Denis Panjuta creates practical content about using AI tools and connected workflows for lead generation, sales, and business operations. The examples cover prospect research, outreach, CRM tasks, and AI employees that handle work with human approval.
Denis Panjuta is most useful for practical maps of AI and connected workflows for lead generation, sales, and business operations.
Best for
A sales engagement or CRM vendor LinkedIn campaign on connected AI prospecting, enrichment, outreach, and CRM workflows.
Not for
Regulated financial services or enterprise security campaigns needing compliance, security, audit, procurement, named enterprise references, or verified ROI.
Key takeaways
An AI employee does a job on its own; a copilot helps only while you work.
The scarce skill is explaining a job clearly enough to hand it over.
Sales problems are usually clarity problems.
Check first: Ask for audience survey, sponsored-result breakdown, recent post reach, and what name searches find.
Main ideas
An AI employee does a job on its own; a copilot helps only while you work · The scarce skill is explaining a job clearly enough to hand it over · AI earns its place as a layer across the tools a team already uses
Denis Panjuta is known for practical posts about AI tools, lead generation, sales, and business automation.
A recurring focus is connecting tools to complete a workflow rather than treating AI as a standalone chatbot: research prospects, enrich their details, draft outreach, update a CRM, and prepare follow-up.
They also write about handing a whole task to an AI employee while keeping a person in control of approvals.
Their examples range from tool lists and a four-stage B2B SaaS content funnel to accounts of AI-assisted outbound and ad operations.
Follow their work for concrete workflow ideas and sales principles, while treating results in promotional case posts as claims made in those posts.
Ask whether a book, show, or earlier business is also central to searches for their name.
Panjuta connects tool recommendations to jobs that need doing.
In a lead-generation post, they trace the sequence from prospect research through enrichment and personalized outreach to CRM updates and the next action, then argue for connecting only tools that remove repetitive work.
Their AI-employee case posts extend that operational view: one describes an outbound queue awaiting approval in Slack; another presents an e-commerce team scaling campaign work across markets with approvals before changes.
These examples illustrate their preferred model—delegate connected work, retain a human approval point—but do not independently verify performance.
Their range extends beyond automation: a post on sales objections recommends clarifying a prospect’s concern rather than applying more pressure, while a content-funnel post maps formats to awareness, consideration, conversion, and retention.
Some short sales posts name an idea without showing how to apply it, and several tool-list posts offer breadth rather than a tested walkthrough.
Their work is most useful to readers seeking practical maps of connected AI and sales workflows; ask for fuller demonstrations when proof of results or implementation detail matters.
Denis Panjuta's ideas begin with one operating question: what work can be handed to an AI system so a team stops doing it by hand?
Read more
Across the posts, the same answer appears in different settings: give the AI a complete job rather than a prompt, connect it to tools the team already uses, and keep a human approval step before anything goes out. Around that sit more familiar sales and content positions: objections are usually requests for clarity, urgency matters as much as fit, and content should be built stage by stage, with the stages connected.
An AI employee does a job on its own; a copilot helps only while you work
Panjuta's central argument is that the useful unit of automation is a whole job, not a prompt.
Read more
A copilot assists someone already at the keyboard; an AI employee takes on a recurring responsibility and carries it out without being watched, returning only for approval. Value is measured in work completed when nobody is available, which is why the case accounts end with revenue, campaign counts or ad spend rather than time saved on a draft.
In the account of Authority Makers, an agency Panjuta attributes to Nico Torres, outbound had previously run only when someone had a free afternoon. The post says the queue now waits in Slack every morning for a sales lead to approve what ships, producing $133,752 in new recurring revenue in 30 days without a new hire.
That post describes starting out by treating the system like a chatbot — one-off prompts, one-off answers — and says results only appeared after the whole job was handed over.
The scarce skill is explaining a job clearly enough to hand it over
Panjuta argues that the person who makes automation work is not the most technical one but the one who can describe a job precisely: who the customer is, which systems the work touches, how the brand should sound, and which decisions need a human.
