Go-to-market leaders adopt AI for content creation because their team is stretched thin, because competitors are moving fast, and because the pressure to produce more never stops. The velocity feels great for a short time. Then something shifts.
Prospects mention emails that feel automated. Sales complains marketing content doesn’t match what they’re saying on the front lines. Customer success uses a different language than the product team. The brand voice that once was distinct, now starts to feel diluted, generic, and like everyone else.
The result: Companies that treat AI like an infrastructure change versus a content tool are setting a new precedent. They are not only protecting their brand, but they’re also building a competitive competency.
The Buying Reality You Can’t Ignore
According to 6sense’s 2024 Buyer Experience Report, buyers already know 3.5 out of their 4.5 vendor options on day one. They’re 69% through their purchase decision before reaching out to you. And 81% of the time, they already have a vendor favorite before talking to sales.
Translation: Your brand is winning or losing deals before your sales team ever gets involved.
The top factors influencing these decisions include vendor reputation and brand, customer service experience, case studies, and thought leadership content. Every single one of these touchpoints is increasingly influenced by AI-generated content.
The companies pulling ahead are building systems to protect brand voice while also scaling output.
Research from Salsify shows that 87% of shoppers will pay more for brands they trust in 2025. When AI-generated content feels off-brand or inconsistent across touchpoints, you’re creating a quality issue while simultaneously eroding the trust that drives revenue.
Why GTM Leaders Now Own AI Brand Governance
AI governance is rapidly becoming a board-level conversation because the risk compounds across every go-to-market function. I see a clear competitive advantage for early adopters by establishing guardrails to protect brand integrity. They’re shipping more content, maintaining higher quality, and building stronger brand recognition. They’re winning deals in the silent 69% of the buying process. And they’re using strategic governance as an enablement tool so all teams can move faster.
The longer you wait to address this systematically, the wider that gap becomes.
The Value of AI In Practice
Industry data and my experience in leading marketing and GTM functions suggests that marketing teams spend significant time on repetitive content work like drafting multi-channel versions, running review cycles, and editing for brand inconsistencies.
Here’s what I’ve seen when teams implement proper AI governance:
- Review cycles drop from 2-3 weeks to under a week; a 60% reduction based on data from one CMO and team I worked with directly.
- Content teams that previously covered 2-3 channels per asset are now covering 8-10 channels.
- New hires ramp faster because they have 24/7 access to brand coaching instead of bottlenecking senior leaders.
That extra capacity is immediately reinvested into high-level strategy.
Automate the repetitive, low-value work. Preserve high-value human judgment. Protect brand voice. This is how scale becomes a competitive advantage instead of a quality risk.
The Three-Part Infrastructure That Works
Moving strategy into practice, here’s a framework I’ve implemented with B2B teams from early-stage startups to established enterprises:
>> Infrastructure Layer 1: Context Documents That Make AI Useful
Most teams hand AI a 3-page brand guide written for humans and wonder why the output is generic. The companies leading this game build AI-native documentation.
To do this, collect 10-15 samples of your best on-brand marketing emails, website copy, sales scripts, and product descriptions. Then use an AI LLM like ChatGPT, Claude or Gemini to build a brand guide specifically for machine consumption. Ask the LLM to extract voice, tone, style, formatting preferences, and, critically, what to avoid.
Similarly, ask the LLM to format the guide for machine readability (Markdown for ChatGPT/Gemini/Copilot, XML for Claude) and deploy it as a single source of truth across every AI tool your teams use.
Almost immediately, teams reduce executions that sound off-brand as AI has proper context documents to work from.
Tip: Your AI documentation should be treated as a living asset. The first version will create quick improvements, but it will require adjustments. Establish a process to evaluate outputs for quality and expect to refine your documentation based on that feedback. This iterative process, often called ‘evals,’ is what turns a good AI system into a great one.
>> Infrastructure Layer 2: Automate Quality Checks Before Human Review
The bottleneck that kills velocity usually appears in review.
Someone writes AI-assisted content. It goes to a manager for review. The manager sends it back with feedback. The writer revises. The manager reviews again. Maybe a VP approves. Three weeks later, it ships.
The best teams are building “brand voice guardians,” or AI assistants that live in Slack or Teams that review content against brand guidelines in real-time.
Here’s how it works in practice: A team member drops their content draft into Slack. The assistant reviews it against the brand samples and guidelines, then returns immediate feedback with detail, like:
- “FAIL – This email is too corporate for the suggested audience. You’re using passive voice in 3 places. Your brand voice is conversational and uses contractions. Here’s what to fix…”
- Or: “PASS – Great work. Voice is on-brand, formatting matches guidelines, tone is appropriate for this audience.”
This prevents senior leaders from spending hours catching basic voice inconsistencies so they can have the time to focus on strategic edits.
