Hello go-to-market (GTM) leaders, strategists, and innovators! 👋 Thank you for dropping by to learn practical AI applications and gain strategic insights to help you grow your business and elevate your teams strategic value.
Quick Take
Last year, companies invested an estimated $35-40 billion in AI. Budget and tools arent the barrier to AI-driven results. The difference lies in approach: some buy tools and hope for transformation; others design connected systems and measure impact from day one.
A global leader in cybersecurity is a case in point. For years, this company has applied AI to stay ahead of fast-moving threats, from advanced threat intelligence to innovation across its security portfolio. Now, that same mindset is redefining how they market, communicate, and operate.
Rather than bolting AI onto existing processes and technology, this company redesigned its marketing workflows from the ground up, building systems where people and AI collaborate side by side. The result: fast execution, higher quality output, and measurable business impact across every stage of the go-to-market engine.
In less than six months, the marketing organization trained 75 trailblazers and embedded 57 custom GPTs into daily work—all under a model grounded in responsible AI principles, with human review and validation at every step.
Security was foundational to their AI deployment. The team partnered closely with its CISO and security teams to ensure every system was deployed safely and aligned with company security standards. They jointly reviewed proposed workflows and partnered together during onboarding.
The company selected ChatGPT Enterprise to ensure company data remains private (never shared for model training or external use), allowing employees to experiment confidently within a secure environment.
Key insights that separate successful companies from those that struggle:
- Shift from tool adoption to work redesign – Treat AI as teammates that change how work gets done, not productivity software you bolt onto existing processes
- Create enablement systems, not just tools – Design workshops, office hours, and spaces for teams to experiment and share what works
- Design connected workflows, not isolated productivity hacks – Link AI teammates and platforms together in systematic processes
- Partner with security from day one – Work closely with CISO and security teams to review workflows, ensure safe deployment, and select platforms that protect company data
- Measure business impact from day one – Track efficiency gains, quality improvements, and new capabilities (not just tool usage)
This shift created a disciplined framework for scaling responsible AI use, strengthening human creativity instead of automating it away.
AI Video Explainer and AI Podcast Versions of This Newsletter
To support different learning styles, this newsletter is available as an 7-min AI video explainer (see below) and a 16-min AI podcast with two AI hosts. If you haven’t seen these AIs in action, they’re worth a view. The tech is advancing in amazing ways. I used Google’s NotebookLM to create these and personally reviewed them for accuracy and responsible AI use.
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Their Human + AI-Assisted Marketing Org
Here’s what this transformation looks like in practice. They redesigned their marketing org chart to include AI teammates that work alongside humans:
New to building human + AI teams? Check out A Leaders Playbook: How a Lean Team Transformed Into a Human + AI Powerhouse for step-by-step guidance for getting started.
This approach goes beyond layering AI onto existing workflows. The redesigned operating model blends human creativity with AI efficiency. The company architected a new model collaboration where human creativity and AI capabilities are interwoven by design:
- 75 trailblazers across marketing functions driving adoption
- 211 AI teammates (i.e., custom GPTs) created during experimentation, refined to 57 now embedded into regular daily workflow
- Marketings proven results helped support the decision to provide enterprise AI access to employees across the organization
The difference compared with companies that see zero return? They built a repeatable framework for human + AI teaming, from experimentation to scale. Each AI teammate has a clear role, owner, and oversight process, and every AI workflow is transparent and auditable, ensuring quality and compliance across global markets.
Stop Random Acts of AI
Their journey began with a single insight: transformation doesn’t happen by adding AI licenses. It happens by re-architecting how people learn and collaborate.
This began with strategic experimentation. The 75 trailblazers, or early adopters, across marketing drove the use of AI, focusing on high-value use cases that showed clear, measurable impact.
But identifying the right people was only the first step. Through my work with them, we created space for teams to learn, experiment, and build confidence. Deploying AI tools without equipping people to use them is where most companies waste money. We focused on inspiring responsible use, teaching practical skills, and embedding AI into real work. They didn’t just adopt AI. They built a human + AI system that works.
Our enablement approach gave people permission to try things and places to share, including:
- Foundational AI workshops to understand how customer journeys and behavior are changing because of AI and how to better serve them with AI teammates working alongside humans
- Applied AI sessions where people saw how to use AI in key use cases relevant to their function plus hands-on exercises where they built their first AI teammates using custom GPTs
- AI automation and agent workshops to help build AI-infused workflows for Marketing Ops
- A dedicated Teams channel for sharing what works and what doesn’t
- Weekly office hours to get help to move from from simple questions to building real solutions that scale
- Show-and-tell sessions where teams shared successes and inspired each other
Within 6 months, we saw widespread adoption across marketing and compelling business results (see “Measure What Matters”). The team continues to transform and scale across the organization.
