Gianni Newman creates practical content on using AI and automation to improve B2B sales work, including sales workflows in Slack and Salesforce and ways to evaluate sales tools.
Gianni Newman is most useful for implementation-level AI sales workflow content and tool evaluation checks.
Best for
B2B SaaS AI sales or sales engagement brands wanting implementation content for sales ops and RevOps.
Not for
Campaigns needing broad reach, guaranteed distribution, media kits, or non-sales audiences.
Key takeaways
A quarterly pipeline briefing can build itself in Slack.
Reps spend only 40% of their time selling, according to the cited figures.
The tool test covers workflow fit, CRM integration, data boundaries, improvement, and ROI.
Check first: Confirm audience demographics, engagement data, and sponsorship terms before contacting.
Main ideas
AI should return selling time or money, not just add another tool · Recurring sales reports should become reusable, scheduled workflows · Vendors should pass a pre-demo test on workflow fit, CRM integration, data boundaries, improvement, and ROI
Gianni Newman creates practical content about applying AI to B2B sales work.
Their posts show a Slack-based sales briefing workflow, explain how AI assistants might return time to selling, and offer criteria for evaluating sales tools.
Follow them for concrete examples and decision checks focused on fitting AI into existing sales workflows rather than adopting tools for their own sake.
Newman connects a practical workflow to a business test.
In the Slack briefing example, they describe summarizing sales activity, building a quarterly canvas, adding cited blockers and enablers, and scheduling the result as a reusable skill.
Other posts suggest measuring time returned to selling and assessing tools for workflow fit, CRM integration, data boundaries, improvement over time, and measurable ROI.
Together, these examples show how an AI workflow could operate and how to judge whether it is worth adopting.
Newman's through-line is that sales AI has to prove itself in the work: return selling time, fit the existing CRM and approval flow, and be measured against a metric chosen before purchase.
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Their posts describe a scheduled Slack workflow that replaces a quarterly manual briefing, the time an AI assistant should give back, and a five-test screen for vendors. They treat prompts as reusable operating assets and vendor claims with suspicion until they pass a workflow and data-boundary check.
AI should return selling time or money, not just add another tool
Newman starts from a simple test for any new AI agent or workflow: does it directly increase revenue, reduce cost, or make the day easier, faster, or cheaper?
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In the productivity post, the same logic becomes a time-recovery argument: sales reps spend only 40% of their time selling, so an AI assistant should be judged by the hours it hands back for account research, first-draft emails, and closing work. They argue that adoption pressure and vendor demos are outrunning proof of return, so the metric should be chosen before the tool is signed.
The Salesforce 2026 State of Sales survey of 4,050 sellers is used to put expected cuts at up to 34% for prospect research and 36% for email drafting; Newman advises starting with the tasks that eat the most hours and tracking the selling time returned.
Newman's money test for any new workflow is whether it increases revenue, reduces cost, or makes the day easier, faster, or cheaper; the productivity post turns it into a time-recovery measure.
Recurring sales reports should become reusable, scheduled workflows
Newman argues that a quarterly pipeline briefing should not take an hour or two to rebuild by hand.
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The alternative is a workflow inside Slack connected to the CRM: summarize seven days of sales activity, turn last quarter's conversations into a growth briefing canvas, update it with cited enablers and blockers, then save the prompt as a reusable Slackbot skill and schedule it for the last Friday of each quarter. The reasoning is that AI output is only as good as the data and the prompt, so the prompt should be written once, saved, scheduled, and judged by a business outcome.
The four-step Slack workflow is the concrete case, ending with a scheduled message containing the new canvas link and a reminder to prep for the quarterly review.
Vendors should pass a pre-demo test on workflow fit, CRM integration, data boundaries, improvement, and ROI
Newman describes a gap between AI adoption pressure and weak returns: Gartner's forecast that AI agents will outnumber sellers 10 to 1 by 2028, with fewer than 40% of sellers expected to say those agents improved productivity, and MIT's finding that 95% of organizations get zero return from GenAI.
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The five tests are workflow fit, CRM integration, clear data boundaries, improvement over time, and measurable ROI.
One executive quoted in the post said a tool that does not plug into Salesforce or internal systems will not be used.
Newman's advice is to write the five criteria down and score every tool before the demo.
The post cites 31% of chief sales officers calling proving AI ROI a top challenge, and asks the reader to pick the ROI metric before signing rather than after.
This extends the earlier money question into a vendor-selection scorecard with integration and data-handling checks.
