Graham Treakle runs businesses and builds AI-supported workflows for business operations, including sales follow-up, CRM tasks, performance reporting, and daily planning.
Practical AI workflow content for small-business operations, especially sales follow-up, CRM tasks and daily reporting.
Best for
A CRM or sales tool vendor selling to small businesses with about 5 to 50 staff.
Not for
Enterprise AI governance, data security, or keynote work on regulatory compliance and risk.
Key takeaways
AI is more useful as recurring jobs than as a chatbot.
Quote follow-up drafts email for review and sending, saving about 30 minutes daily.
Over 20 recurring tasks became hard to manage as errors and failed runs increased.
Check first: Confirm audience demographics, partnership terms, and independent verification for reported gains.
Main ideas
AI is more useful as recurring jobs than as a chatbot · Give AI jobs that make a difference, not just prompts · AI should extend an existing discipline, not replace it
Graham Treakle runs businesses and shares how AI can support day-to-day operations.
In three LinkedIn posts, they describe using Claude and recurring automations for sales follow-up, CRM work, performance reports and daily planning.
The posts trace this approach back to a paper-planner routine that began in 1998.
Readers interested in practical small-business workflows can follow how they put AI to work and rebuild systems when they need it.
Treakle connects specific AI tasks to the operating habits around them.
One post lays out recurring reports and jobs, including quote follow-up that drafts an email for review and sending; another shows how automation results, priorities and risks come together in a printed daily plan.
Treakle also describes more than 20 recurring tasks becoming difficult to manage as errors and failed runs demanded attention.
The concrete examples, human review and acknowledged operational friction show what AI can handle and what still needs oversight.
Graham Treakle argues that AI is most useful when given standing responsibilities rather than opened and prompted on demand.
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Across three LinkedIn posts, they describe an AI operations layer built around their business: scheduled jobs that run on their own, report back and feed a single daily brief.
The through-line is not novelty but maintenance—small, individually unremarkable tasks that together remove administrative friction, attach to sales and CRM stages where time savings can be counted, and fit into a planning discipline Treakle has kept since 1998.
Treakle also sees reliability, not capability, as the real limit: once more than 20 recurring tasks were running, failed runs and error messages competed for attention.
The response was to define AI roles and rebuild the stack from scratch.
AI is more useful as recurring jobs than as a chatbot
Treakle’s central claim is that AI’s bottleneck is remembering to ask, so the fix is scheduled work with dedicated context that runs whether or not you think of it.
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Each job is small; the payoff is cumulative.
The post lists four main Claude workflows: an AI project manager that checks other AI projects at 5:30 AM, flags issues and returns one consolidated status report; a Monday weekly performance report built on a Project holding the right KPIs and reporting structure; a 5:50 PM daily productivity scorecard; and a morning operations and profitability brief that reads the calendar and CRM for opportunities to watch, calls and emails to make, stale deals and daily priorities.
Smaller jobs include a 7-day quote follow-up, daily inbox triage, a daily quote classifier and a subscription payment health check.
Treakle’s summary: none is individually revolutionary, but together they remove a surprising amount of administrative friction.
An AI project manager reviews the other AI projects every morning and reports what ran, what worked and what needs attention—one report instead of checks across multiple processes.
Give AI jobs that make a difference, not just prompts
Treakle’s lesson is that prompts and abstract jobs are not enough; jobs should change something real in a person’s life or business.
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This principle underpins treating AI as an operating layer rather than a tool to consult, and applies outside work as well as inside it.
A recipe generator tailored to flavor profile, health goals, preparation time and local grocery store—a personal job with a concrete outcome, not a generic prompt.
AI should extend an existing discipline, not replace it
Treakle trained in Franklin Covey planning after starting at Central Carolina Bank in 1998, used the official planners for nearly a decade, then adapted the system to journal-sized notebooks, with the schedule on the left page and the five most important tasks plus meeting notes on the right.
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The new Daily Profitability Plan keeps the printed page as the interface.
Treakle still prints it, writes notes on it and checks items off one at a time, describing themselves as a list person.
The morning plan consolidates the schedule, automation results, top priorities, messages waiting for approval, risks and items needing attention, and a sales pipeline snapshot, replacing checks across twenty different processes.