When early duplication emerged (teams building similar GPTs in isolation), the team responded with better coordination.
The VP of Marketing Operations, built a three-pillar measurement framework to track their progress. We measured business focus (efficiency gains), people focus (satisfaction and skill development), and differentiation focus (market positioning through AI search optimization).
She shared her key insight:
This approach reflects the company’s people-first, AI-forward philosophy. The training and enablement focused on investing in their teams future capabilities. By teaching employees to work alongside AI, They positioned their workforce to add more value, stay competitive, and thrive in an AI-driven workplace.
Build Systems, Not Tools
Most companies get stuck at Phase 1 where they primarily use AI as tools for individual tasks. They never progress to Phase 2 (guiding AI as teammates) or Phase 3 (orchestrating AI workflows). I covered this topic in detail in one of my previous newsletters titled: AI Is Redefining GTM Jobs: From Tool Users to Teammates to Orchestrators
The team built what they call an AI Network. Its a layered architecture where different types of AI teammates have clear roles and can work together alongside humans.
Without a system to organize AI teammates, chaos happens fast. Imagine three different teams each building their own content optimizer GPT because nobody knows the others exist. Or someone spending an hour trying to figure out which of 50 GPTs to use for a simple task. This is where most companies hit a wall.
Below is the AI Network with sample elements at each layer: AI teammates/GPTs (circles), specialized AI platforms (diamonds), human work (pentagons, trapezoids, and squares).
Foundation Layer: Core AI teammates that establish organizational knowledge and brand voice
- Brand GPT (maintains consistent messaging across all content)
- Audience GPT (ensures content targets the right buyer personas)
- Compliance GPT (keeps content aligned with legal and regulatory requirements)
Story/Campaign Layer: Specialized GPTs for strategic content and campaign development
- Campaign GPT (develops integrated campaign strategies)
Domain Layer: Function-specific tools that understand product nuances
- Product GPT (specialized for product content)
Asset Layer: Content creation specialists for specific formats
- Blog Post GPT, Email GPT, Social GPT, Event GPT (each optimized for their formats unique requirements)
QA Layer: Quality assurance and optimization specialists
- SEO GPT (optimizes content for search visibility)
- Content Relevance GPT (ensures accuracy and message alignment)
- Localization GPT (adapts content for global markets)
Human in the Loop: Strategic oversight and approval processes
- Reviews, approvals, and strategic direction at key decision points
Automation Layer: Specialized AI tools and automation/integration tools needed to support end-to-end workflows
- Platform integrations that create seamless handoffs between AI teammates and AI business systems like CRM, marketing automation, AI SDR, intent and enrichment platforms.
Most companies focus on individual AI tools when they should be designing AI ecosystems. But this company understood that sustainable AI advantage comes from connected workflows, not isolated productivity hacks. This is why their approach scales while others plateau.
This is also an example of governance in action. The architecture determines whether a task needs a specialized GPT, can be handled by existing tools, or requires a combination with human oversight and support.
Measure What Matters
The company chose a hybrid approach. They use ChatGPT Enterprise as their foundation but build custom workflows that integrate with their existing tech stack. The key is measurement at every level.
Three workflows show how this plays out:
1. SDR Transformation – The SDR team built custom GPT chains that turn company profiles into personalized Outreach campaigns. Instead of generic sequences, each prospect gets messages tailored to their role, company context, and industry challenges. The system even helps SDRs prepare for calls by analyzing tech used and suggesting conversation angles.
Here are some early results using an innovative workflow:
- Time saved: 1-3 hours per day per rep
- Quality improvement: 2-3x better open rates (15% to 40%)
- Scale impact: 100+ hours saved per week across the team
Note in the workflow below that the team applies critical thinking throughout, ensuring that the humans still evaluate tradeoffs and make the final decisions. If interested, here’s a guide on critical thinking with AI including real-life examples.
The SDR operations leader in the Americas region, not only made his team more productive, he also helped them improve the quality of the work and reimagined how work is done. He said:
2. Enterprise Content Creation – The Americas Marketing team automated deep research for enterprise content creation. What used to take 10+ hours of manual research now takes minutes. The AI pulls company information, analyzes competitors, and suggests positioning angles specific to enterprise buyer concerns. The end-to-end workflow is shown below:
While the full automated workflow is still being built, the Enterprise Audience GPT is already in wide use. The Head of Americas Marketing, shares the impact:
3. AI Search Strategy – While other companies optimize for Google rankings, the company optimizes for AI-powered search results. They create content specifically designed to be cited by ChatGPT, Perplexity, and other AI systems. As a result, their Managed Detection and Response (MDR) solution continues to climb in share of mentions across major AI search platforms.
