Go-to-market articles and insights

Your investment team spent three months evaluating a $20M software company. The financials looked solid. The market was there. The product worked. Everything checked out until you sat down with their sales and marketing leader and asked a simple question: "Show me how you actually acquire customers."

What they showed you was a spreadsheet. Not a system. Not a process. Just a sheet with projected pipeline numbers that looked like they came from thin air. When you dug deeper, you found what was actually driving deals: the founder. His network. His ability to get on the phone and close. The company had a scattered marketing motion, no recent thought leadership building brand, and no repeatable customer acquisition process.

You killed the deal.

This isn't an edge case. Most companies in growth mode operate this way. They start with founder-led sales, and fewer companies actually evolve beyond this stage. They grow through founder hustle, not systems. And when you're evaluating acquisition targets or trying to understand the quality of a portfolio company's growth engine, you need a framework to spot the difference between real growth and spreadsheet theater.

The good news: you can evaluate this in diligence. The better news: you can use the same framework to assess and improve any company you acquire.

What a modern growth engine looks like

A real growth engine has layers. Each layer is a system that works independently but connects to everything else. When all layers are operating, growth is predictable and repeatable. It doesn't depend on one person's rolodex.

Here's the simplified system to show how we connect all the pieces:

• GTM Foundation is the base. This is your positioning, messaging, and ideal customer profile. Does the company know who they actually sell to, and can they articulate why customers should buy from them. Messaging either stays consistent across the team or it doesn't. Positioning either gets written down or it lives in the founder's head.
  
• Content is what fuels awareness and demonstrates expertise. This is case studies, thought leadership pieces, nurture sequences, educational content. Content can be produced on a schedule or sporadically. It can align to the buyer journey or just be random top-of-funnel noise.
  
• Campaigns are how you put your strategy in motion. Campaigns can be structured around segments, buyer stages, or specific value props. Done well, campaigns have specific objectives, measurable outcomes, and keep your actions focused and intentional.
  
• Channels are where your audiences live, and serve as the pipelines that you activate to increase awareness and drive engagement. This is linkedIn, email marketing, reddit, paid ads, etc.
  
• Activate and Capture is how you convert the 5% of your audience that’s ready to act. This is a lead capture form on your landing page, lead magnets your audience bites on in direct social outreach, or your customer who replies to the monthly newsletter with a referral. 
  
• RevOps Underneath. Are systems connected or siloed? Do you know your metrics or are you guessing? Do you have a CRM that actually tracks contacts, deals, and engagement across the stack, or are you managing spreadsheets masquerading as systems? RevOps isn't about having the fanciest tools. It's about having a platform that connects your foundation to your measurement, so you can see what's working and what isn't.

Growth is messy, non-linear, and multidisciplinary. Frameworks and systems add clarity and structure that help control the chaos. At scale, companies have to have systems or they break. At early stage, the hope is that if you build the right system from the beginning, you avoid the death march of trying to retrofit it later.

Every company we speak with has a different level of maturity across the framework. The trick is knowing when the existing setup is a long-term liability or an immediate opportunity.

How Do You Actually Spot This in Diligence?

The real work of GTM diligence is asking the right questions about each layer and knowing when you're looking at real answers versus vague reassurance. Spreadsheets are easy to write down. Knowing when to call BS on what you're being told is the hard part.

Here are the five questions we use to structure the conversation:

1. What channels are actually working for growth? Not "what channels do you want to use." What's generating deals right now, and how much pipeline is each channel creating? 
2. How do you create content today? Who's responsible? Is there a schedule? Do people actually use it to sell, or does it sit on a shelf? Does it align to your buyer's journey, or is it random top-of-funnel noise?
3. Walk us through a recent marketing campaign. Who planned it? How did it get organized? What were you trying to achieve? What actually happened? You'll learn more about how a company actually operates from one campaign than from any document they send you.
4. For your active pipeline, what are the different lead sources? Break it down. If it's 90 percent founder referrals and 10 percent everything else, you know what you're inheriting.
5. What's the average deal cycle duration for pipeline in the last 12 months? Not a single deal. Look at the last 12 months. If it's chaotic, you're seeing founder-dependent selling. If it's predictable, you're seeing a system.

The key is listening to how they answer. Do they describe a process, or do they describe people? Do they reference metrics, or do they tell you what they think should happen?

Case Study: The Software Company We Walked Away From

Last year, our team spent two months evaluating a tech-enabled services company that had household name clients, a solid delivery team, and reasonably unique software that was under invested in recently. On paper, it looked solid. Mid-market B2B SaaS business, $20M ARR, good margins, established customer base. The investment thesis made sense.

So we dug into the growth engine.

GTM Foundation was weak. Positioning existed but wasn't consistent across the team. Their content engine was minimal. Campaigns weren't structured. Channels were a mystery. 

More importantly, the company had one person capable of selling. Not one person doing most things. One person the business actually depended on. He was the founder. He closed deals, maintained relationships, knew how the business worked. And he wanted out.

He was tired. He'd been running on fumes for three years, ready to hand it off and move on, and we knew this going in. But what the company underestimated was the degree to which their lack of any growth engine handicapped the deal.

