Brandon Bornancin shares advice on B2B sales discovery and business processes, and promotes Seamless AI’s sales tools, including AI-assisted prospect messaging.
Bornancin’s clearest ideas in these posts are to fix sales practices before adding technology and to use qualification frameworks as guides for conversation, not scripts.
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Their posts also take a more promotional tone when presenting Seamless AI features.
Remove needless work before automating it
Bornancin argues that AI can scale a bad process rather than improve it.
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Before automating, they say, ask whether the work is necessary, can be removed or can be done faster. The priority is choosing the right work, not maximizing the number of AI agents.
Bornancin describes reps entering 12 CRM fields after every demo because leadership once wanted cleaner reporting. If only four fields inform forecasting, automating all 12 preserves eight unnecessary steps.
Bornancin rejects the claim that BANT is obsolete.
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The problem, they say, is turning its budget, authority, need and timeline questions into an interrogation or CRM checklist. The framework should help a salesperson determine whether a real deal exists without making the prospect feel tested.
Instead of asking what budget a prospect has, Bornancin suggests asking what the problem is costing them.
To gauge urgency, they suggest asking what happens if nothing changes.
A prospect can like a product yet have no reason to act.
The posts move between advice on improving sales work and direct promotion of Seamless AI.
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The product post emphasizes a named feature and a quick call to action rather than examining whether the underlying process should change. The contrast shows that the account pairs process critique with product-led messaging.
A Seamless AI post promotes AI Fields, claiming it can create 500 personalized LinkedIn messages in seconds, then asks readers to watch a video and comment “FIELDS.” The post offers no way to assess the feature’s performance.
This is the clearest statement of Brandon Bornancin's argument about AI in sales, and it answers a newcomer’s first question: is this a tool promoter or a process critic?
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The post makes their position clear, beyond the products they promote.
The post opens against a common instinct: don't automate everything.
The example is a sales team where reps fill out 12 CRM fields after every demo because leadership once wanted cleaner reporting.
If only four fields drive forecasting, the other eight add friction, and automating all 12 with AI skips the real question of whether those fields need to exist.
Brandon Bornancin lays out a sequence to run first — does this need to happen at all, can it be deleted, can it be made faster — and only then automate.
The closing line is the thesis: the companies that win won't be the ones with the most agents, but the ones using AI on the right things.
For brands planning a campaign
This post offers a ready-made campaign angle for a sales enablement brief: cut unnecessary CRM steps before automating them.
The 12-field example gives marketers a specific hook, and the four-question sequence provides a structure.
The argument is based on opinion, not data; a campaign built on it should supply its own figures for the time those extra fields cost.
The post shows a recurring teaching method across the account: take a standard sales framework, defend it, and show how it gets used badly.
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Readers deciding whether this person can speak credibly to a sales team should see it early.
Brandon Bornancin pushes back on the claim that BANT is dead, arguing that bad discovery is what died.
The post describes how reps turn the framework into an interrogation — "Do you have budget?
Are you the decision-maker?" — which is box-checking rather than discovery.
The rewritten questions are: "What is this problem costing you today?", "Who else is impacted by this?", "What happens if nothing changes?"
The point is that BANT is a way to work out whether a deal actually exists, not a script, and that a prospect can love a product and still have no urgency to change.
For brands planning a campaign
Useful for an enablement audience, this post models how to train discovery without discarding the vocabulary sales teams already use.
A campaign could pair the original and rewritten questions as a training asset.
The examples come from the author's own practice; ask for call recordings or outcome data before presenting the rewrite as evidence of improved win rates.
Half of this account is product promotion, and this is the clearest example of that mode.
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It shows what a sponsored or product-led post from this person looks like, which matters before commissioning one.
A short product post for a Seamless AI feature called AI Fields, framed around personalizing LinkedIn messages at volume: 500 personalized messages for an entire sales pipeline in seconds.
The call to action is to comment the word "FIELDS" to get started free.
