Overview
You're selling Luma's multimodal AI platform (Luma Agents, Uni-1 model) to enterprise teams in software, e-commerce, design, and film. You'll be doing full-cycle sales in a market that's crowded with AI tools, talking to innovation teams and creative directors who are both excited about and skeptical of generative AI. This is early GTM - they're still figuring out ideal customer profile, pricing, and what use cases actually drive deals.
Role Snapshot
| Aspect | Details |
|---|---|
| Role Type | Full-cycle AE (prospecting to close) |
| Sales Motion | Outbound-heavy with some inbound interest |
| Deal Complexity | Consultative to Strategic |
| Sales Cycle | 3-6 months (AI procurement + security reviews) |
| Deal Size | $50K-250K ACV (estimated for enterprise pilots) |
| Quota (est.) | $800K-1.2M/year |
Company Context
Stage: Series B/C (estimated based on 252 employees and frontier AI focus)
Size: 252 employees
Growth: Actively hiring across GTM roles, recently launched Luma Agents and Ray3 video model
Market Position: Category creator in unified multimodal AI - competing in a crowded generative AI space with OpenAI, Midjourney, Runway, Stability AI
GTM Reality
Pipeline Sources:
- 30% Inbound - Product-led interest from creators who've used Luma's consumer tools, word-of-mouth in AI/creative communities, demo requests from website
- 60% Outbound - Cold outreach to enterprise creative teams, software companies building AI features, e-commerce brands looking for content generation
- 10% Partners/Referrals - Design agencies, AI consultancies, existing customers
SDR/AE Structure: Likely self-sourcing or small SDR team (company is scaling GTM now)
SE Support: Technical demonstrations handled by solutions engineers or research team members for complex POCs
Competitive Landscape
Main Competitors: OpenAI (DALL-E, ChatGPT), Runway ML, Midjourney, Stability AI, Adobe Firefly, Anthropic Claude (for text/image)
How They Differentiate: Unified multimodal model (text, image, video, audio in one system), focus on creative workflow automation with agents, research-first approach
Common Objections: "How is this different from OpenAI?", "Our team already uses Midjourney/Runway", "We're building our own AI tools", "Data security concerns with uploading creative assets", "Pricing unclear for enterprise scale"
Win Themes: Unified creative pipeline (not stitching together multiple tools), agent-based workflow automation, quality of video generation (Ray3), ability to coordinate across media types
What You'll Actually Do
Time Breakdown
Prospecting (35%) | Active Deals (40%) | Internal (25%)
Key Activities
- Prospecting creative and product leaders: You're reaching out to heads of design, creative directors, product teams at mid-market to enterprise companies. Response rates are mixed - AI tool fatigue is real, but the right persona (overwhelmed creative teams, companies shipping AI features) will take meetings.
- Running discovery and demos: You're showing how Luma Agents work, walking through use cases like automated video generation, multi-format content creation, creative workflow automation. Demos are impressive but you'll get questions about edge cases, accuracy, and "what can't it do?"
- Navigating long procurement cycles: AI tools trigger security reviews, data privacy discussions, legal reviews of terms. You'll spend time coordinating with InfoSec, answering vendor questionnaires, and waiting on procurement approvals. Deals that look close slip quarters regularly.
- Defining the playbook: Since GTM is early, you're reporting back what objections you're hearing, which personas respond, what pricing models work. You're part sales rep, part product feedback loop. Expect frequent strategy pivots.
The Honest Reality
What's Hard
- AI tool fatigue is real: Prospects have seen 50 AI demos this year. You're competing for attention and budget against OpenAI, existing tools, and "let's just build it ourselves" mentality.
- Unclear ROI conversations: Creative output is subjective. Quantifying "faster content creation" or "better quality video" is difficult. Finance teams want hard numbers you don't always have.
- Product is evolving fast: Features change, models improve, pricing adjusts. What you sold last month might work differently now. You'll be learning alongside customers.
- Long cycles with uncertain outcomes: 3-6 month deals that hinge on security reviews, budget freezes, or executive whims. Your pipeline will be unpredictable.
What Success Looks Like
- Landing 3-5 enterprise pilots per quarter that convert to paid contracts
- Building repeatable use cases and customer stories in specific verticals (e-commerce product imagery, film pre-production, etc.)
- Close rate of 20-30% from qualified demo to closed deal
Who You're Selling To
Primary Buyers:
- VP/Head of Design or Creative at enterprise software/e-commerce companies
- Chief Product Officer or Head of AI/Innovation at tech companies
- Creative Directors or Production Heads at media/entertainment companies
What They Care About:
- Speed and cost of content production (can they generate 100 product videos instead of shooting 10?)
- Quality and brand consistency (does output match their standards?)
- Integration with existing creative workflows (Adobe, Figma, etc.)
- Data security and IP ownership (who owns the generated content? where does training data come from?)
- Differentiation from consumer AI tools (why not just use free Midjourney?)
Requirements
- 3-5 years selling B2B SaaS, preferably in creative tools, AI/ML platforms, or developer tools
- Comfortable with technical product demos and talking to both creative and engineering buyers
- Experience navigating enterprise procurement and security reviews
- Self-starter mentality - there's no established playbook, you'll be figuring it out
- Genuine interest in AI and creative technology (you'll need to stay current on what competitors are shipping)