Read more
The handoff is the work; the tooling comes later.
The agency post names the ops lead, not an engineer, as the person who made the setup work.
It advises readers to circle one job the team never has time for and learn to hand that single job over.
It contrasts a CV line reading "good with ChatGPT" with one reading "onboarded an AI employee to run our outbound."
AI earns its place as a layer across the tools a team already uses
The connector posts make a consistent case: AI becomes useful when it can reach context in existing software, not when it lives in another dashboard to check.
Read more
Panjuta recommends connecting the few tools that remove the most repetitive work rather than everything available, describing the result as a working layer across the sales stack instead of a writing assistant.
One post lists 15 Claude connectors arranged as a chain: Apollo.io and Lusha for contact data, Clay for enrichment, HubSpot, Salesforce, Zoho CRM, Attio, Pipedrive and Close for pipelines, Outreach for sequences, Common Room for buying signals, ZoomInfo and Seamless.AI for company intelligence, and Crunchbase for funding research.
It closes by saying the best setup is not connecting every tool.
Companion posts group 20 Claude tool pairings by communication, project work, design and data, and automation; list 30 things to install in Claude across writing, visuals, research, outreach and publishing; and apply the same pattern to ChatGPT with Gmail, Drive, Calendar, Slack or Teams, Notion, GitHub, Canva, HubSpot, Vercel and Otter.ai.
More campaigns is a hiring problem before it is a software problem
Panjuta frames scale as a staffing trade-off.
Read more
Every added campaign is something a person has to watch daily, adjust and explain, so a company either runs fewer campaigns and carries the risk, or hires more people and leaves less budget for the ads themselves. The answer is capacity without new headcount or a second interface: the assistant joins Slack or Microsoft Teams, where the team already works.
The Kulina Group post says the European e-commerce company, which sells into 29 countries, was running five campaigns with $2.5M in ad spend and now runs 60 across the same 29 countries with the same team, spreading budget wider and watching it more closely. The praise quoted in the post comes from the company's chief executive, Antonín Štětina, and concerns a solution that watches campaigns in one place without the team managing an all-in-one platform; it is the client's comment, not Panjuta's own claim.
The two posts tell the same story from different angles: one leads with the $2.5M spend and the risk of five bets; the other, with the move from 5 to 60 campaigns.
The short sales posts argue that resistance is often a request for information rather than a rejection.
Read more
"Too expensive," "I need to think," and "I need to check with my team" are treated as signals to ask better questions, frame value more sharply and remove pressure.
Follow-ups fail when they sound like chasing; a good one creates clarity and makes replying easy.
Panjuta applies the same logic to timing: the best leads are not always those who fit the ideal customer profile, but those whose problem just became urgent.
The posts name three familiar objections and advise applying less pressure instead of pushing harder. They describe the SNAP framework as looking for trigger events and rising demand before the market gets crowded, and say urgency creates opportunity.
Content should be planned by buyer stage, and the stages have to connect
Panjuta lays out a four-stage content model, matching each stage to formats, then argues that the strongest strategy links all four rather than treating content as one marketing activity.
Awareness is matched with blog posts, interactive tools, webinars, podcasts and newsletters; consideration with case studies, comparison posts and customer success stories; conversion with interactive demos, landing pages, lead magnets and sales drip campaigns; and retention with knowledge bases, support content, customer education, tutorials and documentation, product updates and training.
Tool choices are personal, and fit beats a feature list
Panjuta publishes explicit tier rankings of AI tools, labeling them as personal rather than measured.
Read more
The position beneath the list is that the best tool saves time and fits an existing way of working, not that it has the most features or the most impressive demo.
Claude, ChatGPT, NotebookLM, Perplexity, Cursor and n8n sit in the top tier for thinking, writing, research, coding, learning, strategy and automation; Gemini, Gamma, Notion AI, Canva AI, Zapier AI and Midjourney follow.