One CMO told us her team went from 2-3 week review processes to less than a week. With an automated brand voice guardian, teams can ship more content, faster, with better brand consistency.
>> Infrastructure Layer 3: Content Repurposing Workflows That Protect Brand at Scale
This is where you unlock the compounding advantage.
Most teams manually adapt content for different channels. Leading teams are building content repurposing systems that take source content like a webinar transcript, podcast script, case study, etc. and automatically generate channel-specific versions while maintaining brand voice.
Using tools like Cassidy, Zapier, n8n, Make, etc., for these workflows, they can:
- Extract key points from source content
- Generate content briefs for each channel
- Route those briefs through your brand voice guardian
- Output first drafts for each chosen channel and asset, like a LinkedIn post, or email series, etc.
- Execute content in the correct channel formats and in brand voice
Your content team is then repositioned to do what humans do best—to be creative, strategic, and create original work. Instead of spending 3 days reformatting a webinar into 10 assets, they spend 3 hours adding strategic polish and creating the next original piece.
Why The Window For Action Is Now
The companies implementing AI governance systems today are establishing advantages that compound over time. They’re producing more content, maintaining brand differentiation, closing deals faster with consistent experiences, and building institutional knowledge that’s hard to replicate.
If your competitor built these systems six months ago, they’ve had six months to:
- Compound brand consistency gains
- Institutionalize team learning and system refinement
- Expand content reach across channels
- Refocus senior talent on strategy
That’s not necessarily an insurmountable lead, but it’s a real one, and every day, month or quarter that passes, the adopters are extending their advantage and multiplying their learnings.
The Implementation Reality (And How to Start Smart)
Successfully implementing AI workflows is not about turning every marketer into a developer, nor is it solely the domain of a central operations team. The most successful companies foster two distinct but related skillsets:
- Foundational AI Literacy For Everyone: Your GTM teams—the writers, sellers, and support representatives—should be empowered to build simple, task-specific AI assistants. Using no-code tools or custom GPTs to create a “brand voice checker” or a “headline generator” can easily become a core competency. This gives each of these functions and individuals immediate leverage and ownership.
- System Architecture For Specialists: Building robust, cross-functional repurposing workflows (Infrastructure Layer 3) is a different challenge. This is where operational talent in Marketing Ops, RevOps, or a dedicated AI enablement role, becomes the critical partner. They are architects who connect the systems, ensure data integrity, and build the automations that the entire team or organization can use.
The goal is partnership, not silos. You want content creators to be skilled users and builders of their own tools, while your operations specialists provide the secure, scalable infrastructure for them to work within.
This can’t be solved with a plug-and-play tool purchase, you need to think of this as an infrastructure shift across the organization. And organizations getting a head start on this type of adoption are using a phased approach based on their maturity. Here is what a potential rollout can look like:
If you’re just starting (no formal AI governance yet):
- Week 1: Create AI-ready brand voice documentation
- Week 2: Deploy to all AI tools your team currently uses
- Week 3: Measure improvement in consistency and “off-brand” feedback
- Expected outcome: Immediate improvement in AI output quality and consistency
If you’re using AI but inconsistently:
- Week 1: Build a basic brand voice checker, even just a custom GPT
- Week 2: Test on recent AI-generated content and look for basic flaws
- Week 3: Deploy in Slack/Teams for team-wide access
- Expected outcome: Measurable reduction in review cycles, elevated content across teams
If you’re ready to scale systematically:
- Month 1: Implement all three infrastructure layers
- Month 2: Train teams and refine based on feedback
- Month 3: Measure capacity gains and expand to new channels
- Expected outcome: 2-3x content output with improved brand consistency
Tip: Start before you feel ready. The advantage goes to teams who treat this as infrastructure to continuously improve, not just a nice-to-have.
The Decision You Need To Make
You can wait and see how AI content governance plays out. You can debate whether it’s truly necessary. You can convince yourself you have time to figure this out later.
Or you can recognize that AI governance infrastructure is becoming a competitive differentiator.
The companies that are leading their categories are the ones treating AI governance as infrastructure. They are building systems that protect brand while enabling scale—they’re establishing processes that ensure quality while increasing velocity, and creating institutional knowledge.
Trust is the key to every transaction. Your teams are already using AI. The time to build systematic governance and gain a competitive advantage with them is now.
_____________________________________________
Julie’s workshop ‘AI and Automation – Free up time for High-Impact Work’, was part of the Women in Revenue (Members-Only) Mini-Series: 4 Ways Leading Women are Harnessing AI to Drive Go-To-Market Impact.
Julie Scotland is an AI advisor with 20+ years of leading rapidly growing go-to-market teams. You can connect with Julie and learn more about Gravi.ai at julie@gravi.ai
To gain access to all Women in Revenue events and resources sign up to be a member here.
