Peter Malick shares and develops practical AI applications for business workflows, including CRM data processing, team research updates, and personalized e-commerce videos.
Peter Malick shows how AI can be applied to specific business tasks.
In posts about InboundAV, they describe turning thousands of DocuSign PDFs into HubSpot-ready data and building a Slack assistant that curates AI and RevOps news for the team.
They also share product and tool experiments, including Jazzbox AI, a planned personalized-video tool for abandoned carts, and a website prototype made with Google AI Studio.
The posts offer practical AI workflows and ideas for choosing a focused problem to solve.
A satirical post about bad cold outreach adds a humorous note.
Malick connects a case for focused AI investment to concrete examples: identify a costly or repetitive workflow, explain how automation handles it, and show what work it frees people to do.
The DocuSign-to-HubSpot example is especially specific, while the Jazzy post shows a smaller, recurring team use.
Jazzbox is presented as a developing e-commerce product, and the Google AI Studio post as a hands-on demonstration.
The mix offers practical examples and a point of view on where AI may help, with satire providing an occasional change of tone.
Peter Malick writes about applied AI as someone running the work, not just commenting on it.
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Across the posts, the recurring argument is that AI pays off in narrow, specific places: one stalled handoff, one document backlog, one recurring team task.
That comes through both in advice to operators deciding where to spend and in descriptions of tools built inside InboundAV, the firm connected with the work.
Another theme: customize tools instead of buying generic ones, whether a news assistant tuned to a team’s niches or a script built for one client’s file mess.
The arguments are public, with steps and time estimates.
Stop asking whether to invest in AI; start asking how
Peter Malick argues the debate has moved on.
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The useful question for any business, whatever its size, is which part of the operation gets AI first: not making big AI bets is starting to look riskier than making them.
Most companies cannot fund dozens of experiments like large technology firms, so the differentiator is disciplined allocation: pick one painful process, one moment in the customer journey, one stalled handoff, or one repetitive task that burns high-value time.
At InboundAV, that means focusing on processes that route and qualify leads, turn unstructured data into clean CRM records, and synchronize platforms.
The post contrasts Microsoft’s worst quarter since 2008 with Google’s heavy spending to defend search, ads, and cloud, then points to OpenAI shutting down Sora as a reminder that even large bets fail. For a typical business, the recommendation is to design one specific process around a single question and outcome.
The framing shifts the question from whether to how, treating “AI everywhere” as something to avoid rather than the goal.
Rather than adopt a generic product, Peter Malick describes building an AI agent for a specific team and audience.
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Called Jazzy, the assistant runs on a Mac Mini dedicated to OpenClaw and connects to Slack.
Each morning, it reads hundreds of articles and posts, curates conversations relevant to the development team, and turns them into short action items.
Malick says customization drove the decision to build: off-the-shelf news bots exist, but OpenClaw let the team tune curation to its specific niches.
The same approach covers internal task management and project tracking.
Jazzy’s daily rundown for the InboundAV team is the concrete output: a filtered list of AI and RevOps conversations, with bite-sized action items instead of a raw feed. Malick describes the work as a collaboration with Fabián Osorio Ricalde on OpenClaw workflows.
One of Peter Malick’s clearest arguments is that unstructured paperwork is where custom automation earns its place.
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A scripted browser process does what a person would do, step by step, then returns structured data.
The scope makes the case: a job that would have taken years of manual entry was done in hours, freeing the client’s team for other work.
Malick says the niche challenge became a capability the firm can offer on other projects.
A client new to HubSpot needed years of DocuSign PDFs imported: thousands of documents with no standard formatting.
The custom process opened a browser, navigated DocuSign’s interface, worked around visual pop-ups, read specific pages, and mapped the data into a clean spreadsheet.
It reportedly processed thousands of documents in hours rather than years of manual entry.
Prompting is now a fast way to build a working website
Peter Malick argues that AI-prompted site builders can now produce usable branding, UX, and UI from a detailed prompt and a clear vision.
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Just months earlier, they did not expect no-code apps to work without significant rework.
The point is not that prompting replaces everything, but that the first working version is now cheap enough to demonstrate in a talk.
