Alex Gonzalez writes about Voice AI for telecom providers and managed service providers (MSPs), focusing on practical ways to measure outcomes, identify automation opportunities, and route callers to AI or human help.
Alex Gonzalez is most useful for explaining practical Voice AI outcomes and provider sales enablement to telecom and MSP audiences.
Best for
This fits Voice AI, telecom or MSP software vendors seeking provider-facing thought leadership and sales enablement.
Not for
This does not fit consumer lifestyle campaigns or broad consumer reach endorsements.
Key takeaways
A slightly robotic AI that works can beat a human-sounding one that does not.
Measure leads, bookings, resolved requests and calls handed to people, not call volume.
Train provider sales teams to spot automation opportunities before the AI request.
Check first: Confirm audience job roles, company types, recent reach, engagement and partnership terms.
Main ideas
A slightly robotic AI that works beats a human-sounding one that doesn't · Measure what an AI call accomplished, not how many calls it answered · Automate the right conversations, not every conversation
Alex Gonzalez writes about how telecom providers and managed service providers can put Voice AI to work.
In their LinkedIn posts, they focus on practical questions: whether an agent completes tasks reliably, what its calls accomplish, when a caller should reach a person, and how provider sales teams can spot useful automation opportunities.
Follow their work for a provider-focused view of Voice AI that looks beyond call volume and human-sounding demos to outcomes and customer experience.
Gonzalez turns broad AI claims into concrete operating choices.
They contrast a natural-sounding agent with one that reliably books appointments and transfers callers, arguing that completed tasks matter more than an impressive demo.
They compare two businesses handling 1,000 AI calls to show why providers need to track leads, bookings, resolved requests and calls handed to people—not just volume.
Their focus is not on automating every conversation: some callers and situations need human help, supported by warm transfers and shared context.
For provider teams, they offer a practical starting exercise: list common customer complaints or changes, then identify a possible AI workflow and the next question to ask.
Their posts present these as ideas they are exploring and building around SkipHold.
Alex Gonzalez writes about Voice AI and business communications for the providers who sell it — managed service providers (MSPs) and telecom companies — rather than the businesses that buy it.
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Across five LinkedIn posts from 2026, the same priorities recur: an AI agent's reliability matters more than how human it sounds; providers should measure what an AI call accomplished, not how many calls it answered; some calls should stay with people; and providers can grow existing accounts by adding automated services instead of seats.
Gonzalez ties these arguments to SkipHold, the communications product the posts describe them as building.
Their posts follow a consistent pattern: a blunt claim, a two-option contrast or list of customer complaints, then advice for providers.
A slightly robotic AI that works beats a human-sounding one that doesn't
Gonzalez's core buying criterion for Voice AI is production reliability, not voice fidelity.
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Asked to choose between two agents for an MSP's customers, they pick the one that sounds less natural but responds faster, follows instructions consistently, completes tasks, and transfers calls correctly.
Voice quality matters, but it wins demos; reliability wins customers.
Gonzalez connects this to telecom's long-standing focus on call quality, routing, and uptime, and says SkipHold is being built around the whole communications experience, not just the voice.
Gonzalez's comparison, in their own words: Agent A sounds incredibly human and demos beautifully; Agent B sounds a little less natural but completes tasks and transfers calls correctly.
Asked which one to trust with 100 business customers, they say they'd look closely at Agent B.
Once the demo ends, customers judge whether the lead was captured, the appointment booked, the transfer handled, and the agent knew when to stop talking.
Measure what an AI call accomplished, not how many calls it answered
Gonzalez argues that call volume, minutes handled, and time saved are useful but incomplete: two businesses with identical AI call counts can have very different understandings of the value delivered.
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They frame outcome measurement as a natural next step for MSPs and telcos, which already measure infrastructure such as call volume, duration, quality, and uptime. AI adds the ability to measure what a conversation accomplished, Gonzalez argues, giving providers an answer that makes their service more valuable.
Two businesses each handle 1,000 AI-answered calls.
Business A has no clear visibility into what happened next.
Business B can see appointments booked, leads captured, requests resolved, and calls that required human assistance.
Same volume, very different picture.
Gonzalez predicts customers will stop accepting "your AI handled 1,000 calls" and start asking what those calls did for the business.
Automate the right conversations, not every conversation
Gonzalez distinguishes the buyer of AI from the person on the other end of the call: the business buying the AI isn't the person calling it.
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Some callers are happy with AI, some want a human, and some situations need one.
