Yiftah Shiri is most useful for practical AI agent and CRM adoption examples for go-to-market teams.
Best for
B2B SaaS selling AI agents to sales and marketing teams, especially adoption-focused video series.
Not for
A CRM or data platform seeking neutral competitor comparisons with independently measured conversion and time-to-value results.
Key takeaways
An AI agent only counts if ordinary staff can build, customise and use it daily.
Scattered answers are the problem a connected company brain is meant to solve.
AI does the volume; people do the moments that matter.
Check first: Confirm bookability, sponsorship policy, and recent audience results before contacting them.
Main ideas
An AI agent only counts if ordinary staff can build, customise and use it daily · Scattered answers are the problem a connected company brain is meant to solve · One generic assistant for everyone is rejected; each seller gets their own
Yiftah Shiri works on go-to-market at monday.com and writes about applying AI to sales, marketing, and CRM work.
Their posts show a consistent focus: AI should solve specific team problems and be easy to use in daily work, not just impress in a demo.
They illustrate that idea with a sales agent tailored to a rep’s deals and customer conversations, and with a “company brain” meant to connect information scattered across business systems.
Follow them for practical examples of how AI might fit into GTM workflows, with an emphasis on adoption and human judgment alongside automation.
Shiri connects big ideas about AI in business to concrete workflows.
In the Closer example, the agent draws on company knowledge but adapts its guidance to an individual seller’s deals, helping with opportunity-specific coaching and customer-facing materials.
The “company brain” post makes the underlying problem legible through questions such as why a customer is at risk or why a deal was lost.
They also resist a one-size-fits-all approach: one post says customers should be able to work through the CRM interface or a preferred LLM, while another frames AI as handling volume and people as handling moments that matter.
Together, these examples help readers weigh practical adoption, context, and the role of people—not just AI capability.
Yiftah Shiri's arguments circle one question: what makes AI useful to a working GTM team?
Read more
Rather than focus on model capability, they judge agents by adoption—whether ordinary staff can set one up, customise it and use it daily; whether it eliminates the digging scattered data forces on people; and whether it fits the tools and interfaces they already use. They pair a simplicity standard with a connected, personalised assistant that knows a specific seller's deals, and draw a clear line between AI handling repeatable volume and people handling judgement calls.
An AI agent only counts if ordinary staff can build, customise and use it daily
Shiri argues that a good AI agent is measured not by what the model can do but by who can deploy it: "any idiot can set it up," customise it and use it effectively every day.
Read more
Large enterprises hire consultancies to strategise and deploy, spend millions and wait months or over a year to get moving—an approach smaller organisations cannot afford.
They list three disqualifiers: if an agent needs a whole team to implement, takes three months to integrate or is not seamless from day one, it is not worth doing.
The measure is build-to-daily-use rate.
Shiri describes deploying agents internally at monday with one KPI in mind: build-to-daily-use rate, meaning real usage rather than demonstrations.
A later post describes Closer, a far more elaborate agent personalised for each seller. The simplicity standard and sophisticated build coexist: Shiri judges it by whether people adopt it, not whether it stays minimal.
Scattered answers are the problem a connected company brain is meant to solve
For Shiri, the hard part of an AI assistant is context, not conversation.
Read more
Answering an ordinary business question can mean pulling data from the warehouse, checking the CRM, reading support tickets, searching Slack, opening dashboards and asking several people what happened. A connected company brain brings sales calls, CRM history, pricing discussions, product gaps, competitor mentions and earlier conversations together in one place, replacing hours of manual chasing with a direct answer.
Shiri borrows a plot point from the show Silo: characters wonder whether a voice is human or AI because it seems to know the history, rules and people. They map that idea onto questions a company brain should answer in seconds: why a customer is at risk, why conversion dropped, why a deal was lost and what to do with a renewal.
One generic assistant for everyone is rejected; each seller gets their own
Shiri explicitly rejects the idea of a generic brain for everyone.
Read more
The team built an internal GTM agent with a shared foundation—product materials, sales content, best practices and customer knowledge—then personalised it so each seller's agent knows their deals, conversations, meetings, selling narrative and communication style. Shared context becomes useful when grounded in the individual's actual work.