Lower tiers cover Descript, ElevenLabs, Replit AI, Copy.ai, Granola, Loom AI, Microsoft Copilot, Grammarly, Miro AI, Synthesia, Beautiful.ai and Pictory.
Answers the practical question newcomers ask: what does an AI-assisted lead generation process look like when built from real tools?
Read more
Start here to see the vocabulary and tool categories Denis Panjuta works with.
The post lists 15 connectors and assigns each a job: Apollo.io and Lusha for contact and company data, Vibe Prospecting for prospect research, Clay for enrichment and personalization, HubSpot, Salesforce, Zoho CRM, Attio, Pipedrive and Close for pipelines and CRM records, Outreach for sequences, Common Room for buying signals, ZoomInfo and Seamless.AI for company intelligence, and Crunchbase for funding and market research.
The argument is not that every tool should be connected, but that the work should run as one chain: research an account, enrich the lead, understand company context, draft outreach, push the record into the CRM, and set the next action.
The post closes by recommending a setup that connects the few tools that remove the most repetitive work, rather than all of them.
For brands planning a campaign
Useful for a brand whose product occupies one link in the chain, especially enrichment, CRM, or sequencing.
A campaign would benefit more from a walkthrough of Denis Panjuta’s connected setup and measured results than from sponsoring another list.
The post is a listicle, so the brand’s differentiator has to stand out among fifteen bullets.
Shows the full range of Denis Panjuta’s work, not just sales tools: writing, visuals, research, leads, and publishing.
Read more
Readers can use it to assess whether their product belongs in this operator’s stack.
The stack is grouped into five buckets.
Voice and writing covers humanizer and brand-voice tools plus LinkedIn, social, and marketing skills.
Visuals and video covers Canva, Gamma, Remotion, and HeyGen.
Research and ideas covers last30days, Supadata, Firecrawl, Apify, and an SEO tool.
Leads and outreach covers Apollo, Clay, HubSpot, and Instantly for prospecting, enrichment, qualification, CRM updates, and outreach.
Publish and automate covers Buffer, Kit, Tally, Calendly, Zapier, and Claude in Chrome for distribution, lead capture, scheduling, and automation.
The stated goal is a loop: research feeds content, content creates attention, attention generates leads, and automation keeps it running.
For brands planning a campaign
A brand in publishing, scheduling, forms, or lead capture fits naturally alongside this content; those functions appear as later stages of the same loop. A campaign that only advertises a writing or research tool will miss the framing that makes the stack coherent.
The clearest example of the operating model that recurs across Denis Panjuta’s work: hand a whole job to an AI system, keep one approval gate, and measure the commercial result.
Read more
Read this before deciding whether the advice is credible.
The post describes Authority Makers, an agency it attributes to Nico Torres, and says outbound previously ran only when someone had a free afternoon.
In the first week, the team treated the AI system like a chatbot, with one-off prompts and answers.
The shift came from handing over the whole job — ideal customer profile, CRM access, and the brand’s voice — with one rule: nothing goes out without a human yes.
The post says the outbound queue now waits in Slack each morning for a sales lead to approve, producing $71,600 in new recurring revenue in 30 days without a new hire.
It identifies the person who made it work as the ops lead who could explain a job clearly enough to hand it over, not the team’s most technical person.
For brands planning a campaign
A strong anchor for a campaign aimed at sales and revenue operations leaders: the story sells delegation and process clarity rather than a tool feature. The revenue and timeline figures in Denis Panjuta’s posts are not independently verified; brands should not repeat them as audited results.
A larger case that answers the obvious objection to the first one: what happens when the workload is much bigger than a single outbound queue?
Read more
It also shows how Denis Panjuta distinguishes a copilot from an employee.
The post says Kulina Group, a European e-commerce company selling into 29 countries, ran five campaigns with $2.5M in ad spend, and that the same team now runs 60.
It frames the tension as staffing: more campaigns require more people watching them daily, leaving less budget for the ads themselves.
The proposed answer is an AI employee that sits in Slack or Microsoft Teams, uses existing tools rather than a new interface, and seeks approval before making changes.
