Benjamin Cohen is the CEO of Proton, which builds AI software for distributors. His posts discuss applying AI to distribution work, including sales training, business data, and customer relationships.
Benjamin Cohen is most useful for explaining AI use in distributor sales, data, and customer work.
Best for
Distributor buying groups or B2B AI software vendors targeting distributors.
Not for
Paid creator sponsorship campaigns with fixed deliverables and audience metrics.
Key takeaways
AI should absorb connective work holding distributor systems like ERP, CRM, and email together.
A new sales rep can ask Pronto for an order instead of learning ERP location.
Customers at a Proton dinner compared what they built and how they used AI.
Check first: Confirm sponsorship or advisory format, deliverables, price, and audience composition.
Main ideas
AI should absorb the connective work that holds distributor systems together · Distributors need one connected AI platform, not more clipboard work between tools · AI should shorten tool training so sales reps can focus on product and customer judgment
Benjamin Cohen is CEO of Proton, which builds AI software for distributors.
Cohen’s posts make the case for AI by showing how it could help people find business information, learn sales work, and spend less time connecting disconnected systems.
The focus is not just on automation: Cohen argues that reducing busywork can leave more room for customer relationships and human judgment.
Posts about customer gatherings, team life, and a podcast conversation with grandfather Larry Cohen add personal stories to the product ideas.
Cohen explains AI through the daily work it is meant to change.
In a post about sales training, a new representative knows what to ask but may not know where to find an order in the ERP.
Cohen describes Proton’s Pronto assistant as a way to ask for the information directly, so training can focus more on judging whether an answer makes sense.
A broader theme is that AI should help people do less work holding business systems together—not simply add another tool.
Cohen describes distributor employees as the human glue between ERP, CRM, product data, email, and call logs, and imagines that better-connected information could give them more time for relationships and creative work.
Why AI should make room for more human work He makes the systems argument with humor, too, comparing disconnected tools to a job that still leaves someone copying information between them.
Cohen also shares practical experiments rather than discussing AI only in abstract terms.
One example turns handwritten meeting notes into next steps: he reads the notes aloud to Granola after an in-person meeting, without changing how he takes notes during it.
Customer stories show another side of the work: Cohen values distributors learning from one another, not only from the software company.
At a Proton dinner, customers compared what they had built and how they used AI; Cohen points to those peer conversations as especially useful because customers understand one another’s problems firsthand.
Customers sharing what they’ve built For readers interested in family business, Cohen also shares a warm, candid podcast conversation with his grandfather Larry Cohen, who had spent more than 60 years at Benco Dental.
Benjamin Cohen's recurring ideas center on AI as a way to remove the manual connective work inside distributor businesses so people can focus on relationships and judgment.
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Cohen pitches Proton as one connected AI platform tied to existing ERP systems, argues AI should shorten tool training for sales reps, and tests AI tools personally, including the failures. Cohen also treats distributor buying-group community and peer learning as central to trust, delivers product and hiring messages through self-deprecating humor, and publicly credits the Proton team.
AI should absorb the connective work that holds distributor systems together
Cohen argues that distributor businesses run on people manually holding ERP, CRM, PIM, and email data together.
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The case starts from a workplace anecdote: a team member was told it would take about 18 months to learn the ERP well enough to become an expert, and Cohen frames that as part of why she joined Proton.ai. The payoff Cohen describes is not headcount reduction but human capacity: when AI can do reasoning-heavy, end-to-end work, people get time to make meaningful connections, cultivate relationships, and be creative.
A distributor employee facing an 18-month ERP learning curve, used as the reason she joined Proton.ai, and Cohen's conclusion that 'the best part of distributors using AI is that humans get to be more human.'
Distributors need one connected AI platform, not more clipboard work between tools
Cohen's product thesis is that departments can each get AI and still leave someone copying information between systems.
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The fix Cohen pitches is one AI platform for distributors, connected to the ERP or ERPs, with people and AI agents working from the same customer and product data.
Cohen uses a 1986 Steve Ballmer pitch—'look at everything you can do in one system'—to frame the problem forty years later, calls the copy-between-tools reality 'an exciting time to be a clipboard,' and says Proton used AI to put Cohen in a plaid suit and shout about it for 83 seconds.
AI should shorten tool training so sales reps can focus on product and customer judgment
Cohen argues AI should make it easier to train a new sales rep.
