0:00
/
0:00

How We Built ChatGPT Enterprise's Sales Team from Absolute Zero: The Complete Playbook with Maggie Holt, Head of GTM

"We made a dramatic decision: unify both organizations into a single go-to-market team."

How We Built ChatGPT Enterprise's Sales Team from Absolute Zero: The Complete Playbook with Maggie Hott, GTM Leadership at OpenAI

About Maggie: Maggie Hott has spent 15 years building go-to-market teams at four unicorns that collectively represent over $50B in market value. She started as the 2nd SDR at Eventbrite, became the first sales hire at Slack (helping scale from $50M to $1B ARR and a $27B Salesforce acquisition), served as Director of Sales at Webflow (scaling from $40M to $140M ARR), and now leads go-to-market at OpenAI where she built ChatGPT Enterprise from scratch. She also runs a venture fund with seven other women investors, backing 30+ founders. These are her personal views, not those of OpenAI.


It was early 2023. OpenAI had just launched ChatGPT, the fastest-growing consumer app the world had ever seen. We were riding an incredible wave, but we had a critical hypothesis: ChatGPT Enterprise would require a fundamentally different go-to-market motion than our API business.

When OpenAI hired me to build ChatGPT Enterprise from scratch, I walked into what can only be described as a beautiful blank slate—and a terrifying challenge.

Our entire sales and go-to-market organization was less than 10 people. No SDRs. No solution consultants. No customer success managers. No sales operations. No RevOps. No marketing enablement. We didn't even have a working Salesforce instance.

What we did have: six incredibly talented account directors and one technical success partner, all laser-focused on selling our API to developers and technical teams.

Here's the complete playbook for how we built what we believe became the fastest-growing enterprise application in history.

The Build vs. Adapt Strategic Decision

Most companies in our position would have taken the "efficient" approach: enable the existing team to sell both products, maybe hire a few specialists, and gradually expand capabilities.

We chose the opposite path: build a dedicated ChatGPT Enterprise team from absolute zero.

This wasn't just about headcount. It was about creating an entirely separate organizational DNA optimized for enterprise selling.

Why We Built Separate vs. Adapted Existing

Product Complexity Was Fundamentally Different

  • API sales required deep technical conversations about integrations, rate limits, and model parameters

  • ChatGPT Enterprise needed business impact discussions about productivity, compliance, and organizational change management

  • The buyer personas didn't overlap—CTOs vs. CHROs, CFOs, and business unit leaders

Sales Cycles Had Different Rhythms

  • API deals often moved quickly with technical evaluation periods

  • Enterprise required lengthy security reviews, compliance discussions, and change management planning

  • Different stakeholders, different timelines, different objection patterns

Go-to-Market Motions Required Different Muscles

  • API was largely product-led with sales-assist for larger accounts

  • Enterprise needed traditional enterprise selling: demos, pilots, RFP responses, and executive alignment

Speed Trumped Efficiency in This Moment The market window was massive but narrow. Every week mattered. Having a dedicated team meant:

  • No competing priorities or split focus

  • Ability to move at startup speed even within a scaling company

  • Clear ownership and accountability for outcomes

Phase 1: Foundation Building (Months 1-3)

The First Three Critical Hires

Enterprise Account Executive #1: Financial Services Specialist

  • 8+ years selling enterprise software to banks and insurance companies

  • Deep understanding of compliance requirements (SOX, PCI, etc.)

  • Existing relationships with CISOs and risk management teams

  • Experience with 12+ month sales cycles and complex procurement processes

Enterprise Account Executive #2: Technology Sector Specialist

  • Background selling to high-growth tech companies

  • Understanding of developer tools and technical infrastructure

  • Experience with both startup buyers and enterprise technology teams

  • Ability to bridge technical and business conversations

Enterprise Account Executive #3: Healthcare/Life Sciences Specialist

  • Healthcare technology sales background

  • HIPAA compliance expertise

  • Relationships with healthcare CIOs and innovation teams

  • Understanding of clinical workflow integration challenges

Why Vertical Specialists First? Enterprise buyers expect deep industry knowledge. They want to know you understand their specific compliance requirements, regulatory challenges, and business context. Hiring generalists would have slowed our credibility-building process by months.

Building the Foundational Systems

Customer Qualification Framework We couldn't use our API qualification criteria. Enterprise buyers had different needs:

  • Company Size: 1,000+ employees (later expanded down to 500+)

  • Budget Authority: Direct access to decision-makers with budget

  • Use Case Clarity: Specific productivity or efficiency goals

  • Security Readiness: Existing enterprise software deployment experience

  • Timeline: Willingness to run pilots and structured evaluation processes

Initial Pricing and Packaging Structure Started with three tiers based on our early customer research:

  • Starter: Small teams, basic enterprise features

  • Business: Department-wide deployment, advanced security

  • Enterprise: Organization-wide, full compliance and customization

Basic Sales Process Framework

  1. Discovery Call: Understand use case, stakeholders, and decision process

  2. Technical Demo: Customized demo showing specific business scenarios

  3. Security Review: Deep dive on compliance, data handling, and enterprise requirements

  4. Pilot Program: Structured 30-60 day pilot with clear success metrics

  5. Business Case Development: ROI modeling and executive presentation

  6. Contract Negotiation: Enterprise terms, security requirements, implementation planning

The Customer Success Foundation

Why Customer Success from Day One Enterprise deals aren't won when the contract is signed—they're won when the customer is successfully deployed and seeing value. We hired our first Customer Success Manager in month two.