They warn against an agent determined to finish a conversation at all costs, arguing that the goal should be to automate the right calls and make the rest easier for human teams to handle.
AI receptionists, human teams, warm transfers, and shared conversation context are parts of one customer experience, not competing products.
Gonzalez's examples of calls that need a person: a complicated billing dispute, a sensitive personal matter, or an urgent issue that requires someone to make a judgment call.
Gonzalez opens with telecom's traditional growth model — more employees, extensions, phone numbers, and locations — and argues that AI changes the economics.
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A customer whose headcount stays flat can still expand its communications needs by automating after-hours reception, appointment scheduling, lead qualification, follow-ups, and internal support requests.
In this framing, growth comes from adding services, workflows, and capabilities to an existing relationship, not adding users.
Gonzalez says SkipHold is built around that model, with AI Receptionists, Managers, and Personal Assistants that providers can offer as managed services.
A customer with 20 employees and 20 phone extensions hires no one, yet starts automating the tasks above. Their communications needs expand significantly while their user count stays the same.
Train provider sales teams to spot the signal before the AI request
Most sales teams are trained to respond once a customer asks for AI; Gonzalez says they should learn to recognize the ordinary complaints that come first.
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Provider account teams already have these conversations, giving providers an advantage over pure AI companies, Gonzalez argues — but only if they turn routine account talk into automation opportunities and make AI another solution the existing team knows when to raise, not a separate sales motion.
Customers won't ask to automate a percentage of inbound communications.
They'll say they're having trouble hiring, opening another location, drowning at the front desk, missing calls after 5, or wanting calls routed differently.
Gonzalez's suggested exercise: take the ten most common customer changes or complaints the team hears, then document whether AI could help, what workflow to show, and what question the salesperson should ask next.
Answers the question an MSP has to settle before putting an AI agent in front of customers: does the voice matter more, or does the job get done?
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Gonzalez sets up two agents.
Agent A sounds impressively human and demos beautifully.
Agent B sounds slightly less natural but responds faster, follows instructions consistently, completes tasks, and transfers calls correctly.
Asked which one to trust with 100 business customers, they pick B and list what customers actually judge once the demo ends: whether the lead was captured, the appointment booked, the caller transferred correctly, and the agent knew when to stop talking.
The closing argument ties reliability to telecom's long-standing concerns with call quality, routing, and uptime, and says SkipHold is being built around the whole communications experience rather than the voice alone.
For brands planning a campaign
Useful for provider-facing positioning that treats Voice AI as production infrastructure, not a demo. A vendor selling to MSPs or telcos can reuse the two-agent comparison to evaluate deployments and counter buyers' concerns that AI voice quality complaints will cost them customers.
Answers what a provider should measure once AI is answering calls, beyond volume and minutes saved.
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Gonzalez compares two businesses that each handle 1,000 AI-answered calls.
Business A has no clear visibility into what happened after the call.
Business B can see appointments booked, leads captured, requests resolved, and calls that needed human help.
Same volume, very different understanding of value.
The post argues that providers have long been strong at measuring infrastructure, such as call volume, duration, quality, and uptime, and that AI adds the ability to measure what a conversation accomplished.
It points to work they describe as Conversations at SkipHold, connecting communications activity with context and outcomes, and predicts customers will stop accepting "your AI handled 1,000 calls" as an answer.
For brands planning a campaign
This is the clearest source for outcome-based reporting language.
It supports messaging for MSPs and telcos looking to reposition Voice AI as a measurable business service rather than a call-handling feature.
The A/B business comparison is simple enough to use in a sales deck or customer-facing framework.
Answers what happens to the caller who wants a person, the objection most likely to stall an AI receptionist rollout.
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Gonzalez argues that the business buying the AI is not the person calling it, and that some callers want a human while some situations genuinely require one, citing complicated billing disputes, sensitive personal matters, and urgent issues that need judgment.
The post warns against an agent determined to finish the conversation at all costs and suggests the goal is to automate the right conversations while making the rest easier for human teams.
AI receptionists, human teams, warm transfers, and shared conversation context are parts of the same customer experience, not competing options.
For brands planning a campaign
Use this when the buyer's objection is caller resistance to AI rather than technology cost.
It gives providers a defensible position on selective automation and a warm-transfer story to sell.
Cite it when a campaign needs to show that an AI receptionist offer does not have to promise full replacement of human staff.
Answers how AI changes the way a telecom or MSP provider grows an account that is not hiring.
