Stop Asking AI to 'Write a Blog Post.' Start Building a Content Engine.
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Stop Asking AI to 'Write a Blog Post.' Start Building a Content Engine.
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Is your content team feeling the pressure? You’re being asked to create more relevant, higher-quality content, faster than ever. So, you turn to AI. But if your strategy is limited to asking a chatbot to “write a blog post about X,” you’re not just missing the point. You’re falling behind.
The real power of AI isn’t in writing a single piece of content. It’s in building a systematic, scalable engine for content production. It’s about moving from individual acts of creation to a unified, automated workflow that delivers quality content at a volume you previously couldn't manage.
In our latest podcast episode, we spoke with James Lamberti, Head of Go-to-Market at the investment firm Georgian, and Brian Schmid, Inverta's AI Practice Lead. They detailed a playbook that has helped companies achieve a 3-4x improvement in content volume without sacrificing quality. It’s not about better prompts. It’s about better systems.
"The goal is not automation. The goal is quality content at scale." - Brian Schmid, AI Practice Lead, Inverta
Build your content production playbook
A production-level AI strategy moves beyond individual heroics. It requires a shared, trained, and routine effort to produce high-quality content. James Lamberti’s team at Georgian developed a playbook that treats content creation less like art and more like engineering. It consists of three core components:
- A Workflow Capability: This is the assembly line for your content. It’s the documented process that outlines every step from idea to publication. While technical tools like N8N or more user-friendly ones like Airflow can automate this, the first step is simply to map out the process manually. What are the steps? Who is involved? Where are the handoffs? You must define the workflow before you can automate it.
- AI Agents: These are specialized AI assistants trained to perform specific tasks within your workflow. Instead of one general-purpose tool, you deploy a team of agents. For example, you might create a Branding Agent trained on your style guide, a Persona Agent that adapts content for different buyers, or even a Compliance Agent to check for regulatory issues. These agents ensure consistency and quality at every step.
- Data Inputs: Your AI engine is only as good as the fuel you give it. This means pointing your agents to the right sources. This isn’t about scraping the open web. It's about leveraging your own hard-won knowledge—your existing white papers, case studies, product documentation, and brand guidelines, even if they live in disparate locations like Google Drive or a PDF. This is what makes the content truly yours.
Start manually to perfect your process
The most common mistake teams make is rushing to automate a broken process. Automating bad prompts just gets you bad content, faster.
The first version of your playbook should be entirely manual. This approach forces your team to test and refine the prompts for each agent, ensuring you get the desired output every time. It’s in this phase you learn what works. Does the Persona Agent truly capture the voice of your champion buyer? Does the Branding Agent correctly apply your tone?
This manual stage builds the foundation for reliable automation. It ensures that when you do connect your systems, you are scaling a process you can trust. It’s also where you identify where humans must remain in the loop—because even the most advanced system needs an editor to ensure the final product doesn’t sound like a robot.
Find or create a content engineer
This new approach requires a new kind of role: the content engineer.
A content engineer is a blend of a traditional content marketer and a marketing operations specialist. They understand what makes content compelling, but they also think in terms of systems, data inputs, and automated workflows. They can look at a brand style guide in a PDF and see it not as a static document, but as structured data that can train an AI agent.
This person is responsible for designing, building, and maintaining the content engine. They work with the content team to templatize outputs, with product marketing to curate the best internal data sources, and with RevOps to integrate external buyer signals. This role can be upskilled from your existing team—look for people in content marketing, product marketing, or RevOps who have a knack for process and systems thinking.
A content engineer understands what good content looks like, but also how data inputs, outputs, and triggers work.
Fuel your engine with signals, not just keywords
Once your engine is running, you can move beyond producing content based on a static editorial calendar. The next level of maturity is creating signal-based content.
Signals are a broad category of buyer activities that tell you what an account needs right now. This includes traditional intent data, but it’s much more than that:
- Are they posting jobs for a new type of role?
- Did they just acquire another company?
- Does their latest 10-K report signal a strategic shift?
Your content engine can be configured to react to these signals. When a target account shows interest in a specific topic, the system can automatically generate a relevant blog post, adapt it to the right persona, and queue it for review. This allows you to get your message in front of buyers at the exact moment they are most receptive, making you part of their consideration set well before they ever reach out.
Where to start
Building a content engine may seem daunting, but the first step is simple. Pick one common, repeatable content format. A blog post is often the perfect starting point. It’s substantial enough to require a process but discrete enough to be manageable.
- Document the current process for creating a blog post.
- Templatize the output. Define the structure every post should follow.
- Build your agents manually in a tool like ChatGPT or Gemini. Start with a "Branding Agent" and a "Persona Agent."
- Curate your data. Point the agents to 3-5 of your best-performing white papers or case studies.
- Run the process manually for a few weeks. Refine, test, and improve.
By focusing on the system, not just the output, you can finally meet the demand for more content without compromising the quality that makes it effective.
Ready to build your own content engine? Listen to Rachel's full conversation with James and Brian for a deeper dive.