First CSM Profile

  • Enterprise software implementation experience

  • Change management background

  • Technical enough to understand AI/ML concepts

  • Business-focused enough to measure productivity impact

Phase 2: Systems and Process Scaling (Months 4-9)

Adding the SDR Engine

Why SDRs for Enterprise? Enterprise deals require extensive relationship building and multi-threading. Our AEs needed to focus on advancing qualified opportunities, not prospecting.

First SDR Hires (3 people)

  • Industry Focus: Each SDR aligned with our AE verticals (Financial Services, Technology, Healthcare)

  • Enterprise Experience: All had experience prospecting into large organizations

  • Multi-Threading Skills: Ability to identify and engage multiple stakeholders

SDR Success Metrics

  • Qualified meetings booked (not just meetings)

  • Multi-stakeholder engagement (average 2.3 contacts per account)

  • Account penetration depth (director level and above)

Implementing Proper Sales Operations

The Salesforce Build-Out This was bigger than just CRM implementation. We needed:

  • Custom Objects: Enterprise-specific fields for compliance requirements, use cases, stakeholder mapping

  • Workflow Automation: Lead routing, opportunity progression, approval processes

  • Integration Stack: Marketing automation, customer success platforms, billing systems

  • Reporting Infrastructure: Pipeline forecasting, activity tracking, conversion metrics

Sales Operations Hire

  • Background: Enterprise SaaS sales operations experience

  • Technical Skills: Salesforce administration, data analysis, process automation

  • Strategic Thinking: Ability to design scalable processes for rapid growth

Building Enablement and Competitive Intelligence

Sales Enablement Program

  • Industry Training: Deep dives on financial services, healthcare, and technology sector challenges

  • Competitive Intelligence: Detailed battlecards for Microsoft, Google, and emerging AI competitors

  • Demo Certification: Standardized demo flows for different use cases and industries

  • Objection Handling: Frameworks for common enterprise concerns (security, compliance, change management)

Content Creation

  • Industry-Specific Case Studies: Early customer success stories by vertical

  • ROI Calculators: Productivity impact modeling tools

  • Security and Compliance Documentation: Detailed technical specifications for enterprise buyers

  • Executive Briefing Materials: Board-ready presentations on AI strategy and implementation

Phase 3: Full Enterprise Motion (Months 10-18)

Scaling the Core Team

Additional AE Hires

  • Expanded to 12 enterprise AEs across verticals

  • Added government/public sector specialist

  • Hired retail/e-commerce focused rep

  • Added manufacturing/industrial specialist

Solution Engineering Team

  • Technical Specialists: Deep AI/ML expertise for technical evaluations

  • Industry Solutions Engineers: Vertical-specific implementation expertise

  • Demo Engineers: Specialized in creating compelling enterprise demonstrations

Expanded Customer Success

  • Implementation Specialists: Focused on enterprise deployment and change management

  • Strategic CSMs: Relationship management for largest accounts

  • Technical Success Engineers: Post-deployment optimization and advanced use case development

Channel Partnerships and Ecosystem

System Integrator Partnerships

  • Deloitte: Enterprise AI strategy and implementation services

  • Accenture: Large-scale transformation projects

  • IBM: Hybrid cloud and enterprise integration

  • PwC: Risk management and compliance implementation

Technology Partnerships

  • Microsoft: Azure integration and enterprise infrastructure

  • Salesforce: CRM integration and workflow automation

  • ServiceNow: IT service management and workflow optimization

  • Slack/Teams: Productivity platform integrations

Marketing Engine Development

Account-Based Marketing Program

  • Target Account Lists: 500 highest-value prospects per vertical

  • Personalized Campaigns: Industry-specific content and outreach

  • Executive Events: CIO roundtables and AI strategy workshops

  • Webinar Series: Industry-specific use case demonstrations

Content Marketing Strategy

  • Industry Reports: AI adoption trends by vertical

  • Executive Whitepapers: Strategic guides for AI implementation

  • Customer Success Stories: Detailed case studies with ROI metrics

  • Thought Leadership: Speaking engagements at industry conferences

Phase 4: The Great Integration (Months 19-24)

The Bold Organizational Decision

By early 2024, we faced a new challenge: many customers wanted both API and ChatGPT Enterprise capabilities. Having separate teams was creating customer confusion and internal inefficiencies.