The time breakdown supports the case.
For a NAMM 2026 pre-show talk, Malick built a fictional six-string electric guitar brand called OnlySix using Google AI Studio: about twenty minutes writing the prompt, three minutes for the build, and twenty minutes tweaking the branding and usability through prompts, with e-commerce functionality as the next step.
Malick says they were skeptical of the phrase “no-code” just months before this demo, making the shift in view part of the argument.
Peter Malick describes a product journey that diverged from the original plan.
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Jazzbox AI began as a possible MicroSaaS side project with system architect Joshua Savage: a small AI-powered e-commerce tool.
It grew into a funded product after an inbound email from AWS offered a nomination for generative AI funding, leading to AWS programs and an introduction to the development firm Innovative Solutions.
Malick’s lesson: stay committed to an idea while leaving room for it to grow beyond its first shape.
The product customizes recovery videos using demographic and psychographic data about the shopper.
When a cart is abandoned, Jazzbox pulls a product image, product description, and shopper information, then generates a personalized video for that buyer.
The beta is planned for Shopify and WooCommerce.
Savage is credited as the system architect and co-builder of the product.
Peter Malick’s satirical playbook makes a practical point by ridiculing signals recipients recognize: “Quick Question” subject lines, flattery about a profile the sender never read, AI personalization with bracketed placeholders, endless follow-up chains, LinkedIn notes about synergies, and claims that a CEO asked for the approach.
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The humor lands because each pattern is familiar. Beneath it is a serious point about deliverability: a CRM or email platform can report near-perfect delivery while a large share of outbound never reaches an inbox.
The post reports 99.4% deliverability alongside roughly 60% of outbound landing in spam, then offers a comment-triggered set of instructions for getting back into the inbox. It cites no source for the figures.
Answers what an InboundAV AI build looks like in practice and why a newcomer should start with it: it is the clearest example of Malick taking one operational bottleneck and describing the workflow in steps.
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Peter Malick describes a client that had just moved onto HubSpot and needed years of DocuSign PDFs imported.
The documents were thousands of unstructured files without standard formatting, and manual entry “would have taken us until the year 2030.”
The response was a custom AI workflow with five steps: open a browser, navigate DocuSign’s interface, bypass visual pop-ups, read specific pages, and map the data into a clean spreadsheet.
According to the post, the bot processed thousands of documents in hours and freed the HubSpot team for other onboarding work.
For brands planning a campaign
Useful for a campaign about practical AI delivery in CRM operations: it shows a narrow, painful data problem turned into a bounded workflow. It gives a concrete example of build scope, process steps, and claimed time saved.
Shows how Peter Malick applies an AI agent to a recurring team workflow and why that matters.
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A newcomer gets a concrete picture of the kind of small, customized automation Malick returns to.
Peter Malick describes buying a Mac Mini to run OpenClaw and working with Fabián Osorio Ricalde on AI-agent workflows for InboundAV.
Their assistant, “Jazzy,” connects to Slack and sends the team a daily rundown of AI and RevOps news.
Each morning Jazzy reads “hundreds of articles and posts,” curates the top conversations for the development team, and creates short action items.
Malick says OpenClaw let them customize the bot for the team’s specific niches rather than use a generic news bot.
For brands planning a campaign
A good fit for campaigns about internal AI enablement, Slack-based agents, and reducing research overhead. The post makes the customization argument clearly: the value is less the news feed than the team-specific filtering and action items.
Answers what Peter Malick is building beyond client work and why they started it.
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It is the strongest starting point for understanding the Jazzbox AI product and the founder narrative around it.
Peter Malick announces Jazzbox AI, an e-commerce tool for abandoned-cart recovery.
The product creates personalized videos: when a cart is abandoned, Jazzbox pulls a product image, product description, and shopper information, then uses demographic and psychographic data to make a custom video for that shopper.
The post describes a year-long path from a MicroSaaS side project with system architect Joshua Savage to an AWS-funded product being built with AWS partner Innovative Solutions.
The beta is described as planned for Shopify and WooCommerce.