The company used the approach I described in my newsletter titled Make Your Brand Sourced and a Top Result in AI Search: Practical Strategies for Marketers. They are also evaluating AI search tools from Profound and Scrunch AI.
Beyond efficiency wins, these are business model changes. The decision to provide enterprise AI access to all employees wasnt based on marketings enthusiasm. It was based on measurable results that proved systematic AI adoption works.
The team is betting that success in one function will create demand in others. Marketing is a key member of the companys AI Committee, working to become an internal catalyst for AI transformation. They’re sharing their success and best practices with other departments like Sales, Support, and Legal.
The Infrastructure Advantage
Here’s what makes them different: they built infrastructure, not just use cases. Think of it as an AI Supermarket.
A supermarket organizes everything so you can see what exists and where to find it. Produce here, dairy there, pasta down aisle five. But the store alone doesn’t make dinner. A recipe tells you what to grab and what order to combine it. The human acts as the chef, calling each ingredient at the right time.
The company built the same thing for AI:
- The Map (GPT Tracker) – A master list of every AI teammate in the org. It tracks each GPT’s purpose, owner, capabilities, and limitations. Think of it as inventory management for AI teammates. Without it, people build duplicates, waste time hunting, or default to the two GPTs they know instead of the ten that could help.
- The Directory (AI Navigator) – A custom GPT that knows all the other custom GPTs and can recommend which ones to use for specific tasks. Tell it what you’re making and it points you to exactly what you need. It can suggest a single GPT for simple needs, or recommend a sequence of GPTs that work together for complex workflows.
- The Recipes (Workflows) – Defined sequences for common tasks. Brand GPT lays the foundation, Campaign GPT builds strategy, Asset GPTs create content, QA GPTs polish before publish. The order is just as important as the ingredients.
- AI Triage System – Simple rules for deciding what to do with AI requests. If you need basic help (writing emails, simple research), handle it yourself with standard AI tools and custom GPTs. If you need complex automation or integrations, it goes to Marketing Ops for prioritization. They have the technical skills and tools to build it properly.
- Governance Structure – Marketing Ops is part of the companywide AI Committee where the company makes decisions about AI tools, policies, and budgets. This means their proven approach influences company-wide AI decisions instead of every department starting from scratch.
The Director of Marketing Data and Analytics, reflected on the importance of the AI triage system.
The Real Choice Every Company Faces
AI tools give you short-term help. Systems give you long-term advantage.
Companies that orchestrate human-in-the-loop AI systems will outpace those that just buy tools. Your competitors will figure this out eventually. The question is whether you’ll beat them to it.
Most companies ask what can AI do for us? The winners flip the question: how do we redesign work so humans and AI collaborate systematically? Heres what that looks like:
- Start with measurement, not tools. Know what success looks like before you buy anything. Track both time saved and new work that wasn’t possible before. Without ways to measure results, you wont be able to prove AI’s business value.
- Think systems, not solutions. Single AI tools help with productivity. Connected workflows create real change. Focus on building processes you can repeat rather than collecting random productivity tricks.
- Build structure for growth. Create simple rules that help teams know when to build, buy, or skip AI solutions rather than expecting everyone to become AI experts.
- Build real support systems for your teams. Run workshops on specific use cases, hold regular office hours for questions, and create spaces where people can share whats working. Intentional support drives faster adoption than leaving teams to figure it out themselves.
This systematic approach is more accessible than most expect. Companies dont need massive budgets, technical teams, or years of planning – just engaged people and clear frameworks. With the right systems, oversight, and culture, AI becomes a trusted teammate that helps people focus on strategy, creativity, and growth.
In a world where many organizations race to automate, this leading cybersecurity company stands out for building AI that elevates human work. It’s not just about faster output. It’s about smarter marketing, stronger governance, and a sustainable advantage for the business.
They chose transformation. The results speak for themselves.
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For applied learning :Explore our applied AI workshops, offering both strategic sessions (use cases and roadmaps) and hands-on building (create AI teammates and workflows during the workshop). You’ll leave with either a clear plan or working solutions.
For team transformation:See real examples—a lean GTM team’s step-by-step playbook and a global cybersecurity leader scaling to 150+ marketers with 57 AI teammates integrated into daily workflows.
For speaking: Here are virtual and in-person events where I’ve covered a variety of AI topics. I’ve also keynoted at many organization and corporate-wide events.
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