Treakle frames AI not as a replacement for the discipline learned from Franklin Covey but as a way to apply it with better information.
A single printed morning plan combines calendar and CRM reads with automation results, still reviewed on paper with a pen.
The method moves from official Franklin Covey planners to black journal-sized notebooks to a printed AI-generated plan. Treakle says the notebook may soon become a three-ring binder of printed plans, calling it a full circle: the tools changed since 1998, but the principles did not.
Sales and CRM follow-up is where the savings are concrete
Treakle ties AI to named sales stages and has it hand work back for human review rather than send on its own.
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A 7-day quote follow-up finds quotes needing follow-up and drafts the email; Treakle reviews, edits and sends it, saving about 30 minutes a day.
A daily quote classifier organizes downloaded quotes, checks the CRM and verifies that each lead is in the correct automation, saving 10 to 15 minutes a day.
A daily productivity scorecard tracks inbound calls answered and missed, outbound calls answered versus voicemail, response time to requests, emails and SMS sent by the system versus by Treakle, new opportunities, and opportunities won and closed.
Treakle reports that lead quality, conversation rate, average sale and available time all improved after using AI more systematically.
The 7-day quote follow-up job finds quotes due for a nudge and drafts the email, leaving Treakle to review, edit and send it—around 30 minutes a day.
Treakle describes the quote follow-up as the first task AI took completely away from the CRM. It came after earlier, smaller jobs and became the pattern for later ones.
Reliability, not capability, is the ceiling on scaling AI operations
Treakle scaled from casual AI use to more than 20 recurring tasks across business and personal systems, then hit a wall: daily error messages and tasks that failed to run demanded more attention, and the setup was not keeping pace with the growth Treakle wanted.
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The response was to rebuild the whole stack, treat AI as a set of roles or employees, and start with a prompt asking an AI to act as an expert in business operations, strategy, growth and AI; recommend how many AI employees Treakle should have and what those roles should be; and help rewrite every project, artifact and scheduled item from scratch. The rebuild’s outcome is not described in the available posts.
The starting prompt asks the model to propose the number and job descriptions of AI employees before any individual workflows are rewritten.
From casual AI use to 20-plus recurring tasks, then a reliability wall, followed by a deliberate rebuild announced as a project whose results would be shared in a few weeks.
Treakle’s open questions point toward data security and executive reporting
Treakle asks the network for their best Claude Projects, scheduled tasks and workflows, naming sales, operations, CRM, productivity, data security and executive reporting as areas of interest.
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That’s a direction of travel, not a settled position; data security and executive reporting remain open questions.
The closing question in the workflows post, which lists those six areas explicitly as the topics Treakle wants ideas about.
This post answers “What does Treakle actually do with AI day-to-day?” and “What concrete workflows have they built?”
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It offers the most detailed example of their hands-on approach, with specific time savings and tasks, making it a strong starting point for understanding their practical expertise.
Treakle explains how to use Claude as an operating layer for business rather than a chatbot.
They list four main workflows: an AI Project Manager that checks other AI projects every morning at 5:30 AM; a weekly performance report every Monday; a daily productivity report at 5:50 PM covering calls, emails, opportunities, and response times; and an operations brief that reviews the calendar and CRM for priorities, stale deals, and follow-ups.
Smaller jobs include a 7-day quote follow-up that drafts emails and saves about 30 minutes a day, daily inbox triage saving 15 minutes, a quote classifier saving 10–15 minutes, and a subscription payment health check.
Their main lesson: give AI jobs that make a difference, not just prompts.
For brands planning a campaign
For a B2B SaaS company assessing Treakle as an expert voice on AI workflow automation, this post shows how they translate AI capabilities into specific, time-saving business processes with measurable outcomes. It offers concrete examples of AI handling sales follow-up, CRM tasks, and reporting in a small business.
This post answers “What is Treakle’s background in planning, and how did they come to AI?” and “How do they blend old and new systems?”
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It helps newcomers understand their philosophy and why they trust AI-supported workflows, making it a good introduction to their approach.
Treakle traces their planning discipline back to a Franklin Covey class in 1998.
They used paper planners for almost a decade, then adapted the system to notebooks, with a schedule on the left page and top tasks on the right.
Recently, they created a Daily Profitability Plan using Claude and AI automations.