We evaluated what it would take to fix this post-close. Build a GTM foundation. Create content. Structure campaigns. Enable a sales team to replace what one person was doing. 

We walked away.

Six months later, the business was acquired by a competitor. Founder checked out like he said he would. Customers started churning. The revenue that looked solid in the data room turned out to be fragile. Every month, the business eroded a little more. The acquirer is now managing decline instead of managing growth.

The issues we identified weren't unsolvable. They were all things we could have fixed with our operating engine. But the founder was unrealistic on valuation, and the deal numbers didn't support what it would cost to fix it. Not all deals work out.

What the acquirer didn't spend: two months of diligence with operators who knew what to look for.

What they're spending now: managing the fallout.

Putting It Together: What the GTM Layers Tell You

When you evaluate a target company, you'll find different combinations:

Strong across all layers. This company has a real growth engine. Post-close, you're scaling and optimizing, not building from scratch. Low risk. Lower post-close investment.

Weak foundation, strong execution. This one is tricky. The team executes well, but execution depends on people. Positioning and messaging aren't locked down. Red flag. When that person leaves, execution falls apart. Moderate risk. Plan for significant change management post-close.

Weak execution, strong foundation. You're buying a platform with solid messaging and positioning. Campaigns and channels aren't built out yet. But you have something to build on. Moderate risk. High post-close investment in building execution, but you're not starting from zero.

Weak across all layers. This is spreadsheet theater. Real growth doesn't exist. You're buying a founder and hoping his network scales. It won't. High risk. Either massive valuation adjustment or pass.

How We Approach GTM Diligence

Other firms skip GTM assessment in PE diligence. Not because it's unimportant. Usually because it's unclear how to evaluate it without being a marketing expert yourself. So it gets skipped, or it gets delegated to consultants who'll hand you a 50-page report that doesn't help you make a decision.

We approach it differently.

Trelliswork joins your deal team as GTM operators during diligence. We sit in management meetings with you and the target company. We help you understand what actually exists and what doesn't. We ask the specific questions that separate real growth from founder-dependent hustle. We help you build a roadmap for what needs to happen post-close if you move forward.

Here's what that engagement looks like:

Two management meetings. We join your team in the room with the target company's leadership. We sit alongside your deal team as an extension of your team, not as external consultants. We dig into their channels, campaigns, and metrics. We ask about process and tooling. We help you get a clear picture of what's actually built versus what's aspirational. You understand the gap between where they are and where they need to be.

A realistic post-deal roadmap. Then we work with you to build a specific GTM roadmap for this company, this market, these people. Not a generic template. A plan that's actually executable. What needs to happen in month one. What needs to happen in quarter one. What gets fixed versus what gets rebuilt. What you're inheriting versus what you're building.

You close the deal with clarity about what you're acquiring and what it actually costs to scale it.

The Cost of Missing This

You can discover this in diligence. You price accordingly. You go in with eyes open about what you're buying and what you'll need to invest post-close.

Or you miss it. You assume that because the company has grown, they have systems. You close the deal. You onboard the company into your portfolio. And somewhere around month three or month four, you realize the growth engine doesn't actually exist. Now you're managing a broken revenue machine while you should be scaling it.

The valuation adjustment you didn't make in diligence becomes the operational headache you own in year one.

Evaluate the layers. Ask the hard questions. Decide what you're actually buying.

That's how you spot the difference between real growth and a spreadsheet.

Ready to derisk your next deal?

Value creators and deal teams use Trelliswork to pressure-test growth engines before the close. We embed with your team during diligence, identify the gaps that don't show up in a data room, and build realistic post-close roadmaps so you know exactly what you're buying and what it costs to fix.

See how we work with investors or get in touch to talk about your next deal.

A Different Kind of Restructuring

Every few months, another headline announces agency layoffs. The narrative focuses on AI displacement and budget cuts. But there's a different story playing out at mid-sized independent agencies.

These firms aren't shrinking. They're restructuring around a simple insight: the work clients pay premium rates for (strategy, relationships, business insight) is different from the work that eats up most of their headcount (content production, campaign execution, technical delivery).

So they're splitting the difference. Keep the strategic layer. White-label the execution layer to specialized GTM partners who do that work faster and cheaper.

Legal Services Got Here First

Law firms faced this inflection point before marketing did.

If you've used traditional outside counsel before you know what it feels like to get a $1,700 invoice for a 30 minute phone call and an updated legal doc. Everyone knew this industry was ripe for disruption, it just wasn’t possible until today's AI came along to handle the legal busywork.

Harvey AI went from zero to an $8 billion valuation by becoming the execution layer for law firms. Partners kept client relationships and strategic counsel. Harvey handled the document review, research, and drafting that used to employ armies of junior associates.

This same dynamic is in play now with GTM firms and marketing Teams.

The Economics Forcing This Conversation

Mid-sized agencies typically run 60-70% of their headcount in execution roles: content writers, campaign managers, SEO specialists, designers, developers, analytics people. These roles are necessary, but they're where margins get compressed.

Execution work is increasingly commoditized. Clients benchmark your deliverables against competitors producing similar outputs with smaller teams and better tooling. Meanwhile, your execution staff expects annual raises, benefits costs climb, and turnover runs 20-30% annually.

