AI Mode Meets the Four Intelligences: Rethinking Search and Automation in the No-Code B2B Era

AI Mode and Cognitive Intent

AI Mode Meets the Four Intelligences: Rethinking Search and Automation in the No-Code B2B Era

In the dynamic no-code B2B era, organizations increasingly rely on intuitive automation platforms and AI-driven search to drive efficiency and innovation. In fact, 84% of enterprises have adopted no-code solutions to enhance agility and innovation (tadabase.io), empowering business teams to build applications and workflows without writing code. At the same time, Google’s new AI Mode for Search – powered by a “query fan-out” technique – is changing how we find information. AI Mode breaks a user’s question into multiple related searches across subtopics, then synthesizes the results into a cohesive answer (Google). This approach promises greater breadth and depth in search results, reaching “hyper-relevant” content beyond what a single query would traditionally return (Google).

However, as we embrace intelligent automation and advanced AI search, a critical factor often goes overlooked: the human element of cognitive-intent. Different people (and teams) approach problems and interpret answers in fundamentally different ways. Elena Buran’s The Evolution of Intelligence (2025) presents a model of four intelligence types – Homo Rationalis, Homo Ethicus, Homo Practicus, and Homo Intuitivus – each with a distinct way of thinking and deciding. In this article, we’ll explore how Google’s AI Mode and its query fan-out relate to these four cognitive styles. We’ll see how each type frames queries and interprets AI answers differently, the risks of misaligned intent, and how no-code automation can be tailored to each intelligence type. Through case scenarios and best practices, we’ll offer a visionary roadmap for aligning AI assistants and search systems with the dominant intelligence profile of your B2B team.

The Four Intelligence Types: A Framework for Cognitive Intent

Elena Buran’s typology of intelligence provides a useful lens for understanding how people seek and use information. Each intelligence type represents a distinct form of human existence — a unique way of being, seeing, feeling, and acting in the world (Buran, 2025). Below is a brief overview of the four types in this framework:

• Homo Rationalis (Logical Type) – This is the person of reason and argument, drawn to what can be clearly expressed, structured, and proven logically (Buran, 2025). They filter reality through language, analytical frameworks, and formal logic. For Homo Rationalis, words are the tools of thought, and linear logic is the road (Buran, 2025). They excel at step-by-step reasoning and articulate communication of ideas.

• Homo Ethicus (Ethical Type) – This type sees the world through people, feelings, and relationships (Buran, 2025). Homo Ethicus grasps facts along with empathy, seeking harmony and ethical equilibrium in social systems (family, team, society). Their cognition is attuned to moral reasoning and empathy – correlated with brain regions like the limbic system and prefrontal cortex responsible for social sensing and ethics (Buran, 2025). For Homo Ethicus, truth lives not only in words — but in people’s hearts (Buran, 2025). It’s important to note this “heart-centric” reasoning is not mere emotion, but a principled, relational logic focused on values and human impact.

• Homo Practicus (Practical/Sensory Type) – The Practicus orientation is toward concrete action, sensory details, and practical results (Bura, 2025). These individuals focus on what can be touched, measured, applied in real-world terms. They evaluate ideas by their real usefulness and tangible effectiveness (Buran, 2025). For Homo Practicus, truth is found in visiable action and result (Buran, 2025). This is the domain of hands-on problem solving, step-by-step execution, and “does it work?” reasoning – essentially practical reasoning grounded in experience and utility.

• Homo Intuitivus (Intuitive Type) – This type reaches beyond words and immediate facts, sensing hidden patterns, emerging possibilities, and the “big picture” behind chaos (Buran, 2025). Homo Intuitivus picks up on subtle signals – energies, motives, symbols, and nascent trends not yet formalized – effectively feeling connections before they are logically or visibly confirmed (Buran, 2025). Their thinking is holistic and systemic, associated with right-hemisphere brain activity for pattern recognition and subconscious insight (Buran, 2025). For Homo Intuitivus, truth lies not only in what is visible, but in what is still forming (Buran, 2025). This intuitive cognition is not simply emotion or guesswork, but an advanced ability to anticipate and “sense what emerges” beyond the data (linkedin.com).

Buran gives each type a “natural” name reflecting its core orientation: Rationalis – word and logic; Ethicus – heart and relationships; Practicus – action and concreteness; Intuitivus – spirit and deep processes (Buran, 2025). One particularly insightful nuance in her model is the distinction between linear and systemic thinking. While we often assume the “logical” mind is most systematic, Buran clarifies that systemic, integrative thinking actually belongs to the intuitive intelligence, whereas the logical-rational mind tends toward structured, sequential reasoning. In other words, Homo Intuitivus is the truly systemic thinker, seeing patterns in the whole, whereas Homo Rationalis excels at orderly, linear logic. Likewise, Homo Ethicus employs a relational logic (grounded in empathy and human context), and Homo Practicus relies on practical logic (cause-and-effect tied to real-world action).

Understanding these differences is more than theory – it directly influences how different people ask questions and what they consider a “satisfying” answer. Below is a summary of how each intelligence type typically frames their intent, what they need from information or automation, and their common goals in a B2B automation context:

Intelligence Type	Query Intent & Thinking Style	Needs from AI/Search	Common Automation Goals
Homo Rationalis  (Logical)	Precise, analytical queries. Tends to ask structured questions seeking clear definitions, data, or logical explanations. Focuses on facts, frameworks, and “proof.”	Clarity and accuracy – well-organized answers with evidence, citations, and logical reasoning. Ability to drill down into details.	Efficiency of structure. Automate processes by formal rules and data. Goals: improve accuracy, consistency, and optimize based on logical criteria (e.g. data validation workflows, compliance checks).
Homo Ethicus (Ethical)	Contextual, people-oriented queries. Often frames questions around impacts on people, values, or relationships (e.g. “how will X affect our team/customers?”). May use empathetic language.	Context and empathy – answers that address human factors, trust, and ethical implications. Nuanced explanations that consider “soft” outcomes (not just cold facts).	Human-centric improvement.  Automate to enhance user or employee experience, fairness, and collaboration. Goals: ensure solutions are equitable, culturally sensitive, and maintain harmony (e.g. customer support chatbots with empathy, HR workflows that improve employee well-being).
Homo Practicus  (Practical)	Direct, task-focused queries. Prefers “how to” or solution-oriented questions aiming at immediate problem solving (“How do I accomplish X?”). Often concise and action-driven.	Actionable answers – step-by-step solutions, best practices, or tool recommendations. Minimal jargon, focusing on what to do next. Speed and reliability of information are key.	Tangible results.  Automate for productivity and cost/time savings. Goals: streamline routine tasks, eliminate inefficiencies, get quick ROI (e.g. task routing, inventory triggers, automated alerts – anything that saves labor and shows immediate effect).
Homo Intuitivus  (Intuitive)	Open-ended, exploratory queries. Tends to pose broad or novel questions (“What are emerging trends in…?”) and hypothetical or multi-faceted questions that seek insight. May use metaphor or abstract language to probe for patterns.	Broad exploration & connections – answers that synthesize across domains, reveal patterns or new ideas. Tolerance for ambiguity: the AI should present multiple angles or clues rather than a narrowly definitive answer.	Innovative outcomes. Automate for discovery, strategy, and innovation. Goals: use automation/AI to experiment, simulate scenarios, and identify opportunities (e.g. trend analysis dashboards, creative brainstorming agents, scenario planning tools).