Each morning, they print a plan that includes their schedule, automation results, top priorities, messages waiting for approval, risks, and a sales pipeline snapshot.
They still print the plan and write notes, saying AI is not replacing discipline but helping apply it with better information.
For brands planning a campaign
This piece demonstrates Treakle’s long-term operational discipline and how AI fits into established habits. For a brand positioning AI as an augmentation rather than a replacement for small-business processes, their perspective and personal story offer credible, relatable evidence.
This post answers “How does Treakle handle scaling and failure in AI operations?” and “What is their process for redesigning systems?”
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It offers a look at their reflective, systematizing approach and how they use AI to solve operational problems.
Treakle describes scaling from casual AI use to more than 20 recurring tasks across their systems.
When daily error messages and failed tasks began demanding too much attention, they decided to rebuild the entire AI operations stack.
They share the exact prompt they used to ask an AI to act as an expert in business operations and AI, recommend how many AI employees they should have and what those roles should be, and help rewrite all projects, artifacts, and scheduled items from scratch.
They plan to share results in a few weeks.
For brands planning a campaign
This post shows Treakle’s iterative approach and willingness to share successes and failures, including the prompt engineering behind their system. For a B2B SaaS company, it demonstrates a credible voice on AI implementation challenges and scaling issues in small-business operations.
Daily Profitability Plan: AI reporting inside a paper planner habit2
Sales and CRM follow-up automation2
Scaling AI operations until reliability breaks, then rebuilding1
AI for personal productivity and everyday life1
AI as an operating layer made of recurring jobs
Graham Treakle’s central argument is that AI becomes more useful when given recurring responsibilities rather than used only as an on-demand chatbot.
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One post lists four main Claude workflows: an AI project manager that checks other AI projects each morning at 5:30, a weekly performance report on Mondays, a daily productivity report at 5:50 PM, and an operations and profitability brief that reviews the calendar and CRM for opportunities to watch, calls and emails to make, stale deals, deals needing attention, and daily priorities. Smaller jobs include seven-day quote follow-up, daily inbox triage, daily quote classification, and a subscription payment health check.
Gives AI scheduled jobs with defined outputs and review points. Uses dedicated Projects with context, KPIs, and reporting structure so recurring tasks run consistently rather than being re-prompted from scratch.
For brands planning a campaign
For a B2B SaaS company assessing Graham Treakle as an expert voice on AI workflow automation for small business operations, this is the strongest topic.
They can describe specific recurring-job designs, review points, and time savings.
Campaigns should focus on concrete workflow examples rather than generic AI productivity messaging.
Daily Profitability Plan: AI reporting inside a paper planner habit
Graham Treakle traces a planning system from Franklin Covey training in 1998 through journal-sized notebooks to a new Daily Profitability Plan built with Claude and automations.
Read more
The printed plan includes a schedule, automation results, the day’s top priorities, messages waiting for approval, risks and items needing attention, and a sales pipeline snapshot. They still print the plan, write notes on it, and check items off, describing AI as helping apply an older discipline with better information rather than replacing it.
Consolidates many process checks into one morning report. Keeps a physical review layer and uses an AI manager to check the work completed by automations.
The post presents this as an ongoing personal change, not a template proven across other businesses.
For brands planning a campaign
Useful for productivity, planning, and sales-pipeline tools that want to position AI as augmenting established routines. Treakle can speak to owners who want better operational visibility without eliminating paper or checklists.
Graham Treakle describes specific sales operations jobs: a seven-day quote follow-up that finds quotes needing follow-up and drafts the email, saving about 30 minutes a day; a daily quote classifier that organizes downloaded quotes, checks the CRM, and verifies each lead is in the correct automation, saving 10 to 15 minutes a day; and a daily productivity scorecard tracking inbound calls answered and missed, outbound calls answered versus voicemail, response time to requests, emails and SMS sent by the system versus by the person, new opportunities, and opportunities won and closed.
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They report that lead quality, conversation rate, average sale, and available time all improved after using AI more systematically.
Connects AI tasks to CRM records and existing follow-up stages, and measures daily call, email, and opportunity metrics.
For brands planning a campaign
Relevant for CRM, sales engagement, and AI email vendors.
A campaign can ask Treakle to walk through a quote follow-up or lead-classification workflow and explain where human review remains.