The agent, named Closer, coaches on a specific opportunity, flags deal roadblocks, creates customer-facing assets to ramp new reps faster, learns with each deal and runs wherever the seller works—Shiri's own in Slack, others in the CRM or in Claude. It began as an internal experiment, useful enough that the team moved to put it into the product.
Shiri treats interface choice as a customer right, not a product constraint: a CRM should work through its own simple interface or a preferred LLM provider, with tooling that supports either route smoothly.
Read more
The priority is flexibility and interoperability, not locking customers into one way of working.
The post makes the point in two lines: keep working in the intuitive interface or run the business through a favourite LLM provider; the tools will support either route.
AI does the volume; people do the moments that matter
Shiri lays out the rule for growing a CRM business with fewer resources: agents handle repeatable, high-volume GTM work, while people handle judgement and moments that carry the relationship.
Read more
The argument is about where automation earns its place, not replacing marketers or sellers.
Shiri lists three agents shown at a marketing event: one turns a discovery call into a demo built on the client's own data; one reaches every free-trial sign-up while intent is at its peak; and a marketing assistant drafts on-brand, legally safe copy without the usual review back-and-forth. The post also names the marketing leaders joining the session.
For anyone weighing what makes an AI agent useful beyond a demo, this post sets out Shiri’s clearest adoption test and explains why ease and time-to-value matter.
Read more
Shiri argues that an AI agent should be easy for ordinary employees to set up, tailor and use every day—not merely impressive in a demo.
They contrast that standard with lengthy, costly enterprise deployments and say monday focused on the build-to-daily-use rate.
The post offers a practical adoption test: can teams get useful work from an agent without specialist implementation?
For brands planning a campaign
For a series about AI adoption in sales and marketing, make daily use—not novelty—the through-line. Show a simple agent task from setup to repeated use, then ask viewers what would prevent their team from adopting it.
Want to see how Shiri applies AI to a real GTM workflow?
Read more
This post offers the most detailed example, including what the agent knows and how a rep might use it.
Shiri describes an internal sales agent called Closer, built on company product, sales, customer and best-practice material.
Tailored to an individual seller’s deals, conversations and communication style, it can coach on a specific opportunity, help new reps create customer-facing materials and work in tools such as Slack or a CRM.
The example makes the “company brain” idea concrete: shared knowledge becomes useful when grounded in a seller’s actual context.
For brands planning a campaign
Follow one opportunity through the agent’s workflow: retrieve relevant context, identify a deal obstacle and help the rep prepare a next step. Keep the demonstration tied to a specific task rather than presenting the agent as a generic assistant.
If “company brain” feels abstract, this post shows the questions Shiri wants it to answer and the business context they say it should connect.
Read more
Using a question raised by the show—whether a voice is human or AI—as an entry point, Shiri describes a company brain that can connect sales calls, CRM history, pricing discussions, product gaps, competitor mentions and prior conversations.
They illustrate the goal with business questions about customer risk, conversion drops, lost deals and renewals.
The post explains the problem the agent is meant to solve: relevant context is scattered across systems and people.
For brands planning a campaign
Use this as a problem-framing episode before a product demonstration.
Follow one business question across scattered information sources, then show what a connected answer would need to include.
Present the speed comparison as Shiri’s claim, not an independently measured result.
Wondering whether Shiri’s CRM view depends on one interface?
Read more
This short post states their preference for meeting users in the tools they choose.
Shiri presents two acceptable ways to work: use the CRM’s interface or work through a preferred LLM provider, with tools intended to support that choice. The point is not that one interface suits everyone, but that the CRM should fit different work habits.
For brands planning a campaign
Use this to anchor a discussion about adoption and interface choice. Compare the same CRM task in the native interface and an LLM workflow, and clarify what capabilities and safeguards each route provides.
For a quick look at the marketing tasks Shiri associates with AI, this post gives three concrete examples and their concise principle for dividing the work.
Read more
Shiri sums up an approach to AI in CRM growth as “AI does the volume” while people handle important moments.
They name three examples for a marketing event: turning a discovery call into a demo using a client’s data, reaching free-trial sign-ups while interest is high and drafting on-brand, legally safe copy.
The examples distinguish repeatable work from the human judgment and review it still requires.