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On day one, a rep already knows to ask what a customer bought last time, but may still need someone to show where that order lives in the ERP. Cohen says the AI assistant Pronto lets the rep ask the question and lets the software find the information, so training can concentrate on product knowledge, customer knowledge, and recognizing when an answer does not look right.
A day-one sales rep asking Pronto for a past order instead of being shown the ERP screen, leaving training to focus on judgment.
Cohen tests AI in their own routine and shares the failures, not just polished wins
Cohen posts about personal AI use alongside the Proton product story.
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The method is to describe a real experiment, leave the caveat or failure in, and sometimes share the workflow with the team.
This is a priority on firsthand practice and practical adoption rather than vendor polish.
It also creates a tension with the product message: the same automation push that Cohen says should free people up produces its own operational mess, and the admission stays in.
Cohen describes running so many AI agents that a laptop loses battery while it is plugged in, a teammate using Claude to find a CPU-hogging process, and Cohen concluding the agents probably should move to the cloud. In another post, Cohen reads handwritten meeting notes aloud to Granola after in-person meetings so it generates next steps tied to the meeting and the people present, and shares the method with the team for events.
Peer learning and distributor community are central to how Cohen builds trust
Cohen treats the Affiliated Distributors community as more than a channel.
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The recurring argument is that distributors learn best from other distributors who have worked through the same problems, and that relationships built around that community matter. Cohen also describes the earlier effort to become an AD Service Provider as a big step for a startup that had a lot of convincing to do.
Cohen describes hosting 30 people for dinner in Nashville, mixing customers and prospects, where conversations had moved from CRM to building agents with Proton and finding uses across entire businesses.
Cohen highlights peer learning: hearing from another distributor who has tried something is different from a vendor showing what is possible.
Cohen credits Chloe Fitch and the customer experience team for handwritten notes, take-home items, and wearable Proton pins so anyone evaluating the company could find someone to question.
A separate post marks the AD Service Provider Excellence Award and a San Diego customer dinner with Aramsco, Dakota Riggers & Tool Supply, General Factory Supply, and Replenex.
Cohen delivers product and hiring messages through self-deprecating humor
A recurring method is to carry a product or recruiting point inside a joke.
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The posts stay short and anecdote-led: a self-deprecating setup, one specific detail, then the product or hiring line at the close. This is part of Cohen's public voice, not a separate topic.
Cohen jokes about renaming themselves Aleksandar on Slack because Proton already had four Aleksandars, then turns it into a recruiting pitch inviting people interested in building AI for distributors to apply.
A Halloween post about a customer photo and basketball player Jayson Tatum uses the same shape.
Building and crediting the Proton team is a public priority
Cohen regularly posts about the team behind Proton, credits named colleagues, and uses company-culture moments to recruit.
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The emphasis is on care in customer work and on the people doing that work, not only on the software.
Cohen credits customer experience lead Chloe Fitch for details that made customers feel welcome, celebrates the team's care after the AD award, and turns the Slack name collision into an open invitation to apply. In the Nashville dinner post, Cohen thanks many named attendees and says they were thinking about how to do the event more often.
Long-run industry history matters through a family conversation
One supplied post shows Cohen recording a podcast with grandfather Larry Cohen, who has spent more than 60 years at Benco Dental.
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Cohen frames the conversation as charming and candid and says Larry tells a story better than Cohen does.
The post shows the interview took place and how Cohen frames it; the packet does not include the podcast audio or its contents, so the interview itself is not evidence reviewed here.
Larry Cohen is the guest and his role is described by Cohen, not quoted directly in the supplied post.
Cohen says not many people get to interview their grandfather about the business he helped build, calls it one of their favorite recordings, and points to the link in the comments.
Answers what problem Cohen says Proton solves and why AI in distribution is framed as human capacity rather than replacement.
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Cohen argues that distributor systems are held together by people.
One example: a team member was told it would take about 18 months to learn the ERP well enough to become an expert, which is part of why she joined Proton.ai.
When AI can take on reasoning-heavy, end-to-end work, Cohen says people get time for meaningful connections, relationships, and creativity.
For brands planning a campaign
Gives a keynote or advisory angle centered on workforce capacity and change management, not just AI product features.
Shortest clear statement of Proton's product thesis and public voice.