You're running hard just to stay in place. The strategic work is where your differentiation lives, but execution overhead consumes capacity you could invest there.

Why Agencies Can't Build This Internally

Three reasons.

The talent market works against you. Engineers who understand AI-native workflows command $300,000+ packages. Even if you land someone good, they need infrastructure and time to build something useful. Most agencies underestimate the investment.

Your team can't retool fast enough. Your content team isn't becoming prompt engineers in a month. Training takes time you don't have, and clients won't subsidize your learning curve.

Your business model fights the change. Agencies built on billable hours struggle when AI compresses delivery timelines. If a project that took 40 hours now takes 15, do you bill for 15 and take the revenue hit? Its an option, but it doesn't represent the value you are creating.

How White-Label Partnerships Work

You keep: Strategy and planning, client relationships, creative direction, business development.

Your partner handles: Content production, campaign management, technical implementation, SEO and paid media, analytics infrastructure.

Everything ships under your brand. Clients interact with your team. The partnership stays invisible.

The Delivery Engine Behind It

A good GTM partner brings more than bodies. They bring a delivery engine: an existing framework, workflow, and system you plug into.

Consider what goes into publishing a single, high quality SEO article. You need a content calendar, keyword strategy to inform topics, and then a workflow for each piece: outline creation, gathering unique perspectives from the client, drafting, adding visuals, internal review, a featured image, client approval. Then internal links, optimization for the buyer journey stage, and finally publication. That’s a lot of moving parts for one article.

At Trelliswork, we use our 10/80/10 framework. The agency owns the first 10% (client relationship, strategic direction) and the last 10% (client review, final approval). We handle the 80% in the middle: all the execution and orchestration that turns strategy into deliverables. Content interactions get packaged up and shipped ready for the agency to present. The end client never sees Trelliswork. The agency maintains full ownership of the relationship while we run the engine underneath.

The Financial Impact

A $15 million agency with 22 employees might look like this:

Current state:

• 8 people in strategy/client services: ~$960K loaded cost
• 14 people in execution/production: ~$1.5M loaded cost
• Operating margin: 12-15%

After restructuring:

• Keep 8 strategy/client services people: ~$960K
• White-label execution (typically 45-55% of internal cost): ~$750K
• New operating margin: 18-23%

That's roughly $750K in annual savings. The flexibility matters as much as the savings: scale execution through your partner when you win big accounts, reduce scope without layoffs when accounts churn and simplify your bench management stress. Your cost structure becomes variable instead of fixed.

What Separates Good Partnerships From Bad Ones

Integration quality means your partner operates as an extension of your team. Shared project management, direct communication, aligned accountability. If you're spending hours coordinating handoffs, you've traded one overhead problem for another.

Strategic depth means your partner brings expertise that improves your work. They've seen patterns across dozens of clients and know what's working. They should elevate your strategy, not just fill orders.

The Competitive Window

Agencies restructuring now are building partner relationships and improving margins while competitors maintain the old model. That advantage compounds. Better margins fund better business development. Better business development wins more clients.

The agencies that wait will face the same economics eventually, but they'll be playing catch-up with less runway.

Making the Shift

First, map your current economics. What percentage of headcount sits in execution roles? What's your fully-loaded cost per deliverable type?

Second, identify the right partner. Look for GTM firms that handle the full execution stack under one relationship. Evaluate integration capabilities, not just deliverable quality.

Third, plan the transition. Start with a single service line or client segment, prove the model works, then expand scope gradually.

The agencies winning in 2026 are maintaining execution quality for clients while shifting their best people to think more strategically. To get there, it means retooling your delivery model. White-labeling is one option, but the shift is structural, not transactional.

Ready to explore what this could look like for your agency? The first step is understanding your current cost structure and where the leverage points are.

Frequently Asked Questions

What does it mean to white-label GTM for agencies?

A partnership where GTM firms handle execution (content, campaigns, SEO, paid media, analytics) under your agency’s brand. Clients never see the partnership. You keep strategy and client relationships.[1] 

How much can agencies save by white-labeling execution?

45-55% reduction in execution costs. For a $15M agency, that’s roughly $750K annually. Savings come from eliminating salaries, benefits, tools, and 20-30% turnover costs.

What functions should agencies keep in-house vs. white-label?

Keep in-house: strategy, client relationships, creative direction, business development. White-label: content production, campaign management, technical implementation, SEO/paid media, analytics.

How is agency white-labeling different from traditional outsourcing?

Traditional outsourcing sends discrete tasks to the lowest bidder. White-label partnerships integrate the partner into your team with shared systems, direct communication, and accountability. Good partners improve your strategy, not just execute orders.

Will clients know we're using a white-label partner?

No. All deliverables ship under your brand. Clients interact only with your team. The partnership stays invisible.

What should agencies look for in a white-label GTM partner?

Integration quality (shared project management, direct communication, aligned accountability) and strategic depth (cross-industry expertise that improves your work). Avoid partners requiring heavy coordination or lacking full GTM stack experience.

Model Context Protocol (MCP) servers let you manage your CRM through natural language instead of endless clicking. You'll learn what MCP servers do, how to create deals and run reports without opening HubSpot, and what limitations to expect while the technology matures.