For brands planning a campaign
Build a campaign segment around the handoff between automation and people.
Demonstrate one of the named tasks, then show where a marketer or seller reviews, personalizes or takes over.
Ask what legal review the copy requires; the post doesn’t establish that it can be skipped.
Analogy-led teaching, with a product demonstration and a link4
Agents anyone can build, customise and use daily2
A company brain as shared context, not one generic brain2
A CRM that meets customers where they already work1
AI does the volume, humans do the moments that matter1
Agents anyone can build, customise and use daily
Shiri argues that the real benchmark for an AI agent is whether anyone—not just a specialist team—can set it up, customise it and use it effectively every day.
Read more
The posts contrast that with enterprise routes through consultancies, months of integration and waits into the following year.
Smaller organisations cannot wait: if an agent needs a whole team to implement, takes three months to integrate or is not seamless from day one, it is a non-starter.
They describe tracking one internal metric, build-to-daily-use rate, as the standard that matters.
Judges agents by build-to-daily-use rate and time-to-value, not model capability or feature lists.
For brands planning a campaign
Fit for a video series that takes a GTM team from an empty setup to a working daily agent in one sitting. Script the setup, not just the finished result, and make the time-to-value claim concrete.
A company brain as shared context, not one generic brain
Shiri opens with a television plot where a voice seems to know the history, context, rules and people, then maps it to a business problem: answering one question today can mean pulling from the data warehouse, the CRM, support tickets, Slack and dashboards.
Read more
A connected company brain brings sales calls, CRM history, pricing discussions, product gaps and competitor mentions into one place to answer questions such as why a customer is at risk, why conversion dropped or why a deal was lost. On top of that brain, their team built a personal agent for each seller that knows their deals, conversations and selling style, and lives in Slack, the CRM or Claude.
Start with a business question that currently requires several tools and people, then show the same question answered from one connected context.
Shiri rejects one generic brain for everyone; the point is a per-seller agent that knows that seller’s narrative and deals.
For brands planning a campaign
Structure a series around one buyer question per episode, with the agent demonstrating the answer in a real surface such as Slack or the CRM. Personalisation for each rep is central, so avoid a generic “one assistant for the whole company” angle.
A CRM that meets customers where they already work
The shortest post states the position directly: keep working inside the product’s own interface or drive the business through a preferred LLM provider, with tooling to make either route work smoothly.
Read more
It argues for flexibility and interoperability rather than demonstrating a feature.
For brands planning a campaign
Useful for content aimed at buyers worried about switching tools or being locked into one interface. Show the same workflow from two surfaces to make the interoperability point visible.
AI does the volume, humans do the moments that matter
Shiri names this rule as the basis for growing monday CRM with fewer resources.
Read more
The examples are specific: an agent that turns a discovery call into a demo built on the client’s own data; an agent that reaches every free-trial sign-up while intent is at its peak; and a marketing assistant that drafts on-brand, legally safe copy without the usual review back and forth. The pattern: automate high-volume, repeatable GTM work while people keep the judgement calls.
Divides GTM work into volume tasks for agents and judgement moments for people, then illustrates each side with a named workflow.
For brands planning a campaign
Build a series around one high-volume GTM task per episode: show the agent handling it end to end, then state what the human still owns. Trial-signup timing and copy review are the most concrete hooks and can carry a shorter format.
Analogy-led teaching, with a product demonstration and a link
The posts share a recognisable arc: open on a mainstream reference or widely felt anxiety, translate it into a business problem, walk through an internal example and close with a product pointer.
Read more
The most developed version uses a television season’s “is that voice human or AI” question to introduce the company-brain argument.
Product posts follow the same path from internal experiment to something useful enough to ship, ending with a link to monday CRM.
Shiri also notes a conference appearance where the practical examples were to be shown live.
Start with an analogy or shared anxiety, move to a concrete internal example, then show a demonstration or link. The written posts are conversational, using short lines and deliberate repetition for emphasis.
Shiri’s posts are text posts on one channel, with no video transcript or long-form article. Ask for an on-camera sample before assessing delivery style.
For brands planning a campaign
A series that opens on a familiar anxiety before showing a working example fits the approach Shiri already uses. Their posts favour concrete demonstrations over abstract AI commentary.