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Gonzalez opens with the traditional growth model in telecom: more seats, more extensions, more phone numbers, more locations.
The counterexample is a customer with 20 employees and 20 extensions who does not hire anyone but starts to automate after-hours reception, appointment scheduling, lead qualification, customer follow-ups, and internal support requests.
Their communications needs expand anyway.
The argument is that AI lets providers grow a customer relationship by adding services, workflows, and capabilities rather than users, and that SkipHold is built around AI Receptionists, Managers, and Personal Assistants that providers can offer as managed services.
For brands planning a campaign
This frames the commercial case for upsell-oriented messaging: recurring services attached to existing accounts, not headcount-driven seat expansion. A software vendor or distributor selling through MSPs and telcos can use it to show partners where new revenue comes from when their customers' employee counts are flat.
Answers how a provider can turn conversations its team is already having into an AI practice, without a separate sales motion.
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Gonzalez notes that customers will not ask to automate a percentage of inbound communications.
They will say they are having trouble hiring, opening another location, drowning at the front desk, missing calls after 5, or wanting calls routed differently.
Those sound like routine account-management conversations but are also AI openings.
The recommended exercise is concrete: take the ten most common customer changes or complaints the team hears, and next to each one document whether AI could help, what workflow to show, and what question the salesperson should ask next.
The post argues this is an advantage providers have over pure AI companies because those conversations are already happening.
For brands planning a campaign
Directly usable as enablement material.
A vendor selling Voice AI through channel partners can adapt the ten-complaint exercise into a workshop or worksheet.
The post also supplies language for positioning AI as another solution the existing account team knows when to raise.
Provider-first framing and a repeatable post structure5
Voice AI reliability as the buying criterion1
Measuring what an AI call accomplished1
Selective automation and human handoff1
Account growth by adding services, not seats1
Teaching sales teams to spot automation signals1
Voice AI reliability as the buying criterion
Gonzalez argues that a slightly robotic AI that works beats a human-sounding AI that doesn't.
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The post gives MSPs two agents to compare: one that sounds human and demos well, and one that sounds less natural but responds faster, follows instructions, completes tasks, and transfers calls correctly.
After the demo, customers judge whether the lead was captured, the appointment booked, the transfer handled correctly, and the AI knew when to stop talking.
They tie that to telecom habits around call quality, routing, reliability, and uptime, and say SkipHold is built around the whole communications experience, not voice alone.
Voice quality matters; the claim is about priority, not a dismissal of natural-sounding voice.
For brands planning a campaign
Useful for vendors whose differentiator is uptime, routing, or task completion rather than voice fidelity. It offers a production-readiness argument and a short task checklist for a demo, benchmark, or comparison page.
Gonzalez argues that call volume, minutes handled, and time saved don't show the value delivered.
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The post contrasts two businesses that each handled 1,000 calls with AI: one with no visibility into what happened next, and one that could see appointments booked, leads captured, requests resolved, and calls that needed human help.
In this framing, the same call volume yields very different understanding of value.
They position outcome measurement as an opportunity for MSPs and telecom providers who already measure communications infrastructure well, and tie the idea to Conversations at SkipHold.
For brands planning a campaign
A framework for analytics dashboards, business reviews, or ROI messaging aimed at providers. The paired 1,000-call example is an easy structure for a post, slide, or landing-page section.
Gonzalez argues that AI answering every call doesn't mean it should.
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The business buying the AI isn't the person calling it, and some callers want a human while some situations—a complicated billing dispute, a sensitive personal matter, or an urgent judgment call—need one.
The goal is to automate the right conversations and make the rest easier for humans to handle.
AI receptionists, human teams, warm transfers, and shared conversation context are presented as parts of one customer experience, not separate products.
For brands planning a campaign
Gives vendors language to address AI fatigue or automation backlash without retreating from the category. Supports positioning around hybrid coverage, escalation design, or agent assist rather than full automation.
Traditionally, growing a telecom account meant selling more seats, employees, extensions, phone numbers, and locations.
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Gonzalez describes a 20-person customer that automates after-hours reception, appointment scheduling, lead qualification, customer follow-ups, and internal support requests without hiring anyone.
Communications needs expand while user count stays flat.
The takeaway for MSPs and telecom providers: growth can come from adding services, workflows, and capabilities rather than users.
SkipHold is presented as more than a single AI receptionist, spanning AI Receptionists, Managers, Personal Assistants, communications, and the tools providers need to offer them as managed services.
