Yong S. Kim

The foundations named stay consistent across posts — ideal customer profile, messaging, CRM hygiene, a playbook, and a coaching structure — and most of the writing is spent on what goes wrong when tools arrive before those exist.

• The test Kim offers a buyer is blunt: would this strategy work without the technology?
• If not, the tool will not save it.

In one post Kim lists the failure modes directly: a team with no defined stages gets AI follow-ups on deals that were never qualified; a rep who does not know the ICP gets a faster way to prospect the wrong accounts; a forecast built on gut feel gets a dashboard that makes gut feel look like data.

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The named causes are organisational rather than technological — inconsistent messaging, no qualification framework, no coaching cadence — and the consequence is reps improvising their way through deals that should have been disqualified early. The priority Kim sets is engineering that system first, which is why the same argument reappears as the reason a new tool will not help.

Writing about healthcare SaaS companies, Kim describes leadership pouring money into more leads, more tools, and more headcount while revenue stays flat, and calls a CRM dashboard a prettier view of the same mess. The same post closes with the line the rest of the writing keeps returning to: stop managing the funnel, start engineering the system underneath it.

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The proposed fix is deliberately unglamorous: every AI use case gets a trigger, a defined output, and an owner, with the manager checking the output. Kim's framing of the alternative is that telling a team to use AI more is a wish rather than an instruction, because nothing about the work changes.

The scene Kim describes is a team where every rep had access to AI and almost nobody used it the same way twice — one drafting cold emails, another occasionally using it for call prep, most opening it when they remembered. The two workflows named in response are a one-page account summary produced after every discovery call and a set research prompt run before every first meeting, both checked by the manager.

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A scoring model finds patterns in historical wins, so if those wins were the wrong customers, the model learns the wrong lesson and scales it. Kim's stated priority is completing the ideal-customer work first and treating the model's ranked output as a test of that definition rather than as the definition itself.

The outcome Kim warns about is a prioritised list of leads that look like the company's past rather than its future, and the mistake named as most expensive is not buying the wrong tool but turning on the right one before anyone has defined what a good lead actually is.

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Conversation intelligence platforms already hold the material a playbook needs, in Kim's account, and most sales organisations leave it unmined. The constraint Kim attaches is that surfacing the data is not the same as using it: AI produces the signal, and a manager still has to do something with it, which is why coaching capability is listed alongside process as something a new tool cannot substitute for.

Kim's two illustrative questions are which objections come up most in deals that stall at proof of concept, and what reps who win against the top competitor say in discovery that others do not — described as playbook intelligence sitting in a Gong library already. The same interest in management shows up in the complaint about managers who inspect instead of coach.

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Turnover then takes the institutional knowledge with the person who held it.

• The alternative Kim proposes is AI built into the workflows the existing team already runs, on the reasoning that it needs no handoff, ramp-up, or dedicated manager.
• Kim does not rule outsourcing out entirely — the position is scoped to customer-facing pipeline work rather than stated as a blanket rejection.

The costs Kim counts beyond the hourly rate are hours spent reviewing work, rewriting instructions, managing time zones, and retraining after each round of turnover, with the customer knowledge walking out of the door. The replacement question offered is what it would take to make the existing team faster at the same work.

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One recurring format is the pre-purchase question list aimed at people about to take a vendor call; another is a readiness assessment that reports where AI could genuinely help, where the team is not set up to get value yet, and what to prioritise instead of chasing every new tool. Kim states explicitly that the assessment carries no cost and no sales pitch.

One post offers a roughly ten-minute AI Readiness Assessment covering where AI could move the needle, where data, process, and adoption gaps block value, and what to prioritise first.

• A parallel test turns on a single question: can a manager look at any deal in the CRM right now and state the current stage, next step, key stakeholders, and risk?
• A no is presented as a data problem that adding AI would only scale.

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If you read one post to decide whether this voice is useful to you, read this one.

Kim walks through a failure pattern described from work with healthcare SaaS companies: leadership buys more leads, more tools, more headcount, and revenue still doesn't move.

• The named causes are specific — no consistent messaging, no qualification framework, no coaching cadence — and so is the consequence: reps left to freelance their way through deals that should have been disqualified in week one.
• A CRM dashboard, in this account, only gives "a prettier view of the same mess."
• The closing line, "Stop managing the funnel.
• Start engineering the system underneath it," is the thesis the rest of the writing keeps returning to.

For brands planning a campaign

For a healthcare SaaS team asking for a sales overhaul, this is the natural opening document: ask for the system diagnosis — messaging, qualification, coaching cadence — rather than a lead-generation or tooling purchase.