Adding a deal to HubSpot takes about 17 clicks and two and a half minutes. You navigate to the right pipeline, tab through edit fields, fat-finger something, delete it, try again. By the time you're done, you've lost momentum on whatever you were actually doing.

Now imagine speaking a 7 second update and having the deal created automatically. The pipeline is correct. The close date is set. The contact is associated. You never opened HubSpot.

That's what Model Context Protocol servers make possible. They put a conversational layer between you and your business systems so you can talk to tools like HubSpot in plain English instead of clicking through menus.

Behind the scenes

Think of MCP servers as translators. When you ask a question or give a command in plain English, the MCP layer converts that into the correct API calls for whatever system you're connecting to. The responses come back in readable form rather than raw data.

With a standard API integration, you need to know the exact endpoint, the correct formatting, the required fields. You're writing code or at minimum configuring something technical. With MCP, you describe what you want and the protocol handles the translation.

The Model Context Protocol documentation covers the technical architecture if you want to go deeper. The short version: MCP creates a standard way for AI applications to connect with external data sources and tools. Instead of each integration requiring custom code, platforms can publish MCP servers that any compatible AI interface can use.

HubSpot recently released a bi-directional MCP server. This means you can both read data from HubSpot and write data back to it. You can ask "show me all deals closing this month" and also say "create a new deal with these details."

Worth noting: write access through HubSpot's MCP server is currently in beta. Your company or partner admin needs to opt in before you can create or update records through the connection. Read access works immediately once you connect.

The headless CRM experience

The term "headless" comes from software architecture, where you separate the interface from the underlying functionality. A headless CRM means you get the data management and pipeline tracking without being forced into the CRM's interface.

For teams that spend their days in Claude, Slack, or Notion, this is welcome. Instead of context switching into HubSpot to update a deal, you update it from wherever you already are. The CRM becomes infrastructure rather than a destination.

Here's an actual prompt that creates a complete deal record:

"Add a new deal to HubSpot called 'NewCo GTM Assessment', assign it to me, associate the deal with Robert Duncan, set this to first deal stage in our pipeline, close date Mar 31, and set the lead source to 'Client Referral', deal type new business"

That single request replaces opening HubSpot, navigating to the pipeline, clicking "Add Deal," filling in the deal name, setting the amount, selecting the pipeline stage from a dropdown, picking a close date from the calendar widget, searching for and associating the contact, setting the deal type, configuring the lead source property, and finally clicking save.

The MCP server processes the whole thing, creates the deal record, makes all the associations, sets every field, and confirms what it did. Seconds instead of minutes.

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What this looks like in practice

Setting up the HubSpot MCP connection in Claude means enabling the connector in your settings. Once connected, you'll authorize specific tool functions the first time you use them. After that initial setup, the connection persists.

The real value shows up in recurring work. Updating deal stages, adding notes, changing amounts, associating new contacts. Each of these tasks involves multiple clicks and screen navigation in the traditional interface. Through MCP, they become single requests.

The time savings compound quickly. If you're managing a pipeline with 20 active deals and touching each one even once per week, you're looking at hours reclaimed monthly.

Notice in the example prompt that you can reference contacts by name ("Robert Duncan") rather than hunting for record IDs or email addresses. You can use relative terms like "first deal stage in our pipeline" instead of memorizing exact stage names. The MCP layer handles the translation to HubSpot's internal structure.

Ad-hoc reporting without the formatting fights

Anyone who has built reports inside HubSpot knows the frustration. You want a specific view of your data, but the report builder has opinions about how that data should look. Column limits. Visualization restrictions. Export formats that require cleanup before they're usable.

MCP changes this. Instead of conforming to what HubSpot's reporting interface allows, you describe what you want to see and get it back in whatever format makes sense.

"Show me all deals by stage with associated contacts and last activity date, sorted by days since last touch."

The response comes back as structured data you can use however you'd like. No clicking through configuration screens. No wrestling with chart types that don't fit your data.

The real gain here is iteration speed. In HubSpot's report builder, changing a filter or adding a column means navigating back through configuration screens. With MCP, you refine your query conversationally. "Actually, filter that to just deals over $50k" or "Add the deal owner column" becomes a quick follow-up rather than a multi-click detour.

The tradeoff is a lack of standardized reporting while you're in iteration mode. You want your data to build on itself over time, to tell a story with trends and patterns, not start from scratch every week. This is where the opportunity to rethink what you really want your data to tell you comes in.

We're finding a middle ground that works right now. We keep a base set of reports that run clockwork week over week. They drive accountability and data checks. Then all the exploration off of that data goes back to MCP prompts, augmenting the data story for that particular week.

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Hubspot's MCP Limitations

MCP servers are still maturing. You'll encounter quirks. Sometimes the connection needs re-authorization. Sometimes the AI layer needs gentle reminders that it does have access to the tools you've configured.

The technology requires the right setup. You need Claude's desktop app or similar interface that supports MCP connections. You need the specific MCP server for the tool you're connecting to. Not every platform has released one yet.

Write operations require beta access through your HubSpot admin. If you're testing this personally, you may be limited to read operations until your organization enables the beta features.