The personalization claim appears in an attached video, which was not reviewed; ask for the video to assess it.
For brands planning a campaign
This is the format a brand would get for product-level content rather than process advice: a fast hook, a named feature, and a comment-to-get-access call to action.
A campaign about improving sales process should draw on the argumentative posts shown in the other two examples, not this promotional register.
Before repeating any performance claim, ask for the video supporting it.
Bornancin argues that AI automation in sales operations should come last.
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The example: a sales team must fill 12 CRM fields after every demo because leadership once wanted cleaner reporting.
If only 4 fields drive forecasting, the other 8 create friction.
Automating all 12 with AI would scale a process that may not need to exist.
Before automating anything, ask: Does this need to happen?
Can it be deleted?
Can it be made faster?
Only then automate.
Winning companies, the post concludes, will not be those with the most AI agents, but those applying AI to the right work.
A three-question filter before automation: Does the step need to happen?
Can it be removed?
Can it be made faster?
Automation comes last, not first.
For brands planning a campaign
This is the closest match to a proposed post or short video on trimming CRM steps before automating them. A co-created piece could use the 12-fields-to-4 example as its spine and apply the three-question filter to one real pipeline stage, giving the sponsor a concrete audit rather than a general AI endorsement.
Bornancin pushes back on the claim that the BANT qualification framework is obsolete.
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The problem, they argue, is that salespeople turn qualification into an interrogation — 'Do you have budget?', 'Are you the decision-maker?', 'When are you looking to buy?' — reducing a conversation to CRM box-checking.
The alternative is to surface the same four signals through open diagnostic questions: what is this problem costing you today, who else is impacted by this, and what happens if nothing changes.
The framework stays; the delivery changes.
A prospect can like the product and still have no urgency or reason to change.
Turn scripted qualification questions into open questions that surface the same information without asking for it directly.
In a sales-content landscape where BANT is frequently declared dead, Bornancin keeps the framework and blames the execution instead.
For brands planning a campaign
For a sales enablement sponsor, this supports a piece on discovery quality rather than AI tooling. It pairs well with CRM-process content: both argue that the underlying sales behaviour, not the software, needs fixing.
A short post promotes Seamless AI's AI Fields feature, claiming it can produce 500 personalized LinkedIn messages for an entire pipeline in seconds, each framed around a prospect's pain points.
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The post directs readers to watch a video and comment 'FIELDS' to get started for free. It is product-led content: a named feature, a bold speed claim, a demo asset, and a comment-to-unlock call to action.
A product-feature demo with a watch-the-video prompt and a comment keyword for lead capture.
For brands planning a campaign
Bornancin's posts already include vendor demo content for Seamless AI. A sponsored post would sit alongside promotional product material, so it should either stand apart as independent sales advice or account for the audience's existing expectation of demo-style posts.
Two posts announce 40-plus open roles at Seamless AI, listing sales development representatives, account executives, channel and partnerships roles, legal counsel, lifecycle marketing, and product marketing.
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Both emphasize 100% U.S. remote work, benefits, uncapped growth, and the company's status as a fast-growing SaaS business, and both close with a call to apply. These employer-brand posts target candidates rather than buyers.
For brands planning a campaign
This shows a second content stream: talent and company-growth messaging tied to Seamless AI. Sponsors should not expect every post to serve a sales or enablement audience; a campaign plan should identify which stream the paid content belongs in.
The posts document one named brand relationship: Seamless AI, a sales-intelligence software company.
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Bornancin promotes a Seamless AI product feature for personalized prospect messaging and posts hiring announcements for more than 40 open roles at the company, writing about Seamless as "we" and "our newest feature." Ask Bornancin to clarify whether their role is employment, ownership, or another arrangement.
Seamless AI
Disclosed partnership
Bornancin promotes Seamless AI's AI Fields feature, which generates personalized LinkedIn messages for a sales pipeline, and invites readers to comment "FIELDS" to try it for free.