They report improvements in conversion and sales; ask for supporting evidence before using those results as campaign claims.
Scaling AI operations until reliability breaks, then rebuilding
Graham Treakle writes about starting with casual AI use and reaching more than 20 recurring tasks across business and personal systems.
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As tasks and reports multiplied, daily error messages and failed tasks took more attention, and the setup was not keeping up.
They then decided to rebuild the stack, define the roles AI should fill, and start with a prompt asking how many AI employees to have and what those roles should be.
The post says results will be shared in a few weeks.
Treats AI as a set of roles, audits and rewrites projects, artifacts, and scheduled items from scratch, and uses an executive summary as a starting point for new prompts.
The rebuild outcome is not described in the posts here, so this is a stated plan and a stated problem, not a described resolution.
For brands planning a campaign
For B2B SaaS companies selling orchestration, monitoring, or AI operations platforms, Treakle’s reliability pain point offers a useful campaign angle: moving from many fragile automations to a managed system. The rebuild is a stated plan, not a completed project.
Graham Treakle applies the same recurring-job approach outside business.
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One post mentions a recipe generator customized to flavor profile, health goals, preparation time, and local grocery store, and a daily productivity report that gives an end-of-day scorecard. The argument is to give AI jobs that make a difference in life, not just prompts.
For brands planning a campaign
Broadens Treakle’s appeal beyond business operations. Consumer AI, meal planning, or personal productivity brands could use this angle; the posts offer only brief examples, so ask for more detail before building a campaign around their depth in these areas.
Across the posts, the arc moves from casual AI use to more than 20 recurring tasks, then to a reliability wall, and then to a planned rebuild.
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One post says lead quality, conversation rate, average sale, and available time are up. Treakle also asks the network for ideas on sales, operations, CRM, productivity, data security, and executive reporting.
The public account shows a shift from one-off chatbot use to scheduled jobs and then to deliberate system design.
For brands planning a campaign
Offers a narrative for small business owners moving from AI experimentation to operational dependency. Treakle also identifies data security and executive reporting as areas of interest; ask about their approach to each before building a campaign that depends on them.
They say they went to work for Central Carolina Bank in the fall of 1998 and built their first planning system there using a Franklin Covey course and planner.
Franklin Covey remained their personal framework after 2008, adapted for notebooks.
Claude powers the AI workflows described across all three posts: scheduled reports, CRM follow-up, quote handling, and a daily profitability plan.
Ask whether any of these relationships involve a sponsorship, affiliate arrangement, or paid partnership, and which businesses Treakle runs; the posts do not name them.
Claude (Anthropic)
Mention
Claude is the platform behind Treakle’s AI operations stack.
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The posts describe recurring scheduled jobs rather than one-off prompts: an AI project manager that checks other automations at 5:30 a.m. and reports status; a Monday performance report; a 5:50 p.m. daily productivity scorecard; a morning operations and profitability brief; seven-day quote follow-up that drafts the email; inbox triage; a quote classifier; and a subscription payment health check.
Treakle later rebuilt the stack around named AI roles, saying more than 20 recurring tasks run across their systems.
The posts describe use, not a commercial relationship; ask for confirmation of any sponsorship, affiliate arrangement, or paid partnership with Claude or Anthropic.
Treakle says that in the fall of 1998, fresh out of college, they went to work for Central Carolina Bank.
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Shortly after they started, the bank sent them to a multi-day Franklin Covey planning class, which began the planning habit described in the posts. The material covers a past job, not a current relationship with the bank.
Graham Treakle writes about running a small business with AI as a set of standing jobs, not a chatbot opened only when needed.
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The clearest entry point is the post listing recurring Claude workflows — a project manager that checks the other automations each morning, a Monday performance report, an end-of-day scorecard, and a morning brief built from the calendar and CRM.
It names the jobs, their schedules, and the time each saves.
Read the planning post next: it traces the same practice back through Franklin Covey training in 1998 and years of paper planners to the printed morning plan they still review by hand.
That makes concrete the argument that AI extends an existing discipline rather than replacing it.
Finish with the rebuild post, which describes passing 20 recurring tasks, hitting daily errors and failed runs, and starting over by asking an AI to recommend which roles it should fill.