This table encapsulates how differently each type approaches search and automation. Next, we dive deeper into how Google’s AI Mode (with query fan-out) interacts with these intent styles, and what can go right or wrong when AI systems don’t recognize the user’s cognitive orientation.

One Question, Four Interpretations: How Each Type Uses AI Search

Modern AI search like Google’s AI Mode can handle complex questions by fanning out into sub-queries and pulling together diverse information (blog.google). But the effectiveness of this “AI search brain” depends on aligning with the user’s intent. A query posed by a Rationalis mind vs. an Intuitivus mind might be the same words but imply very different expectations. Let’s examine how each intelligence type typically frames queries, interprets answers, and interacts with AI Mode’s query fan-out – along with the risks when there’s a mismatch.

Homo Rationalis – Analytical Queries and Linear Reasoning

The Rationalis user approaches search in a highly analytic, methodical way. Their queries tend to be well-defined and specific, as if crafting a precise question for an encyclopedia or database. For example, a Rationalis operations manager might ask: “What is the most cost-effective workflow automation tool for document approval, based on reliability and security ratings?” Every element of the question is deliberate – they’re implicitly expecting a comparative analysis with hard facts (cost, reliability data, security certifications).

Interpreting Answers: Homo Rationalis evaluates AI answers through a lens of logic and evidence. They will quickly zero in on the structure and validity of the response. Does the answer define terms clearly, cite data or sources, and draw a logical conclusion? An AI Mode answer that provides a neatly organized comparison (perhaps a mini-report citing reliability stats and security standards for each tool) will satisfy them. If, instead, the answer is too high-level or lacks justification, the Rationalis user may distrust it. This type thrives on transparency – they often want to see the underlying sources or reasoning. Fortunately, AI Mode’s integration of follow-up queries and linked sources caters to this need by providing citations and the ability to drill down (blog.google).

Query Fan-Out Benefits and Pitfalls: Google’s query fan-out can be a great ally for Rationalis. By breaking a complex query into subtopics, the AI can fetch comprehensive coverage – exactly what a rational mind craves. In our example, AI Mode might simultaneously search for “cost-effectiveness of no-code automation tools”, “workflow tool reliability comparisons”, and “security certifications of top platforms”, then merge the findings. The breadth of sub-queries ensures thoroughness, reducing the chance that an angle important to the Rationalis (say, security) is missed. As Google puts it, this technique lets AI “dive deeper… helping you discover even more of what the web has to offer” (blog.google) – aligning with the Rationalis user’s desire for exhaustive information.

The potential hurdle is if the AI’s synthesized answer doesn’t present information in a clear, logically structured way. A Rationalis will be frustrated by a disorganized or tangential summary. They prefer a linear presentation: e.g. an introduction, a factual comparison (perhaps a table of features/pros-cons), and a reasoned recommendation. If query fan-out yields a lot of data, the AI must not overwhelm or jump steps in reasoning. Consistency matters too – if sub-results are conflicting (one source says Tool A is cheapest, another says Tool B is), the Rationalis user expects the AI to note and reconcile that conflict logically. Misinterpreting a Rationalis query – for instance, treating it like a broad exploratory question – can lead the AI to give an unfocused answer, missing the mark. The risk is an answer that sounds generic or inconclusive, leaving the Rationalis user thinking the AI “didn’t really answer the question”.

Summary: Homo Rationalis frames queries like a logical probe and wants answers with structure, evidence, and clear reasoning. AI Mode’s fan-out can satisfy them by gathering all the relevant facts, but the AI must deliver those facts in an orderly, rational narrative. When aligned, this partnership yields high-confidence decisions – when misaligned, the result is dissatisfaction or the need for the user to manually verify facts (defeating the purpose of “intelligent” search).

Homo Ethicus – Empathetic Queries and Relational Reasoning

A user with an Ethicus mindset brings a relational, values-driven approach to search. Their queries often incorporate human context or ethical criteria. For example, a Homo Ethicus founder designing a customer service chatbot might not simply ask, “What’s the best no-code platform for chatbots?” Instead, they are likely to ask: “What is the best no-code chatbot platform for providing empathetic, culturally sensitive customer support?” The addition of terms like “empathetic” and “culturally sensitive” signals that the quality of the interaction is as important as technical features. In other cases, an Ethicus-oriented manager might pose a query in more narrative form: “How can we automate our sales emails without losing the personal touch and respect for customer privacy?” Such questions bundle practical needs with ethical or relational concerns.

Interpreting Answers: Homo Ethicus looks for answers that acknowledge the human element. In the first example, if the AI Mode answer only ranks chatbot platforms by cost and features but says nothing about user experience or cultural factors, the Ethicus user will find it lacking. They are scanning the answer for clues that the solution will “feel right” for people. This could include mentions of user feedback, inclusivity features (e.g. multilingual support for cultural sensitivity), or how the automation maintains empathy (perhaps through tone or personalization). An Ethicus interpreter tends to weigh tone and implications: an answer that is technically correct but cold or purely profit-driven may be received poorly. They prefer an answer that is well-rounded – covering not just “what” to do, but “who” it affects and “how” it aligns with our values.