Kim describes the concrete ways AI makes a weak process louder: follow-ups on deals that were never qualified, a faster route to the wrong accounts for a rep who doesn't know the ICP, and a forecasting dashboard that makes gut feel look like data.

• The test offered is blunt: would this strategy work without the tech?
• If the answer is no, the tool won't save it.
• The post names auto-written emails, AI call notes, and lead scoring as real tools that work — the objection is to buying them before the fundamentals exist.

For brands planning a campaign

Use this as the gate in an AI-readiness engagement: every proposed AI use case has to pass the without-the-tech test before it gets budget, and failing cases point back to process work.

The test is whether a manager can look at any deal in the CRM right now and state the current stage, the next step, the key stakeholders, and the risk.

• When they can't, Kim calls it a data problem rather than a technology problem, and describes the failure mode of stacking AI on top: "adding AI to a dirty CRM doesn't fix it.
• It scales it."
• The post is framed as the piece Kim most wants healthcare SaaS CEOs to read before their next vendor call.

For brands planning a campaign

A diagnostic or advisory engagement can open with this deal-level CRM test; the answers decide whether pipeline hygiene or AI enablement comes first, and give the client a visible before-and-after.

The scene is a team where every rep has access to AI and almost none uses it the same way twice — one drafts cold emails with it, another occasionally uses it for call prep, most open it when they remember and close it when they get busy.

• Leadership's instruction was "use AI more," which Kim calls a wish, not a workflow.
• The fix described is deliberately boring: every AI use case gets a trigger, a defined output, and an owner, checked by the manager.
• Two named examples: a one-page account summary after every discovery call, and a set research prompt before every first meeting.

For brands planning a campaign

This is the shape a worked engagement can take — pick a handful of recurring sales moments, specify trigger, output, and owner for each, and have the manager inspect the output so adoption is measurable rather than aspirational.

Kim's argument is that scoring models learn from historical wins, so if those wins were the wrong customers, the model learns the wrong lesson and scales it — leaving "a prioritized list of leads that look like your past, not your future."

• The most expensive mistake, in this account, isn't buying the wrong tool; it's turning on the right tool before the team has defined what a good lead actually is.
• The post says a longer piece walks through the ICP work that should come first.

For brands planning a campaign

For a team about to enable lead scoring, front-load the ICP definition and treat the model's ranked output as a test of that definition rather than as the definition itself.

Kim describes what the sector looks like now: AI tools everywhere, results flat.

• High open rates alongside a flat pipeline, reps who feel busy without moving deals, dashboards full of data nobody knows how to act on.
• The missing foundation is named for this buyer in particular — persona depth, multi-stakeholder sequencing, and messaging built for a compliance-trained buyer.
• The post says the linked piece separates where AI is genuinely delivering from where it is creating false confidence.

For brands planning a campaign

For a healthcare SaaS brief, this is the post to quote in the proposal: it names the compliance-trained buyer and multi-stakeholder sequencing as the parts of the system that have to precede tooling spend.

Kim claims most sales orgs are sitting on a goldmine inside their call intelligence platform and not mining it.

• Two example questions show what the mining looks like: which objections come up most in deals that stall at proof of concept, and what do reps who win against the top competitor say in discovery that others don't.
• That is playbook intelligence, in Kim's phrasing, sitting in a Gong library right now.
• The constraint attached is that surfacing it requires managers who know how to use the data, not just review it — "AI gives you the signal.
• Your manager has to do something with it."

For brands planning a campaign

A coaching or enablement engagement can start from the call recordings the team already has: mine win/loss language into a short playbook, then build the manager cadence that turns it into coaching instead of reporting.

Kim offers a free AI Readiness Assessment for sales teams and sales leadership.

• It takes about ten minutes and returns three things: where AI could genuinely move the needle, where the team isn't set up to get value yet (data, process, and adoption gaps), and what to prioritise first instead of chasing every new tool.
• The post states there is no cost and no sales pitch attached, and invites readers to compare their results against expectations.

For brands planning a campaign

A low-commitment first step with a healthcare SaaS team: run the assessment, then use the gaps it surfaces as the scope for a diagnostic or advisory engagement.

Kim counts the costs of outsourcing beyond the hourly rate: hours spent reviewing work, rewriting instructions, managing time zones, and retraining every time the outsourced team turns over — with customer knowledge walking out the door with the people who held it.

• The suggested replacement question is "what would it take to make our own team faster at this?" with AI built into existing workflows adding capacity without handoffs and keeping customer context with the people who already have it.
• The post concedes outsourcing still has a place for low-context work, but not for the pipeline.
• A near-identical version of the same argument appears in a separate post on the same theme, which suggests it is a settled position rather than a one-off.