The posts describe one clear commercial relationship: Yiftah Shiri works at monday.com in a go-to-market role and writes about applying monday's AI agents and CRM product to sales and marketing.
Read more
They also describe an internal monday agent called Closer that is being moved into the product. Ask whether any sponsorship, affiliate, client or investment relationships exist beyond what the posts disclose, and whether any other companies are partners or clients.
monday.com
Employment
The posts describe Yiftah Shiri as part of monday.com's go-to-market team.
Read more
One says the team built an AI agent on monday, fed it the organization's product, sales and customer knowledge, and is now bringing the internal agent, called Closer, into the product.
Another says "we deployed that here on monday" and points readers to monday CRM.
A third explains "how we use AI to grow monday CRM with fewer resources."
A fourth describes speaking at G-CMO Europe, hosted at "our monday.com London office."
Together, the posts show employment at monday.com and a role promoting its CRM product.
Ask for sponsorship terms or details of any agency relationship.
Start with Yiftah Shiri’s AI-agent adoption argument, then the company-brain context argument, then the GTM division of labour.
Read more
The through-line is practical AI for sales and marketing teams: agents should be usable daily, grounded in a seller’s own deals and tools, and focused on repeatable volume while people handle the judgement calls. Read the Silo analogy post for the context argument, the Closer post for the personalised agent build, and the G-CMO Europe post for concrete GTM workflows.
Conor Brennan-Burke argues that AI agents fail when business context is scattered across Slack, Notion, email, CRM and people’s heads.
Read more
Connecting more sources without a layer above them, they say, produces confident but disconnected answers. That aligns with Yiftah Shiri’s company-brain argument and rejection of a generic assistant.
Brennan-Burke writes from the perspective of building a context-layer product at Hyperspell, using the blind-mice parable to explain source conflict.
Shiri focuses on GTM adoption, daily-use metrics and CRM workflows at monday.
Both address context, but from different angles: infrastructure vendor versus go-to-market practitioner.
Rashel Hariri writes for marketing and go-to-market operators about how teams work, including candour, decision speed and fractional leadership.
Read more
The audience overlaps with Shiri’s marketing and GTM readership. Both use short LinkedIn posts to turn everyday team friction into a practical argument.
Hariri’s posts focus on team communication and a fractional GTM career, rather than AI-agent adoption or CRM context. Shiri’s arguments centre on agent deployment, daily use and connected customer data; Hariri’s on human candour, ego and decision delay.
Shalin Jain describes building an AI-native CRM, using AI agents to mine support and success calls for expansion opportunities, and bringing context together so reps stop jumping between tools.
Read more
That matches Shiri’s arguments for connected context, per-seller usefulness and agent-assisted GTM efficiency.
Jain speaks as the founder-operator of a bootstrapped support-software business, framing the change as an operating-system reset across the company.
Shiri writes from inside monday CRM’s GTM organisation, illustrating specific marketing and sales agents.
Both discuss AI-native CRM and context, but Jain draws on rebuilding one company, while Shiri draws on deploying agents to a GTM team.
Yiftah Shiri’s posts address AI agents and CRM use in go-to-market teams, making the subject matter a potential fit for campaigns explaining AI adoption to sales and marketing audiences.
Read more
Before booking, request recent audience results, including professional roles or seniority, supported by survey results or another documented source.
Yiftah Shiri's posts do not show a creator partnership programme.
Read more
They promote monday CRM — the product behind the GTM work they write about — and a G-CMO Europe marketing event held at monday.com's London office.
Both are employer product and event promotion, not a sponsorship offer, and no post discloses a paid partnership with an outside brand.
No sponsor policy, rate card, format list, campaign minimum, deadline or separate partnership contact is published.
Buyers should ask whether sponsored collaborations are available, in what format and through which contact.
The documented subject matter is practical AI agents for sales and marketing teams, CRM adoption and time-to-value discipline for smaller organizations.
Shiri has presented this material publicly, including an agent that turns a discovery call into a demo on the client's own data.
Strong fit on subject matter and teaching approach. Bookability is a separate question; the material reviewed does not answer it.
Read more
Brief
Brief A: a B2B SaaS company selling an AI agent to sales and marketing teams wants a short video series explaining AI agent adoption in plain terms: how a go-to-market team sets up an agent, customises it and gets daily use out of it.