For brands planning a campaign
For UCaaS and MSP-facing vendors, this is an upsell and packaging narrative: sell capability expansion within existing accounts. Useful for partner enablement content on recurring revenue and managed-service bundling.
Most sales teams are trained to respond when a customer asks for AI, Gonzalez writes; instead, they should learn to recognize the signals that come before the request.
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Customers say they're having trouble hiring, opening another location, dealing with an overwhelmed front desk, missing calls after 5, or wanting call routing changed.
These sound like routine account conversations but double as automation opportunities.
They give providers an advantage over pure AI companies because their teams are already in those conversations.
Gonzalez suggests an exercise: list the 10 most common customer changes or complaints the team hears, then document whether AI could help, what workflow to show, and what question the salesperson should ask next.
For brands planning a campaign
A reusable enablement asset for channel partners. Source material for field-facing content such as discovery question cards, one-pagers, or training modules that embed AI into an existing sales motion.
Provider-first framing and a repeatable post structure
Across the posts, Gonzalez writes for MSPs and telecom providers rather than the businesses that buy communications.
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Topics cluster around Voice AI, UCaaS, managed services, customer experience, and sales enablement. The posts follow a repeatable shape: a blunt claim or reframe in the opening line, a two-option contrast or a list of things customers actually say, a turn to what providers should do, and a closing pointer to skiphold.com with a consistent hashtag set.
For brands planning a campaign
Gives a brand a template for co-created or ghostwritten thought leadership and a clear picture of the audience the existing posts reach. The contrast-pair opening can be reused for product comparisons or objection handling.
The posts describe one brand relationship: SkipHold, the Voice AI and business-communications product Alex Gonzalez writes about.
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They say "we" are building it and link to skiphold.com in every post. Ask whether they own the company or work for it.
SkipHold
Disclosed partnership
Every post promotes SkipHold, a Voice AI and communications platform for telecom providers and MSPs, and closes with a link to skiphold.com and the #SkipHold hashtag.
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Gonzalez writes in the first person plural about the product — "we're building SkipHold around the entire communications experience, not just the AI voice," "we've built SkipHold around more than a single AI receptionist," and "that's one of the ideas we're exploring with Conversations at SkipHold" — indicating a working involvement in the business.
Ask whether they own the company or are employed by it.
The posts explain the product's positioning: measuring what AI-handled calls accomplished rather than call volume, automating the right conversations instead of all of them, and giving providers a way to grow existing accounts through added services.
Alex Gonzalez writes short LinkedIn posts about Voice AI and business communications for managed service providers (MSPs) and telecom companies that sell and support those services, rather than for the businesses that buy them.
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Start with the argument that a slightly robotic AI that works beats a human-sounding one that doesn't, then read the post on measuring what an AI call accomplished rather than how many it answered, the case for automating only the right conversations and handing the rest to people, the account-growth post about adding services instead of seats, and the sales-enablement post on spotting automation signals before a customer asks for AI.
Each post follows the same shape: a blunt claim up front, a two-option contrast or a list of things customers actually say, then what a provider should do about it.
The tags below group the subjects and the audience so you can move from one strand to another; the similar creators write for overlapping audiences from a different angle.
Finkelsteyn writes for the enterprise telecom world with the same analytical habit as Gonzalez: make a claim, then break the obvious answer into a few models and show why no single one is complete.
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The post on aggregators, consultants, agents, TEM platforms and MSPs takes a communications problem apart the way Gonzalez takes Voice AI apart when contrasting two agents. The telecom-spend guessing post reaches the same conclusion as their post about call volume: the number everyone tracks is not the number that matters.
Finkelsteyn writes for enterprise buyers managing telecom, covering cost visibility, contracts, invoices and carrier relationships, with a healthcare-style diagnosis of what a customer actually needs.
Gonzalez writes for providers selling AI services, focusing on positioning, reliability and account growth.
Finkelsteyn's subject is spend and lifecycle accountability, not Voice AI.
Hale covers the same ground in the same format: a hook-first LinkedIn post on AI handling calls and after-hours follow-up, written for someone deciding whether to adopt it.
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The AI-spending post promises small business owners calls answered 24/7, leads followed up in seconds, no-shows cut in half and front desk overhead that does not scale with headcount. It uses the buyer-then-benefit structure Gonzalez uses, with the same framing of missed calls as a systems problem rather than a staffing one.