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Cohen uses a 1986 Steve Ballmer pitch to frame the problem: look at everything you can do in one system.
Forty years later, Cohen says departments can have AI but still leave someone copying information between tools.
Proton's stated fix is one AI platform for distributors connected to ERP systems, with people and AI agents working from the same customer and product data.
Cohen also used AI to put themself in a plaid suit and shout about it for 83 seconds.
For brands planning a campaign
Useful for a keynote segment on one-system versus clipboard work, and it shows Cohen will use humor and video to explain the product.
Answers how Cohen works with AI day to day, not just what Proton sells.
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For in-person meetings, Cohen takes notes on paper, then reads them into Granola and ties them to the meeting and the people who were there.
Granola generates the next steps.
Cohen shared the method with the team for events and other in-person meetings and notes that someone can try it without changing how they take notes during the meeting.
For brands planning a campaign
Shows firsthand experimentation with AI tools beyond Proton, useful for authentic keynote examples on practical adoption.
Shows how Cohen talks about industry partnerships and customer trust.
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Cohen describes Proton trying to get into AD, becoming a Service Provider as a big step for a startup with convincing to do, and an early vote of confidence that still matters.
Cohen thanks Kevin Druecker and Sebastian G. for partnership work and praises the entrepreneurial streak among AD members who want to grow and are curious about doing things differently.
The post also mentions a customer dinner in San Diego with Aramsco, Dakota Riggers & Tool Supply, General Factory Supply, and Replenex.
For brands planning a campaign
Evidence for distributor-community credibility and relationship-building, useful for advisory or keynote positioning with AD members.
Shows what Proton's customer community looks like in practice and how Cohen describes peer learning.
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Cohen describes hosting 30 people for dinner in Nashville, mixing customers and prospects.
The conversations had moved from CRM to building agents with Proton and finding uses across entire businesses.
Cohen highlights peer learning: hearing from another distributor who has tried something is different from a vendor showing what is possible.
Cohen credits Chloe Fitch and the customer experience team for handwritten notes, take-home items, and wearable Proton pins so anyone evaluating the company could find someone to question.
For brands planning a campaign
Demonstrates community-led customer proof and an event format for brand or agency campaign planning.
Distributor buying-group community and channel relationships3
One AI platform connected to the distributor's existing systems2
Uses AI tools in his own working routine, failures included2
AI should give distributor staff time back for relationship work1
Long-run industry history through a family conversation1
AI should give distributor staff time back for relationship work
Cohen's core argument is that distributor businesses run on people holding ERP, CRM, PIM and email data together by hand, and that AI should absorb that connective work.
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One post describes a colleague who was told it would take about 18 months to learn the ERP well enough to be an expert, and frames that as the reason she joined Proton.ai. Cohen's stated payoff is that people then get to do what people do best: make meaningful connections, cultivate relationships and be creative.
The case is built from a single workplace anecdote and then generalised to the whole distribution sector, rather than from market data.
For brands planning a campaign
This is a strong keynote frame because it is about freeing staff capacity rather than cutting headcount. A brief should ask Cohen to bring the specific distributor workflows and numbers behind the claim, since the supplied post stays at the anecdote level.
One AI platform connected to the distributor's existing systems
Cohen pitches Proton as one AI platform for distributors, connected to the ERP or ERPs, with people and AI agents working from the same customer and product data.
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The problem is illustrated with a Steve Ballmer reference from 1986 and the image of someone still copying information between tools, which Cohen calls an exciting time to be a clipboard. A separate post describes a day-one sales rep asking Pronto, the AI assistant inside Proton, for a past order instead of being shown where to find it in the ERP, so that training can concentrate on product knowledge and on recognising when an answer does not look right.
For brands planning a campaign
This is the product-level argument a software buyer would want Cohen to defend in public. For an event, ask for a concrete walkthrough or demo rather than the marketing line, and check how the 'one platform' claim holds across different distributor ERP setups.
Uses AI tools in his own working routine, failures included
Cohen posts about personal AI use, not only the product.
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One post describes running so many AI agents that a laptop loses battery while it is plugged in, and the internal exchange that followed, ending with the admission that the agents should probably move to the cloud. Another describes reading handwritten meeting notes aloud to Granola after in-person meetings so it generates next steps tied to the meeting and the people present, a method Cohen says worked well enough to share with the team for events.
