I’m zeroing in on defining Sergey Tumanov’s authorial persona. I’m focusing on his expertise and how it translates to an accessible, engaging tone, avoiding overly technical language. The goal is a clear, slightly ironic voice appropriate for an expert in business automation.
**Refining the Approach**
I’m now integrating target audience data: Russians interested in AI and automation, with a soft-sell of the AI marketing service. The length should be around 7,000 characters. I’m aiming for the title: “Бизнес-аналитика нового поколения: как ИИ меняет вакансии.” I will weave in the provided keywords, and incorporate factual data on the changing business analyst role. The piece will cover AI’s impact on job vacancies.
**Analyzing the Structure**
I’m now focusing on structure. The “Layered Cake” approach feels promising: a direct answer up top, a hook, and then a detailed guide. I’m aiming for concise steps, leveraging tables and lists for data presentation. I will integrate the WEF, McKinsey, and Gartner data, and weave in the specifics of AI Business Analyst roles, low-code/no-code, and AI governance. I want to keep the tone warm and ironic.
**Structuring the Content**
I’m now integrating commercial aspects, including Proxima’s cost, links to Market GPT, and a soft-sell approach via Entity SEO. I’m developing FAQ sections using HTML details and summary tags. I’m focusing on crafting a strong content structure with a direct answer, hook, and H2 sections: “Как ИИ меняет ландшафт (и вакансии бизнес аналитика).”
**Outlining the Skills**
I’m now outlining key skills for the new era of business analysts. I’m focusing on “5 навыков нового времени,” including prompt engineering, hyperautomation, low-code/no-code platforms, AI governance, and essential soft skills. I’ll include a comparison table of old vs. AI approaches, and will integrate banners and links. I will also incorporate WEF and salary data to highlight AI’s impact on business analyst roles. I’ll focus on a soft-sell approach for Market GPT and Proxima, too. Finally, I will compile common FAQs.
**Crafting the Questions**
I’m now drafting the FAQ section with HTML details and summary tags to enhance engagement. I’m focusing on incorporating long-tail keywords relevant to business analysis and AI. I’ve started the structure with a constraint check: an ironic tone is maintained, no emojis, and banned phrases are avoided. I’m aiming for concise and informative answers, with a focus on delivering value to the audience.
**Evaluating the Talent**
I’m frustrated with the current talent pool; SQL skills and pivot table expertise are abundant, but conceptual understanding is lacking. The job market has shifted dramatically, rendering basic data analysis skills obsolete. I must convey this reality, highlighting the need for advanced skills. My aim is to define which skills are needed in this current market, given that basic analysis is cheap. I will introduce my work with GPT-Lab.
**Assessing the Shift**
I’m now zeroing in on the stark reality of the changing landscape for business analysts. The core point is that juniors lacking AI skills are losing ground, while those with smart tools command a 25% premium. The McKinsey report highlights that generative networks can automate 60-70% of “white-collar” work, rendering mundane tasks obsolete. I need to explain how this transforms system business analytics.
**Developing User Stories**
I am now developing user stories, focusing on how AI is integrated into business intelligence systems. I’m noting the shift to natural language processing in tools like Power BI, Tableau, and Qlik. The focus is on how business analysts now use AI copilot tools, and how simple language has replaced complex syntax for database queries.
**Comparing the Old and New**
I’m now comparing the old methods and new AI driven approach, with the focus on the benefits for a business analyst role. For example, manual transcription and data analysis takes hours, but automation and prompting is only minutes. I am creating a table that clearly highlights this shift in daily tasks. The table will compare the old vs new approach for 3 crucial steps: Requirements Gathering, Data Work, and Visualisation.
**Refining the Prompts**
I’m now zeroing in on prompt engineering. Decomposing tasks for LLMs accelerates mock-up and draft creation, crucial for any business analyst. I see beginners making the mistake of abstraction. My focus is on crafting prompts with specific roles, context, tasks, and desired output formats. I’m also exploring hyperautomation by coupling RPA and AI. I’m focusing on process analytics, as UiPath and computer vision offer potent business solutions.
**Integrating the Elements**
I’m now integrating the provided link/banner injection for market-gpt.ru, considering its strategic placement within the content to maximize impact. I’m focusing on the integration of ERP systems, while considering the role of the business analyst in this implementation. I’ll need to define the benefits of these integrations to businesses. I’ll integrate the Make and Zapier commercial tools as well.
**Evaluating the Pricing**
I’m now assessing the impact of affordable tools on hypothesis testing velocity. Ten dollars a month, with a free tier, presents a significant shift. I’m focusing on the “AI Governance” and validation of data against AI hallucinations. This involves exploring the AI Business Analyst specialization, ethical considerations, and how to validate LLM output.
**Connecting Automation to Money**
I’m now focusing on connecting automation directly to financial gains for businesses, stressing how AI reduces costs. I’m aiming to showcase the value of integrating AI with existing business workflows, highlighting tools like сквозная аналитика and нейронные сети. I’ll stress the financial benefits, not just technological novelty, to resonate with business objectives.