For brands planning a campaign
Relevant to e-commerce AI, personalization, founder stories, and product-launch content. It shows Malick moving from client workflow experiments into a named product, which can support a campaign about applied AI rather than abstract commentary.
Shows Peter Malick’s hands-on testing and teaching style with a concrete tool.
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A newcomer sees how they turn an AI capability into a short, repeatable demonstration.
Peter Malick describes a talk at the NAMM 2026 pre-show where they built a website in just under an hour using Google AI Studio.
The breakdown is 20 minutes writing the prompt, 3 minutes waiting for Google to build the site, and 20 minutes prompting tweaks to branding and usability.
The demo was “OnlySix,” a fictional six-string electric guitar brand, and Malick presents it as evidence that AI-prompted website builders now produce functional results.
For brands planning a campaign
Works for campaigns about accessible AI tools, rapid prototyping, and creative demos. The time breakdown makes the claim easy to inspect and gives a campaign a usable before/after structure.
Answers how Peter Malick thinks about AI priorities and where they believe the leverage is.
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Start here to understand the strategic argument behind the more tactical build posts.
Peter Malick argues that the useful question is no longer whether to invest in AI but how.
The post points to Microsoft’s worst quarter since 2008 and Google’s heavy AI spending, then says most businesses cannot fund dozens of experiments.
The recommended approach is to apply AI to one painful workflow, one critical customer-journey moment, one stalled handoff, or one repetitive process.
At InboundAV, Malick says the team focuses on workflows that route and qualify leads, turn unstructured data into clean CRM records, and streamline task management.
For brands planning a campaign
Supports thought-leadership campaigns aimed at operators and go-to-market teams deciding where to start with AI. The post gives a clear point of view—focused implementation over broad platform bets—and ties it to named workflow categories.
Shows Peter Malick’s voice and their attention to outbound email problems.
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A newcomer gets a fast sense of how they explain a common B2B annoyance and where their humor sits.
Peter Malick writes a mock playbook for “100% SPAM in 2026,” listing tactics such as “Quick Question {{first_name}}” subject lines, fake profile praise, AI-generated personalization with bracketed placeholders, endless follow-up chains, “synergies” messages, and claiming “Our CEO asked me to personally reach out.” The satire turns serious at the end: Malick says a CRM or email platform can report 99.4% deliverability while 60% of outbound emails land in spam, and offers instructions to comment “NO SPAM.”
For brands planning a campaign
Useful for outbound, email deliverability, and sales-communication campaigns that want a recognizable voice. The post shows Malick making a practical point through satire.
Peter Malick argues that most companies do not need AI across the whole business.
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The useful question is which workflow to fix first: qualifying leads, cleaning messy CRM data, syncing platforms, or addressing a handoff where deals consistently stall. A narrow, high-value problem is where a smaller team can outperform a horizontal tool that tries to help with everything.
Pick one painful workflow at a critical moment in the customer journey, then design the build or purchase around a single sharp question and clear outcome.
The recommendation still allows for big bets to fail: OpenAI’s heavy spending followed by the shutdown of Sora serves as a counterexample. The point is allocation discipline, not avoiding AI investment.
For brands planning a campaign
A good fit for positioning around targeted AI deployment for operations and go-to-market teams, rather than broad platform rollouts. The argument gives brands a concrete way to talk about where to start.
Custom scripts that read documents people do not want to read
A client onboarding into HubSpot needed years of DocuSign PDFs imported across thousands of unstructured files with no standard formatting.
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Peter Malick describes a custom AI workflow that opens a browser, navigates the DocuSign interface, works around visual pop-ups, reads specific pages, and maps the data into a clean spreadsheet.
The job took hours instead of years of manual entry.
The same approach has since been used for a daily news bot and a task-management bot.
Five-step browser automation: open, navigate, bypass pop-ups, read pages, and map data into a structured output.
For brands planning a campaign
Strong material for data migration, CRM hygiene, and onboarding-automation campaigns that can show a concrete before-and-after. The work is easy to explain to buyers struggling with legacy documents.
Peter Malick runs an OpenClaw assistant called Jazzy on a Mac Mini, connected to Slack.