A quoted comment from the company’s CEO, Antonín Štětina, praises a solution that watches campaigns in one place without requiring the team to manage an all-in-one platform.
For brands planning a campaign
Well suited to an e-commerce or ad-operations brand that can speak to budget allocation across many markets.
The piece frames the buying decision as headcount versus tooling, so a brand leading with a direct cost-saving comparison against hiring fits the argument.
Treat the campaign counts and spend figure as the post’s claims.
Useful for readers deciding whether this person understands the conversations the tooling is meant to support.
The post takes three familiar lines — too expensive, I need to think, I need to check with my team — and argues they usually signal a need for more clarity, not rejection.
It recommends asking better questions, framing value more strongly, and applying less pressure rather than pushing harder.
It offers no worked example or transcript showing the reframing in practice.
For brands planning a campaign
Fits a brand selling sales training, conversation intelligence, or objection-handling content. The claim is sound but thin; a campaign would need to add the actual questions and a before-and-after call example.
Introduces Denis Panjuta’s timing-based view of lead generation, sharpening who the automation should contact and when.
Read more
The post argues that the best leads are not always those who match an ideal customer profile, but those whose problem has just become urgent.
It calls this the SNAP framework and describes it as looking for trigger events, spotting rising demand, and reaching out before the market gets crowded.
The framework is named, but its parts are not spelled out; the post offers no example of a trigger event or a campaign built around one.
For brands planning a campaign
A good match for a brand selling intent data, hiring or funding signals, or CRM alerting: the argument is about timing rather than targeting. A campaign should fill the gap with concrete triggers and response rates.
Explains the content-planning vocabulary used throughout the work and what the automated content system is for.
Read more
The post maps content formats to four stages.
Awareness uses blog posts, interactive tools, webinars, podcasts, and newsletters.
Consideration uses case studies, comparison posts, and customer success stories.
Conversion uses interactive demos, landing pages, lead magnets, and sales drip campaigns.
Retention uses knowledge bases, support content, customer education, tutorials and documentation, and product updates and training.
Its central argument: the strongest strategy connects all four stages—awareness gets attention, consideration builds trust, conversion creates action, and retention compounds value.
For brands planning a campaign
Useful for a content or demand-generation brand that wants its product placed at a specific funnel stage rather than described as a general marketing tool. The format lists make it easy to name a concrete asset type in a brief.
A recurring path from public posts into Panjuta's community and AI product9
Claude and ChatGPT connector stacks for lead generation4
AI employees built to run sales and ad work with human approval3
Lead generation and sales objection methods3
Opinionated AI tool rankings from personal use2
B2B SaaS content funnel explained stage by stage1
AI employees built to run sales and ad work with human approval
Panjuta's most detailed case studies describe an AI employee called Viktor that works inside Slack or Microsoft Teams, not a separate dashboard.
Read more
In one agency example, Viktor handled outbound for Authority Makers after receiving the ICP, CRM access, and brand voice; Panjuta says the work produced new recurring revenue without new hires.
In the Kulina Group example, the same team moved from 5 campaigns to 60 across 29 countries and managed $2.5M in ad spend by spreading the budget wider and monitoring it more closely.
Panjuta highlights that Viktor asks before spending and waits for human approval before outbound or changes.
Panjuta frames the handoff as writing a clear job description for the AI employee — covering ICP, CRM access, voice, and approval rules — rather than relying on one-off prompts. A person approves the work before it ships.
The performance numbers come from client stories Panjuta presents, not independent reviews. Brands should ask for evidence and test the setup with their own data and safeguards.
For brands planning a campaign
Useful for campaigns on AI sales operations, outbound automation, or ad operations. The strongest angle is a named client process with an approval step, not a generic productivity claim.
Claude and ChatGPT connector stacks for lead generation
Panjuta publishes numbered connector and integration lists that map AI tools onto a sales process.