We made a dramatic decision: unify both organizations into a single go-to-market team.

The Integration Process

The Scope: 500 people across both teams The Challenge: Most people had been at OpenAI less than 6 months The Change: Everyone got new roles, new managers, new workflows, and had to learn the opposite product

Integration Timeline

  • Week 1: Announced the change and new organizational structure

  • Week 2-4: Individual role assignments and team restructuring

  • Month 2: Cross-training program launch (API sellers learning ChatGPT Enterprise, ChatGPT Enterprise sellers learning API)

  • Month 3: New process implementation and system integration

  • Month 4-6: Performance optimization and culture integration

The Results

  • Faster Execution: Eliminated duplicate processes and conflicting priorities

  • Better Customer Experience: One team, one relationship, unified solution selling

  • Reduced Costs: Eliminated redundant systems and overlapping functions

  • Enhanced Capabilities: Every seller could now handle both simple API needs and complex enterprise requirements

Integration Lessons Learned

What Worked

  • Clear Communication: Transparent about the why behind the change

  • Fast Timeline: Ripped the band-aid off quickly rather than prolonged transition

  • Investment in Training: Intensive cross-product education program

  • Leadership Alignment: United leadership team modeling the integration

What Was Challenging

  • Learning Curve: Everyone had to master new products and processes simultaneously

  • Cultural Integration: Merging two different team cultures and ways of working

  • System Complexity: Integrating different CRM instances and operational tools

  • Customer Communication: Managing customer relationships during the transition

The Key Success Factors

Hire for Chaos, Not Comfort

Every early hire was a "chaos translator"—someone who thrived in ambiguous, rapidly changing environments. We specifically avoided people who needed clear processes and defined roles. Instead, we found builders who could create structure from nothing.

Maintain Extremely High Hiring Standards

Despite the pressure to scale quickly, we never compromised on quality. One exceptional enterprise seller was worth three average ones, especially in the early days when they were helping define our entire go-to-market approach.

Invest in Industry Expertise Early

Generic enterprise selling skills weren't enough. The investment in vertical specialists from day one paid massive dividends in credibility, deal velocity, and win rates.

Build for Scale from the Beginning

Even with a small team, we designed processes and systems that could handle 10x growth. This meant more upfront investment but prevented costly rebuilds later.

Embrace Bold Organizational Changes

The integration decision was risky and painful, but it was the right move for customers and the business. In AI, the pace of change requires constant organizational evolution.

The Numbers Behind the Success

While I can't share specific revenue figures, here are some metrics that demonstrate the impact:

  • Team Growth: 10 to 500 people in 24 months

  • Win Rate: Maintained >60% win rate even during rapid scaling

  • Sales Cycle: Average enterprise deal closed in 4-6 months (fast for enterprise AI)

  • Deal Size: Average contract value increased 5x through pilot program optimization

  • Customer Success: >95% of enterprise customers expanded usage within first year

  • Integration Success: Post-integration, unified win rates increased 15% over separate teams

What I'd Do Differently

Start with Sales Operations Earlier

We should have hired sales operations in month one, not month four. The operational debt we accumulated in early months took significant effort to clean up.

Invest More in Change Management

Enterprise AI adoption requires significant organizational change management. We should have built change management expertise into our customer success function earlier.

Build Competitive Intelligence Faster

The enterprise AI landscape evolved incredibly quickly. We should have invested in systematic competitive intelligence gathering and analysis from day one.

Create More Structured Onboarding

With rapid hiring, our onboarding process became inconsistent. A more structured program would have reduced time-to-productivity for new hires.

The Broader Lessons for Building Enterprise Teams

Product-Market Fit Looks Different in Enterprise

Consumer product-market fit is about engagement and retention. Enterprise product-market fit is about business impact and organizational change. The metrics and success criteria are fundamentally different.

Enterprise Buyers Want to Buy from Experts

Generic enterprise selling doesn't work in specialized markets like AI. Industry expertise and technical depth are table stakes, not differentiators.

Speed Matters More Than Perfection

In rapidly evolving markets, the team that moves fastest often wins, even if their processes aren't perfect. Iteration speed trumps initial optimization.

Culture Scales Through People, Not Processes

Our best cultural decisions were hiring decisions. The right people created the right culture, which then influenced all our processes and systems.

Customer Success is Revenue, Not Cost

In enterprise software, customer success directly drives expansion revenue, renewal rates, and reference-ability. It's not a support function—it's a growth engine.


Building ChatGPT Enterprise from zero taught me that enterprise go-to-market isn't just scaled-up SMB selling. It requires different people, different processes, different systems, and different organizational structures.

The most important lesson: in fast-moving markets like AI, organizational agility matters more than organizational perfection. The teams that can build, scale, and adapt quickly will win.

Thanks for reading The Secrets To Scaling in The Age of AI! Subscribe for free to receive new posts and support my work.