Query Fan-Out in Action: The query fan-out technique can either rescue or miss an Ethicus query depending on how it breaks down the question. Ideally, AI Mode will detect the ethical and relational angle as one of the subtopics. In the chatbot query, beyond searching for “best no-code chatbot platforms,” it might also fan out a query like “ensuring empathy in customer chatbots” or “culturally sensitive AI customer service practices”. If it does, the synthesized answer could include a segment about which platforms allow personalized scripting or sentiment analysis (to keep empathy), or community feedback about the customer experience. This would directly address the Ethicus user’s intent and delight them with a thoughtful answer.

On the other hand, there’s a risk: if the AI misinterprets or de-emphasizes the ethical aspect, it may fan out only along technical lines (price, AI features, integration options) and omit the “soft” criteria. The resulting answer might read as “The top chatbot platforms are X, Y, Z with these features…” and nothing about empathy. The Ethicus user will feel their real question was ignored. Another risk is the AI might treat words like “empathetic” as just sentiment and respond with a generic blurb (“Important to maintain a personal touch with customers”) without concrete guidance. Query fan-out is most helpful to Ethicus when it explicitly includes relational sub-queries (like “impact on team morale”, “customer trust considerations”, etc.). It’s less helpful if the sub-queries stay superficial on human factors.

Summary: Homo Ethicus frames queries blending facts with values, and they interpret answers for human-centric insight. They need AI search to be context-aware enough to address ethical, sensitive, and relational subtext – not as an afterthought, but as a core part of the answer. When AI Mode surfaces those facets (e.g. pulling an empathy best-practice alongside product info), it validates the Ethicus user’s intent. If it doesn’t, the result can be a tone-deaf answer that erodes trust. In the worst case, misalignment here could lead to implementing an automation that works technically but backfires with people – the very outcome Homo Ethicus is keen to avoid.

Homo Practicus – Pragmatic Queries and Practical Reasoning

Practicus users are all about getting things done. Their queries are straightforward, aimed at immediate problem-solving or task execution. A Homo Practicus team member in HR, for instance, might ask: “How can I automatically route incoming job applications to the right hiring manager?” – a clear, outcome-focused question. They often prefer queries phrased as “How to…”, “Best way to…”, “Tool for doing…”, etc., zeroing in on the action needed. There’s little interest in theory or broad analysis; they want the quickest path from point A to point B.

Interpreting Answers: When an AI answer arrives, the Practicus thinker rapidly scans for actionable content. They are happiest with bullet lists, step-by-step instructions, or a direct recommendation: e.g. “Use Tool X’s automation rule feature: Step 1 do this, Step 2 do that.” If the answer starts with a long-winded explanation of the problem or too much background, a Practicus user may grow impatient or skip ahead. They essentially look for “What do I need to do, and what will it accomplish?” Metrics or examples can help (e.g. “Route rules can cut response time by 50%”). An answer that ends with a clear next step (“Click here to deploy the solution now”) is gold. In contrast, an answer heavy on abstract considerations (“One must consider organizational change…”) is likely to lose them. Practical reasoning filters out fluff in favor of concrete guidance.

Query Fan-Out Impact: On the surface, Google’s query fan-out – which “explodes” a query into many sub-queries (wordlift.io) – might seem like overkill for the Practicus style. After all, the user just wants a quick solution, not an essay. But fan-out can still be advantageous if the AI uses it to quickly gather the best answer. In our example, AI Mode might parallel-search subtopics like “no-code tool for email routing in HR”, “auto-forward resume by department”, and “case study HR task automation”. This behind-the-scenes breadth could ensure that the single answer presented to the user is robust and time-saving – perhaps mentioning the top one or two methods and even pitfalls to avoid, all in one go. The Practicus user essentially outsources the comparative research to the AI, expecting a distilled recommendation.

The key is that the final answer must remain concise and focused. Query fan-out should not manifest as a sprawling, overwhelming response. If the AI dumps too much information (“Here are five possible methods with detailed pros and cons…”), the Practicus user might feel it’s too much work to sift through – ironically the very work they hoped the AI would do for them. So the balance is crucial: the AI can fan-out widely, but it should then filter and present just the actionable highlights. Done right, this means the Practicus gets a high-quality answer in minimal time (e.g. “Use Platform Y’s routing feature – it’s a no-code solution popular in HR, with easy setup. Here’s how to implement it…” plus maybe a direct link).

Risks of Misinterpretation: If AI Mode misreads a Practicus query as an open-ended exploration, it might produce a verbose answer or pose rhetorical questions (some AI models do this to “cover all bases”). For a Practicus user, that’s a fail. The danger is they will abandon the AI answer and manually hunt for a straightforward tutorial or video. Another risk is if the AI’s fan-out covers lots of theoretical subtopics (“history of workflow automation” or “pros/cons of HR automation strategy”) that are irrelevant to the immediate question – including that in the answer wastes the user’s time. Practicus users measure success by efficiency: the right answer is the one that solves the problem now. So AI Mode best serves them by using its expansive search power to deliver a single, clear course of action. When aligned, the user feels empowered (“Great, I know exactly what to do next”). When not, the user feels AI was a detour and might revert to more manual or familiar methods.

Homo Intuitivus – Exploratory Queries and Systemic Intuition

The Intuitivus approach to querying is exploratory, imaginative, and future-oriented. These users often ask questions that are broad or abstract, sometimes even unconventional. For example, a Homo Intuitivus innovation lead might ask: “What emerging employee sentiment patterns should we consider in our HR automation strategy for the next 5 years?” – a question that blends data, human behavior, and a future timeline. Or they might pose a highly open question like, “Could our internal communication bot be used to boost innovation culture?” The Intuitivus style is comfortable with ambiguity; the query is often a starting point to find hidden connections or insights. They might also use metaphor or analogy, e.g., “Is there a ‘Wisdom of Crowds’ approach to our knowledge base automation?” – expecting the AI to catch the reference and explore it.

Interpreting Answers: Homo Intuitivus users are looking for sparks – insights that trigger their intuition or confirm a hunch. They interpret AI answers less literally and more for patterns or novel connections. An Intuitivus might read an answer and between the lines spot a trend or idea that isn’t explicitly stated. For instance, if the AI answer to the sentiment query mentions several disparate factors (like “remote work challenges”, “need for recognition,” “cross-team communication issues”), the intuitive thinker may synthesize those into a larger insight about company culture shift. In terms of satisfaction, Intuitivus users appreciate answers that bring together diverse angles and inspire further questions. If an answer feels too final or narrow, they might find it uninspiring. They often enjoy when the AI surfaces something they weren’t explicitly asking but is intriguingly relevant – a serendipitous find that feeds their creative process.