Tool tips framed as personal experiments, with the caveat or the failure left in rather than edited out.
The agent setup is a personal anecdote about Cohen's own laptop and says nothing about Proton's product capabilities.
For brands planning a campaign
Supports advisory and workshop credibility because Cohen can speak from hands-on practice. Verify the tool claims independently before repeating them in promotional material.
Distributor buying-group community and channel relationships
A recurring subject is the Affiliated Distributors community and Proton's place in it.
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Cohen describes hosting a 30-person customer dinner in Nashville where customers compared what they had built with Proton, and a San Diego dinner timed around the AD Service Provider Excellence Award.
Cohen recalls the earlier effort to become an AD Service Provider as a little startup with a lot of convincing to do, thanks named partner contacts, and points to the entrepreneurial streak among AD members.
The customer experience team wrote handwritten notes and handed out wearable Proton pins so that anyone evaluating the company could find someone to question.
For brands planning a campaign
Cohen's standing inside distributor buying groups is a real asset for an event or advisory engagement, and named AD member companies form a concrete customer pool to reference. Note that the AD award is recognition from an industry body, not independent product benchmarking.
Cohen often carries a product or recruiting message inside a joke.
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A Halloween post about a customer photo and basketball player Jayson Tatum, a post about renaming himself Aleksandar on Slack because the company already had four of them, and the clipboard line in the Ballmer post all use comedy as the delivery vehicle. The posts stay short and anecdote-led.
Self-deprecating setup, one specific concrete detail, then a product or hiring line at the close.
For brands planning a campaign
Suggests Cohen is a low-stiffness speaker who can hold a room. For a formal keynote, agree with the event organiser how much of the slot should be humour and how much should be technical substance.
Cohen posts about building the Proton team and about open roles.
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The Slack name post turns an internal naming collision into a recruiting pitch inviting people interested in building AI for distributors to apply. Cohen credits named colleagues, including customer experience lead Chloe Fitch for the details that made customers feel welcome, and celebrates the team's care after the AD award.
For brands planning a campaign
Gives Cohen a people-and-culture angle to pair with the technology story, which suits leadership or employer-brand events as well as product audiences.
Long-run industry history through a family conversation
Cohen recorded a podcast episode with grandfather Larry Cohen, who has spent more than 60 years at Benco Dental, and describes it as one of his favourite recordings, with the link placed in the comments.
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This is the only supplied post touching on family or on the long history of the distribution industry.
The post shows the interview took place and how Cohen frames it. The packet does not include the podcast audio or its contents, so the interview itself is not evidence reviewed here.
For brands planning a campaign
Shows Cohen can carry a long-form conversation and draws on multi-generational distribution experience. Treat the podcast content as unverified until the recording itself is supplied.
Benjamin Cohen is CEO and founder of Proton, an AI software company for distributors, and the business relationships visible in the supplied posts run mainly through that company.
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Proton is a service provider to Affiliated Distributors and counts distributors including Replenex, Aramsco, Dakota Riggers & Tool Supply, and General Factory Supply among its customers.
Cohen also writes about tools used in daily work, such as Granola and Claude, and about the family connection to distributor Benco Dental.
No post discloses a paid sponsorship or affiliate arrangement.
Proton
Owned business
Cohen is CEO and founder of Proton, which builds AI software for distributors.
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The posts describe Proton's platform and its Pronto AI assistant, customer dinners, and hiring.
Cohen shared a photo sent by Matt Cohen, Replenex's president, of someone in a Jayson Tatum jersey using Proton, and thanked Replenex for attending a Proton customer dinner.
Cohen reads handwritten meeting notes aloud to Granola, which turns them into next steps, and recommended the approach to the team for events and other in-person meetings.
Explore Benjamin Cohen's work by starting with the AI-and-distribution argument, then the distributor community posts, then the personal AI experiments and humor-led recruiting posts.
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The tags group the recurring subjects, formats, and audiences. The similar creators below are useful next follows if you want other B2B technology and sales thinkers who explain adoption through concrete operational examples, though each works in a different sector.
Both explain emerging technology through specific operational use cases.
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Baillot's retained posts describe a Unity-based multi-user geospatial AR platform, WorldEngine, and an emergency response system for first responders, including drone-to-ground collaboration. The posts follow a similar pattern to Cohen's: name the problem, describe the system, and point to a concrete application.