See how Newman turns a recurring sales report into a repeatable AI workflow, with practical steps rather than a general argument about AI.
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Newman lays out a four-step Slack workflow: connect Slack to the CRM, prompt it for a seven-day sales activity summary, turn last quarter's conversations into a sales growth briefing canvas, then ask Slackbot to update the canvas with enablers, blockers and channel citations.
The prompt is saved as a reusable Slackbot skill and scheduled for the last Friday of each quarter.
They set a simple test for any new AI tool or workflow: does it increase revenue, reduce cost, or make the day easier, faster, or cheaper?
For brands planning a campaign
A collaboration could ask Newman to rebuild a recurring client report inside Slack and define the money metric before starting. That gives B2B SaaS marketers a practical example of improving sales productivity with AI without adding another standalone dashboard.
See what problem an AI sales assistant is meant to solve and where a team should start, with a clear workflow change to weigh against productivity claims.
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Newman cites Salesforce's 2026 State of Sales survey of 4,050 sellers to argue that reps spend only 40% of their time selling.
The post says sellers expect AI agents to cut prospect research time by up to 34% and email drafting time by up to 36%.
It also cites Salesforce's 2024 report finding 83% revenue growth among sales teams using AI versus 66% without it, and Gartner finding that teams given AI-enabled next best actions are 2.6 times more likely to achieve commercial growth.
They advise starting with tasks that consume the most hours, such as account research and first-draft emails, then tracking the selling time returned.
For brands planning a campaign
An advisory session could use the 40% selling-time gap to frame a time-recovery audit: choose account research or first-draft emails, measure hours returned, and connect the result to pipeline capacity. The post gives B2B SaaS marketers a concrete productivity narrative and a starting task list.
Use Newman's buying checklist to assess AI sales tools before sitting through another demo.
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Newman points to a gap between vendor promises and results: Gartner predicts AI agents will outnumber sellers 10 to 1 by 2028, yet fewer than 40% of sellers will say those agents improved productivity.
The post also cites MIT's GenAI Divide research finding 95% of organizations getting zero return from GenAI.
They list four vendor criteria from MIT's executive interviews: the tool fits the workflow, plugs into the CRM, has clear data boundaries, and improves over time.
They add a fifth: measurable ROI, noting that 31% of chief sales officers call proving AI ROI a top challenge.
Their advice is to score every tool against the five criteria before the demo.
For brands planning a campaign
A content collaboration could turn the five criteria into a scoring sheet for a B2B SaaS audience, with the ROI metric chosen before the demo. An advisory session could use the same criteria to compare AI sales tools without relying on vendor claims.
Sales pipeline workflows inside Slack and Salesforce1
AI sales assistant business case: productivity and revenue data1
A five-test scorecard for choosing sales AI tools1
Sales pipeline workflows inside Slack and Salesforce
Newman outlines a four-step process connecting Slack to a CRM: one prompt summarizes the past seven days of sales activity and produces a prioritized follow-up list; a second turns last quarter's conversations into a sales growth briefing canvas; Slackbot updates the canvas with cited enablers and blockers; finally, the prompt is saved as a reusable skill and scheduled for the last Friday of each quarter.
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The post's test for any new AI agent or process is whether it directly increases revenue, reduces cost, or makes the day easier, faster, or cheaper.
Starts with a recurring task, builds a reusable, scheduled process around it, and judges the result by a business outcome—not the novelty of the tool.
For brands planning a campaign
A collaboration could center on a reproducible process demo or a working session for sales or RevOps teams. The brand should show the actual Slack and CRM steps, the prompt, and the metric the process is meant to move.
AI sales assistant business case: productivity and revenue data
Newman cites a 40% selling-time figure and Salesforce's 2026 State of Sales survey of 4,050 sellers, which puts expected time savings at up to 34% for prospect research and 36% for email drafting.
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The post also cites Salesforce's 2024 report, in which 83% of AI-using sales teams saw revenue growth versus 66% without, and Gartner's 2026 finding that teams given AI-enabled next best actions were 2.6 times more likely to achieve commercial growth. The advice: start with tasks that consume the most hours, such as account research and first-draft emails, and track the selling time returned.
Uses analyst and vendor survey data to frame the problem, then focuses the rollout on measurable time sinks.
For brands planning a campaign
A vendor with an AI sales assistant could commission an implementation-focused piece built around those two starting tasks, with a before-and-after measure of selling time. Tie claims to the cited reports and the product's actual capabilities, not a general promise of pipeline growth.