For brands planning a campaign

Useful when a brief involves offshore SDRs or outsourced research: it gives the case for automating inside the existing team, along with a stated limit — low-context work is still a reasonable place to outsource.

It multiplies it.

• Strong process gets faster.
• Broken process breaks at scale."
• The foundations named are consistent across posts — ideal customer profile, messaging, CRM hygiene, a playbook, and a coaching structure.
• The failure modes are concrete: automated follow-ups on deals that were never qualified, a faster way to prospect the wrong accounts, and a dashboard that makes gut feel look like data.

Ask a single question before buying: would this strategy work without the tech? If the answer is no, the tool will not save it.

For brands planning a campaign

A vendor selling AI sales tools into this audience should expect the process question before the product question. Feature-led campaigns are likely to be read as the distraction Kim warns about; leading with a readiness or process diagnostic fits the argument the audience already follows.

Examples: Source 1 · Source 2 · Source 3 · Source 4

One post offers a free AI Readiness Assessment that takes about ten minutes and reports where AI could genuinely help, where the team is not set up to get value yet (data, process, adoption gaps), and what to prioritise first.

• Another promotes five questions to ask before the next vendor call.
• A third asks whether a manager can look at any deal in the CRM and state its current position, next step, key stakeholders, and risk — and treats a no as a data problem that AI would only scale.

Frame the diagnostic as value in itself: explicitly "no cost, no sales pitch attached."

For brands planning a campaign

Co-branded readiness assessments, checklists, and pre-purchase question lists are the native format here, and they map cleanly onto a campaign that wants measurable engagement before a sales conversation. A sponsored diagnostic works better than a sponsored product claim.

Examples: Source 1 · Source 2 · Source 3 · Source 4

Leadership's instruction was "use AI more" — which Kim calls a wish, not a workflow.

• The fix is deliberately boring: every AI use case gets a trigger, a defined output, and an owner, with the manager checking it.
• A discovery call triggers a one-page account summary; a first meeting triggers a set research prompt.

Same trigger, same output, every time, with a named owner and a manager check.

• "Individual habits don't scale.
• Workflows do."

For brands planning a campaign

Enablement and workflow tooling that ships with concrete trigger-and-output templates fits this audience. A demo showing the artefact a rep produces after a specific meeting will read as a workflow; a general capability tour will read as another tool nobody uses the same way twice.

Examples: Source 1 · Source 2

AI supplies the signal; a manager has to do something with it. The management problem recurs elsewhere — a new account executive will not thrive without onboarding, a playbook, and someone coaching the first ten deals, and a manager who inspects instead of coaches is listed among the problems a new tool will not fix.

For brands planning a campaign

Conversation intelligence, call review, and sales coaching offers align with material Kim already publishes. A campaign that demonstrates turning call data into a specific coaching action matches the argument; one that stops at surfacing metrics does not.

Examples: Source 1 · Source 2 · Source 3

The described symptoms: AI tools everywhere and results nowhere, high open rates with flat pipeline, reps who feel busy but are not moving deals, dashboards full of data nobody knows how to act on.

• The groundwork named is specific to the buyer — persona depth, multi-stakeholder sequencing, and messaging built for a compliance-trained buyer.
• In an earlier post the pattern is leadership pouring money into more leads, more tools, and more headcount while revenue does not move, because there is no consistent messaging, qualification framework, or coaching cadence.

For brands planning a campaign

A vertical campaign aimed at health-tech sales leaders can use this framing directly. Generic SaaS messaging will not match the specificity the audience sees in Kim's writing, and multi-stakeholder and compliance-aware buying are the details a brief should include.

Examples: Source 1 · Source 2

Scoring works by finding patterns in historical wins; if those wins were the wrong customers, the model learns the wrong lesson and scales it.

• The result is a prioritised list of leads that look like the company's past rather than its future.
• The same point appears in the five questions to ask before a vendor call: an unclear ideal customer profile is not fixed by a new tool.

Complete the ideal-customer-profile work before switching scoring on.

For brands planning a campaign

ICP research, data quality, and scoring tools positioned as a second step after definition fit this audience. Vendors that sell scoring as a fix for an undefined ICP are arguing against the published position.

Examples: Source 1 · Source 2 · Source 3

A single offshore hire is manageable; a team becomes a second management layer with someone owning quality control, someone covering the time-zone gap, and someone rewriting instructions whenever the process changes.

• Turnover then takes the institutional knowledge with the person.
• AI, in this framing, does not need a handoff, a ramp-up period, or a manager to catch its mistakes, and adds capacity to people who already know the customers.

Kim does not rule outsourcing out entirely: "Outsourcing still has a place for low-context work. Your pipeline isn't it." The position is scoped to customer-facing pipeline work, not a blanket rejection.