Baillot works on AR and geospatial systems for public safety, logistics, and entertainment; Cohen works on AI software for distributor ERP, CRM, and sales workflows.
Both treat technology adoption as a systems problem.
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Terry argues that electrification is also interconnection, creating interdependent software, hardware, data, networks, APIs, people, and processes, with new questions about data ownership and cybersecurity. Cohen makes a comparable systems argument about distributor software, where AI should connect ERP, CRM, and product data instead of leaving someone to copy information between tools.
Terry's posts center on EV charging infrastructure, public infrastructure, and cross-sector convening; Cohen's posts center on AI for distribution businesses and a specific software platform.
Both teach B2B sales and AI adoption through concrete examples rather than abstract claims.
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Levitin uses a water-price analogy to show why sellers who sound the same compete on price, and contrasts two salespeople using AI with very different judgment and customer knowledge. Cohen uses anecdotes from distributor work and sales-rep training to make similar points about judgment, context, and learning.
Levitin focuses on sales differentiation, sales training, and manager learning across industries; Cohen focuses on AI platform adoption inside distributor businesses and on building Proton's team.
Audience evidence for Benjamin Cohen is one-sided: there is a measured following, but no measured audience composition.
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The single number available — 12,572 LinkedIn followers as of 8 October 2026 — says how large the reachable network is, not who is in it.
For a keynote or advisory engagement on AI-driven distribution transformation, that is enough to justify a conversation and not enough to justify a booking on audience grounds alone.
The supplied posts show who Cohen writes for.
They address distributors and the people inside them: the staff who hold ERP, CRM and PIM data together, the sales reps being trained on those systems, and the distributor companies Proton sells to.
Posts about running AI agents on a laptop, reading handwritten meeting notes into Granola, and using the Pronto assistant inside Proton are written for practitioners weighing AI in distribution, not for a general business readership.
That is evidence of content appeal and intended audience, not of who actually reads, follows or shows up.
What is missing is the composition question.
No platform demographics, audience survey or event attendance data was supplied, so nothing here verifies geography, age, job function or seniority — usually the first things a keynote brief asks about.
Next step for a buyer: request the LinkedIn follower demographics or another documented audience source, plus recent results for comparable topics, depth and formats, so the reach figure can be matched to the room you are planning for.
The supplied material does not document any sponsorship, paid partnership, speaking, or advisory format for Benjamin Cohen.
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Cohen’s LinkedIn posts show work as CEO of Proton, an AI platform for distributors: customer dinners, an Affiliated Distributors service-provider award, AI-agent experiments, sales-training ideas, and product demonstrations.
Those are business and customer-relationship posts, not creator partnership offers.
No sponsor policy, deliverable list, price, exclusivity term, approval process, disclosure practice, or official inquiry route appears in the reviewed posts.
The actionable gap for a buyer planning a keynote or advisory engagement is direct confirmation from Cohen or Proton that such engagements are offered and what they require.
Customer logos, award mentions, and event photos should not be read as sponsorship packages.
Strong fit for a distributor-facing keynote or advisory engagement.
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Brief
A distributor buying group or a B2B software company selling AI tools to distributors wants Benjamin Cohen for a keynote or advisory engagement on AI-driven distribution transformation.
Reasons
Cohen is CEO of Proton, an AI platform for distributors, and the supplied posts are written directly to that audience. The content addresses the work distributors do: holding ERP, CRM, PIM, and email data together, training new sales reps, and deciding where AI can take over reasoning-heavy tasks.
The supplied posts give Cohen concrete, inspectable examples for a keynote: a colleague told it would take about 18 months to learn the ERP well enough to become an expert; a day-one sales rep asking Pronto for a past order instead of being shown where to find it; reading handwritten meeting notes into Granola after in-person meetings; running so many AI agents that a laptop loses battery while plugged in.
Cohen has distributor-community credibility through Proton's Affiliated Distributors Service Provider Excellence Award and customer dinners in Nashville and San Diego. The posts name distributor companies and describe peer learning between customers, which is useful for an event built around trust and adoption rather than a pure product pitch.
The voice is practical and low-stiffness. Cohen uses humour and short anecdotes to carry product, hiring, and industry points, which suits a conference slot that needs to keep a room of operators engaged.