Ask for an update on the outcome of that plan.
The through-line is maintenance over novelty — small jobs attached to sales and CRM stages, counted in minutes saved, and kept inside a human review step.
Tveter also writes about putting Claude directly into sales and CRM work, walking through named features and how a rep or admin should apply each one day to day — the same hands-on, step-by-step approach Treakle takes to their recurring jobs.
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Tveter reviews specific products a team buys and configures, such as a HubSpot sales plugin and deal-progression features, including what admins must switch on. Treakle writes as an owner about a stack they built for one small business and the strain of keeping it reliable as it scales past 20 tasks.
Jones turns Claude into repeatable operational jobs for a specific desk — a project per client, one intake call converted into several deliverables, a top performer's writing captured as a reusable Skill — while keeping the human in the final decision.
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Treakle uses the same structure for quote follow-up and lead classification.
Jones applies this inside staffing and recruiting, and the posts lean on pitches, free sessions, and vendor-facing figures such as job descriptions drawing more applicants. Treakle's posts stay within one owner's business and report personal time savings rather than market statistics.
LeBrun shares practical first-person lessons for people running or choosing an owner-operated small business, and shares Treakle's premise that technology should absorb routine work while people retain judgment, leadership, and the customer relationship.
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LeBrun focuses on evaluating and operating franchise and service businesses, including staffing the numbers as if a manager were already in place. AI appears as a general backdrop rather than the subject; LeBrun does not describe specific automation workflows or tools.
Graham Treakle’s posts suggest a potential voice for practical AI workflow content aimed at small-business operators, though whether that audience matches B2B SaaS buyers is unclear.
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The three posts focus on hands-on operations: scheduling recurring AI tasks, managing CRM follow-up, producing daily performance reports, and rebuilding an AI operations stack.
They point to interest in applied automation rather than abstract AI thought leadership.
The only measured audience figure is a LinkedIn following of 3,398, observed on 8 October 2026.
Before placement, ask for recent audience data or a survey covering industry, job function, seniority, company size, and geography, plus results from recent posts or campaigns on small-business AI workflow automation.
That will help determine whether the following overlaps the target B2B SaaS buyers.
Graham Treakle's posts focus on their AI workflow systems for business operations, including scheduled reports, CRM follow-up, inbox triage, and daily planning.
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They do not describe sponsored placements, rates, audience packages, content requirements, or an official inquiry route. Before planning a campaign, ask directly about available partnership options and terms.
A CRM and sales-engagement vendor selling to small businesses (roughly 5–50 staff) wants a practitioner voice to show how AI takes over routine quote follow-up, lead classification, and daily pipeline reporting while keeping a human approval step.
Reasons
Treakle writes about this exact work. One post describes a seven-day quote follow-up job that finds quotes needing follow-up and drafts the email, leaving review, editing, and sending to a person and saving about 30 minutes a day. The same post describes a daily quote classifier that organizes downloaded quotes, checks the CRM, and verifies that each lead sits in the correct automation, saving 10–15 minutes a day.
The daily productivity scorecard tracks the metrics a sales-tool vendor usually sells against: inbound calls answered and missed, outbound calls answered versus voicemail, response time to requests, emails and SMS sent by the system versus by the person, new opportunities, and opportunities won and closed. A campaign can ask Treakle to walk through those numbers as they appear in a morning report.
The framing matches the product category. Treakle argues for giving AI recurring responsibilities rather than re-prompting a chatbot, and describes Claude as an operating layer with scheduled jobs, dedicated projects, and an AI manager that checks whether the other jobs ran correctly. That makes a more natural match for process automation, orchestration, or CRM automation products than for a general AI thought-leadership slot.
The material shows a human review point, useful for a vendor that has to answer the objection that AI will send something wrong. Treakle describes reviewing, editing, and sending the drafted follow-up rather than letting it go out untouched.
The narrative arc runs from casual AI use to more than 20 recurring tasks, a reliability wall, and a deliberate rebuild. Buyers already using several fragile automations will recognise the middle of that story.
Limits
The posts show a LinkedIn following of 3,398 but no audience demographics, so fit with the vendor's target industry, seniority, or company size cannot be confirmed. Ask for an audience survey or platform audience data before committing spend.