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In the same period Bornancin posts two hiring announcements for Seamless AI covering 40+ U.S. remote roles across sales, legal, marketing, and recruiting, describing the company's mission and culture in first-person terms ("our newest feature," "At Seamless, we don't just give you a job"). The posts show sustained public promotion of the company's product and recruiting, but none states whether the relationship is employment, ownership, or another arrangement, so the specific role is unstated.
To follow the sales thinking, start with posts that take a position on practice: cutting unnecessary steps before automating them and keeping BANT qualification while making discovery conversational.
Product posts for Seamless AI, including a feature demo with a comment-to-unlock call to action, and hiring announcements for the same company sit alongside that advice.
Readers browsing the LinkedIn page will find advice, product promotion and employer-brand messaging in one feed.
Topic tags point to the practice posts; format tags mark the demo and hiring posts so they can be read separately.
Readers can also sample three other LinkedIn creators below who write about adjacent sales and AI subjects from different angles.
Writes about putting AI into B2B outbound: systems that respond, qualify, follow up and book meetings without a person in the loop.
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Argues that AI amplifies an existing sales process rather than replacing it, and must be trained on a team’s messaging, ICP and qualification framework.
Higgins makes the case with his system’s running numbers, including outbound touches, reply rate and appointments booked while he was away from work, and an AI assistant built on a company’s playbook. Bornancin’s comparable posts focus on deciding whether a task merits automation and how reps should conduct discovery conversations.
Publishes step-by-step AI and go-to-market playbooks for B2B sales: choosing buying signals, turning research into a repeatable AI process and laying out rules that change as a company grows.
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Ruha organizes her teaching by revenue stage, from roughly $1M ARR upward, with named tool stacks and a counted set of rules for each stage. Bornancin focuses on individual process decisions, such as which CRM fields to keep or how to ask a qualification question without turning it into an interrogation.
Teaches sales leadership and account prioritization through close readings of outside material and their own selling experience, including sorting accounts by what a customer spent last year against what they could spend next year.
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Griego builds posts around analogy and personal history, such as lessons from a book about shepherding, and lands on a simple two-column spreadsheet as the tool. Bornancin makes shorter, more direct arguments about sales process and AI, mixing product promotion into the same feed.
Brandon Bornancin's measurable audience is on LinkedIn, which shows 115,012 followers as of 6 October 2026; a linked Instagram account shows 24,641 followers on the same date.
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These are the only measured audience figures presented here.
The posts do not show how many followers work in B2B SaaS sales, their seniority, employers or locations, and no reach or engagement figures are attached to the five posts from 4 September–2 October 2026.
The posts show the publishing mix a buyer would be placing alongside: sales-discovery teaching, such as the BANT post; AI process advice about deleting unnecessary CRM steps before automating; a Seamless AI feature post about personalised prospect messaging; and hiring notices.
For a campaign focused on AI and sales processes, ask for recent results on comparable topics and formats — such as a text post on cutting wasted CRM steps or a short product video — including reach.
Ask separately whether an audience survey documents job function and seniority, and whether the audience overlaps with the sales-enablement buyer's target list.
The posts here contain no documented partnership formats for Brandon Bornancin.
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Across five LinkedIn posts, their commercially oriented content promotes Seamless AI, the company they appear to lead: product posts about its AI Fields feature and using AI on the right processes rather than automating everything, plus two hiring announcements listing more than 40 open roles at the company.
The posts do not present a sponsorship offer.
A brand cannot determine price, lead time, deliverables, or approval process from the published material.
The hiring and product posts show they produce promotional content for their own company, which a buyer may read as a sign of channel fluency, but it is not a documented sponsorship product.
Before planning a campaign, ask for an official partnerships or sponsorship page, a contact route for brand inquiries, examples of sponsored or paid partnerships, and specifications such as formats offered, turnaround, and usage rights.