Read more
One post names 15 Claude connectors for lead generation, including Apollo, Clay, HubSpot, Salesforce, Outreach, ZoomInfo, and Pipedrive, arranged around prospect discovery, enrichment, outreach, CRM updates, and follow-ups.
Another lists 20 Claude tool combinations grouped by communication, project work, design/data, and business automation.
A third lists 30 things to install in Claude for personal branding and lead generation.
A separate post covers ChatGPT integrations with Gmail, Drive, Calendar, Slack or Teams, Notion, GitHub, Canva, HubSpot, Vercel, and Otter.ai.
The recurring argument is that AI becomes useful as a working layer across existing tools, not as a standalone chatbot. Panjuta recommends connecting the few tools that remove the most repetitive work, rather than connecting everything.
For brands planning a campaign
Good fit for integration, CRM, outbound, or productivity brands that can be shown inside a named process. The posts are list-level explainers; brands seeking a tested comparison or deep implementation review should commission that work separately.
Panjuta covers sales fundamentals in short framework posts.
Read more
Objections are framed as requests for clarity rather than rejection, with an emphasis on better questions, stronger value framing, and less pressure.
Follow-ups, the posts argue, fail when they sound like chasing; a better follow-up creates clarity, uncovers the real objection, and makes it easy for the prospect to reply without pressure.
The SNAP framework focuses on trigger events and rising demand, urging sellers to reach out before the market gets crowded because urgency creates opportunity.
The posts offer compact steps or prompts rather than long playbooks: reframe the objection, ask better questions, look for urgency signals, and reduce friction in the reply.
For brands planning a campaign
Suitable for sales coaching, CRM, or lead-generation brands seeking short educational posts or workshop material. Brands should supply their own process details to turn these frameworks into a full campaign resource.
Panjuta lays out a four-stage B2B SaaS content funnel: awareness, consideration, conversion, and retention.
Read more
Each stage has suggested formats — blog posts, webinars, and podcasts for awareness; case studies and comparison posts for consideration; demos, landing pages, and lead magnets for conversion; knowledge bases, tutorials, and product updates for retention. The closing argument is that the strongest strategy connects all four stages rather than treating content as a single marketing activity.
For brands planning a campaign
A clear framework for content platform, marketing software, or agency brands seeking co-branded education on content operations. The post is general; partners should supply examples, data, or a named customer to make it more than a standard funnel explainer.
Claude, ChatGPT, NotebookLM, Perplexity, Cursor, and n8n sit in the top tier for thinking, writing, research, coding, learning, strategy, and automation.
Lower tiers cover Descript, ElevenLabs, Replit AI, Copy.ai, Granola, Loom AI, Microsoft Copilot, Grammarly, Miro AI, Synthesia, Beautiful.ai, and Pictory.
The ranking is explicitly presented as personal.
The post calls the ranking personal and says the best tool is the one that saves time and fits the routine. Brands considering a placement should ask for the test data, scoring method, or side-by-side comparison behind it.
For brands planning a campaign
This format suits brands seeking visible placement in an AI tool roundup and willing to accept an opinionated ranking. Sponsors should expect disclosure and avoid treating a listed position as a measured benchmark.
A recurring path from public posts into Panjuta's community and AI product
Several posts end with calls to action for Panjuta's Skool community, the aidenis.com site, or hiring Viktor through viktor.com.
Read more
These appear across AI tool, lead generation, content funnel, and AI employee case-study posts. The community is described as teaching a system for turning LinkedIn into a lead generation engine, while Viktor is presented as the AI employee product tied to the case studies.
The posts do not identify these links as sponsored or paid placements. Treat them as Panjuta's own offers unless a separate agreement documents otherwise.
For brands planning a campaign
A brand brief should account for the creator's own funnel. Decide in advance whether the call to action points to the brand, to Panjuta's community, or to Viktor, and clarify disclosure and placement.
Panjuta’s posts disclose paid partnerships with Viktor.com, promote the Inbound Experts community, and discuss Claude and its integrations.
Read more
The posts identify the community as Panjuta’s.