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It reads hundreds of articles and posts each morning, curates what matters to the development team, and turns it into short action items.
Malick built it rather than using an off-the-shelf news bot because the agent can be tuned to the team’s specific niches.
The same internal-automation habit covers task management and project tracking.
Dedicated hardware plus an agent framework, wired into the team’s chat tool, with curation rules set by the team rather than the vendor.
For brands planning a campaign
Relevant to internal-ops and productivity campaigns, and to audiences weighing build-versus-buy for workplace AI. The recurring theme is customization for a specific team, not generic aggregation.
Peter Malick describes building a demo site with Google AI Studio in roughly an hour: about twenty minutes writing the prompt, three minutes for the build, and twenty minutes of prompting tweaks to improve branding.
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The result, a fictional six-string guitar brand called OnlySix, was presented at a NAMM 2026 pre-show talk. The claim is not that no-code replaces everything, but that AI-prompted builders can now produce usable branding, UX, and UI from a vision and detailed prompting.
A detailed prompt, a rapid generated build, then iterative prompting passes for branding and layout before adding e-commerce functionality.
Peter Malick notes being skeptical of the phrase 'no-code' only months earlier, which makes the change of view part of the story.
For brands planning a campaign
Suits demo-led or workshop formats and campaigns aimed at small teams testing whether AI-assisted web building is worth the time. The time breakdown is the persuasive part.
Peter Malick describes starting Jazzbox AI with system architect Joshua Savage as a possible MicroSaaS side project.
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It grew into an AI e-commerce tool that generates personalized videos to recover abandoned carts: it pulls a product image, description, and shopper information, then uses demographic and psychographic data to tailor the video to the buyer. An inbound email from AWS led to funding through AWS generative AI programs and an introduction to development firm Innovative Solutions, which is now building the beta for Shopify and WooCommerce.
Start narrow, follow the funding and partner opportunities that emerge, and expand scope when the concept proves bigger than the original plan.
For brands planning a campaign
Useful for founder-journey and product-launch content aimed at people building in AI, and for e-commerce campaigns focused on cart recovery. The AWS and partner details give a brand something concrete to react to.
Peter Malick urges teams to stop asking whether to invest in AI and start asking how.
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The argument draws on Microsoft’s worst quarter since 2008 and Google’s heavy spending to defend search, ads, and cloud, concluding that not making big AI bets is starting to look riskier than making them. Most businesses cannot scatter money across dozens of initiatives, so the differentiator is thoughtful allocation rather than aggression.
Compare what companies with deep pockets can afford with a normal business’s constraints, then narrow the focus to the few areas where better speed or quality would move revenue or retention.
For brands planning a campaign
Fits commentary and analysis placements aimed at operators and leadership teams deciding where AI budgets go. It gives brands a timely hook without requiring a product pitch.
A satirical playbook lists six ways to kill cold outreach: “Quick Question” subject lines, fake profile flattery, AI-generated personalization, endless follow-up chains, LinkedIn notes about synergies, and the claim that the CEO asked for the approach.
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The humour carries a real point about deliverability: Peter Malick says a CRM or email platform can report 99.4% deliverability while around 60% of outbound lands in spam, and offers a comment-triggered guide to getting back into the inbox.
Use a recognizable example to ridicule each pattern, then pivot to the deliverability problem beneath it.
For brands planning a campaign
A fit for deliverability, sales-engagement and RevOps campaigns where humour and pattern recognition can earn attention. The comment prompt offers a ready-made engagement mechanic.
Peter Malick flags recent Salesforce developments and their implications for mid-market teams, framing the post as a take and an invitation to compare interpretations.
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The post is a short teaser with hashtags and no worked reasoning. Ask for a worked example before positioning them as a subject-matter expert.
For brands planning a campaign
The post signals interest in CRM platform changes, but offers little analysis. Ask for the supporting analysis; the value here is the audience and subject, not the depth.
Malick’s posts document three working relationships most clearly: Jazzbox AI, a company they started that makes AI-personalized videos for abandoned-cart recovery; AWS, which awarded Jazzbox funding through programs supporting generative-AI projects; and Innovative Solutions, the AWS-approved development firm building the Jazzbox beta with them.