Query Fan-Out Advantages: AI Mode’s query fan-out is almost tailor-made for Homo Intuitivus. By issuing multiple sub-queries, the AI can cover a whole landscape around the question. This aligns with the Intuitivus love of systemic, big-picture insight. To the intuitive query above, the AI might fan out into searches like “trends in employee sentiment 2025”, “HR automation future outlook”, “employee innovation culture drivers”, and even analogous domains (“what boosts innovation in companies similar to ours”). When the results come back together, the answer could read like a mini horizon scan: touching on technology, psychology, and organizational trends. Such an answer can validate the Intuitivus user’s sense that everything is connected. It might even present a surprising link (e.g., correlating sentiment patterns with innovation output) that propels the user’s thinking in a new direction. This is the ideal scenario: the AI augments the user’s intuition, acting as a partner in discovery by bringing in information from many sources.

There is, however, a subtle challenge: the AI must not suppress ambiguity too much. Intuitives are comfortable with not having a black-and-white answer; they’d rather see multiple possibilities and emerging ideas. If the AI’s synthesis tries too hard to conclude or close the question definitively, it may feel limiting. For example, if the AI answer said, “Employee sentiment has no significant effect on innovation, focus on something else,” an Intuitivus might distrust that narrow conclusion (and possibly think the AI missed the point). They would prefer something like, “There are several emerging patterns (A, B, C) that could influence your strategy, and here are some potential approaches to leverage them…” In other words, leave room for exploration. Query fan-out, used well, will provide a rich menu of insights that the Intuitivus user can further inquire about (and indeed AI Mode allows follow-up questions easily). Used poorly, it could deliver an information overload – but Intuitives tend not to mind sifting through rich information, as long as it’s relevant.

Risks of Misalignment: The biggest risk is misinterpreting an Intuitivus query as a demand for a simple factual answer. Because intuitive queries can sound broad or even vague, a less savvy AI might either oversimplify (answering only one narrow facet and ignoring the rest) or provide a generic answer that doesn’t delve into the nuance. This would leave the Intuitivus user unsatisfied, perhaps feeling the AI was too superficial. Another risk is if the AI lacks knowledge in a novel area the Intuitivus query points to – the answer could miss creative connections that a human intuitive leap might find. However, with tools like Gemini 2.0 powering AI Mode, the system is designed for advanced reasoning and multimodal input (blog.google), which should help in capturing complexity. The bottom line: Homo Intuitivus seeks an AI collaborator that expands their perspective. If AI Mode’s fan-out casts a wide net and responds with a visionary breadth, the Intuitivus user is likely to be delighted and inspired. If not, they’ll treat the AI as just another conventional search tool and might pursue answers through more creative means (human brainstorming, etc.).

The Risk of Misinterpreted Intent in AI Search

The above explorations highlight a crucial point: when an AI search assistant misreads the user’s intent type, the result can be miscommunication or even missteps in decision-making. In a B2B context, this risk is amplified. For example:

• A Rationalis team might ask a question expecting a detailed analysis, but if the AI gives a breezy summary (perhaps assuming a Practicus-level need), the team could make a decision without sufficient data, or lose confidence in the AI and revert to manual research.

• An Ethicus-minded leader might seek guidance on an automation strategy “that keeps our culture strong”. If the AI ignores the cultural aspect, the resulting action could harm team morale or public image – a costly misalignment of values.

• A Practicus operations officer might query an AI for quick setup instructions, but if the AI over-delivers a plethora of options and information, precious time is lost and the user may abandon the AI’s advice, reducing trust in the tool.

• An Intuitivus strategist might pose a forward-looking question and get an answer that is too literal or dismissive of the unknown. This could cause the company to miss out on innovative ideas, because the AI failed to validate a nascent opportunity the question was hinting at.

Misinterpreting intent isn’t just an inconvenience – it can lead to implementing the wrong automation solutions, overlooking critical considerations, or friction between teams and their AI tools. In essence, when AI search doesn’t appreciate the cognitive style behind the query, it risks delivering information that is technically correct but contextually wrong.

Google’s query fan-out approach does mitigate some of this risk by covering multiple interpretations of a question in parallel. By design, it tries to ensure that if a question has facets (technical, human, short-term, long-term), the AI will touch on many of them (blog.google). This reduces the chance of completely missing the user’s underlying intent. However, it’s not foolproof. The AI must still decide which findings to emphasize in the final answer, and that is where understanding the user’s priorities (their intelligence type) matters. Ultimately, the solution is not one-size-fits-all: truly effective AI assistants in the enterprise will need to adapt their response style to the user – much like a skilled human advisor would adjust communication when speaking to a CEO (big picture Intuitivus) versus a CFO (detailed Rationalis) or an HR manager (relational Ethicus).

In the next section, we illustrate these differences in practice with concrete scenarios, and then we’ll propose how to align AI and automation tools to each intelligence type for maximum success.

Automation in Action: Case Scenarios for Each Intelligence Type

To make this discussion more tangible, let’s consider real-world B2B scenarios where a team or leader of a certain intelligence type undertakes an automation project. In each case, we’ll see how their dominant cognitive style influences their approach – from the questions they ask, to the design decisions they make, to the way they leverage AI Mode and no-code platforms. These scenarios also reveal potential friction points and how aligning AI assistance with their style leads to better outcomes.

Case Scenario 1: Homo Rationalis Team – Data-Driven Process Optimization

Context: A finance operations team at a mid-size enterprise is tasked with automating the company’s expense approval workflow using a no-code platform. This team’s culture is predominantly Rationalis – analytical and detail-oriented. The team lead, a process analyst, begins by formulating very clear requirements: they need an automation that can route expense reports based on amount, department, and project code, with conditional logic for different approval chains.

Approach: The Rationalis team starts by researching and planning. They use Google’s AI Mode to query things like “Best practices for building approval workflows with no-code tools” and “Data validation rules for financial approvals automation” . Their queries are specific and often include domain jargon (they might even search for regulations or compliance issues related to expense approvals). The AI Mode’s fan-out yields a comprehensive overview: for example, it provides a summary of top no-code platforms for finance processes, highlights a case study of a similar company’s workflow (with metrics on error reduction), and lists key validation rules (like flagging duplicate receipts or out-of-policy expenses). The Rationalis team is pleased to see citations and links in the AI’s answer, which they click to read further details (blog.google). One team member cross-checks a source on data security (important for financial data integrity). This thorough upfront research – aided by AI Mode’s broad search – satisfies their need for a logically sound plan.