Hale addresses small business owners as buyers and stays at the level of promised outcomes. Gonzalez addresses MSPs and telecom providers as the sellers who have to position, price and enable the same capability, going deeper into production reliability, outcome measurement and account growth inside a channel.
Collins writes practical, channel-first material for the people running and selling managed services.
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The quarterly business review framework — business update, executive summary, security review, service experience, roadmap, risk, recommendations — rests on the same account-growth argument Gonzalez makes about AI services: the relationship grows when the conversation is about business outcomes rather than a service update, and a $3,000/month client can become a $10,000/month one.
Collins focuses on account management and sales practice.
One of the two posts is a personal story about a rejection and the resilience it produced, with almost nothing on AI.
Gonzalez's material focuses on Voice AI positioning and provider enablement.
Where Collins teaches how to run the meeting, Gonzalez argues about what to sell into it.
Alex Gonzalez is a plausible choice for a Voice AI thought-leadership campaign aimed at MSP and telecom provider teams.
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Across five LinkedIn posts (7–9 October 2026), they address providers directly: "Imagine you're an MSP evaluating two AI agents for your customers"; "if I were building an AI practice inside an MSP or telecom provider, I'd do something simple"; "providers should train them to recognize the signals that come before one."
Recurring themes include measuring what an AI call accomplished rather than how many calls it answered; teaching sales and account teams to spot automation opportunities in ordinary account conversations; growing accounts through added workflows rather than added seats; and keeping human handoff available for sensitive or complex calls.
The posts signal whose interests the content serves, but don’t verify audience composition.
The LinkedIn account linked from the posts had about 2,770 followers when observed on 11 October 2026; no engagement, impression or per-post breakdown was provided.
Before committing budget, ask whether Gonzalez has audience survey results or another documented source covering follower job function, company type and geography, and request recent reach and engagement figures for posts comparable in topic and depth to the planned deliverables.
Alex Gonzalez's five posts are first-person commentary on Voice AI for telecom providers and MSPs, each ending with a link to skiphold.com.
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For a brand considering them as a subject-matter voice on Voice AI positioning and provider enablement, the posts offer a clear format: short, structured arguments for MSP and telecom readers, built around a single idea, a two-sided comparison or a small framework a sales team could reuse, and ending with a product link.
The posts are promotional in shape, so any commissioned content would appear alongside their own SkipHold messaging, not in a neutral editorial setting.
Ask directly whether Gonzalez takes on sponsored or commissioned content, and confirm deliverables, audience terms, pricing and partnership details before proceeding.
A Voice AI company selling through MSPs wants a short thought-leadership series and a sales-team worksheet on finding automation opportunities in existing accounts.
Decision
Strong fit for the subject matter and audience. Gonzalez’s posts address MSP and telecom providers directly. Their post on sales signals offers a practical exercise: list common customer changes or complaints, then map each to a possible AI workflow and a follow-up question. Examples include hiring difficulties, an overwhelmed front desk, missed after-hours calls, and requests to change call routing. A campaign could turn that approach into useful provider enablement rather than generic AI promotion.
Commission a series of provider-facing posts plus a worksheet for account teams. Before committing, ask for audience and recent reach data, as the posts don’t specify followers’ job roles or company types. Ask for partnership terms as well.
Brief
A telecom or MSP software vendor wants a customer-facing framework explaining how to evaluate Voice AI beyond call volume and how to handle calls that need a person.
Decision
Good fit for a framework or thought-leadership campaign grounded in operational outcomes and human escalation. Gonzalez contrasts call counts with outcomes such as appointments booked, leads captured, requests resolved, and calls needing human help. They also argue for automating appropriate conversations while making human handoff easier for the rest. Together, those ideas offer a more practical message than promising to automate every call.
Build a framework around outcome reporting and escalation: track what each call accomplished, identify calls that should reach a person, and show how context can pass to the human team. Don’t present these posts as evidence of product performance or proof that a particular vendor achieves those results.
When to choose a specialist
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Brief
A consumer brand wants a broad-reach lifestyle endorsement, or a company wants a campaign aimed primarily at consumers rather than telecom and MSP providers.
Decision
Poor fit based on the five posts here. They focus on Voice AI, telecom, MSPs, managed services, and provider sales enablement—not consumer lifestyle content. The available follower count doesn’t establish whether Gonzalez’s audience matches a consumer campaign.
Consider a creator with relevant consumer work and documented audience composition, or request reliable audience and performance data before reconsidering Gonzalez.