Newman describes the gap between pressure to adopt AI and weak returns, citing Gartner's forecast that AI agents will outnumber sellers 10 to 1 by 2028, with fewer than 40% of sellers expected to say those agents improved productivity, and MIT's finding that 95% of organizations get zero return from GenAI.
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The five tests are fit with existing processes, CRM integration, clear data boundaries, improvement over time, and measurable ROI. They advise scoring every tool against the five before a demo; 31% of chief sales officers call proving AI ROI a top challenge.
Turns vendor selection into a pre-demo scorecard, requiring evidence on integration, data boundaries, and a metric chosen before purchase.
For brands planning a campaign
This structure suits buyer-education collaborations, especially for a product that can demonstrate CRM integration, data handling, and a named ROI metric. Prepare evidence for each scorecard criterion; the tool will be assessed against them, not simply endorsed.
Across the posts, Newman teaches through numbered steps, short lists, scoring criteria, and citations to Salesforce, Gartner, and MIT research.
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The Slack post links to a video walkthrough; the tool-selection post ends with a concrete instruction to write down the five criteria and score every vendor before the demo.
For brands planning a campaign
Make brand assets executable: a fill-in prompt, scoring sheet, source list, or short recorded walkthrough will fit the format better than a general thought-leadership essay. Supply the operational detail and evidence to make the asset credible.
Newman writes about sales AI that has to prove itself: connect to the CRM, save the prompt as a reusable workflow, and choose the ROI metric before buying.
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Start with the Slack pipeline-briefing post for the four-step workflow, then read the post on giving reps back selling time and the five-test vendor scorecard. For a different angle, Vandan Jain connects forecast risk to coaching and pre-call context, Greg Spence argues for fixing the sales operating system before adding headcount, and Askia Sisay satirizes the pipeline and forecast habits that surface at quarter end.
Both treat disconnected sales systems as the core problem and propose a unified place for deal risk, rep coaching, and pre-call context.
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Jain writes about the divide between weekly forecast calls and one-on-one coaching, and reps entering calls without customer examples or pricing guidance spread across three tools.
Jain's posts focus on coaching and rep skill gaps, naming PitchSense AI as the product in development. Newman's focus is reusable Slack workflows and a five-test scorecard for choosing AI tools, supported by Salesforce, Gartner, and MIT research rather than product build notes.
Both argue that sales AI and headcount should not be added on top of a broken operating system.
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Spence writes about clarifying GTM strategy, CRM architecture, and forecasting governance before scaling; Newman writes about choosing AI tools that fit the existing workflow and CRM before signing.
Spence writes from a sales leadership and org-scaling perspective, focusing on hiring sequence, enablement, and compensation design across industries. Newman focuses on AI workflows and tool selection inside Slack and Salesforce, using numbered steps and cited analyst data rather than career-spanning examples.
Both write about the gap between how sales teams report pipeline and forecast and how deals actually move.
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Sisay's forecast post lists the deal types that appear at quarter end; Newman's Slack workflow turns last quarter's conversations into a briefing canvas and prioritized follow-up list.
Sisay teaches through satire and list-based humor about sales habits and CRM behavior. Newman uses step-by-step instructions, vendor scorecards, and cited research.
For a campaign aimed at B2B SaaS sales productivity, Gianni Newman is a plausible topical-reach choice for sales-operations and sales-leadership readers.
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Their posts discuss Salesforce, Slack workflows, AI tool evaluation, sales productivity, and RevOps, framing the content for sales and revenue-operations practitioners rather than a general AI audience.
The only published audience figure is a LinkedIn follower count of 1,526, observed on 9 October 2026.
To assess audience fit, ask for LinkedIn follower demographics or an audience survey covering job function, seniority, company size, and geography, plus recent results for comparable posts on AI sales workflows.
Gianni Newman's three posts offer no partnership terms, media kit, rate card, or inquiry route.
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They cover AI sales workflows: a Slack-plus-CRM pipeline briefing routine, an evaluation of AI sales tools against buyer criteria, and Salesforce and Gartner figures on seller time and AI assistant returns.
None announces a brand collaboration or paid relationship, or describes a sponsor package.
Salesforce, Slack, Gartner, and MIT appear as tools or research sources, not partners.
For buyers, the posts suggest negotiating directly rather than choosing from published deliverables and rates.
Ask whether Newman accepts paid work and what terms and deliverables they offer; activity on other channels or in other formats is not visible here.
Good fit: a B2B SaaS company selling an AI sales assistant or sales-engagement product that wants implementation-level content for sales operations, RevOps and frontline sales leaders, rather than another generic AI think piece.