One thing to expect: the MCP tool connections reset periodically and require re-authentication. Part of this is because these integrations are actively evolving. Just be prepared to reconnect from time to time as things stabilize.

There's also a learning curve in how you phrase requests. Being specific helps. The example prompt above works well because it includes everything in one request: deal name, amount, pipeline stage, close date, contact association, lead source, and deal type. The more complete your prompt, the more accurate the result.

What this means for GTM operations

Nobody loves their CRM. This is an industry-wide truth. The systems exist because pipeline visibility matters, because forecasting requires data, because revenue operations need structure. But the interfaces are friction-heavy by design. They prioritize data capture over user experience.

MCP servers let teams interact with CRM data from wherever they already work. If your revenue team lives in Slack and Claude and Gmail, they can now update HubSpot from those environments. The CRM still does its job. You just don't have to live inside it anymore.

That connects to something we keep seeing in go-to-market operations. The tools that win are the ones that reduce busywork, not just move clicks from one interface to another.

For teams evaluating their tech stack, MCP compatibility is worth paying attention to. The platforms investing in these connection protocols are the ones building for where workflow is actually heading.

Getting started

If you want to experiment with MCP servers and HubSpot, the workflow is similar whether you're using Claude or ChatGPT:

1. Install Claude's desktop application (or use ChatGPT's interface)
2. Navigate to settings and enable the HubSpot connector
3. Authorize the connection to your HubSpot instance
4. Start with simple read queries to test the connection
5. Contact your HubSpot admin about enabling beta write access
6. Move to write operations once beta access is confirmed

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The initial setup takes about fifteen minutes. After that, you're working in a different mode. Less clicking, more doing.

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Frequently Asked Questions

Q: Do I need to be technical to use MCP servers with HubSpot?

No. The entire point of MCP is removing the technical barrier. You interact through natural language prompts like "create a new deal called X and assign it to me." The protocol handles the translation to HubSpot's API structure. If you can describe what you want in a sentence, you can use MCP.

Q: What is a headless CRM?

A CRM you interact with through APIs or AI prompts instead of its native interface. You get the data management without living inside the software.

Q: Is MCP reliable for daily use?

It's maturing. Connections reset periodically and need re-authentication. Best for teams comfortable with occasional troubleshooting as the technology stabilizes.

Q: Can MCP create deals in HubSpot?

Yes, but write access is in beta. Your admin must enable it. Read access works immediately. Once enabled, you create deals with a single prompt.

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Every B2B marketing leader has heard the warnings by now. Google traffic is declining. ChatGPT is replacing search for research. Your buyers are asking LLMs for vendor recommendations, and you have no idea if your brand even shows up.

The SEO tool vendors see this shift too. SEMrush, Ahrefs, and newcomers like Mentions are rolling out "AI visibility" features that promise to track how often your brand appears in LLM responses. The pitch is compelling: if your buyers are using AI to research solutions, shouldn't you know whether ChatGPT recommends your competitors instead of you?

We decided to test these tools across our own properties and several client sites. Not to write an official vendor comparison (yet), but to answer a practical question: is there any real benefit to investing in AI visibility tracking today?

The short answer: not yet.

The tools show promise, but the data infrastructure isn't there. More importantly, anyone claiming they've cracked the code on how to manipulate LLM visibility is lying. These systems are still largely a black box, and the theories about how to game them are just that: theories.

What we found, and what you should focus on instead.

The Current State of AI Visibility Tools

The concept makes sense in theory. Just like you track keyword rankings in Google, you should track "prompt rankings" in ChatGPT, Claude, Gemini, and Perplexity. You want to know how often your brand appears, what prompts trigger mentions, and how you stack up against competitors.

The tools we tested claim to deliver these insights. In practice, they provide dashboard scores without the substance you need to act on them.

Zero Visibility Doesn't Mean Zero Impact

If you're not a household brand with massive search volume, expect to see mostly zeros in your dashboard. The tools assign you an "AI visibility score," but provide almost no context for what drives that number.

One client site we tested scored a 0. Their direct competitor scored a 35/100. Sounds concerning, right? Except there's no breakdown of which prompts drove that 35, how many actual appearances that represents, or what content made them visible. You can't reverse engineer success when the underlying data isn't exposed.

This isn't like traditional SEO, where you can identify long-tail keywords, see monthly search volume, and build an optimization plan. The LLM vendors aren't sharing prompt data at scale. OpenAI doesn't provide a "Search Console for ChatGPT." Neither does Anthropic for Claude, or Google for Gemini.

The AI visibility tools are trying to fill a gap that's mostly still empty.

The Data Problem Is Foundational

These tools are building on quicksand.

Google Search Console exists because Google wants site owners to improve their content. Better content creates better search results. The incentive structure works for everyone.

LLM providers haven't opened up that same transparency yet. You can't see which prompts mentioned your brand, how often you appeared in responses, what context surrounded those mentions, or whether users took action afterward.

Some tools are scraping what they can or running test prompts at scale to simulate visibility. But it's a thin dataset compared to the depth available for traditional search. Until the LLM vendors provide real transparency, these tracking dashboards are measuring shadows.