A worked campaign example
Angle
Turn ordinary account conversations into a Voice AI discovery checklist—not a pitch for automating every call.
Brief
An MSP-facing Voice AI vendor wants a practical campaign angle for account managers who hear customer complaints but do not yet recognize them as automation opportunities.
Execution
Open with a familiar signal: a customer says the front desk is overwhelmed or calls are being missed after 5. Ask the account manager to identify the affected call types, what the customer might want handled, and the next discovery question. Then show how to assess the result: calls answered are not enough; measure leads captured, appointments booked, requests resolved, and calls transferred for human help. Close by asking which situations should remain with staff.
Sample follow up question
Which calls are being missed or delayed, what should happen on those calls, and which cases need a person rather than automation?
The angle combines Gonzalez’s concrete ten-complaint exercise with their distinction between call volume and business outcomes, while preserving their caution that some calls call for human judgment. It gives a provider team a usable discussion starter without claiming that automation is suitable for every type of call.
A better choice when the campaign targets enterprises buying telecom, not MSPs or telcos selling it.
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Finkelsteyn's posts examine how enterprises have tried to solve the “telecom problem” through aggregators, consultants, agents, TEM platforms, and MSPs, and argue for one partner spanning engineering, contracting, and invoicing.
The “guess your telecom spend” exercise is the sharpest example: after a few questions at a conference, Finkelsteyn guessed an organization's monthly telecom spend and was right about half the time; one infrastructure manager estimated $20,000 a month against a real figure closer to $170,000.
If the campaign aims to make enterprise buyers question their current telecom setup, cost visibility, or contract structure, this material speaks to that decision more directly than Gonzalez's posts.
Finkelsteyn does not position or sell AI.
The posts cover telecom lifecycle management and spend visibility, not Voice AI outcomes, escalation design, or how a provider packages and sells an AI service.
A campaign about measuring what an AI-handled call accomplished, or helping MSP sales teams spot automation opportunities, would lose the provider-facing argument Gonzalez supplies.
Choosing Finkelsteyn means trading Voice AI subject matter for enterprise telecom estate expertise.
A better choice when the campaign targets small-business owners as end customers, rather than MSPs and telcos as channel partners.
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Hale's posts speak directly to owners: the “$600 billion” AI spending headline; why that spending makes AI cheaper to try (a deployment that took enterprises 18 months and $2M two years ago now takes 14 days with no upfront cost); and outcomes that matter to a smaller business — calls answered around the clock, leads followed up in seconds, fewer no-shows, and front-desk overhead that does not scale with headcount. For demand generation aimed at businesses that would buy an AI receptionist, that framing is closer to the reader than provider enablement.
The channel disappears from the story.
Hale's posts say nothing about selling through MSPs, training account teams to raise AI in existing accounts, or reporting AI results as a managed service — the core of Gonzalez's angle.
Both posts also carry the same text, making one argument rather than two, and cover a narrower range of themes than Gonzalez's five posts.
Choosing Hale means trading provider enablement for direct end-buyer messaging.
A better choice when the campaign is about how MSPs run and grow client accounts, rather than AI product positioning.
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Collins's quarterly business review framework lays out a working slide structure — business update, executive summary, security review, service experience review, strategic roadmap, risk review, recommendations — and ties it to a commercial result: turning a $3,000-a-month client into a $10,000-a-month strategic account.
The detail is operational, down to telling a story with the data (“we blocked 4,219 malicious emails this quarter”) and quantifying risk so a C-suite will pay attention.
A vendor selling into MSPs and trying to shape how they conduct client reviews would find that closer to the decision than Voice AI messaging.
Collins writes as an MSP sales and account-management practitioner, and the QBR piece raises AI only as a question to ask clients.
The posts contain no Voice AI positioning, provider enablement for AI services, or method for measuring AI outcomes.
A campaign about what a 1,000-call month actually accomplished, or about automating the right conversations and handing the rest to people, would need Gonzalez's framing rather than Collins's.
Choosing Collins means trading AI subject matter for MSP account practice.
Five LinkedIn posts dated 7–9 October 2026 show Gonzalez’s approach to Voice AI: prioritize reliable task completion over a natural-sounding demo, measure what calls accomplish, and route conversations to people when they need human judgment.
Gonzalez also writes about helping providers find automation opportunities in customer needs and grow accounts through new services.
The posts name SkipHold and link to its website; they do not establish Gonzalez’s role or ownership.
This is a focused view of those recent posts, not a survey of Gonzalez’s full body of work.