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Malick also writes in first-person plural about InboundAV, an AI-workflow and CRM services business, describing its client case studies and tools.
Ask whether they own the business or work there; the posts don’t say.
HubSpot, OpenClaw, and Google AI Studio appear as platforms central to the work, not as stated sponsors or clients.
Jazzbox AI
Owned business
Malick says they started Jazzbox AI, an e-commerce tool that generates personalized videos to recover abandoned carts.
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They first brainstormed it as a side project with system architect Joshua Savage, and it is now in its first full development sprint. The post says the beta will be available for Shopify and WooCommerce.
Malick says AWS contacted them after noticing Jazzbox was being built on its platform and nominated the project for funding.
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Jazzbox received funding through several AWS programs that support generative-AI projects built on AWS. The post links the funding to ongoing development, not sponsorship or advertising.
Malick describes Innovative Solutions as an AWS-approved development company they met through AWS funding and the team they are working with on the Jazzbox beta.
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They say their conversations stood above those with the other AWS-approved developers they met.
Malick writes about InboundAV in first-person plural terms, describing client AI work "at InboundAV," its HubSpot team, and AI-driven workflows the business builds for lead routing, CRM data cleanup, and task management.
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Ask whether Malick owns InboundAV or works there; the posts don’t say.
HubSpot is central to the client work Malick describes: after a client onboarded to HubSpot, years of DocuSign PDFs had to be migrated into the CRM, and Malick says the automation freed InboundAV’s HubSpot team.
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A separate post discusses Salesforce and HubSpot market developments. Ask whether there is a sponsorship, employment, or partner relationship with HubSpot; the posts don’t specify one.
Malick says they bought a Mac Mini dedicated to running OpenClaw and used the AI agent to build "Jazzy," an assistant that reads hundreds of articles daily and posts a curated AI and RevOps news rundown to the team’s Slack.
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OpenClaw is a tool they run, not a named sponsor or partner.
Start with Malick's practical build posts, then move to the argument pieces.
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The clearest entry points are descriptions of tools built to solve real problems: a script that reads years of DocuSign PDFs into HubSpot, a Slack news assistant tuned to one team's niches, and a demo website built with Google AI Studio in about an hour.
Together, they show the working method: pick one narrow problem, build a small custom tool, and report the steps.
The commentary posts explain the reasoning: allocate AI to one painful workflow rather than everywhere, and use satire to expose cold-email patterns that keep outbound landing in spam.
The product-launch story about Jazzbox AI is the most personal thread, tracing an idea from a possible side project to a funded cart-recovery product.
Read the case studies alongside the commentary for a fuller picture; they work best as one set rather than a dated sequence.
Both write about wiring AI agents into marketing operations and showing the setup rather than the theory.
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Corto S. describes turning Claude Code and GPT-6 into a Klaviyo execution engine that analyzes an account, finds opportunities, and runs CRM workflows, then using the same approach to plan a full monthly campaign calendar, flows, and creative directions. Malick describes a similar pattern for lead routing, CRM cleanup, and internal assistants: applying a narrow tool to a recurring operational job.
Corto S. focuses on email and CRM production for Klaviyo and Shopify brands, packaging workflows as guides for other marketers.
Malick's work ranges more widely and stays closer to internal operations: document migration, a Slack news assistant, hosting a demo build, and founding a cart-recovery product.
They put less emphasis on packaging repeatable playbooks for an outside audience.
Both teach ecommerce marketing through specific worked examples rather than general advice.
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Lindsey Lopez walks through an abandoned-cart sequence that misfires when email and SMS are not coordinated, shows a timed cross-channel version, invites flow questions, and argues that email and SMS are relationship channels that educate and engage rather than only promote. Malick likewise takes a concrete workflow and explains what changes when it is done properly.
Lopez focuses on channel orchestration for DTC brands, recommending that marketers stop batching email and text and time them together. Malick's examples center on custom automation, agents, and product builds, so the technical depth and AI framing differ even where the ecommerce audience overlaps.