About the author
Service page feature
The RevRoom podcast
Is your content team feeling the pressure? You’re being asked to create more relevant, higher-quality content, faster than ever. So, you turn to AI. But if your strategy is limited to asking a chatbot to “write a blog post about X,” you’re not just missing the point. You’re falling behind.
The real power of AI isn’t in writing a single piece of content. It’s in building a systematic, scalable engine for content production. It’s about moving from individual acts of creation to a unified, automated workflow that delivers quality content at a volume you previously couldn't manage.
In our latest podcast episode, we spoke with James Lamberti, Head of Go-to-Market at the investment firm Georgian, and Brian Schmid, Inverta's AI Practice Lead. They detailed a playbook that has helped companies achieve a 3-4x improvement in content volume without sacrificing quality. It’s not about better prompts. It’s about better systems.
"The goal is not automation. The goal is quality content at scale." - Brian Schmid, AI Practice Lead, Inverta
Build your content production playbook
A production-level AI strategy moves beyond individual heroics. It requires a shared, trained, and routine effort to produce high-quality content. James Lamberti’s team at Georgian developed a playbook that treats content creation less like art and more like engineering. It consists of three core components:
- A Workflow Capability: This is the assembly line for your content. It’s the documented process that outlines every step from idea to publication. While technical tools like N8N or more user-friendly ones like Airflow can automate this, the first step is simply to map out the process manually. What are the steps? Who is involved? Where are the handoffs? You must define the workflow before you can automate it.
- AI Agents: These are specialized AI assistants trained to perform specific tasks within your workflow. Instead of one general-purpose tool, you deploy a team of agents. For example, you might create a Branding Agent trained on your style guide, a Persona Agent that adapts content for different buyers, or even a Compliance Agent to check for regulatory issues. These agents ensure consistency and quality at every step.
- Data Inputs: Your AI engine is only as good as the fuel you give it. This means pointing your agents to the right sources. This isn’t about scraping the open web. It's about leveraging your own hard-won knowledge—your existing white papers, case studies, product documentation, and brand guidelines, even if they live in disparate locations like Google Drive or a PDF. This is what makes the content truly yours.
Start manually to perfect your process
The most common mistake teams make is rushing to automate a broken process. Automating bad prompts just gets you bad content, faster.
The first version of your playbook should be entirely manual. This approach forces your team to test and refine the prompts for each agent, ensuring you get the desired output every time. It’s in this phase you learn what works. Does the Persona Agent truly capture the voice of your champion buyer? Does the Branding Agent correctly apply your tone?
This manual stage builds the foundation for reliable automation. It ensures that when you do connect your systems, you are scaling a process you can trust. It’s also where you identify where humans must remain in the loop—because even the most advanced system needs an editor to ensure the final product doesn’t sound like a robot.
Find or create a content engineer
This new approach requires a new kind of role: the content engineer.
A content engineer is a blend of a traditional content marketer and a marketing operations specialist. They understand what makes content compelling, but they also think in terms of systems, data inputs, and automated workflows. They can look at a brand style guide in a PDF and see it not as a static document, but as structured data that can train an AI agent.
This person is responsible for designing, building, and maintaining the content engine. They work with the content team to templatize outputs, with product marketing to curate the best internal data sources, and with RevOps to integrate external buyer signals. This role can be upskilled from your existing team—look for people in content marketing, product marketing, or RevOps who have a knack for process and systems thinking.
A content engineer understands what good content looks like, but also how data inputs, outputs, and triggers work.
Fuel your engine with signals, not just keywords
Once your engine is running, you can move beyond producing content based on a static editorial calendar. The next level of maturity is creating signal-based content.
Signals are a broad category of buyer activities that tell you what an account needs right now. This includes traditional intent data, but it’s much more than that:
- Are they posting jobs for a new type of role?
- Did they just acquire another company?
- Does their latest 10-K report signal a strategic shift?
Your content engine can be configured to react to these signals. When a target account shows interest in a specific topic, the system can automatically generate a relevant blog post, adapt it to the right persona, and queue it for review. This allows you to get your message in front of buyers at the exact moment they are most receptive, making you part of their consideration set well before they ever reach out.
Where to start
Building a content engine may seem daunting, but the first step is simple. Pick one common, repeatable content format. A blog post is often the perfect starting point. It’s substantial enough to require a process but discrete enough to be manageable.
- Document the current process for creating a blog post.
- Templatize the output. Define the structure every post should follow.
- Build your agents manually in a tool like ChatGPT or Gemini. Start with a "Branding Agent" and a "Persona Agent."
- Curate your data. Point the agents to 3-5 of your best-performing white papers or case studies.
- Run the process manually for a few weeks. Refine, test, and improve.
By focusing on the system, not just the output, you can finally meet the demand for more content without compromising the quality that makes it effective.
Ready to build your own content engine? Listen to Rachel's full conversation with James and Brian for a deeper dive.