A B2B SaaS sales enablement company wants a creator to front an AI-and-sales-process piece: a LinkedIn text post or short video on cutting unnecessary CRM steps before automating.
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Decision: commission it, with two conditions.
The closest post argues that automation should come last.
It already contains the exact asset this campaign needs: a running example of reps filling 12 CRM fields after every demo when only 4 drive forecasting; the claim that automating all 12 would scale a process that may not need to exist; and a filter to run first (does this need to happen, can it be deleted, can it be made faster, then automate).
The angle comes from the creator rather than being assigned.
Surrounding posts support the wider topic: the BANT post takes a standard sales framework and shows how it is used badly, the register a sales enablement buyer wants.
Both posts offer opinion and reasoning rather than measurement, so the fit is with the argument and teaching method, not data.
Condition one: the sponsor must bring its own numbers.
The CRM post does not measure the time cost of extra fields or what changed after trimming them, so any claim of saved hours has to come from the brand's own customers or pilot.
Condition two: keep the sponsored piece in process-advice mode, not product-demo mode.
The account already publishes Seamless AI feature posts with a comment-to-unlock call to action and two hiring announcements for more than 40 roles at the same company.
The audience may expect vendor promotion, so the new piece needs a clear signal that it is independent sales thinking.
Before booking, ask for reach on a text post about CRM process or wasted pipeline work specifically.
The LinkedIn account shows 115,012 followers as of 6 October 2026, but the posts do not identify how many are sales leaders, and there are no recent reach figures for comparable posts.
The posts also provide no sponsorship rate card, disclosure example, or brand-inquiry route; settle those directly before booking.
When to choose a specialist
A consumer brand, such as a direct-to-consumer fitness or meal-kit company, wants ongoing Instagram lifestyle content plus a paid partnership package with published rates and audience demographics that prove consumer reach.
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Decision: decline based on the published material, or park it until the missing pieces are collected.
Four things break the fit.
First, there is no documented sponsorship product: the posts contain no media kit, rate card, audience package, sponsored-post disclosure, or inquiry route, so a buyer cannot determine price, lead time, deliverables, or approval.
The only commercial content is promotion of Seamless AI, the company Brandon Bornancin appears to lead, and its hiring.
Second, the consumer brand's proof requirement points to what is absent.
The published audience figures are two follower counts—115,012 on LinkedIn and 24,641 on Instagram as of 6 October 2026—with no reach, engagement, geography, job function, or seniority attached.
A consumer brand seeking demographic validation has nothing to underwrite a spend.
Third, no Instagram posts appear here, so there is no indication of consumer-lifestyle output; the second largest account is being asked to carry the campaign while its content is unexamined.
Fourth, and the reason more money would not fix it, the content register is wrong for the buyer.
Every post speaks to sales reps, sales leaders, or job candidates: AI and process advice, discovery training, a sales-tool demo, and recruiting.
A consumer brand buying this channel would be buying an audience assembled around B2B selling.
If the brand still wants to test it, first ask for an official partnerships or sponsorship page, one disclosed sponsored example, an Instagram performance record showing reach on comparable lifestyle content, and audience composition data.
A worked campaign example
A sales enablement sponsor wants to commission a LinkedIn text post or short video on cutting unnecessary CRM steps before automating.
Angle.
Use the creator's own sequence as the spine and make it an audit rather than an AI endorsement.
Open on the counter-intuitive thesis of the original post: AI does not fix a bad process, it scales it.
Then keep the worked example concrete: reps fill 12 CRM fields after every demo because leadership once wanted cleaner reporting, although only 4 drive forecasting, leaving the other 8 as friction.
Next, run the three-question filter on a stage a sales operations team will recognise, such as post-demo logging or pre-call qualification notes: does this step need to happen, can it be deleted, can it be made faster?
Only then introduce automation for whatever remains, positioning the sponsor's tool for those steps rather than the whole process.
Format and length.