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Why Newman suits this campaign: their posts already take the form this buyer needs.
The Slack piece is a four-step runbook, not an argument: connect Slack to the CRM, prompt for a seven-day activity summary with a prioritised follow-up list, turn last quarter's conversations into a growth briefing canvas, then save the prompt as a reusable Slackbot skill scheduled for the last Friday of each quarter.
The tool-selection piece is a five-test scorecard a reader can write down and use before a demo: fit with the sales process, CRM integration, clear data boundaries, improvement over time, and a metric chosen before purchase.
The productivity piece gives the business case, opening on reps spending only 40% of their time selling and narrowing the rollout to the tasks that eat the most hours: account research and first-draft emails.
That combination matters to a company seeking implementation-level AI sales-productivity content.
The usual failure is a promise with no procedure attached.
Newman supplies the procedure, attaches a money test, and names the measurement point: does the workflow increase revenue, reduce cost, or make the day easier, faster or cheaper, and how much selling time did it return?
The natural deliverables are a recorded working session or a prompt-and-scorecard pack, not a keynote.
Their post links to a video walkthrough of the Slack routine, showing that the format is already familiar.
Conditions to settle before committing: the audience is small, with 1,526 LinkedIn followers as observed on 9 October 2026.
Ask for audience demographics, employer and seniority mix, engagement figures and post performance before estimating reach or access to budget holders.
The posts state no sponsorship offer, rate card or media kit, so negotiate directly and agree deliverables, usage rights and disclosure in writing.
When to choose a specialist
Poor fit: a campaign that needs broad-category awareness, large reach, or a documented sponsorship package with assured distribution.
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Two campaign needs fail for the same underlying reason.
The first is a vendor buying reach, such as a launch campaign that wants a media kit, guaranteed impressions and a creator with an order of magnitude more followers than 1,526.
Newman's posts offer no sponsorship terms, rate card, audience package or inquiry route, so there is nothing packaged to buy.
Ask for audience composition and engagement data before assessing reach.
The second is a campaign aimed at a different audience, such as data engineering, marketing operations beyond the sales stack, or finance buyers: every post sits inside the Salesforce, Slack and RevOps world and is written for people running a sales floor or a revenue-operations function.
A subtler mismatch is a campaign that needs high publishing volume or a long track record of consistent creator output.
Newman has three posts from a three-day window in late September 2026.
That shows how they teach and what topics recur, but not their cadence, audience growth or how the advice has held up over time.
What still works for a less demanding campaign: Newman would be a reasonable fit for a vendor that wants critical buyer education in the sales-AI category.
The five-test scorecard puts vendors under scrutiny rather than flattering them, so a brand should expect the same scrutiny of its own product.
The poor fit is about scale and packaging, not competence.
A worked campaign example
Need: a sales-engagement platform wants a content collaboration plus one advisory session on cutting account-research and first-email time for mid-market sales teams.
Campaign angle: a selling-time audit.
Frame the collaboration around one measurable claim: hours returned to selling.
Newman already uses this frame when citing Salesforce's 2026 State of Sales survey of 4,050 sellers, where sellers expect AI agents to cut prospect research time by up to 34% and email drafting time by up to 36%.
Step one, the advisory session.
Pick one recurring report or task the client's own customers rebuild every quarter, and run it as the Slack post does: define the prompt, define the saved skill or template, put it on a schedule, and agree the single money metric before anything is built.
A 45 to 60 minute working session with the client's sales-ops lead is enough to produce a usable process rather than a discussion.
Step two, the published asset.
Create a prompt-and-template pack with two working pieces: the reusable activity-briefing prompt and its schedule, plus a scored vendor comparison built on five tests—fit with the sales process, CRM integration, data boundaries, improvement over time, and a pre-agreed ROI metric.
Add a one-page worksheet where readers write down their own metric and the two tasks they will automate first.
Every resource should work when copied out of the page, with the client's product shown only where it actually does the work.
Step three, the post showing the results.
Publish a short written or recorded walkthrough of the session output, stating the before-and-after time estimate plainly and attributing it to the specific task measured, not to a general promise of pipeline growth.
Newman already cites Salesforce, Gartner and MIT by name, so claims should carry the same specificity or be dropped.
Step four, the pitch. Use the published asset as the entry point for a follow-up session with the client's prospect list, with the worksheet as the qualification mechanism: teams that cannot name their metric are not ready.
Before planning, verify audience composition and engagement; the only stated audience figure is 1,526 followers as of 9 October 2026.