Resources
About the author
Service page feature
The RevRoom podcast
Stop Asking AI to 'Write a Blog Post.' Start Building a Content Engine.
Speakers
Other Helpful Resources
Is your content team feeling the pressure? You’re being asked to create more relevant, higher-quality content, faster than ever. So, you turn to AI. But if your strategy is limited to asking a chatbot to “write a blog post about X,” you’re not just missing the point. You’re falling behind.
The real power of AI isn’t in writing a single piece of content. It’s in building a systematic, scalable engine for content production. It’s about moving from individual acts of creation to a unified, automated workflow that delivers quality content at a volume you previously couldn't manage.
In our latest podcast episode, we spoke with James Lamberti, Head of Go-to-Market at the investment firm Georgian, and Brian Schmid, Inverta's AI Practice Lead. They detailed a playbook that has helped companies achieve a 3-4x improvement in content volume without sacrificing quality. It’s not about better prompts. It’s about better systems.
"The goal is not automation. The goal is quality content at scale." - Brian Schmid, AI Practice Lead, Inverta
Build your content production playbook
A production-level AI strategy moves beyond individual heroics. It requires a shared, trained, and routine effort to produce high-quality content. James Lamberti’s team at Georgian developed a playbook that treats content creation less like art and more like engineering. It consists of three core components:
- A Workflow Capability: This is the assembly line for your content. It’s the documented process that outlines every step from idea to publication. While technical tools like N8N or more user-friendly ones like Airflow can automate this, the first step is simply to map out the process manually. What are the steps? Who is involved? Where are the handoffs? You must define the workflow before you can automate it.
- AI Agents: These are specialized AI assistants trained to perform specific tasks within your workflow. Instead of one general-purpose tool, you deploy a team of agents. For example, you might create a Branding Agent trained on your style guide, a Persona Agent that adapts content for different buyers, or even a Compliance Agent to check for regulatory issues. These agents ensure consistency and quality at every step.
- Data Inputs: Your AI engine is only as good as the fuel you give it. This means pointing your agents to the right sources. This isn’t about scraping the open web. It's about leveraging your own hard-won knowledge—your existing white papers, case studies, product documentation, and brand guidelines, even if they live in disparate locations like Google Drive or a PDF. This is what makes the content truly yours.
Start manually to perfect your process
The most common mistake teams make is rushing to automate a broken process. Automating bad prompts just gets you bad content, faster.
The first version of your playbook should be entirely manual. This approach forces your team to test and refine the prompts for each agent, ensuring you get the desired output every time. It’s in this phase you learn what works. Does the Persona Agent truly capture the voice of your champion buyer? Does the Branding Agent correctly apply your tone?
This manual stage builds the foundation for reliable automation. It ensures that when you do connect your systems, you are scaling a process you can trust. It’s also where you identify where humans must remain in the loop—because even the most advanced system needs an editor to ensure the final product doesn’t sound like a robot.
Find or create a content engineer
This new approach requires a new kind of role: the content engineer.
A content engineer is a blend of a traditional content marketer and a marketing operations specialist. They understand what makes content compelling, but they also think in terms of systems, data inputs, and automated workflows. They can look at a brand style guide in a PDF and see it not as a static document, but as structured data that can train an AI agent.
This person is responsible for designing, building, and maintaining the content engine. They work with the content team to templatize outputs, with product marketing to curate the best internal data sources, and with RevOps to integrate external buyer signals. This role can be upskilled from your existing team—look for people in content marketing, product marketing, or RevOps who have a knack for process and systems thinking.
A content engineer understands what good content looks like, but also how data inputs, outputs, and triggers work.
Fuel your engine with signals, not just keywords
Once your engine is running, you can move beyond producing content based on a static editorial calendar. The next level of maturity is creating signal-based content.
Signals are a broad category of buyer activities that tell you what an account needs right now. This includes traditional intent data, but it’s much more than that:
- Are they posting jobs for a new type of role?
- Did they just acquire another company?
- Does their latest 10-K report signal a strategic shift?
Your content engine can be configured to react to these signals. When a target account shows interest in a specific topic, the system can automatically generate a relevant blog post, adapt it to the right persona, and queue it for review. This allows you to get your message in front of buyers at the exact moment they are most receptive, making you part of their consideration set well before they ever reach out.
Where to start
Building a content engine may seem daunting, but the first step is simple. Pick one common, repeatable content format. A blog post is often the perfect starting point. It’s substantial enough to require a process but discrete enough to be manageable.
- Document the current process for creating a blog post.
- Templatize the output. Define the structure every post should follow.
- Build your agents manually in a tool like ChatGPT or Gemini. Start with a "Branding Agent" and a "Persona Agent."
- Curate your data. Point the agents to 3-5 of your best-performing white papers or case studies.
- Run the process manually for a few weeks. Refine, test, and improve.
By focusing on the system, not just the output, you can finally meet the demand for more content without compromising the quality that makes it effective.
Ready to build your own content engine? Listen to Rachel's full conversation with James and Brian for a deeper dive.
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