When building the automation, the Homo Rationalis style shows in how they structure the workflow: meticulously. They create a flowchart with every conditional branch mapped out clearly. They use the no-code platform’s visual logic builder to implement rules, and they test each rule with sample data (attempting edge cases like an unusually high expense or an ambiguous project code) to ensure the logic holds. Throughout, they keep documentation of the workflow logic – essentially writing an internal whitepaper on how the automation works and why certain rules were chosen.

AI Alignment: During development, they continue to consult AI Mode for specific questions, e.g., “How to implement exception handling for approvals in [ToolName]” or “API vs built-in integration for attaching receipts – which is more reliable?” The AI’s answers, thanks to query fan-out, often come back with bullet-pointed options and technical explanations. Because the AI recognizes the technical phrasing, it returns high-precision answers (often referencing the tool’s documentation or user forums). The Rationalis team cross-verifies these tips in documentation (old habits die hard) but generally finds the AI’s guidance solid.

Outcome: The result is a rock-solid expense approval automation that is fully documented and optimized. The Rationalis team’s thorough approach, amplified by AI Mode’s ability to fetch extensive data and examples, means the solution is thoroughly vetted. They avoided pitfalls (like knowing from the AI results to include a step for handling policy exceptions) and achieved their goals of accuracy and compliance. The only drawback: the project took slightly longer than initially hoped because the team insisted on deep understanding and testing – but for them, that’s a feature, not a bug. When presenting the workflow to management, they provide analytic evidence of its effectiveness (e.g. “We expect a 30% reduction in processing time and zero policy violations, citing a similar case” – something they got from the AI-sourced case study). This boosts confidence across the board.

Takeaway: A Homo Rationalis team shines when AI and no-code tools support their need for logic and data. The AI Mode’s exhaustive query fan-out complemented their research phase perfectly, and the no-code platform’s capacity for explicit logic mapping fit their linear reasoning. Aligning with Rationalis meant giving them facts, structure, and control – which led to an optimized, reliable automation deployment.

Case Scenario 2: Homo Ethicus Founder – Designing an Empathetic Customer Service Chatbot

Context: The founder of a growing e-commerce startup is a classic Ethicus leader – very attuned to company values and customer relationships. The startup is implementing a no-code customer service chatbot to handle common inquiries. The founder’s top priority is that the bot delivers helpful answers with empathy and reinforces the brand’s friendly, inclusive ethos.

Approach: From the outset, the Homo Ethicus founder frames the project not just as a tech installation but as an extension of the customer experience. In meetings with her team, she emphasizes questions like, “How do we ensure the bot doesn’t frustrate people?” and “What if a customer is upset – can the bot recognize that and respond kindly?” These concerns guide their plan.

When she turns to AI Mode for research, her queries reflect this blend of practical and ethical considerations. She asks:* “What are best practices for creating an empathetic customer service chatbot?”* and “No-code chatbot platforms with multilingual and empathetic response capabilities” . The AI’s query fan-out tackles this from multiple angles. It might search product comparisons (platforms that allow training on tone and sentiment), psychological insights (“empathetic language in customer service”), and even diversity considerations (“cultural nuances in automated customer support”).

The synthesized AI answer comes back with a holistic set of recommendations. For example: a list of top no-code chatbot builders that enable custom tone and sentiment analysis, a note on enabling a language detection feature for multilingual support, and a tip that training the bot on past chat transcripts (with both positive and negative examples) can improve its empathy. It even cites a study about customer satisfaction increases when using compassionate language – which deeply resonates with the founder’s ethos.

Design and Implementation: Armed with this information, the founder chooses a chatbot platform known for its NLP sentiment detection. Using the no-code interface, she and her team design conversation flows that include checkpoints: if the bot detects a negative sentiment (angry or frustrated language), it triggers a different path with a more apologetic tone and offers to escalate to a human agent gracefully. They craft the bot’s responses carefully, often phrasing things in a friendly, reassuring manner. The founder even involves a few long-time customer support reps in reviewing the bot scripts – ensuring that the empathy honed by human agents over the years is distilled into the automation.

They use AI Mode again to refine these scripts: e.g., “Polite alternatives to say 'I don’t understand' in customer support” – the AI suggests phrases like “I’m sorry, I’m not sure I got that. Let me try again or connect you to a team member,” confirming their approach. Another query, “How to handle sensitive customer data in chatbot interactions ethically,” leads the AI to provide guidelines on privacy (don’t ask for more personal info than needed, reassure about data protection) – an ethical angle the founder also weighs carefully.

AI Alignment: The Ethicus founder finds AI Mode especially helpful for sanity-checking the human impact of the automation. At one point, she asks the AI, “What are common complaints customers have about chatbots?” The answer (fanned-out from forums and surveys) alerts her to things like bots not understanding slang, or giving canned responses that feel insincere. She uses this insight to adjust the bot’s programming – adding some common slang to the bot’s understanding and programming a few “I understand how you feel” style responses that acknowledge feeling sensing before problem-solving. By anticipating these nuances, the team ensures the bot won’t inadvertently come off as uncaring.

Outcome: When the chatbot launches, it quickly handles a large volume of inquiries (order status, return policies, etc.), taking pressure off the human support team. More importantly, customer feedback is positive – many users comment that “it doesn’t feel like a typical bot”. The bot’s empathetic touches – apologizing for inconveniences, using friendly language, seamlessly handing off to a human when needed – preserve the company’s relationship-centric brand. The founder is satisfied that automation did not come at the cost of customer goodwill. In fact, the automation enhanced consistency in tone (every customer gets a polite interaction, whereas human reps might have off days).

There is an added benefit internally: the approach has become a selling point in marketing, as the company proudly advertises that their AI is “built with empathy.” All of this was achieved by aligning the no-code technology with a Homo Ethicus mindset – prioritizing relationships and ethics at each step. The AI assistant (Google’s AI Mode) served as a valuable consultant, providing both technical options and context on human-centric best practices, ensuring the founder’s values were encoded into the final product.