Both write as operators about applying AI to retention marketing and the data underneath it.
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Larry Kim describes testing visitor-identity providers that were often wrong, rebuilding the identity layer from the ground up, and using it to power AI audience prediction that decides who receives, gets suppressed, or gets reactivated.
They also write about removing the product-management layer and letting AI carry the translation work between insight and output.
Malick makes a parallel argument about allocation — put AI where it moves revenue or retention, not everywhere — and about small custom tools replacing generic ones.
Kim is building a data and identity platform at CustomersAI and writes about product organization and market direction, using revenue-per-send and attribution as yardsticks. Malick builds bespoke workflows for individual problems and a smaller cart-recovery product, focusing more on the mechanics of the build than on an identity layer or company structure.
Peter Malick’s LinkedIn posts show practical business-AI examples, including CRM data processing and an AI news process for a team.
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That content may suit a B2B technology campaign about applying AI to operations. Before booking, request recent LinkedIn audience demographics and documented evidence of job function or seniority if the campaign depends on those attributes.
LinkedIn followers
8,352
LinkedIn profile; observed 7 October 2026 · 2026-10-07
Peter Malick's posts don't describe a sponsorship, advertising, or paid-partnership program.
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Brand and platform names appear as tools, clients, or vendors in their work—not as sponsors of their content.
Malick writes about building AI workflows at InboundAV, starting Jazzbox AI (an AI e-commerce video product developed with AWS support and an AWS-approved development partner), and running an OpenClaw-based news assistant on a Mac Mini bought for the purpose.
Google AI Studio appears in a demo built for a talk at the NAMM 2026 pre-show; Salesforce and HubSpot appear in market commentary.
For a commercial arrangement, ask directly about format, scope, rates, exclusivity, and disclosure terms.
Their posts don't publish an offer or application process.
The clearest partnership-adjacent examples concern vendors on their own products: Jazzbox was funded through AWS generative-AI programs and built in sprint work with an AWS-approved development team, while InboundAV client work involved a custom AI script to read thousands of unstructured DocuSign PDFs into HubSpot.
These examples describe Malick as a buyer and builder of technology partnerships, not a seller of audience access.
A strong fit is a B2B vendor selling CRM, data, or operations software to mid-market teams that wants a creator to show where a narrow AI process actually helps.
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The need in view: a CRM or RevOps platform wants content aimed at operations leads and go-to-market teams who know they should use AI somewhere but cannot tell which job to start with.
Malick's posts match that need directly.
One argues that most companies do not need AI everywhere; they need it in one painful process, one critical moment in the customer journey, or one handoff where deals stall.
That is the vendor's own buying conversation, phrased the way an operator would phrase it.
The HubSpot migration example provides the concrete before-and-after: thousands of unstructured DocuSign PDFs with no standard formatting, a five-step process that opened a browser, navigated the interface, worked around pop-ups, read pages, and mapped data into a clean spreadsheet, and a job finished in hours rather than years of manual entry.
A viewer can picture that build scope, and a vendor can attach its product to it.
Malick also works in the same direction as the buyer.
The Slack-based news assistant on a Mac Mini, built rather than bought so the curation rules could be tuned to the team's niches, makes the same case for customization as a platform vendor.
The task-management bot and internal processes follow the same pattern of small, specific automations.
The audience section shows only a follower count, with no demographic or job-function information.
A vendor that needs to confirm buyers are watching should ask for audience data.
There is no published partnership format or rate card, so scope and disclosure terms need to be negotiated.
The case study is Malick's own account of client work, not an independently audited result; ask for verification before building a campaign around the number.
Reasonable uses include a demo-led series on automating one process at a time, a written walkthrough of a data migration, or commentary placements on where AI budgets should go.
Long-form video walkthroughs, screen-recorded build sessions, and paired-written pieces all suit the work they already do.
When to choose a specialist
A genuinely poor fit is a consumer musical-instrument or guitar brand that wants to reach guitar buyers through Malick's apparent connection to the instrument.
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One post describes a fictional six-string electric guitar brand called OnlySix, presented at a NAMM 2026 pre-show, which might prompt a buyer to pitch the brand.