Reasons
Every post reviewed addresses this brief. Yiftah Shiri writes about AI agents for go-to-market teams and argues that the real benchmark is whether an ordinary employee can set one up, customise it and use it effectively every day—with monday tracking build-to-daily-use rate as the measure that mattered. That is an adoption story, not a capability story, as the brief requires.
The teaching structure already suits a video series. Shiri's posts open with a widely shared reference or anxiety, translate it into a business problem, walk through an internal example, then point to the product. The clearest example uses a television season's 'is that voice human or AI' question to introduce a company brain that answers why a customer is at risk or why a deal was lost. A series built the same way—familiar problem first, working example second—would match their approach.
The posts offer ready-made episode subjects with named workflows: an agent that turns a discovery call into a demo on the client's own data; an agent that reaches every free-trial sign-up while intent is at its peak; and a marketing assistant that drafts on-brand, legally safe copy. Each gives an episode a visible task, suited to the brief's practical register.
Shiri explicitly rejects the generic-assistant framing. The sales agent, Closer, was built so each seller gets their own version, informed by their deals, conversations and selling style, and accessible in Slack, the CRM or Claude. A series promising 'one assistant for the whole company' would contradict their stated position.
Interface flexibility offers a natural mid-series episode. One short post says customers can keep working in the CRM's own interface or run the business through a preferred LLM provider, with tooling either way. Show the same task done both ways rather than just making the claim.
What would decide or block it
The five written posts include no video transcript, recorded session or on-camera material, so they cannot show delivery style, pacing or screen presence. Ask for recent video or webinar recordings before committing to a filmed series.
Shiri's posts publish no sponsorship format, rate, deadline or partnership contact, and disclose no paid partnership with an outside brand. The posts are employer product and event content. First confirm whether sponsored work is available, in what format and through which contact.
No measured audience data was available. The posts show subject matter, not readership; get separate evidence on reach and role composition before planning media spend.
When to choose a specialist
Not a fit for a campaign seeking a competitor comparison. This assessment concerns that brief, not whether Shiri ever comments on competitors or takes sponsored work.
Read more
Brief
Brief B: a CRM or data platform competing with monday.com wants a creator to publish a neutral, side-by-side comparison of three vendors' AI agents, backed by independently measured conversion and time-to-value results, for enterprise revenue-operations leaders.
Reasons
Every post promotes monday CRM, the monday product its agents run on, or a G-CMO Europe event held at monday.com's London office. Shiri's flagship example, Closer, is described as an internal monday experiment being brought into the product. Asking them to rank a competitor alongside or above monday would put them in direct conflict with the product they write about. No post discloses a sponsorship relationship that might explain that stance.
A campaign seeking independently measured results would be a poor fit. Shiri's posts offer internal targets and their own comparisons: build-to-daily-use rate as an internal KPI, and a claim that a connected brain answers a question in seconds instead of ten people at two hours each. These are their positions, not third-party measurements, and neither comes with a method or source a comparison video could cite.
Shiri's posts do not identify the audience for a campaign targeting enterprise revenue-operations leaders. They write about go-to-market teams generally; ask for audience data before proceeding.
The commercial mechanics are also unclear. There is no published sponsor policy, rate card, format list or separate partnership route. Even if the conflict were waived, ask how to commission a three-vendor comparison with a fixed deadline.
The register also works against the brief. Shiri's posts use analogies and direct readers to the product, landing on a demonstration and a link. A neutral multi-vendor comparison requires a different stance: open criteria, a disclosed method and comparable evidence. The posts do not show them working in that mode.
A worked campaign example
Angle
Run the whole series against one standard: can an ordinary person on a go-to-market team set up the agent, customise it and use it every day without an implementation project? Give each episode one task and show it end to end, including the setup rather than just the finished result.
Brief served
Brief A — the B2B SaaS company explaining AI agent adoption to sales and marketing teams.
Constraint to respect
Keep the series grounded in Shiri's demonstrated work and stated positions. Do not cast them in a neutral multi-vendor comparison or write a script that contradicts their per-seller, product-directed framing.