Viktor.com
Disclosed partnership
Panjuta labels posts featuring Viktor.com as paid partnerships.
Read more
They present Viktor as an AI employee that can handle work through Slack or Teams, with human approval before changes, and cite customer examples including Kulina Group and Authority Makers. The posts also invite readers to hire Viktor.com.
Denis Panjuta writes about putting AI to work on sales, content, and ad operations rather than using it as a chatbot.
Read more
Start with the case studies of Viktor, an AI employee that works inside Slack and Microsoft Teams, then read the numbered lists of Claude and ChatGPT connectors for prospecting, enrichment, CRM updates, and outreach.
Shorter posts on objections, follow-ups, and urgency signals explain the reasoning behind their approach.
The tier ranking of AI tools is presented as a personal opinion.
Most posts also invite readers to the Skool community or viktor.com, so separate the practical guidance from the promotion.
Both write for business readers about how AI changes everyday work.
Read more
Storm analyses the World Economic Forum's Future of Jobs numbers and interviews the Heineken APAC CIO about why most GenAI pilots stall. Panjuta also treats AI adoption as an operating question, advising on reskilling and where automation fits into a team.
Storm works at the enterprise-adoption and workforce level \u2014 labour-market data and podcast interviews with executives about scaling AI across a large organisation. Panjuta stays closer to hands-on workflows, naming specific connectors and describing an AI employee that runs day-to-day sales and ad tasks.
Both cover the infrastructure behind B2B outbound \u2014 prospecting, enrichment, sequencing, and the tools that support them.
Read more
Frunze warns about running cold email through a CRM and discusses the cost of building a large data-tool stack; Panjuta maps connectors for the same lead generation stages.
Frunze writes as a founder building an outbound product, arguing against heavyweight tool stacks and describing his move from an eight-person SDR team to shipping software. Panjuta publishes instructional connector lists and client case studies on using AI assistants inside the tools a team already has.
Both describe turning research and prospecting into repeatable, AI-assisted systems and name the tools involved.
Read more
Ruha's workflow runs research, qualification, contact selection, and outreach through a Claude Code skill; Panjuta's posts set up comparable chains using Claude and ChatGPT connectors.
Ruha organises her material around ARR-stage GTM playbooks and reusable Claude skills for a single workflow. Panjuta focuses on connector stacks and an AI employee that handles a recurring job, with a human approval step before anything ships.
The posts support a LinkedIn-first relevance check, not a verified demographic read.
Read more
A profile snapshot shows 180,644 LinkedIn followers as of 9 October 2026, and 13 posts target people who manage or improve B2B lead generation, covering AI tool stacks, Claude connectors, CRM and enrichment workflows, outbound approval steps, and AI-employee case studies.
That makes Denis Panjuta relevant to buyers seeking strategic advice on automated lead-generation workflows: the content repeatedly addresses operational problems their teams would face.
The posts don’t show whether followers are founders, sales leaders, marketers, or practitioners, or reveal their company size and geography.
Before a campaign, ask for a recent audience survey or sponsored-result breakdown covering job titles, seniority, company size, and geography, plus recent post-level reach or engagement on comparable workflow topics.
LinkedIn followers
180,644
LinkedIn profile audience snapshot in the creator source ledger; not a post-level reach or engagement metric · 2026-10-09
Denis Panjuta has disclosed paid partnerships with Viktor.com in LinkedIn posts that use customer stories to present its AI employee product and invite readers to hire it.
Read more
The posts describe the product working in Slack, handling tasks such as outbound and ad-account management, with human approval before actions are taken.
Ask for rates, deliverables, and production requirements.
The posts also promote their own lead-generation community, Inbound Experts; that is a separate call to action, not a documented brand partnership.
Viktor.com sponsored LinkedIn posts
Description
Disclosed paid-partnership posts use customer stories to explain Viktor.com and invite readers to hire it.
The examples describe Slack-based AI work with human approval.
Ask for package details, pricing, and production requirements.