Ask whether Newman takes paid work, since their posts mention no offer, terms or contact route.
Also confirm availability beyond the demonstrated topic range, which covers three posts from 23 to 25 September 2026.
If the audience is mostly individual contributors with no buying role, keep the collaboration as an advisory and education engagement and do not build a lead-generation forecast on it.
Choose Jain for a campaign about coaching and enablement within the sales conversation, rather than automating a pipeline report.
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Both posts argue that the forecast call and coaching conversation are usually the same underlying fact viewed from two angles: a stalled deal and a rep's skill gap.
Almost no system makes that connection plainly, leaving managers to merge the views from memory each week.
The second post works through one deal: the buyer questioned price; the rep tends to defend before asking why; the call recorder holds the first detail, a shared drive holds a customer example that proves value, and the manager knows the third.
No single tool sees all three.
The build Jain describes puts them into a pre-call brief with a six-minute practice drill on handling price.
For buyers seeking role-play, pre-call preparation or rep-level skills content, that is a more specific fit than quarterly reporting.
Jain is writing toward PitchSense AI, the product named in both posts, making the content a product argument rather than neutral procedure.
Both posts offer analysis and a pitch, not a runbook: there is no prompt, schedule or copyable step list.
The Salesforce figure Jain cites—51% of sales leaders saying disconnected systems hold AI back—frames a problem rather than teaching a method.
A collaboration would also put the buyer's product beside a named competitor in the same category, which a vendor may not want.
Choose Spence for a campaign built around the operating-model argument, not a step-by-step process: fix the sales engine before adding headcount or tools.
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The first post argues that adding reps to a shaky operating system creates more noise and meetings, not predictable revenue.
It lays out an order of work: clarify GTM strategy, define strategic account management standards, build enablement before ramp pressure, establish forecasting and KPI governance so pipeline means the same thing to everyone, and make CRM architecture usable rather than mandatory box-checking.
Spence cites a reference point of going from zero to 11 BDRs at C-Suite Network and scaling national sales organizations four times.
The second post makes the cross-industry case, listing CPG manufacturing, retail, SaaS, B2B, digital media, industrial services, automotive, HVAC and commercial signage, and citing a 30% increase in operational cadence and revenue 26% beyond annual quota in enterprise commercial signage.
This suits a leadership or fractional-advisory session on sequencing AI adoption behind a revenue model that already works.
Spence's material stays above the tooling: there is no prompt, AI process or measured before-and-after on a named task.
A session would therefore produce priorities and an operating sequence, not a copyable implementation asset.
The posts also have rough editing—'common since', 'sames result'—which matters if the deliverable is published prose rather than an internal working session.
Both are from June 2026, roughly three months older than Newman's September posts, and neither discusses AI tools in detail.
Choose Sisay when the campaign's goal is recognition rather than instruction: content that makes a sales audience nod at the exact ways forecasting and CRM discipline break down.
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The two posts are satirical catalogues: quarterly resolutions abandoned by Friday, bulk CRM entry from memory ('Did I talk to a Mike or a Mark?'), and forecast fictions—'The Resurrection', 'The Verbal', 'The Inbound That Saves Us', "The Sandbag's Cousin", 'The Hail Mary'.
They close with the line that a deal appearing in the last five days and skipping three stages is not a forecast but a wish.
For a launch or awareness push aimed at engagement and a shared vocabulary among sales teams, that framing travels further than a process diagram.
The posts name the problem but offer no AI process, prompt, metric or procedure.
They are aimed at the emotional experience of the job rather than buyers deciding what to deploy, making them a weaker fit for vendors seeking to demonstrate implementation value.
Ask for a separate method or resource pack if the campaign needs practical guidance on AI sales productivity.
Gianni Newman's three LinkedIn posts published between 23 and 25 September 2026 cover a Slack and CRM pipeline-briefing workflow with a scheduled quarterly canvas, a case for AI sales assistants built on Salesforce and Gartner figures, and a five-point scorecard for choosing sales AI tools.
The posts show 1,526 followers on 9 October 2026.
Six posts from three other sales creators published between June and October 2026 cover sales AI, forecasting, CRM discipline, coaching, and sales leadership.
The posts cite Salesforce State of Sales 2024 and 2026, Gartner, and MIT; those figures appear as Newman presented them rather than as independently checked numbers.
The linked video walkthrough has not been assessed here.
The profile does not include books, shows, businesses, earlier career material, engagement data, audience demographics, publishing history beyond those three days, or sponsorship terms; those remain open gaps.