Takeaway: For Homo Ethicus leaders, automation must align with values and human needs. AI tools that can incorporate ethical and relational knowledge (like AI Mode pulling empathy best practices) become powerful allies. In this case, because the AI and platform were used in a way that respected the founder’s Ethicus intent – not ignoring the “soft” requirements – the automation succeeded both functionally and empathetically. It highlights how crucial it is to bake in empathy and ethics when automating customer-facing processes, especially when your team’s dominant intelligence type is Ethicus.

Case Scenario 3: Homo Practicus Team – Streamlining HR Task Routing

Context: A Human Resources operations team at a large corporation is under pressure to improve efficiency. They decide to use a no-code automation tool to streamline task routing in HR, such as automatically assigning incoming employee requests (leave applications, benefits questions, etc.) to the appropriate HR staff. This team’s style is strongly Practicus – very practical, deadline-driven, and focused on quick wins. They don’t want a perfect system next quarter; they want a good solution next week.

Approach: From kickoff, the Homo Practicus team is action-oriented. They identify a clear problem: requests often sit idle because it’s unclear who should handle them, leading to delays. The goal: route each request instantly to the right person based on category (payroll, leave, IT issue, etc.). They outline the basic requirements on a whiteboard in one sitting and immediately move into implementation on their chosen no-code platform.

Their interaction with AI Mode is targeted and minimalistic, as their queries show. Instead of deep research, they ask very pointed “how to” questions: “How to auto-assign support tickets by category in [ToolName]?” or “Example of HR ticket routing automation” . The AI’s responses, via query fan-out, are succinct and to the point – which suits them perfectly. For the first query, AI Mode might pull the tool’s knowledge base and forum tips, responding with: “Use [ToolName]’s rule engine: create categories (Payroll, Benefits, IT, etc.) and use a condition-action rule: IF category is X THEN assign to Y (specific HR rep). Ensure each request form has a category field. Here’s a step-by-step….” accompanied by a link to a tutorial. This is exactly what they needed – basically, the answer and the instructions in one. They follow the steps and get a basic workflow running in a matter of hours.

They also leverage AI Mode to quickly troubleshoot. When testing, they encounter an issue: some requests aren’t getting categorized correctly because employees write long descriptions. A team member asks the AI, “How to handle uncategorized tickets automatically?” The AI suggests implementing a default assignment (e.g., route to a coordinator if no keyword matches) and maybe prompts them to consider adding a simple keyword detection using an AI service if available. The team decides on the simpler route (default assignment rule) – true to Practicus form, they want the straightforward fix now rather than an elaborate AI classification model that would take longer.

Design and Implementation: Within a couple of days, the no-code workflow is live: new requests from the HR portal trigger the automation, which parses the category (based on a selected dropdown in the form) and routes it to the designated person’s task list or email. The Practicus team keeps the design minimal – just a series of IF/THEN rules in the no-code interface. They don’t worry about documenting much beyond a one-page SOP, and they intend to refine as needed on the fly.

AI Alignment: During deployment, a question arises: Should they notify employees when their request is assigned? The team is initially inclined to skip that (extra work, and they figure employees just care about resolution). But one member recalls seeing something in the AI’s earlier answer or maybe a related search snippet about “closing the loop”. So they quickly ask AI Mode: “Should automated ticket assignment include notification to requester?” The AI fan-out brings back a quick rationale: automated acknowledgment can improve customer (employee) satisfaction and sets expectation (“Your request about X has been forwarded to Y”). It cites a best practice from ITSM (IT service management) where such notifications reduce duplicate follow-ups. Convinced by this practical benefit, they add an auto-reply feature in the workflow – a minor addition that pre-empts a lot of “Did you get my request?” emails.

Outcome: The HR task routing automation immediately shows results: requests are now routed in seconds to the right people, and nothing falls through the cracks. The team observes a drop in average response time within the first month. They achieved their efficiency goal with minimal fuss. Importantly, because they stuck to out-of-the-box features of the no-code tool and followed proven examples (often supplied by the AI queries), they avoided overcomplicating the solution. The simplicity means it’s easy to maintain.

One month later, when a new type of request emerges (say a new HR service), they quickly update the rules themselves – the no-code interface allows them to drag in a new condition in minutes. This agility delights them and their management. In a retrospective meeting, one team member quips, “This was the smoothest project we’ve done – it just works”. The combination of their Practicus decisiveness and the AI’s ability to deliver instant expertise (no lengthy research needed) proved powerful.

They didn’t concern themselves with academic debates or long-term implications; their focus was immediate ROI, and that’s what they got. The small addition of notification (inspired by AI) also had a side benefit: employees appreciated the prompt acknowledgment, which reflected well on HR’s responsiveness (a nice outcome that the Ethicus-minded folks in HR also approved of!).

Takeaway: A Homo Practicus team flourishes when AI and no-code tech are tuned to pragmatism and speed. The AI Mode’s fan-out was leveraged in a minimalist way – to confirm the quickest how-to path and troubleshoot issues on the spot. By aligning with Practicus priorities (actionable info, no extraneous detail), the team avoided analysis paralysis and delivered a functional automation in record time. This scenario shows that understanding the Practicus mindset – “get it done, make it work now” – and providing AI assistance in that spirit leads to rapid wins in automation.

Case Scenario 4: Homo Intuitivus Team – Innovation Pipeline Automation

Context: A product innovation team at a tech company, led by a visionary Intuitivus VP, is tasked with improving how the company generates and develops new product ideas. They opt to create an “innovation pipeline” system using a no-code platform integrated with AI. The team’s dominant style is Intuitivus: they thrive on big-picture ideas, anticipate future trends, and often rely on creative intuition for decision-making. The goal is somewhat open-ended – make our idea generation and vetting process smarter and more future-focused.

Approach: Unlike the other scenarios, this project starts with an exploratory phase. The Intuitivus team doesn’t have a rigid plan; instead, they brainstorm what an ideal innovation pipeline could do. Some ideas: use AI to scan emerging market trends and suggest ideas, have a knowledge base of past brainstorming sessions to inspire new ones, maybe an automated way to connect far-flung ideas (e.g., link a trend in AI with a customer need in an unrelated sector to spark a product concept).

They turn to AI Mode with open queries to feed this exploration. One query might be: “What emerging no-code AI tools can help with innovation management?” – to discover what’s even possible. The query fan-out likely retrieves info on tools that do trend analysis, idea management platforms, and even case studies like “How Company X crowdsources innovation with AI.”

Another query: “Patterns of successful product innovation in tech – any frameworks?” This might bring back synthesized insights from innovation literature (e.g. mention of the “three horizons” model, or how cross-pollination of ideas works).