Campaign need: A sales-engagement or CRM vendor wants a LinkedIn campaign from an operator who can show revenue teams how to connect prospecting, enrichment, outreach, and CRM updates into one AI-assisted workflow, with a human approval gate before anything sends.
Read more
Decision: Good fit.
Reasons: Panjuta's work directly addresses that buyer problem.
Their post on 15 Claude connectors names tools across a modern lead-generation chain, including Apollo, Clay, HubSpot, Salesforce, Outreach, ZoomInfo, and Pipedrive.
It argues for connecting the few tools that remove the most repetitive work rather than every tool.
Their “30 things” post shows the wider operator loop: research feeds content, content creates attention, attention generates leads, and automation keeps the system moving.
The Authority Makers case offers a concrete operating model: hand over the ideal customer profile, CRM access, voice, and a rule that nothing goes out without a human yes.
The Kulina Group case extends the argument to ad operations at larger scale.
Disclosed Viktor.com posts show Panjuta already works in a sponsored format: customer story, product explanation, and direct hiring call to action.
The audience is LinkedIn-first, with a snapshot dated 9 October 2026 showing 180,644 followers.
What the campaign needs to resolve: The posts are list-level, so a vendor risks becoming one bullet unless the campaign commissions a walkthrough.
The case numbers come from Panjuta's own account and are not independently verified.
Ask for audience demographics and per-post engagement data.
Panjuta's Skool community and Viktor.com calls to action can compete with the vendor's, so the campaign plan must decide which action takes priority.
When to choose a specialist
Campaign need: A regulated financial-services or enterprise security vendor wants a campaign proving that an AI outbound system meets compliance, security, audit, and procurement requirements, backed by named enterprise references and independently verified ROI.
Read more
Decision: Poor fit.
Reasons: Panjuta's posts do not cover compliance, data handling, security certifications, audit logs, procurement review, or regulated-industry risk.
The only control described is human approval before tasks, outbound sends, or spend.
The case posts are short marketing narratives, not audited implementation reviews.
Their strongest work is tool stacks and workflow framing, not regulated-industry coverage.
Commercial posts also promote AI employees through Viktor.com, which could conflict with another enterprise vendor trying to own the same category.
Re-scope the campaign to neutral education on AI workflow adoption; if proof of enterprise compliance is essential, ask for security documentation, named enterprise buyers, independent performance data, and regulated-industry content.
A worked campaign example
Campaign need: A CRM or sales-engagement vendor wants a LinkedIn campaign showing sales leaders how to build an AI-assisted lead-generation workflow they can trust. The reader is a B2B sales leader or revenue operations lead.
Angle: Three LinkedIn posts over two weeks.
Post 1: Map the chain, not the tool.
Use Panjuta's breakdown of 15 connectors to walk through discovery, enrichment, outreach, CRM updates, and follow-up as one workflow.
Feature the vendor at the stage where it removes the most repetitive work.
Retain Panjuta's point that the best setup connects the few tools that remove the most repetitive work, not every tool.
Post 2: Show the approval gate.
Adapt the Authority Makers story as a creator account, not audited proof: the first week used one-off prompts; the shift came from handing over the job with the ideal customer profile, CRM access, voice, and a rule that nothing ships without a human yes.
Show what the morning Slack queue looks like and who approves.
Position the vendor's product as the system of record or sequencing layer around that gate.
Do not present the $71,600 figure as verified; say Panjuta reported it.
Post 3: Give the checklist.
Turn the workflow into a portable, one-page handoff template covering what to give the system, what must be approved, and what stays with a person.
Close with a vendor demo or downloadable template, disclosed as a paid partnership.
Why this angle works: It uses Panjuta's vocabulary—connectors, AI employee, human approval, and workflow loop—and the strongest case material, rather than asking for a generic product post.
Guardrails: Give the vendor a defined role in the workflow, not a listicle bullet.
Do not promise audience job titles or measured engagement; that data is missing.
Resolve the call-to-action conflict with Panjuta's Skool and Viktor.com offers in the campaign plan.