A 45 to 60 second vertical video works better than text because the filter can appear as three sequential on-screen steps; a roughly 200-word text post with the same structure is the fallback.
Either version should end with a single action, such as downloading the sponsor's pipeline-step audit, not a comment keyword.
The account's existing comment-to-unlock posts are product demos, and this piece should read differently.
What the sponsor must provide.
The source post does not measure the cost of the extra CRM fields, so the sponsor must provide the time or cost figure and own that claim.
Any before-and-after result should come from a named customer or its own pilot data.
What to confirm before publishing.
Ask whether the sponsor's disclosure rules require a label, since the posts show no disclosed sponsored example.
Confirm that the piece will run as independent process advice with the sponsor credited at the end, which suits the account's argumentative posts better than its demo posts.
Also confirm whether a product mention fits the campaign, given the account already carries Seamless AI feature promotion for a comparable sales-outreach use case.
A second viable variant, if the sponsor wants the fuller version of the same argument, is a paired text post on BANT discovery, letting the sponsor own the training angle rather than the tooling angle; the sponsor must still bring outcome data absent from the source posts.
When a campaign needs the second half of the argument: what happens once the steps that survive the cut have been automated.
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Higgins' posts describe an outbound system that handles response, qualification, follow-up and booking with no human in the loop, and report one run's results — 32,300 outbound touches at a sub-1% bounce rate, a 7.28% reply rate, 187 sales opportunities and 125 appointments booked without human interaction.
The second post makes the same case Brandon makes, that AI amplifies a process rather than replacing it, then names what a team has to feed the system: its messaging, ideal customer profile, objection handling, follow-up sequences and deal qualification framework, with named client examples attached.
If the sponsor's video has to show the automated end state with numbers on screen, this creator can carry it.
Brandon Bornancin's posts tie back to an AI product they say the team built.
Campaign figures are self-reported, with no method shown; ask for the methodology before repeating them or testing them in a pilot.
The argument also lands on building or buying the automation layer, a different ask from the delete-first thesis: it shows teams how to run the surviving steps well, not why several CRM fields should not exist.
When the audience owns the whole go-to-market motion and wants a staged checklist rather than one process critique.
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Ruha's posts assign rules to revenue bands, from $0–1M up to $25M+ ARR, name the tools used at each stage, and add a 'where it goes wrong' line per band — hiring SDRs before the founder can sell, buying more tools when the process is the problem.
The second post walks through turning AI lead generation into a repeatable system: pick buying signals first, encode research and qualification as a Claude Code skill, teach the writing style from real messages, and compress three days of research into under three hours.
A sponsor focused on pipeline generation rather than CRM hygiene fits her framing closely.
The rules are prescriptions, not measured results: a target like keeping CAC payback under 18 months is stated without showing how it was reached, and most of the 62 rules sit in an image, with only the summary text available to review.
The post also offers a video and the agents used, putting a lead-capture pitch alongside any sponsor message; the named tools it recommends can compete with the sponsor's product.
Ask how the approach handles trimming an existing CRM step before automating it.
When the campaign should be about the manager's job rather than the tool.
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Griego's posts explain why capable reps go under: a territory twice the size it should be, CRM admin, worry about comp, and the pull of the bad-fit deal that is close at hand.
He offers a method a brand could build a video around — score every account on last year's spend and next year's upside in about 30 seconds, sort into A, B and C, and protect time for the top tier — and says the matrix was adopted across Griego's company, preceded two number-one years, and was later extended to pulling live CRM data into an AI workspace so more variables can be re-ranked than a spreadsheet holds.
For a sponsor selling sales coaching, management training or rep productivity, this speaks to the buyer's actual complaint.
The posts rely on analogy — sheep that roll onto their backs, a shepherd's staff, Psalm 23 — and personal recollections from employers Brandon does not name, with no figures attached.
No AI product or tool is named beyond 'an AI workspace'.
The material is a poor fit for a campaign about a CRM form and which fields deserve to exist.