Reported gains in lead quality, conversion rate, average sale, and available time are Treakle's own account. They are not independently verified, so present them as stated results, not measured outcomes.
The posts describe no sponsorship or partnership formats. Establish rates, deliverables, and an official inquiry route directly.
When to choose a specialist
Poor fit
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Brief
An enterprise AI governance and data-security platform selling to large-company security and compliance leaders wants a keynote voice on model governance, regulatory compliance, and AI risk frameworks for complex organisations.
Reasons
The posts focus on operational small-business practice: scheduling recurring jobs, CRM follow-up, inbox triage, reporting, and rebuilding a personal AI stack. They do not address governance frameworks, model risk, audit requirements, or regulation—the subjects a keynote aimed at security leaders needs to cover.
Data security appears only as a topic Treakle asks the network for ideas about, alongside sales, operations, CRM, productivity, and executive reporting. The posts show no demonstrated expertise or approach to data security.
The posts provide no audience demographics for enterprise security buyers. The only measured figure is a LinkedIn following of 3,398, with no industry, job function, seniority, company size, or geography data. Ask for platform audience data before booking a keynote intended to reach security leaders.
A keynote also requires evidence the material does not show: the examples are first-person accounts of a system Treakle built, not a tested framework applied across other organisations. The rebuild post is a stated plan with results promised later, so there is no completed enterprise case to present.
The content does not cover likely buying triggers for the platform, such as policy enforcement and compliance reporting across many users. It gives readers no concrete reason to connect Treakle to that decision.
When they might still work
A narrower version is defensible: a panel or session on practical adoption friction inside a small or mid-sized business, where Treakle speaks as an owner-operator who has hit reliability problems at more than 20 recurring tasks. The platform's own subject-matter expert should handle governance and compliance; Treakle should offer only the practitioner view.
A worked campaign example
Angle
Build the campaign around one job and one objection. The job is the seven-day quote follow-up that finds quotes needing attention, drafts the email, and waits for review. The objection is that automation will send something wrong or lose the personal touch. Treakle's material answers that with a concrete detail: the draft is reviewed, edited, and then sent, saving about 30 minutes a day. The campaign line is a task removed from the CRM with a person still holding the send button, not a fully autonomous sales agent.
Brief
The CRM and sales-engagement vendor in the good-fit scenario wants a short campaign showing prospects what a working quote follow-up job looks like inside a small business, including where a person checks the output.
Deliverables
A written walkthrough of the quote follow-up job: what triggers it, what it looks at in the CRM, what the draft contains, and exactly where the human review and edit step sits.
A short video or screen recording in which Treakle opens the drafted follow-up, edits it, and sends it, narrating the review decision.
A companion piece on the daily quote classifier, showing how a lead is checked against CRM records and the correct automation, and what happens when a record fails that check.
A prompt or setup example. Treakle publishes a full prompt in another post, so a worked starting prompt fits the existing material and gives the audience something portable.
A follow-up post on reliability: what broke as the number of recurring tasks grew, and what the vendor's tooling does about failed runs. This connects to the stated rebuild problem without presenting the rebuild as finished.
Gaps
The posts here do not include audience demographics, engagement rates, or any past sponsored-post results.
The posts document no partnership terms, rates, or inquiry route. Agree on these directly before planning the campaign in detail.
The rebuild post promises results later. If the campaign depends on what the rebuilt system achieved, collect that evidence first.
Measurement
Ask the vendor to collect audience composition data before spend, so the campaign’s reach among small-business sales roles is known.
Track demo requests and trial starts from the campaign links. Both are observable.
Label the reported gains in lead quality, conversion rate, average sale, and time saved as Treakle’s account, not verified outcomes, or test them separately with the vendor’s own customers.
Why it works
Each campaign claim points to something in the posts: the follow-up job, time saved, review-and-send step, classifier, or productivity metrics. That gives the campaign specific support for its claims about AI.
The angle uses their framing, not the vendor’s. Their argument for recurring jobs with defined outputs and a check of completed work runs through more than one post, so the campaign reads as the creator’s position, not a script.
It targets a decision small-business buyers face—whether to let software handle quote follow-up—and addresses the fear that often blocks it.
Jones instead when the vendor's buyers are staffing and recruiting firms rather than general small businesses.