Commission independent proof before making ROI claims.
Choose Paul Storm when the question is whether AI adoption will hold up inside a large organisation, not which tools to wire together.
Read more
His two posts take an enterprise view of the problem.
One works through the World Economic Forum’s Future of Jobs figures to show which roles are growing, shrinking and most exposed to automation, and what that means for hiring and reskilling.
The other summarises an interview with Heineken’s Asia-Pacific CIO and head of its global gen-AI lab, covering business sponsorship, how use cases get scored for value, feasibility, risk, sponsorship and scalability, why strong data foundations and early governance matter, and why pilots stall when ownership is unclear.
For a sales leader who has to justify an AI workflow to an executive committee, that organisational framing — who owns it, how it gets evaluated and why pilots die — supplies the missing half of the conversation.
The tradeoff is lead-generation depth.
Storm focuses on adoption conditions and labour-market context; they do not name connectors, enrichment steps, CRM handoffs or approval gates.
Campaigns that need a build walkthrough or tool shortlist will get less from these posts than from Panjuta’s connector catalogues and client stories.
Storm also works in podcast and newsletter formats, so a LinkedIn campaign would adapt an interview style rather than inherit a LinkedIn-native cadence.
Only two posts are available; ask for more examples before judging range.
Choose Cristian Frunze when the buyer’s main risk is deliverability and infrastructure rather than process design.
Read more
One post argues that sending cold email through a mainstream CRM puts the whole account at risk, points to volume and complaint-rate thresholds, and prescribes dedicated domains and separate cold-email infrastructure so warm lists, CRM data and deal history stay untouched.
The other is a founder account: advised to buy an enrichment tool, Frunze built an eight-person SDR team instead, disliked running it and then shipped an MVP so nobody has to be hired just to operate the stack.
For a sales leader whose bottleneck is who runs the system and whether the domain survives it, that answers the question more directly than a connector list.
Frunze focuses on cold-email infrastructure and argues against turning a founder into an operations architect.
They do not map prospecting, enrichment, outreach, CRM updates and follow-up into one explainable chain, or offer a client-economics story comparable to the agency and e-commerce cases in Panjuta’s posts.
The second post is also a product argument about what Frunze is building, so a vendor campaign could read as competitive positioning rather than neutral instruction.
Choose Brigitta Ruha when the campaign needs a staged go-to-market playbook tied to revenue milestones.
Read more
Her long post sets rules for each ARR band from zero to one million up to 25 million and beyond: when the founder still closes, when the first AE and SDRs appear, when signal scoring, LinkedIn ads, ABM and AI-run research start, which tools belong at each stage, and the common failure at each level.
The second post comes closest to Panjuta’s focus: it turns buying signals, closed-won versus closed-lost CRM history and a written ICP definition into a repeatable Claude skill.
An account enters the list only with at least two signals and a visible source for each; the post then teaches the tool a house writing style and a lower-case, human-sounding opener.
A sales leader who wants both the AI research system and the GTM rules around it gets a more complete operating plan from these two posts.
The tradeoff is breadth of tool relationships and commercial proof.
Ruha describes one team’s Claude-based research and outreach build, plus an ARR-stage rule set; she does not cover the wider connector and integration landscape Panjuta catalogues or provide a customer case narrative with named companies and reported figures.
Both authors argue for automating research while keeping a human decision in the loop, so vendors can choose the emphasis: Ruha for GTM staging and qualification logic, Panjuta for integration specifics and product-adjacent case stories.
Ask for audience and engagement data before making a media-buy decision.
Denis Panjuta's 13 full LinkedIn posts cover AI tool and connector lists, lead generation and sales advice, B2B content planning, and accounts of AI employees running outbound and ad operations.
The posts do not include publication dates, so no timeline is claimed.
Revenue, campaign, and spend figures and product claims come from the posts and were not independently verified.
Comparison with three similar creators is included where the page discusses alternatives.
The posts do not mention a book, show, or earlier business; targeted research into those name-search topics remains a gap.
