SDR Guide: Talk Track & Qualification
This page mirrors the downloadable SDR Guide PDF for search indexing. For comprehensive demand generation materials, see the Demand Generation Pack.
The 30-Second Pitch
"Orion AI Outcomes helps enterprises stop piloting AI and start deploying it. Most organizations are stuck experimenting—we provide a structured methodology that addresses not just the technology, but the value identification, data readiness, governance, user experience, and economics that actually make AI adoption succeed."
Pain Signals to Listen For
| Signal | What They Say | What It Means |
|---|---|---|
| Pilot Purgatory | "We've done several AI pilots but none have scaled" | Need structured methodology |
| Shadow AI | "People are using ChatGPT on their own" | Ungoverned risk + unmet needs |
| Executive Pressure | "Leadership wants AI ROI this year" | Time pressure for results |
| Governance Gaps | "We're not sure how to govern AI" | Protection framework needed |
| Failed Initiatives | "We tried [vendor] but it didn't stick" | Adoption never addressed |
Discovery Questions
1. "What AI initiatives have you tried in the past 18 months?"
Listen for: pilots, POCs, failed projects, specific vendors
2. "How did those initiatives go?"
Listen for: "didn't scale," "couldn't get adoption," "governance issues"
3. "Are employees using AI tools on their own?"
Listen for: ChatGPT, Claude, Copilot usage—indicates unmet needs
4. "What's driving the interest in AI right now?"
Listen for: board pressure, competitive threat, efficiency mandate
5. "Who owns AI adoption at your organization?"
Listen for: CIO, CDO, or "nobody"—all are valid but inform approach
Ideal Customer Profile
| Attribute | Ideal |
|---|---|
| Revenue | $500M+ |
| Employees | 2,000+ |
| Industry | Healthcare, Financial Services, Legal, Telecom, Sports/Entertainment |
| AI Maturity | Has experimented but not scaled |
| Cloud Status | Using or evaluating AWS, Azure, or GCP |
Disqualifying Factors
- Company has fewer than 500 employees (scope too small)
- No cloud presence or commitment (infrastructure barrier)
- No executive sponsorship for AI (cultural barrier)
- Looking for staff augmentation only (not our model)
Objection Handling
"We're already working with [Big Consulting Firm]"
"That makes sense—they're great at large transformations. The challenge we see is that AI adoption requires a different approach than traditional transformation. How's adoption going on the initiatives they've delivered?"
"We want to build internally"
"Internal teams are essential for long-term AI success. What we typically see is that the initial methodology—figuring out where AI is worth applying and how to govern it—benefits from external pattern recognition. Have your internal teams deployed AI at scale before?"
"We're not ready for AI yet"
"What would 'ready' look like? Often organizations feel unready because they're thinking about enterprise transformation rather than agent-specific readiness. The gap between perception and reality is usually smaller than expected."
"We just need help with data"
"Data is certainly foundational. What we've seen is that data readiness for AI is different from general data quality—it's about whether specific agents can operate, not whether data is perfect. Have you defined what 'ready' means for specific AI use cases?"
Qualification Criteria
Strong fit if 3+ of these are true:
- Have attempted AI pilots that didn't scale
- Executive pressure for AI results
- Using or committed to AWS/Azure/GCP
- Willing to invest in methodology, not just technology
- Have budget authority or access to it