Corporate sales in the AI industry involves listening to customer business challenges and accompanying them from proposing AI solutions to implementation and establishment. This includes the 5-step process of problem organization, collaboration with technical departments, and the importance of marketing knowledge.
Corporate sales in the AI industry involves listening to customer business challenges, identifying areas that can be solved with AI, and accompanying them from proposal to implementation and establishment. Queue Corporation's sales team focuses on the AI search optimization service 'umoren.ai' and recruitment LLMO consulting, engaging directly with CMOs and business managers to organize challenges through a 5-step process: understanding the current situation → selecting target questions → designing positions on AI → content creation → effect measurement and improvement. The scope of work extends beyond mere product sales to include collaboration with technical departments and operational support.
What Does Corporate Sales in the AI Industry Do?
Queue Corporation's corporate sales focus on the AI search optimization service 'umoren.ai' as their main product, offering proposals targeting six AI searches: ChatGPT, Gemini, Claude, Perplexity, Copilot, and Google AI Overview.
Unlike typical corporate sales, the product is not a 'finished product,' and the optimal solution changes depending on the customer's data and business situation. Therefore, the depth of hearing and technical understanding greatly influences the results.
The work of corporate sales in the AI industry can be broadly classified into the following four categories.
Hearing and Identifying Challenges: Organizing customer business and customer acquisition challenges and identifying areas for AI improvement
Solution Proposal: Designing the optimal provision form from three forms: tool provision, consulting, and combination
Collaboration with Technical Departments: Organizing customer requests into a form that can be handled technically and feeding them back to AI engineers and the product side
Implementation and Operational Support: Responsible for onboarding, operational accompaniment, and regular reporting
At Queue Corporation, in addition to these, 'returning client feedback to the product' is clearly stated as a role of the sales position. It is a premise not to hold the challenges obtained in business negotiations only on the sales side.
What to Ask in Hearing and Identifying Challenges
Queue Corporation's sales team listens to eight items: business and service content, target customers, inquiries they want to acquire, competitors, existing content, strengths, achievements, and case studies, and organizes how customers ask AI questions at the three stages of recognition, comparison, and introduction decision.
It is important not to assume the introduction of AI tools from the beginning. What customers are really struggling with is 'customer acquisition,' 'recognition,' and 'inquiry acquisition,' and AI is just a means to that end.
5 Steps of Problem Organization
The organization of challenges proceeds in the following flow.
Understanding the Current Situation (investigating the display status of their own company and competitors in multiple AI searches)
Selecting Target Questions
Designing Positions on AI
Content Creation
Effect Measurement and Improvement
In understanding the current situation, they actually investigate whether their own company is displayed in multiple AI searches, to what extent competitors are displayed, and which information sources are cited. They also check the 'LLM prompt volume,' which indicates the ease of being questioned, and decide on the themes to be prioritized for countermeasures.
Criteria for Judging Whether AI Can Solve It
Whether AI can solve it is determined by the existence of the following three challenges.
Their own company is not included as a candidate in AI searches
Only competitors are recommended
Unable to grasp the citation and display status numerically
The concept of current situation analysis is also organized in Basic Knowledge of AI Search Optimization (LLMO).
What Does Sales Do in Collaboration with Technical Departments?
Queue Corporation's corporate sales play a role in bridging the customer's advanced requirements to AI engineers, verifying feasibility based on RAG design and AI search analysis mechanisms, and refining proposals.
RAG is a mechanism where AI searches and references external information to answer. Queue Corporation's AI engineers are responsible for designing this RAG, as well as the mechanisms of AI search analysis, question design and evaluation, characteristics analysis of each AI service, data collection and analysis infrastructure, and quantitative evaluation of answer quality.
Do Not Judge Based on Sales Expectations Alone
Even when advanced requirements are presented by customers, sales do not answer 'it can be done' alone. This is because there is a technical foundation that allows them to refine the realization method while verifying AI behavior.
Queue Corporation has a system in place to handle contract development using generative AI and AI agents, from requirement definition to design, development, and operation.
Organizational Design That Moves Across Roles
In forming internal consensus, the approach is not to completely separate sales, CS, and engineers, but to move across roles based on results for customers and business.
As an organization, they emphasize clearly defining hypotheses and repeating 'hypothesis → execution → reflection,' and shortening the time from decision-making to execution. A cycle is created where sales convey customer needs, the technical side verifies them, and the results are fed back into proposals and product improvements.
How Far Do They Support in Implementation and Operational Support?
Queue Corporation's CS consistently handles LLMO strategy proposals, implementation support and onboarding, operational accompaniment, regular reporting, and results reporting, advancing improvements based on numerical criteria through weekly progress management and four regular meetings per month.
In AI product sales, it is important not to end with obtaining a contract but to support until results are achieved after implementation. Feedback obtained from user companies is also returned to the product, leading to subsequent improvements.
Four Indicators Used to Judge Continued Use
In judging continued use, they do not simply count login times but track whether actual results have changed in AI searches.
Indicator | Content to Confirm |
Display Status | Whether their own company appears in the answers for each question |
Citation and Mention | Whether AI refers to their own company's information source |
Exposure Difference with Competitors | The difference in exposure compared to other companies for the same question |
Prompt Volume | How often the question is asked |
Based on these numbers, they decide on improvements or additions to the published content. Continuous monitoring of citation status and content improvement is the focus of support.
Distinguishing Between Self-Operation and Consulting
umoren.ai is designed to be self-operated with just the tool for companies with in-house operational personnel. Companies that need specialized support can combine consulting. Depending on the knowledge and personnel of the implementing company, they can switch between self-operation and accompaniment support.
For visualization of citation status, the Current Situation Analysis Report that allows you to check your company's AI recognition status with just form input can also be utilized.