The post speaks directly to staffing leaders and lists five ways Claude is used on a recruiting desk: a separate Claude Project per client, loaded once with job specs, rate cards, and meeting notes; turning a single intake-call transcript into five outputs (job description, candidate outreach email, client recap, interview questions, and a LinkedIn post); converting a top recruiter's submittal style into a reusable Skill so the whole desk writes to that standard; connecting email, calendar, and drive to a scheduled Monday morning update; and keeping the hiring call with people, not the model, on the stated grounds that even Anthropic does not let Claude make the hiring decision.
Two things make this a strong swap for the same campaign decision.
First, the draft-then-decide step the CRM vendor wants to show is stated plainly.
Second, the post closes with a two-day live session schedule, so an event or webinar component can build on content that already has a registration hook.
The workflows center on candidates and submittals, not sales.
The material does not cover quote follow-up, lead classification against CRM records, pipeline snapshots, or a daily profitability report, so a vendor selling quote-to-cash or pipeline tooling would be buying a different job than the one in its brief.
The post repeats a third-party figure that job descriptions written with Claude drew 25 percent more applicants; attribute it to the original source or leave it out.
It reads as a curated how-to, not a first-person account of a system that broke at scale and was rebuilt—the reliability narrative the vendor gets from Graham Treakle.
A campaign would also run alongside their live-session promotion and a partner hashtag, so timing and messaging would need coordination.
Choose Chris Tveter instead when the buyers are HubSpot-using sales teams or RevOps functions and the campaign is about AI that ships inside the CRM rather than a personal automation stack.
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One post walks through all eight pre-built skills of HubSpot's free Claude sales plugin and what each is for: call prep, a prioritized daily brief, logging a call by talking through it in plain language, drafting the recap email and saving it in HubSpot, pipeline pulse before a weekly review, contact lookup, CSV or pasted-list contact import with field mapping and deduplication, and a general lookups-and-updates catch-all.
It also gives the vendor the key detail for addressing the trust objection: before Claude writes anything to the account, it shows the proposed change and asks for confirmation.
A second post covers HubSpot Deal Progression in public beta, including the new Catch-Up tab, the requirement that conversation recording be on, configuration of MEDDIC, BANT, or SPICED with the tab showing which criteria are met, Deal Plans that generate a branded buyer portal without a HubSpot login, and a re-engage prompt after 14 days of silence.
It flags a migration detail: under the default layout, the Overview tab is renamed About.
That suits an enablement or change-management angle focused on what reps will actually see on Monday.
The material covers HubSpot features.
A vendor outside the HubSpot ecosystem, or one competing with it, would fund content that walks buyers through a rival's roadmap and admin settings.
There is no small-business owner's account of quote follow-up or lead classification, no stated time savings, and no rebuild story.
Instead, the author explains how they would put each skill to work, with a short note on daily use; the practical detail is guidance, not a documented build.
The post also covers beta features and admin activation steps, so claims could date quickly.
The vendor would need to re-verify the specifics before publishing.
Choose Rich LeBrun instead when the campaign's buyer is a prospective small-business or franchise owner and the message is human judgment over full automation.
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The posts support that argument directly.
One tells buyers to run franchise numbers as if a manager were already in place and to pay that role what it is worth, arguing that as technology takes over routine work, people who bring judgment, leadership, and real connection become more valuable.
The other points to services people genuinely need, such as health and wellness, personal care, and specialized support, where technology can smooth scheduling and follow-up but customers still want a knowledgeable person in front of them.
For a product whose selling point is that a person still approves the send, this is a useful supporting voice from someone who states 30 years owning and operating businesses.
There is no AI workflow content.
The posts do not name a tool, describe an automation, or cover quote follow-up, lead classification, or daily reporting, so this is not a substitute when the buyer task is to show software taking routine work off a small business.
The posts also run an existing audience funnel, closing with a follow prompt and a pointer to a comment link, so a campaign would need to fit that structure rather than introduce one.
Use this as a second voice on the human-decision half of the message, not as the practitioner demonstrating the job.
This profile draws on three full LinkedIn posts about recurring Claude workflows, Treakle’s daily planning routine, and a planned rebuild after reliability problems.
Reported business gains and time savings are Treakle’s own account; the rebuild is described as a plan, not a completed result.