The transformation argument is framed around human capacity, not headcount cuts: AI absorbs connective busywork so people can make connections, build relationships, and exercise judgment. That is a defensible keynote frame for distributor leaders who are worried about change management.
Watchouts
The reviewed material does not document keynote fees, availability, or a formal advisory offer. Those terms need direct confirmation from Cohen or Proton.
Audience composition is unverified. The packet supplies one LinkedIn follower figure and no demographics, so a buyer should not book on audience-size claims alone.
Product claims about Proton, including the one-platform and connected-ERP claims, are Cohen's descriptions. An event organiser should ask for a concrete walkthrough or demo rather than repeat the marketing line.
The podcast with Larry Cohen is mentioned but the audio or transcript was not supplied, so it cannot be used as reviewed evidence.
When to choose a specialist
Weak fit on the evidence supplied.
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Brief
A B2B software vendor or general business media brand wants a paid creator sponsorship campaign with fixed deliverables, guaranteed audience composition, and performance metrics tied to reaching a broad set of enterprise software buyers.
Reasons
The reviewed material documents no sponsorship, paid partnership, speaking, or advisory format for Cohen. There is no sponsor policy, deliverable list, price, exclusivity term, approval process, disclosure practice, or official inquiry route in the supplied posts. That is not proof Cohen never accepts sponsorships, but it is insufficient evidence to plan a paid creator campaign with fixed deliverables.
The audience evidence is one-sided. A LinkedIn follower count of 12,572 as of 8 October 2026 shows a reachable network, not who is in it. No platform demographics, audience survey, or event attendance data was supplied, so geography, age, job function, and seniority are unverified. A brief that requires matching a specific enterprise software buyer profile cannot be accepted on this evidence.
Cohen's posts are tied to Proton's own product and customer relationships. A third-party software vendor asking for a neutral product comparison or an endorsement outside that context would need a different kind of evidence and a clear disclosure arrangement that the packet does not show.
A performance-guaranteed social campaign would also need baseline engagement and conversion data. The packet has post text and one follower count, not campaign analytics.
Watchouts
If the buyer only needs a distributor-community keynote or an advisory conversation, this weak-fit assessment does not apply. The poor fit is specific to a paid, metrics-guaranteed creator sponsorship brief.
Direct confirmation from Cohen or Proton could change the picture on formats and terms, but it has not been supplied here.
A worked campaign example
Assets
A one-page campaign brief for the event organiser with the three concrete examples, the clipboard frame, and the proposed audience interaction.
A pre-event survey asking attendees which systems their teams manually reconcile and how long new hires take to learn the ERP.
A post-event advisory session offer, subject to Cohen and Proton confirming availability and terms.
A permission-cleared reference list of publicly named distributor customers from the posts, only if those customers agree to be cited.
Brief
A distributor buying group books Benjamin Cohen for a 25-minute keynote plus moderated Q&A at its annual technology conference. The audience is distributor owners, operations leaders, and sales managers evaluating AI adoption.
Campaign angle
Title the session around the clipboard problem rather than around AI in the abstract: 'The Clipboard Is Not a Strategy: What AI Changes for Distributor Teams.' The promise is practical: show three places where distributor staff currently act as the glue between systems, then show what changes when an AI assistant can retrieve the information and a human keeps the judgment.
Handoff
The event organiser should confirm fee, travel, and format directly with Cohen or Proton; ask for a product walkthrough if the session will include live Proton claims; and request audience demographics or past event recordings if the booking depends on proven room fit.
Outline
Open with Cohen's own unpolished AI experiment: running enough agents that a laptop loses battery while plugged in. This establishes that the speaker uses the tools, not just sells them.
Move to the 18-month ERP learning example. Ask the room how long it takes a new hire to become expert enough in their ERP to stop asking colleagues where things are.
Use the 1986 Steve Ballmer pitch and the clipboard line to frame the one-system problem: departments can have AI and still leave someone copying information between tools.
Show the day-one sales rep example. A rep knows to ask what a customer bought last time; the software should find the order, while training focuses on product knowledge and recognizing when an answer does not look right.
Bring in the customer dinner format as proof of peer learning: distributors comparing what they built, not a vendor showing what is possible. Name the AD community context and the Service Provider Excellence Award as the relationship backdrop.