For brands planning a campaign

Outsourced SDR and offshore pipeline teams face a sceptical read with this audience.

• AI-enabled capacity tools fit the argument, and messaging that positions offshore headcount for pipeline work contradicts it.
• Campaigns addressing the caveat — low-context work — still have room.

Examples: Source 1 · Source 2

Hudhayfah M.'s posts on referrals not being a growth strategy and on a sales system that gives weekly consistency sit close to Kim's insistence that sales is a system rather than a funnel.

Hudhayfah M. writes from the position of a B2B SaaS founder building a controllable outbound channel, often using LinkedIn as the example. Kim writes about sales teams inside healthcare SaaS organisations, with more emphasis on AI readiness, CRM hygiene, qualification, and coaching cadence.

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Walder's accounts of an AI-native AE-to-CSM handoff and a deal qualification process that explains its reasoning parallel Kim's trigger-output-owner workflows and his interest in call libraries as coaching material.

Walder describes workflows observed firsthand inside one company and leans on day-in-the-life detail. Kim writes as an adviser diagnosing process gaps across teams, and favours diagnostics, question lists, and process principles over an inside-company narrative.

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Basu's 100-day plan for new CMOs starts with auditing closed-won deals, channel-to-pipeline fit, MQL-to-SQL rates, and recent losses before launching new work. That is the same sequencing Kim applies to sales: fix the system under the funnel, define the ideal customer, and only then add AI.

Basu addresses marketing leaders and the marketing system, including multi-stakeholder B2B content. Kim addresses sales leaders and their process, with healthcare SaaS as the recurring worked example.

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Both supplied posts make that case directly: referrals are not a growth strategy because you cannot decide when someone recommends you, control how many introductions arrive next month, or forecast revenue around them.

• The proposed alternative is a channel the company controls, with LinkedIn outbound as the named example, plus a weekly system that tracks who is being contacted, how many conversations start, how many meetings get booked, and what needs improving.
• That makes this the better pick for an early-stage or founder-led team where the diagnostic should end in a first working outbound cadence rather than an AI roadmap.

The writing stays at the level of pipeline predictability.

• It says little about the reps already in place: no qualification framework, no coaching cadence, no argument about CRM hygiene, and no AI readiness angle at all.
• There is no healthcare SaaS or compliance-buyer context.
• Compared with Yong S.
• Kim, it treats "build a system" as the answer without the step that tests whether the current system is sound.
• A company that already runs a 30 to 60 person sales team and a CRM full of deals gets less from this than a company still deciding how to generate demand in the first place.

Examples: Source 1 · Source 2

The two supplied posts describe concrete builds: an account handoff that pulls context from call recordings, email, CRM data, enrichment systems, and internal notes to assemble a structured customer handoff, recommend which CSM should support the account, route the result through account-executive approval, and generate branded kickoff material; and a deal-qualification workflow that scores a deal against a rubric, compares it to past deals, flags missing information, and explains the reasoning behind its score instead of just returning a number. That is the stronger choice when the engagement is an implementation showcase for a team that has already accepted the AI direction and wants a build to aim at.

These are first-person observations about one company's internal tooling, credited to named builders, rather than a method a buyer can apply to their own team.

• There is no readiness assessment, no test of whether a sales process is sound, and no healthcare SaaS context.
• The posts assume that investing in AI-native workflows is the right move, where Yong S.
• Kim's starting question is whether the fundamentals support that investment before any tool is bought.
• The record is also thinner: two posts, both from May 2026, and no advisory framing or client work described.

Examples: Source 1 · Source 2

The 100-day plan for a new marketing leader walks through auditing closed-won deals, mapping channels against pipeline produced, checking the real MQL-to-SQL rate, auditing attribution, and interviewing recent losses and losses, then aligning the ideal customer profile with sales, agreeing on what a good lead looks like, and putting bottom-of-funnel content and landing pages live.

• A second post breaks a B2B deal into its six stakeholder questions, from champion and executive through finance, IT and security, operations, and legal, and argues that most teams build content for only one of them.
• For a healthcare SaaS company whose deals need several approvers, that mapping is closer to the daily problem than a general AI-readiness pitch.

This is a marketing-system view.

• There is no rep-level coaching, no AI readiness assessment, and no workflow design where each AI use case gets a trigger, a defined output, and an owner.
• For a sales team asking whether AI will help and where to start, it addresses alignment and content rather than the sales process underneath them.
• Compared with Yong S.
• Kim, it covers the demand-generation half of the problem more thoroughly and the sales-system half less.
• The record is also two posts from September and October 2026, with no client outcomes or delivery detail in the supplied material.

Examples: Source 1 · Source 2