Episodes
Title
Episode 1 — The three-month problem
What it shows
Open on the anxiety managers already feel: everyone wants the AI stamp, but almost nobody has it. Set up the contrast: enterprise routes through consultants and quarter-long integrations, while smaller organisations need efficiency now, not a year later. Close by naming the series' test: daily use, not demo impressiveness.
Why this fits
This argument draws directly on Shiri's posts about ease, time-to-value and day-one usability as the real benchmarks. It gives the series a spine instead of three unrelated product demos.
Title
Episode 2 — One question, one answer, in one place
What it shows
Pick a question a seller actually asks: why is this customer at risk, or why did we lose the deal? Show where the answer currently lives—the data warehouse, CRM, support tickets, Slack and dashboards—along with the three people they need to ask. Then show the same question answered from connected context, with the sources visible on screen.
Why this fits
It dramatizes the company-brain argument and keeps the episode focused on a buyer question rather than a feature tour. Note on screen that the speed comparison is the creator's own claim, not a measured benchmark.
Title
Episode 3 — Your agent, in your tools, and where the human steps in
What it shows
Build a personal agent for one named rep that knows their deals, conversations and selling style. Run the same workflow twice — once inside the CRM and once inside the rep's preferred LLM surface — so viewers see the interoperability claim in action. End with the handoff: the agent drafts; the human reviews and takes over. Keep legal review of drafted copy in a person's hands on camera.
Why this fits
It carries their per-seller, not one-generic-brain position, uses the discovery-call-to-demo and trial-signup examples as the worked tasks, and follows the rule that AI does the volume while people take the moments that matter.
Handoff
End the series with a single-page checklist viewers can use with their own team: pick one recurring GTM task, time how long the setup takes, and track daily use for two weeks. That turns viewing into an internal decision.
Series title
From demo to daily use: a three-part series on AI agents your team will actually open
What to confirm before booking
Ask Shiri about sponsorship availability, format and rate; their posts do not document these, so the commission route is the first open item.
Ask for recent video or webinar recordings to assess on-camera delivery; Shiri's posts are written only.
Ask for audience information; Shiri's posts do not show post performance or reader roles.
In his “five blind mice” post, he explains how disconnected sources can lead agents to confident but misleading answers.
Together, these make a strong fit for a problem-focused series on context and reliability.
This angle focuses more on the infrastructure and reasoning behind agent failures than on making GTM agents easy for sales and marketing teams to use every day. Choose Yiftah Shiri when practical adoption and daily usability are the priority.
His SaaStr.ai London post frames AI-native change as an operating-model reset, not just a product feature.
These examples give founders concrete operating choices to discuss.
The focus is company operations and CRM, rather than step-by-step guidance for sales and marketing teams adopting agents in their daily work. Yiftah is the closer fit for a practical end-user adoption series.
Choose Rashel for a campaign about the people side of change: fractional GTM leadership, team communication and candid working practices.
Read more
In her post on candour, she gives specific examples—raise concerns early, debate ideas directly and move on after decisions—that can support a thoughtful angle on how teams adapt to new tools.
These examples do not explain AI agents or demonstrate GTM workflows, so they offer less subject-matter grounding for an agent-adoption series. Choose Yiftah for practical AI-agent adoption; choose Rashel when the need is team culture and communication.
This page draws on five LinkedIn posts written by Yiftah Shiri: the "any idiot can set it up" argument for agent adoption, the company brain explained through the TV series Silo, the Closer sales agent built inside monday, a short statement on CRM and LLM interface choice, and a G-CMO Europe session on agents in marketing.
The posts do not show publication dates, so the order and pacing of these views cannot be established.
The posts come from a single channel and include no video transcripts, long-form articles, interviews, or third-party reporting.
Brand relationships are stated where the posts state them.
Shiri describes working in monday.com's go-to-market team and writing about monday CRM and its agents; the posts do not mention sponsorship, a rate card, or a partnership route.
Similar-creator comparisons rest on a small sample of other creators rather than a full review of each creator's output.
Several gaps remain open and are worth naming for anyone reading this for a decision: there is no on-camera or recorded material to judge delivery, no audience data to judge who reads the posts, no video or webinar material, and nothing establishing signature books, shows, or earlier career work.
The posts do not establish these things; that is not a finding about the person.