But OnlySix was a website-building demonstration for Google AI Studio, not an instrument connection.
The content was about prompt writing and iteration speed, with a time breakdown of roughly twenty minutes to write the prompt, three minutes to build the site, and twenty minutes of prompting tweaks.
The guitar brand was a fictional example used to make the website demo concrete, not a subject Malick covers.
Nothing in the material shows guitar technique, gear reviews, playing, or a musician audience.
A guitar brand booking on the OnlySix surface would be buying the wrong subject and would likely reach an AI-tooling audience instead of players.
The same misjudgment applies to any consumer brief that needs broad reach, verified buyer demographics, or lifestyle endorsement.
The content targets operators, founders, and agency teams deciding how to allocate AI effort—the opposite of a mass-consumer audience.
The published material includes no sponsorship program, rate card, inquiry route, or paid-partnership offer, so a consumer brand would need to ask about format, disclosure, and how to book before planning a campaign.
Booking on follower count alone would mean ranking a business-technical creator for a consumer job.
A worked campaign example
Need: a CRM or RevOps vendor sells to mid-market companies and wants a campaign that makes its product the obvious answer to one painful data problem. Target reader: an operations or go-to-market lead with years of legacy documents and messy records and no appetite for a platform-wide rollout.
Angle: a three-part series called "Fix one process first." Each piece picks a single bottleneck and works it end to end.
Piece one, the argument.
Use the position that the useful question is how to invest in AI, not whether to.
Open with the allocation point: most companies cannot fund dozens of experiments, so the leverage is in one painful process, one critical customer-journey moment, one stalled handoff, or one repetitive job that burns hours a week.
Close with the vendor framing the same way: not a platform that helps with everything, but a focused tool for one job.
Piece two, the build.
Use the HubSpot migration as the spine.
Walk through the problem as it appears in the post—thousands of unstructured DocuSign PDFs with no standard formatting, and manual entry measured in years—then break the five steps into a visible sequence: open, navigate, bypass pop-ups, read pages, map into a structured output.
The vendor's product enters at the mapping and CRM-write step, shown as the place clean records land rather than as the thing doing every step.
Do not present the vendor product as the whole process; the credibility comes from showing the surrounding automation honestly.
Piece three, the second use.
Extend the same method to the internal Slack news assistant: hundreds of articles read each morning, curated to the team's niches, turned into short action items.
The point for the buyer is that tuning to a specific team is the difference between a useful agent and a generic feed, which mirrors the vendor's customization claim.
Add a short segment on the task-management bot so the pattern reads as repeatable rather than a one-off.
Deliverables and placement: one long-form video walkthrough with screen capture, one written version with the same five steps for readers who will not watch, and one short commentary piece for the decisions stage. The article should include the blank version of the process as a working resource readers can complete for their own document pile.
What makes this land: the campaign asks the reader to choose one process, the content shows one process finished, and the vendor is attached to the finishing step. Each piece stands alone and adds a new decision or action rather than repeating the same argument.
Before pitching, settle the engagement's format, scope, and disclosure terms with Malick, since nothing published sets them.
If the campaign wants to use the hours-saved figure, ask for verification; it is Malick's own account, not an independently audited result.
Ask for audience information too: the available data is a follower count without demographics.
If the vendor needs a guarantee that the audience includes CRM buyers, resolve that before booking rather than assuming it.
Choose Corto S. when the campaign needs to show AI work happening inside a marketing or CRM platform, not around a data-cleanup project.
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The posts describe turning Claude Code into a Klaviyo execution engine: a roughly ten-minute setup connects account data, frameworks, and operating rules, then runs separate workflows for flows, campaigns, segmentation, deliverability, and analytics while the marketer reviews and approves.
A second post extends the idea with GPT-6 Astra, pairing it with an existing CRM workflow to plan a monthly campaign calendar, generate campaign angles, build lifecycle flows, produce several creative directions, and write headlines, copy, and calls to action ready to recreate inside Klaviyo.