One Thing Works: The ICP Exercise

Mentions does something valuable that has nothing to do with tracking. Their onboarding takes a crack at creating your ideal customer profile after some general questions. It identifies competitors, articulates differentiators, and maps the questions buyers naturally ask based on services and does a decent job of it so you can quickly edit or replace what it creates. 

This exercise matters. It pushes you to think about brand positioning in the context of conversational queries, not just keyword strings. If someone asks an LLM, "What tools help B2B companies improve pipeline visibility without adding headcount?" how should your brand be described in that response?

That's a strategic question worth answering, regardless of whether you can track the results.

But after building that foundation, the tool primarily tracks prompts that explicitly mention your company name. Of course you appear in those results. The more valuable question is whether you surface in category-level or problem-level queries where your name isn't mentioned at all.

Those are the prompts that drive net-new awareness. And the tools can't reliably track them yet.

Traditional SEO Vendors Are Adding Bolt-On Features

SEMrush and Ahrefs are layering AI visibility modules into their existing platforms. On the surface, this seems efficient. You already pay for these tools, so why not get AI metrics in the same dashboard?

The risk is they're applying an SEO framework to a fundamentally different problem. AI visibility isn't just about keywords and backlinks. It's about how your brand narrative gets synthesized into conversational responses. It's about authenticity, context, and the authority signals that LLMs use to decide what's worth citing.

If you optimize for trigger words without understanding how LLMs construct answers, you might improve a score that doesn't correlate with actual buyer influence.

What You Should Do Instead

If the tracking tools aren't ready, what's the alternative? Focus on the fundamentals that drive AI visibility, whether you can measure it precisely or not.

Build Content That LLMs Want to Reference

LLMs are trained on public content and retrieve from indexed sources during inference. If your content is thin, generic, or keyword-stuffed, it won't surface in AI responses no matter what your dashboard says.

Write content that demonstrates real expertise. Address specific buyer problems with depth. Provide clear points of view backed by experience. This is what gets cited when an LLM synthesizes an answer.

Forget the tricks. There are no tricks yet. Anyone who tells you they've reverse-engineered the ranking algorithm for ChatGPT is selling you something they don't have. These systems are black boxes, and the theories floating around are mostly speculation dressed up as strategy.

What works is the same thing that's always worked: create content valuable enough that it becomes a source of truth in your space.

Make Your Content Crawlable and Structured

LLMs need to access your content to reference it. That means basic technical hygiene matters more than ever.

Ensure your site is crawlable. Use clear URL structures. Format your pages with proper headings, lists, and semantic HTML. Make it easy for both traditional search engines and AI systems to parse what you're saying.

If you have key service pages, product explainers, or methodology documentation, structure them clearly. Use headings to break up sections. Include definitions for important terms. Link to authoritative sources that support your claims.

This isn't new advice. It's just more important now.

More kudos to Mentions in this feature area, they provided the most depth on suggestions of structured content that might improve your “score.” Most were obvious: write about the topics that involve your services or customer problems identified in their ICP analysis.  However, Mentions also attempted to diagnose general some visibility problems with your brand and suggested content pieces unrelated to services (e.g. write a blog post specifically focused on who is Trelliswork so that the LLMs can fill in the gaps on what they glean from pages, FAQs, and services pages).

Link to Authoritative Sources (And Earn Backlinks)

LLMs weight authority when deciding what to include in responses. That authority comes partly from who links to you and who you link to.

Build relationships with credible sources in your industry. Contribute to publications that matter. Get cited in research reports, analyst briefs, and case studies from recognized firms.

When you publish your own content, link out to authoritative sources that support your points. This isn't just good practice for readers. It signals to AI systems that your content exists in a network of credible information.

Define How You Want to Be Described

Think about how you want an LLM to describe your company when someone asks about your category. What's your core differentiation? What problems do you solve that competitors don't? How would you explain your value in two clear sentences?

Document this. Make it public. Repeat it consistently across your site, case studies, thought leadership, and any content you control.

LLMs synthesize from available sources. If your positioning is clear and consistent everywhere, that's what gets reflected in AI responses. If it's muddled or contradictory, the LLM will struggle to represent you accurately.

Test Your Own Visibility Manually

You don't need a paid tool to understand your AI presence. Open ChatGPT, Claude, Perplexity, or Gemini. Ask the questions your buyers would ask. See what shows up.

Try variations:

• "What are the best tools for [your category]?"
• "How do B2B companies solve [problem you address]?"
• "What should I look for when evaluating [your solution type]?"

Does your brand appear? If yes, how is it described? If no, look at what does appear. What made that content authoritative enough to reference? What sources get cited?

Reverse engineer those patterns. Look at the structure, depth, linking behavior, and positioning of the content that wins. Then build your own content strategy around those observations.

This is manual and time-consuming. But it's more actionable than a dashboard score you can't interpret.

Promote Your Content in Traditional Ways

AI visibility doesn't replace traditional distribution. It complements it.

Keep promoting your content through email, social, partner channels, and any other distribution you've built. The more your content gets read, shared, and linked to, the stronger the authority signals become. Those signals matter for both traditional search and AI discoverability.

Don't abandon what works in pursuit of a new metric you can't control yet.

The Vanity Metrics Problem

You could add these AI visibility tools to your stack today, get a score, and have no idea what it means or what to do about it.

