Demand Generation Pack

Third-Party Demand Generation Pack

This package equips external demand generation partners, channel partners, and internal SDR teams with everything needed to generate qualified leads for Orion AI Outcomes (OAIO) engagements.

Downloadable Assets

Core Materials

Industry One-Pagers

What is Orion AI Outcomes?

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."

Key Differentiators:

• Adoption-focused: Success metric is adoption, not model accuracy
• Structured methodology: Six pillars covering the full adoption lifecycle
• Cloud partnerships: AWS, Microsoft, and Google subsidize engagements
• Proven approach: Built on patterns from hundreds of enterprise engagements

The Problem We Solve

Pain Points to Listen For

When prospects describe these situations, they're OAIO candidates:

Market Statistics

Use these to establish credibility:

• 62% of enterprises are stuck in AI experimentation/piloting (McKinsey)
• 67% of CEOs expect AI ROI within 1-3 years (KPMG)
• Only 7% have achieved full-scale AI deployment
• 74% of enterprises with structured AI programs report positive ROI (Wharton)

Ideal Customer Profile

Company Characteristics

Target Personas

Disqualifying Factors

Don't pursue if:

• 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)

Qualifying Questions

Use these to determine fit and urgency:

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

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

Industry Messaging

Healthcare

Pain: "Clinical AI is risky without proper governance. Shadow AI in healthcare is a compliance nightmare."

Hook: "How are you ensuring AI governance for clinical applications? Most health systems are struggling with the gap between AI potential and safe deployment."

Value: Structured approach to clinical AI that addresses HIPAA, patient safety, and clinician trust.

Legal Services

Pain: "Law firms are experimenting with AI for document review and research, but adoption is low because lawyers don't trust the output."

Hook: "Are your attorneys actually using AI, or just testing it? The adoption gap in legal AI is huge—how are you addressing trust?"

Value: Framework for AI adoption that builds attorney confidence through proper validation and oversight.

Tax & Audit

Pain: "Audit teams could benefit enormously from AI for anomaly detection and compliance checking, but the accuracy requirements are stringent."

Hook: "How confident are you in AI-assisted audit findings? Most firms struggle with the documentation and validation required."

Value: Governance and validation frameworks that make AI defensible for audit purposes.

Telecom

Pain: "Customer service AI is table stakes, but most implementations frustrate customers more than they help."

Hook: "What's your CSAT on AI-powered interactions vs. human? Most telecoms see AI hurting NPS before it helps."

Value: Experience-centric AI design that improves customer satisfaction rather than degrading it.

Financial Services

Pain: "Model risk management for AI is complex. Regulators are watching, and explainability requirements are real."

Hook: "How are you handling model risk management for AI initiatives? Most financial services firms are struggling with regulatory expectations."

Value: AI governance frameworks designed for regulated industries with built-in explainability.

Sports & Entertainment

Pain: "Fan engagement AI has huge potential, but the data integration across venues, ticketing, and concessions is a mess."

Hook: "How connected is your fan data across touchpoints? AI for fan experience requires data readiness that most organizations lack."

Value: Agent-specific data readiness that doesn't require enterprise transformation.

Cloud Partnership Value

Why Cloud Partners Matter

Orion AI Outcomes is partnered with AWS, Microsoft, and Google. This means:

For Customers:

• Cloud partners subsidize OAIO engagements (reduces customer cost)
• Technical validation from cloud experts
• Access to latest AI services and features
• Reference architectures and best practices

For Sales:

• Co-selling opportunities with cloud sales teams
• Partner-sourced leads
• Joint go-to-market events
• Shared customer success stories

Positioning by Cloud

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?"

Lead Handoff Process

What Happens After Qualification

1. SDR books meeting with Orion OAIO sales lead
2. Orion conducts discovery call with prospect
3. Orion proposes Pillar 1 engagement (Value & Adoption assessment)
4. Cloud partner engaged for co-sell support if applicable

Information to Capture

When handing off a lead, include:

• Company name, size, industry
• Primary contact (name, title, email, phone)
• Pain points identified (use signals from above)
• Cloud platform in use
• Budget/timeline indications
• Competitive landscape (other vendors in play)

Related Materials

• GTM Marketing Overview — All marketing programs
• CTO Whitepaper — Thought leadership for nurture
• Executive Dinner Program — For qualified executives
• Pitch Deck — For direct sales conversations