For a buyer selling email or CRM execution software, that is closer to the product than Peter Malick's migration story: the demonstration ends in the tool, on a monthly cycle, rather than in a cleaned spreadsheet.
Corto S.'s workflows assume Klaviyo data and ecommerce email operations.
The posts don't address unstructured documents, CRM data quality, or how to choose which operational workflow to fix first across a business—the argument Peter Malick makes for mid-market operators.
They also close with a lead-capture offer for a packaged setup or free guide, rather than a narrative case study.
For a broad B2B operations campaign, this answers a narrower, platform-specific question.
Choose Lindsey Lopez when the campaign targets retention messaging for direct-to-consumer ecommerce and the buyer is a lifecycle email or SMS platform.
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One post describes a targeted three-part educational sequence for a DTC client around a key product category, which the post says drove revenue growth while generating reviews and advocacy.
The second diagnoses the failure pattern of unintegrated channels: an abandoned cart email at 11:00, a discount SMS at 11:05, and an upsell email at 11:15.
It then shows a coordinated version with timing rules between email and SMS.
For a platform pitching sequencing discipline and cross-channel orchestration, this is a ready-made teaching example with a before and after.
The posts take the opposite position from an AI-workflow campaign.
The orchestration post says directly that a brand does not need fancy AI or expensive tools, making it a poor fit for a vendor selling AI capability.
There is no build content, tooling walkthrough, or discussion of how the underlying workflows are implemented.
The posts date to June 2025, earlier than Peter Malick's 2026 posts, so they cannot speak to current tooling or recent platform changes.
Treat this as a fit for lifecycle and retention messaging, not AI operations.
Choose Larry Kim when the campaign is about the data layer beneath AI, or about AI changing an operating structure rather than automating one task.
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One post explains why visitor identity data failed for email, citing testing where providers were only 5 to 30 percent accurate.
It then describes building a first-party identity solution now used by over 1,000 brands, with additional work on sending upper-funnel Meta events and predicting which customers should receive, be suppressed from, or be reactivated in campaigns.
The argument is that identity quality becomes the dividing line because AI can't fix a wrong signal.
A second post describes removing the product management function and using event modeling to standardize inputs and outputs so AI can generate interface, prototype, code, documentation, and training artifacts directly.
It reports engineers moving two to three times faster, with communication issues at zero.
That's a founder-level claim about where AI removes a coordination layer, a more strategic register than Peter Malick's fix-one-workflow-first position.
Both posts are first-person accounts from someone running the company being described, so figures such as the two-to-three-times speedup and identity accuracy range come from the person making the change, without independent measurement.
There is no step-by-step build for readers to follow, so these posts can't provide the inspectable workflow resource that Peter Malick's five-step document migration does.
The claim about eliminating product managers may also sit awkwardly beside a vendor whose operations buyers or stakeholders include product managers; handle it carefully in a campaign aimed at those roles.
Use this when the buyer wants a data or organizational thesis, not a demonstration of a product inside a customer workflow.
This page is built from seven public LinkedIn posts from Peter Malick's own account, read in full.
Six are first-person accounts of building, testing, or launching AI tools; one is a short framing note about Salesforce and HubSpot for mid-market teams.
The posts cover client work at InboundAV, a Slack news assistant built on OpenClaw, a demo site built with Google AI Studio, the launch of Jazzbox AI, an argument about allocating AI effort to one workflow, and a satirical piece on cold-email failures.
The posts do not state publication dates and several refer to 2026 events, so the material is treated as a set rather than a dated sequence.
Name-search coverage was checked against the opening of the page to make sure the work readers would search for is represented.
The similar-creator comparisons use two public posts each for three other creators, dated between June 2025 and October 2026, and rest on what those posts show about ideas, teaching approach, and format.
Limits worth knowing before relying on this page: the only account-level figure available is a follower count observed 7 October 2026, with no demographic or job-function data behind it.
No post states partnership formats, rates, or an inquiry route.
The time-savings figures in the data-migration and website-build posts are Malick's own accounts, not independently audited results.
The posts do not say whether Malick owns InboundAV or is employed there, and the posts say nothing about books, shows, or earlier career.