The AI doesn’t hand them a single blueprint (nor do they expect it to). Instead, it provides seeds of ideas: one answer highlights an AI service that predicts technology hype cycles, another points to a no-code platform that can aggregate employee ideas and use voting (crowdsourcing element), and another cites an example of a company using a chatbot to encourage employees to submit ideas casually. The Intuitivus team absorbs all this, and their own intuition connects the dots. They decide to combine several approaches: they’ll set up a no-code system that periodically pulls in data from trend-watching APIs (for external inspiration), allows anyone in the company to submit ideas (internal crowdsourcing), and uses an AI model (via integration) to match new ideas with relevant market trends or past internal projects. Essentially, they design a systemic solution that spans boundaries – very much reflecting an intuitive, holistic outlook.

Design and Implementation: When building this, the Homo Intuitivus style is evident. They create a workflow that’s more networked. For instance, instead of a strict stage-gate process for ideas, they design it so ideas can loop back for more input, merge with other ideas, or spawn spin-offs – mimicking how creativity often works in non-linear ways. The no-code platform they use has flexibility for parallel workflows, and they leverage that heavily.

They use AI Mode along the way as a creative collaborator. For example, they ask, “What metrics can gauge early-stage innovation success?” – not a straightforward question, but the AI might fan out knowledge from innovation management research, suggesting measures like “innovation pipeline velocity” or “portfolio diversity index” and explain them. This helps the team decide what data to capture in their system for evaluating ideas. Another query: “Creative ways to encourage employees to contribute ideas” yields suggestions like gamification or hackathon events; they integrate this by adding a feature where contributors earn points or badges via the system.

Sometimes the Intuitivus team asks questions just to spark discussion, not because they expect a directive answer. For example, “Could an AI predict which product ideas will succeed?” The AI might answer with caveats, mentioning factors in successful innovation and perhaps referencing experimental AI models that tried to predict startup success. This answer triggers a debate in the team – ultimately they decide not to overly rely on predictive scoring (too many false negatives for breakthrough ideas), but they do incorporate a lightweight AI review step that flags similar ideas in the database or relevant market research for each new idea (more of an augmented intuition tool than a judge).

AI Alignment: The Intuitivus team finds AI Mode’s wide-ranging answers energizing rather than overwhelming. They frequently follow up on interesting threads the AI provides. Because they are Intuitives, they don’t require the AI to be certain – they treat it as a partner throwing possibilities on the table. In fact, they sometimes intentionally ask AI Mode highly speculative questions (“What if we applied open-source principles to our innovation process?”) not for a final answer, but to see what information comes back (e.g., maybe a reference to companies that tried open innovation, or cautionary tales). This back-and-forth probing is akin to a brainstorming session with the AI. Query fan-out ensures that even far-out questions return something useful – often drawing from multiple domains – which is exactly what this team wants.

Outcome: The resulting innovation pipeline system is unconventional but effective. Within a quarter of launch, the company sees a surge in idea submissions (the gamification and ease of the system encourages participation). More impressively, some of the ideas are genuinely novel combinations of trends and internal know-how – a testament to the system’s design that surfaces those connections. For example, one team submitted an idea for a new service that merges AI-driven data insights with the company’s traditional product, inspired by a trend report that the system circulated. This idea gets fast-tracked and becomes a successful pilot project.

The VP credits the synergy of human intuition and AI assistance for the improved pipeline. She notes that the AI didn’t replace their intuitive judgment, but it expanded their horizons: “The AI would show us patterns or analogous examples from other industries that we might have missed. That often validated a hunch or led us to pivot our thinking constructively.” The team’s comfort with exploring uncertainty meant they fully exploited AI Mode’s ability to provide breadth and depth. They also set up a periodic AI-generated “trend digest” in the system (an idea that came from one of their AI queries), which keeps the creative juices flowing company-wide.

One subtle benefit: the project itself became a story of innovation. It demonstrated to the organization how a forward-thinking team can partner with AI and no-code tools to create something new. This has sparked interest from other departments, who now ask how they might tailor the approach (with their own style, be it more Rationalis or Practicus) for their needs.

Takeaway: A Homo Intuitivus-led initiative thrives when given flexibility, diverse inputs, and room for iteration. AI Mode’s query fan-out provided a rich canvas of information, which the intuitive team used as fuel for their creative process. By aligning the AI’s capabilities with an Intuitivus mindset – one that values systemic connections and future possibilities – the team built a pioneering automation that likely would not emerge from a strictly linear planning process. This scenario underscores how AI can augment intuitive intelligence by revealing the unseen links and giving form to “hunches,” which in a business setting, can be the source of breakthrough innovation.

Aligning AI and No-Code Platforms with Team Intelligences

The scenarios above underscore a pivotal insight for B2B leaders: the effectiveness of AI assistants and no-code automation is amplified when tailored to the dominant intelligence type of the team using them. In practical terms, this means that implementing “one-size-fits-all” AI features isn’t enough – we should design adaptive AI modes or workflows that cater to different cognitive styles. Below are visionary yet practical recommendations for aligning AI search and automation tools with each intelligence type:

• Homo Rationalis Teams: Design for transparency and control. Ensure AI assistants provide well-structured, evidence-backed responses. For instance, AI search interfaces could offer a “logic mode” that presents information in outlines, with source links and options to drill into data (something AI Mode already hints at with its cited links). No-code platforms should cater to Rationalis by allowing clear mapping of logic (visual flowcharts, decision tables) and offering simulation/testing features to validate each rule. Provide capabilities for in-depth analysis (e.g. built-in reporting or the ability to export data for analysis) so Rationalis users can trust and verify the automation’s outcomes. By feeding their need for clarity, you gain their confidence in the automation.

• Homo Ethicus Teams: Infuse empathy and context awareness. AI assistants interacting with Ethicus users (or delivering answers about people-centric issues) should be tuned to recognize values-laden language and respond with appropriate context. For example, an AI query about “team motivation” should automatically include morale and cultural aspects in its answer. We might envision an “ethics filter” or relational context plugin for AI Mode that, when enabled, ensures every answer addresses stakeholder impact and ethical considerations. No-code tools should incorporate templates or modules for common ethical frameworks (like bias checks, consent confirmations, accessibility features) so that Ethicus teams can easily embed these into their automations. Additionally, providing a way to simulate human feedback (e.g. a feature where the automation can prompt “How would this change affect user experience?” and get AI-suggested answers) could help Ethicus teams foresee relational outcomes. Aligning with Ethicus means the technology must speak to the heart and conscience, not just the mind.