If your score is high, great. But why? If it's low, what's the actual fix? The tools don't provide enough depth to connect visibility to action.

This is dangerous for marketing leaders who need to justify spend and show progress. A static or declining AI visibility score without context creates pressure to "do something" without clarity on what that something should be.

You risk adding another dashboard that looks important but doesn't drive real decisions. That's the definition of a vanity metric.

Our score has ranged from 30-80%, but when you dig deeper – you start to see why. It is giving us credit for questions and response that really aren’t real. No one would ask these questions about Trelliswork:

What to Watch For as These Tools Mature

These tools will  no doubt get a lot better as LLM providers open up more transparency. When that happens, AI visibility tracking will become essential infrastructure, just like SEO tools are today.

What needs to happen for these tools to cross the threshold from "interesting" to "must-have":

Real prompt-level data. You need to see which specific prompts triggered your brand, how often, and in what context. Not aggregated scores, not when your brand was in the question from the start, but granular visibility into what's working to find you in the haystack.

Actionable recommendations. The tools need to analyze why certain content surfaces and provide specific guidance on what to change. "Improve your AI visibility score" isn't helpful. "Add more structured data to your service pages and link to these three authority sources" is.

Competitive context that matters. Knowing your competitor scored higher is useless without understanding what they did to earn that score. The tools need to surface the content, structure, and positioning differences that drive visibility gaps.

Validation that scores correlate with outcomes. Until there's proof that a higher AI visibility score leads to more inbound interest, pipeline, or revenue, these metrics remain theoretical. The tools need to connect their scores to business impact. We all expect this to change quickly so that the LLM providers can monetize beyond a paid chat interface.

Set a calendar reminder to revisit this space in 3-6 months. 

Where We Land

AI visibility tools are trying to solve a real problem. Buyer behavior is shifting toward AI-supported research, and you need to understand your presence in that environment.

But the infrastructure to track and optimize that presence is still too early. The tools from Mentions, SEMrush, and Ahrefs show the right strategic thinking. They understand what needs to be measured. They're building the frameworks and interfaces. The underlying data layer just isn't robust enough yet to deliver actionable value for most B2B brands. Although, as noted above we think Mentions.so is leaping ahead because it appears to have been designed from the start for this task. We are excited to watch this platform continue expanding.

If you're a high-volume, high-recognition brand, you might extract some directional insights. For everyone else, you're better off investing in the fundamentals : deep content, clear positioning, strong technical structure, and authentic authority building.

We'll keep testing these tools as they evolve. When the data catches up to the dashboards, we'll be the first to tell you. Just not today.

While everyone's talking about AI replacing jobs, there's a bigger shift happening that nobody's discussing openly: companies are quietly abandoning expensive agency relationships and in-house marketing teams for outsourced GTM partners.

And the numbers tell you why.

The Math Driving This Shift

A typical marketing leader costs your company between $162,000 and $298,000 annually when you factor in salary, benefits, and overhead. That's for one person. Build out a full marketing team and you're looking at $500,000 to $1 million before you've run a single campaign.

Now compare that to what's happening in professional services. Accenture just cut 11,000 people who couldn't be reskilled on AI fast enough. The firm's workforce dropped from 791,000 to 779,000 in three months while saving over $1 billion. If consulting giants are struggling to justify their traditional staffing and operational models, what chance do mid-market companies have with their bloated marketing departments?

The writing's on the wall. Traditional work models are changing quickly. Work that took 100 hours now takes 10 with AI. Most firms are choosing to partner.

Marketing is hitting the same inflection point with specialized GTM firms offering AI-enhanced services at a fraction of traditional costs. These aren't your grandfather's outsourcing shops. They're AI-first operations that can deliver what used to require a large team with a single specialist and smart automation.

Sam Altman said AI will handle "95% of what marketers use agencies, strategists, and creative professionals for today" — and it'll be "nearly instant and at almost no cost." While that timeline might be aggressive, the direction is clear. Companies are already acting on it.

Why Outsourced GTM Firms Are Winning

Companies are discovering three uncomfortable truths:

Building in-house is expensive and slow. You need AI talent at premium rates, infrastructure from scratch, and 6-12 months before you see results. Most companies get it wrong the first time and have to start over.

Traditional agencies are trapped in the old model. They're built for billable hours, not outcome-based pricing. Their economics don't work when AI collapses delivery time by 50-80%. The Big 4 collectively invested over $4 billion in AI initiatives, but they're struggling to transform without cannibalizing their core business.

Specialized GTM firms have already made the transition. They've spent the last two years figuring out AI-powered workflows. They know which tools work, how to structure teams, and how to deliver outcomes at scale. When you partner with them, you skip the trial-and-error phase entirely.

The best part? You get specialized expertise without the overhead. Need SEO? PPC? Content? Demand gen? Outsourced GTM providers can start next week, no long-term contracts, no benefits packages, no onboarding nightmares. 

There is of course a difference in quality of which firms dig in to understand your business and those who do high quality work. In other words, doing proper buyer diligence isn’t going away. 