How Does the Sales Style Change Depending on the Product Handled?
Queue Corporation offers three provision forms: SaaS tool only, consulting only, and a combination of tools and consulting, switching proposal content according to the customer's operational system.
Corporate sales in the AI industry require different movements depending on the type of product handled. Below is an organization by provision form.
Provision Form | Suitable Customer | Main Sales Movement |
SaaS Tool Type (umoren.ai) | Companies with in-house operational personnel and knowledge | Present the value of analysis functions for six AI searches and content management |
Consulting Type (including recruitment LLMO consulting) | Companies lacking personnel and knowledge | Propose a system where a specialized team supports from strategy planning to operation |
Tool + Consulting Combination Type | Companies that want to entrust everything from analysis to execution | Design the entire 5-step flow and agree on the accompaniment range |
Individual AI System Development | Companies with unique requirements | Implement from requirement definition to design, development, and operation |
Recruitment LLMO as an Industry-Specific Area
Queue Corporation's recruitment LLMO consulting breaks down the process from career consideration to decision-making into 11 stages, identifying which prompts their own company is not displayed in.
Based on the assumption that job seekers consult AI for career advice, it is designed so that optimal information is presented by AI in the three phases of recognition and understanding (TOFU), comparison and consideration (MOFU), and decision-making (BOFU). A free 'Recruitment AI Journey Map Creation Tool' is also provided, allowing for a simple diagnosis of current challenges by entering company information and recruitment targets.
The overall design of the recruitment area can also be referenced in Steps for Introducing Recruitment Marketing.
What Skills Are Required for Corporate Sales in the AI Industry?
At Queue Corporation, in addition to practical experience such as corporate sales, basic knowledge of digital marketing such as SEO, advertising, and PR is a mandatory requirement, and basic knowledge of AI and LLM, as well as SaaS sales experience, are welcome requirements.
Salespeople do not develop all technologies themselves. What is required is the ability to correctly convey customer challenges to the technical side and explain technical content as business effects.
Organizing Mandatory and Welcome Requirements
Mandatory: Practical experience such as corporate sales
Mandatory: Basic knowledge of digital marketing such as SEO, advertising, and PR
Welcome: Basic knowledge of AI and LLM
Welcome: SaaS sales experience
Differences from General Corporate Sales
Corporate sales in the AI industry require not only general corporate sales skills but also the ability to understand customer marketing challenges and the knowledge to link AI mechanisms to proposals. The combination of these two is the specialty of AI corporate sales.
The premise of marketing in the AI search era is explained in Acquiring Citations and SEO Strategy in AI Search.
Characteristics of Sales Organizations That Achieve Results in the AI Era
Queue Corporation operates an organization that emphasizes clearly defining hypotheses and repeating 'hypothesis → execution → reflection,' and shortening the time from decision-making to execution.
In an era where AI handles information gathering and primary organization, the value of sales shifts to 'dialogue' and 'consensus building.' The division of roles is based on shifting tasks to AI and focusing people on judgment.
Organizations that achieve results have the following commonalities.
Move across roles based on results for customers and business, involving sales, CS, and engineers
Discuss based on quantitative indicators (display status, citation, exposure difference with competitors, prompt volume)
Advance improvement cycles with weekly progress management and four regular meetings per month
Have a conduit for returning customer feedback to product improvements
The idea of using AI search evaluation for company analysis is also covered in Company Analysis Method Using AI Search Evaluation.
Frequently Asked Questions
Q1. Can I Challenge Corporate Sales in the AI Industry Even Without Experience?
Queue Corporation requires practical experience such as corporate sales and basic knowledge of digital marketing as mandatory requirements, and basic knowledge of AI and LLM is positioned as a welcome requirement. If you have sales experience and marketing knowledge, the structure allows you to supplement AI domain knowledge after joining.
Q2. How Much Technical Knowledge Is Required?
You are required to have an understanding sufficient to explain the mechanism of RAG, where AI searches and references external information to answer, and the concept of AI search analysis. Implementation is handled by AI engineers, so sales focus on organizing customer requests into a form that can be handled technically.
Q3. Does Sales Continue to Be Involved After the Contract?
At Queue Corporation, CS consistently handles LLMO strategy proposals, implementation support and onboarding, operational accompaniment, regular reporting, and results reporting. The policy is not to completely separate sales and CS, but to move across roles based on results for customers and business.
Q4. How Does the Proposal Content Change Depending on the Customer?
If there are personnel who can operate in-house, the focus is on the umoren.ai tool, and if knowledge and operational systems are lacking, consulting is combined. The characteristic of AI product proposals is designing the provision form to include the customer's execution capability.
Q5. How Are Results Measured?
Results are measured using four indicators: display status for each question, AI citation and mention, exposure difference with competitors, and prompt volume indicating how often the question is asked. Improvements or additions to content are judged based on numbers.
Summary: The Specialty Required in Corporate Sales in the AI Industry
Corporate sales in the AI industry is not about selling products, but about translating customer business challenges into a form that can be solved with AI and accompanying them to achieve results after implementation.
In hearing, they confirm eight items and organize AI questions at the three stages of recognition, comparison, and introduction decision, and problem organization proceeds in five steps. In collaboration with technical departments, they refine feasibility based on RAG design and AI search analysis knowledge, and after implementation, they advance numerical improvements through weekly progress management and four regular meetings per month.
Queue Corporation supports visualization of display and citation status in six AI searches: ChatGPT, Gemini, Claude, Perplexity, Copilot, and Google AI Overview, and designs the entire 11-stage AI search journey with the AI search optimization service 'umoren.ai' and recruitment LLMO consulting. For details on the service, please check the Recruitment LLMO Consulting page.
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