• Homo Practicus Teams: Streamline for action and quick value. For Practicus-dominant users, AI and no-code platforms should emphasize speed, simplicity, and direct results. This could mean an AI assistant has a “get-to-the-point” mode that delivers answers in bullet points or checklists, perhaps literally with a one-click “execute this” suggestion if applicable (e.g. the AI not only tells what to do, but can trigger a no-code workflow or provide the command). No-code platforms serving Practicus users should offer pre-built automation recipes and wizards that accomplish common tasks with minimal setup – essentially fast-tracking the “do” part. Because Practicus teams may not ask the AI many deep questions, the platform can proactively surface optimization suggestions (“Your workflow could be 20% faster if you… [click to apply]”). Moreover, success metrics should be front-and-center (time saved, tasks automated count, etc.), giving Practicus teams immediate feedback on the value. By aligning with their pragmatic mindset, we make the automation not only easy to build but also easy to justify and celebrate.

• Homo Intuitivus Teams: Enable exploration and pattern discovery. To empower Intuitivus users, AI tools should offer broad exploratory features – think of an AI assistant that can switch to a “brainstorm mode” or “systemic view,” where instead of one answer, it gives a map of related ideas, emerging trends, or even provocative questions back to the user. Google’s query fan-out is a great foundation; future enhancements could allow users to visualize the web of sub-queries and dive into each branch, almost like an interactive mind map of their question. No-code platforms for Intuitivus teams should be flexible and integrative: allow easy mashups of different services (to connect dots), support iterative prototyping (to quickly try out new flows), and possibly include simulation environments (so Intuitives can play out “what-if” scenarios with their workflows).

  
  

  Including AI-driven pattern recognition inside workflows can also augment intuitive leaps – e.g. an automation that periodically analyzes data for anomalies or trends and alerts the team with “Something unusual is happening; maybe worth a look.” Aligning with Intuitivus means giving the AI/automation a role as a co-creator and scout into the unknown, rather than a strict executor of known tasks.

In practice, many teams are a mix of types, and individuals themselves can embody multiple intelligences. Therefore, truly adaptive AI systems might first try to infer the user’s style (perhaps via preferences, the phrasing of queries, or even an interactive onboarding quiz) and then adjust accordingly. Imagine a future Google Search that, noticing a user consistently clicks factual sources and asks follow-ups for data, switches to a more Rationalis-toned response by default; whereas another user who asks philosophical or future-oriented questions gets a more Intuitivus-toned expansive answer. In enterprise no-code platforms, user personas could be configured – e.g., a “Rationalis mode” for power users vs. an “Practicus mode” for those who just want quick templates.

Ultimately, rethinking search and automation with the four intelligences in mind leads to more human-centric technology. It acknowledges that “intelligent” automation isn’t just about the AI’s intelligence – it’s about complementing the user’s intelligence. When an AI assistant understands how you think, not just what you ask, it can deliver information in the form that you find most actionable and meaningful. When a no-code tool aligns with your team’s cognitive culture, it can be adopted more smoothly and deliver results that truly fit the way you work.

Conclusion

The no-code revolution and AI-powered search are opening incredible frontiers for B2B teams. Google’s AI Mode and query fan-out technique exemplify how AI can synthesize vast information in seconds – but the interpretation of that information remains in human hands. Elena Buran’s four intelligences framework – Homo Rationalis, Ethicus, Practicus, Intuitivus – reminds us that those hands (and minds) come in different forms. By designing our AI systems and automation strategies to respect these cognitive-intent differences, we unlock the full potential of intelligent automation.

In the coming years, we can envision AI search assistants that dynamically adjust to the user’s thinking style, delivering not just the right answer, but the answer delivered in the right way. We can foresee no-code platforms that guide users based on whether they prioritize data, people, action, or vision, making automation design as natural as thinking out loud to a helpful colleague. This is a future where advanced technology doesn’t homogenize how we solve problems, but rather amplifies each organization’s unique intelligence mix.

In summary, “AI Mode meets the Four Intelligences” is more than a catchy phrase – it’s a call to align our smartest tools with our richest human cognitive diversity. B2B decision-makers and teams that recognize their dominant intelligence type can use this insight to choose the right AI and automation approaches: whether it’s insisting on logical rigor, building in ethical guardrails, streamlining for efficiency, or exploring uncharted ideas. The no-code AI-driven era will belong to those who not only harness technology, but do so in a way that augments their natural strengths. By rethinking search and automation in these terms, we ensure that our AI partners are not just powerful, but truly intelligent – intelligently understanding the people they serve.

Sources

1. Buran, Elena; Miloradovich, Egor. The Evolution of Intelligence: Homo Intuitivus, Rationalis, Ethicus, Practicus. Verbs-Verbi Press, 2025. https://www.verbs-verbi.com/post/the-evolution-of-intelligence-free-pdf-book-on-human-intelligence-and-ai

2. Solis, Aleyda. “Google AI Mode’s Query Fan-Out Technique: What Is It and What Does It Mean for SEO?” aleydasolis.com, May 25, 2025.https://www.aleydasolis.com/en/ai-search/google-query-fan-out/

3. Google. “AI in Search: Going Beyond Information to Intelligence.” Google I/O 2025.https://blog.google/products/search/google-ai-overviews-ai-mode/

4. Google. “Expanding AI Overviews and Introducing AI Mode.” Search Central, May 2025.

5. Fiorelli, Gianluca. “How Publishers Can Adapt to AI Mode and LLMs.” LearningSEO.io, 2025.

6. Mueller, John. “Top Ways to Ensure Your Content Performs Well in Google’s AI Experiences.” Google, 2025.

7. Rotenberg, V.S. “Search Activity and Adaptation.” Neuroscience and Behavioral Physiology, 1984.

8. Damasio, Antonio. The Feeling of What Happens: Body and Emotion in the Making of Consciousness. Harcourt, 1999.

9. Kahneman, Daniel. Thinking, Fast and Slow. Farrar, Straus and Giroux, 2011.

10. Fiorelli, Gianluca. “A Guide to Semantics or How to Be Visible Both in Search and LLMs.” LearningSEO.io, 2025.

    
    

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