The Full-Stack Advantage

Instead of juggling 4-5 partners for different marketing functions, companies in 2025 are opting for full-stack GTM solutions that handle everything under one roof: Website, branding, campaign management, SEO, Google Ads, landing pages, social media management, and more. The reality of today’s marketing world is that connecting the dots between all of these pieces is the hard part, and is necessary if you want to drive higher quality outputs and outcomes. 

This isn't generic outsourcing. The age of "send it to the cheapest vendor" is over. Success belongs to companies that treat outsourced GTM vendors as strategic growth partners. The good ones bring proven frameworks so you don't waste months testing bad messaging and wrong channels.

GTM strategy requires rigorous work. It's time-consuming. Outsourcing it helps you beat competitors while saving costs. With the right partner, you get top-notch results without building the entire capability in-house.

What About Quality?

This is where most objections surface. "Can an outsourced team really understand our business like an in-house team would?"

Agencies are already proficient in the strategies and tools needed to run campaigns. This enables them to find the most cost-effective solution that delivers the highest revenue regardless of industry. An in-house team needs months learning the ropes. Marketing outsourcing companies pinpoint problems and provide solutions immediately.

GTM providers working with multiple clients develop pattern recognition that single companies never achieve. They've seen what works across industries, verticals, and customer segments. That institutional knowledge is worth more than having someone sit in your office.

The Three Options in Front of You

You can build in-house, hire a traditional agency, or partner with a specialized GTM firm.

You can build in-house, hire a traditional marketing agency, or partner with a specialized GTM firm.

01 Building in-house is the most expensive and risky path. You're betting your company can compete with specialized firms that do this all day, every day. You need AI talent at premium rates, infrastructure from scratch, and 6-12 months before you see results. Most companies get it wrong the first time and have to start over.

02 Hiring a traditional marketing agency feels safe but comes with hidden costs. You're paying for billable hours in a world where AI has collapsed delivery time by 50-80%. Their economics are broken, and they're struggling to transform without cannibalizing their core business. You get overhead without the outcomes.

03 Partnering with a specialized GTM firm gives you speed, expertise, and flexibility without the capital commitment. These firms have already made the AI transition. They know which tools work, how to structure teams, and how to deliver outcomes at scale. You skip the trial-and-error phase entirely and focus on what you do best while experts handle the growth engine.

Doing nothing is always the easy fallback option, but it’s a false choice. It just means watching competitors move faster while your costs stay fixed. DThe companies making changes today aren’t just cutting costs — they're restructuring around a new operating model where AI and specialized partners replace generalist teams.

The Choice is Yours

This isn't about AI replacing humans. It's about companies choosing the most effective delivery model for outcomes their customers actually want to pay for.

Traditional marketing departments and agencies are expensive, slow, and struggling to adapt. Specialized GTM firms offering a full outsourced option have already adapted. They're AI-first, outcome-focused, and built for the economics of today.

The question isn't whether to outsource. It's whether you can afford not to.

Smart companies are making the shift now, before their competitors do. The ones waiting to see how this plays out will be the case studies about what happens when you move too late.

Which side of that equation do you want to be on?

Ready to explore an outsourced GTM partnership? The firms winning in 2025 aren't the ones with the biggest teams — they're the ones with the smartest operating models, and can start next week.

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Frequently Asked Questions

Q: What is outsourced GTM?

A: Outsourced go-to-market (GTM) refers to specialized firms that provide comprehensive marketing and sales execution services under your brand, handling everything from strategy to execution without requiring you to build internal capabilities or hire full teams.

Q: Can you outsource marketing effectively?

A: Yes. Companies now outsource marketing to specialized partners who deliver faster results at lower costs than in-house teams, with average savings of $10,000-$25,000 monthly for small operations and significantly more for enterprise companies.

Q: What does outsourced content mean?

A: Outsourced content is professionally created marketing material (articles, campaigns, ads, landing pages) produced by external specialists but branded and published as your own work, giving you expert output without building content teams internally.

Q: Is outsourcing GTM strategy worth it?

A: Outsourcing GTM strategy makes sense when specialized partners bring proven frameworks and cross-industry pattern recognition that internal teams take months to develop, letting you beat competitors while reducing operational costs by 40-60%.

Q: How much does it cost to build an in-house marketing team?

A: A marketing leader alone costs $162,000-$298,000 annually with benefits and overhead, while a full marketing team can run $500,000-$1 million before launching a single campaign, not counting the 6-12 month ramp-up time.

Q: What's the difference between traditional agencies and outsourced GTM firms?

A: Traditional agencies bill by the hour and struggle with AI transformation, while outsourced GTM firms are AI-first operations built for outcome-based pricing, delivering what used to require 20-person teams with 3-5 specialists and smart automation.

Q: What marketing functions can be outsourced?

A: Full-stack outsourced GTM Team can handle SEO, Google Ads, Meta campaigns, landing page design, social media management, content creation, demand generation, and complete GTM strategy under one partnership instead of juggling 4-5 separate vendors. Depending on the vendor, some can also work downstream to offer a full revenue operations solution.

Q: How long does it take to see results from and outsourced GTM partnership?

A: Outsourced GTM firms deliver immediate results because they bring proven strategies and tools, eliminating the months of learning curve that in-house teams require, with many companies seeing measurable impact within the first 30-60 days.

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