Close with a first step the audience can try in a week: pick one workflow where someone copies information between ERP and CRM, ask the AI assistant to retrieve the last order, and keep a human checking whether the answer looks right.
For the same keynote or advisory booking, choose Levitin when the room is sales managers and reps and the organisers want the rep-level half of the transformation.
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Levitin's supplied posts work that task directly.
One walks through sales differentiation with a concrete price ladder — faucet water, a case of Dasani at $6–$8, a FIJI bottle at Deer Valley at $7–$9 — and argues that when every seller claims the best service or the highest quality, buyers fall back on price.
The other names the AI trap: two salespeople can now produce the same AI-written prospecting email, but only one understands the customer, knows how to create tension, and uses AI to sharpen the message.
For an audience that wants a method for creating perceived difference, and a rule for where AI should not remove the friction that produces learning, this is closer to their daily work than a platform-level account of distributor systems.
The session stops being about distributor operations and becomes about how individuals sell and learn.
The supplied posts do not touch distributors, ERP or CRM workflows, or a distributor customer community, and there is no counterpart to Cohen's 18-month ERP learning curve or the day-one rep retrieving a past order.
Levitin's frame is sales training and buyer perception, not systems and data glue, so the booking promise and the audience both change.
No audience, availability, fee, or engagement evidence for Levitin is in the packet.
For the same booking, choose Terry when the organisers want the transformation framed as interdependence rather than as an AI product rollout.
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Terry's supplied posts argue that electrification is also interconnection, that interconnection creates interdependence, and that connecting the physical world brings new cybersecurity risks and new questions about who owns data when private companies run technology on public infrastructure.
The second post is a working convening: a small Los Angeles dinner mixing policy, government, private sector, and university researchers on software interconnection and interoperability in the built environment, with seats opened to Terry's network.
For a session on connected assets, data ownership, and what happens when one system stops talking to another, this argument is more developed than anything in Cohen's material.
The subject matter is the built environment, EV charging, and public infrastructure, not distributor product data or sales workflows.
A distributor buying group would be trading a peer-community speaker with named distributor customers for an infrastructure and policy audience, and would have to rebuild the session around a different industry's failure modes.
Terry's posts also include a personal call for introductions, which suits a convening format more than a stage keynote.
The packet holds only two posts and no audience, availability, or commercial evidence.
For the same booking, choose Baillot when the audience is field operations, public safety, or logistics teams evaluating spatial AR rather than distribution teams evaluating AI.
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Baillot's supplied posts describe ARCortex's WorldEngine, a Unity-based multi-user geospatial AR platform that combines mapping, visual positioning, and eyewear, and ERIS, a first-responder system that uses a drone's overhead view to cut the time ground personnel take to locate and respond to an event.
The second post offers to help others build similar multi-user, geo-referenced applications.
If the conference track is about augmented reality in the field, Baillot is closer to the technical task than Cohen.
The buyer decision becomes a different one: AR development and integration instead of AI for distribution.
Nothing in the supplied material covers distributors, ERPs, sales reps, or customer communities, and both posts read as vendor promotion for specific deployments rather than an operator's account of adopting a tool.
A distributor buying group would be trading a speaker with distributor-community credibility for a specialist in another technology and market.
Each alternative here rests on two posts, so the wider catalogue is unverified.
This profile draws on 11 LinkedIn posts published by Benjamin Cohen and on retained LinkedIn posts from three other creators used for the similar-creator comparison.
Cohen's posts cover Proton's AI platform for distributors and its Pronto assistant, a day-one sales-rep training example, customer dinners and the Affiliated Distributors Service Provider Excellence Award, personal AI workflow experiments such as reading handwritten meeting notes into Granola, hiring and culture posts, and a podcast conversation with Cohen's grandfather, Larry Cohen, about Benco Dental.
Comparator posts span June 2024 to October 2026; individual publication dates were not available for Cohen's posts, and a LinkedIn follower count of 12,572 was recorded on 8 October 2026.
No podcast audio, video, or transcripts were reviewed, so podcast content is reported only as Cohen describes it.
The material documents no sponsorship, affiliate, speaking, or advisory arrangement, and no audience demographics, so those remain open questions rather than settled facts.
Recent engagement
Across their last 9 LinkedIn posts, the median post got 167 reactions and 12 comments. Checked 2026-11-02.
