems lacked accurate, current, consistent entity representations. The resulting drift created financial losses, reputational damage, and legal liability.
Detecting Entity Drift Through AI Audits and Consistency Checks
You can’t fix entity drift until you detect it. Systematic auditing should be core to any entity optimization strategy.
Conduct Regular AI Query Audits.
Query major AI platforms about your brand monthly. Ask: “What does [Company] do?”, “Who are [Company’s] main products?”, “Who are the executives at [Company]?” Run these across ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini. Document responses and look for inconsistencies between platforms, discrepancies with actual positioning, outdated information, conflation with competitors, and fabricated details.
Check Knowledge Graph Representation.
Audit your presence in Google’s Knowledge Graph, Wikidata, and industry databases. Verify entity attributes (founding date, headquarters, executives), relationships (to parent companies, subsidiaries, products), classifications (industry, market), and associated facts (revenue, employee count) are accurate and current.
Track Cross-Platform Consistency.
Create a spreadsheet documenting how your brand is described on Wikipedia, LinkedIn, Crunchbase, industry directories, and review platforms. Compare descriptions for variations in company name, business model, product listings, leadership, and history. Prioritize corrections on the most authoritative platforms.
Monitor Competitor Mentions and Conflation.
Note which competitors are mentioned alongside you and whether competitor attributes are incorrectly attributed to you. If AI consistently conflates your brand with Competitor X, investigate why, similar names? Overlapping markets? Ambiguous information online?
Establish Feedback Loops With AI Platforms.
When you discover significant drift, use platform error-reporting mechanisms. Document errors with screenshots, provide corrected information with authoritative sources, and follow up.
Stabilizing Brand Meaning Across Knowledge Graphs and Embeddings
Detection identifies problems; stabilization solves them.
Create Canonical Entity Definitions.
Develop a single, authoritative source of truth documenting official company name and variations, complete business model description, product and service listings, current leadership team, founding information and history, market positioning and differentiation, verified partnerships, and company statistics. Publish this prominently on your website using schema markup.
Implement Comprehensive Schema Markup.
Use Organization schema for company information, Product schema for offerings, Person schema for executives, Place schema for locations, and FAQ schema for common questions. These explicitly tell AI systems what your entity is, reducing ambiguity that leads to drift.
Maintain Wikipedia And Wikidata Accuracy.
Wikipedia represents 7.8% of all ChatGPT citations according to an analysis of 30 million citations, more than any other source. If your company has a Wikipedia page, monitor it regularly. For Wikidata, claim your entity and ensure all structured data is correct. Wikidata directly feeds many knowledge graphs.
IMG SOURCE: Profound
Synchronize Information Across Major Platforms.
Systematically update LinkedIn, Crunchbase, industry directories, review platforms, news databases, and partner sites to match your canonical definition. Consistent information reduces conflicting data points that cause entity drift.
Use Consistent Naming Conventions Relentlessly.
Pick one official company name and use it everywhere. If variations exist (abbreviations, DBA names), clearly establish relationships using schema markup. Train employees, partners, and stakeholders to use the official name. Inconsistent naming is a primary driver of entity conflation.
Build Entity Linkage Across Platforms.
Help AI systems understand that mentions across platforms refer to the same entity by using sameAs relationships in schema, maintaining consistent identifiers (website URL, social handles), cross-linking between verified profiles, and participating in entity verification programs.
Update Entity Information Proactively.
When your brand changes: new products, executive transitions, new markets, business model evolution; immediately update entity information across all major platforms. Create an entity update checklist triggered by any significant change. This prevents lag between reality and AI representation.
Monitor And Correct Relationship Drift.
Maintain accurate information about relationships to parent companies, subsidiaries, products, executives, partners, and competitors. When relationships change, update them everywhere they’re referenced.
The Future of Brand Identity in AI
Entity drift will intensify before it improves. As AI-driven discovery grows, more brands will compete for accurate representation in limited LLM outputs. Knowledge graphs will grow larger, increasing opportunities for drift.
The brands that thrive will treat entity optimization as a core discipline. They’ll establish canonical brand definitions, implement structured data everywhere, maintain consistency across all platforms, proactively update information, and systematically audit AI representations.
This isn’t about gaming systems. It’s about ensuring the AI ecosystem has accurate, consistent, authoritative information about your brand. When you make that job easy for AI systems, they reward you with accurate representation. When you leave it to chance, drift is inevitable.
The question is whether you’ll detect and correct it before it costs you customers, reputation, and market position. In the age of AI-driven discovery, your brand identity in AI matters as much as anywhere else, because if AI gets your brand wrong, potential customers might never learn the truth.
And if you want to make sure your brand is cited right and doesn’t get wrongfully misinformed, book a call with us at ReSo.
