AI Enablement

Turning Emerging Capabilities into Scalable Impact

I specialize in translating AI innovation into practical enablement applications—not just explaining what AI can do, but actually building the learning experiences, workflows, and validation systems that prove it works at scale.

What Sets This Work Apart

Most "AI enablement" means teaching people about AI. My approach is different: using AI as an enablement tool itself, then designing the training that helps organizations adopt these new capabilities.

I build AI-powered learning systems: Conversational assistants integrated into technical training. Generative AI simulations validating sales capabilities. Prompt engineering frameworks enabling automated assessment generation.

I measure what matters: Not completion rates—capability validation. Not survey scores—business outcomes. Every AI application I design includes evaluation frameworks proving the technology delivers on its promise.

I bridge technical and strategic: Understanding both prompt engineering mechanics and organizational change requirements. Speaking credibly with AI researchers about model behavior while translating implications for program leadership.

Core AI Capabilities

Prompt Engineering: Designing system prompts, conversation flows, and evaluation criteria for generative AI applications. Second Nature simulations with multiple distinct customer personas. Copilot workflows generating technically accurate assessment items.

LLM Evaluation Design: Building frameworks that validate AI output quality—not just accuracy, but consistency, appropriateness, and alignment with learning objectives. Assessment rubrics for AI-generated content. Scoring models for simulation conversations.

Conversational AI Integration: Implementing AI assistants within learning platforms. Bongo AI case-study evaluation in Azure training. Context-aware prompting enabling personalized guidance without human facilitators.

AI Workflow Optimization: Redesigning processes to leverage AI assistance while maintaining quality standards. Assessment creation workflows reducing 25% of manual effort. Content review cycles incorporating AI-suggested improvements.

Projects Demonstrating AI Innovation

AI-Powered Sales Simulations: Pioneered enterprise deployment of generative AI role-play for Microsoft technical validation. Four competency-based simulations covering opportunity identification, discovery, value articulation, and negotiation-each featuring realistic customer personas with technical objections and decision criteria. Automated scoring with human-readable feedback at scale.

Cloud Health Foundations AI: Integrated AI-assisted evaluation into Azure architect training, enabling learners to validate comprehension of governance frameworks (Cloud Adoption Framework, Well-Architected Framework) through case-study analysis with automated feedback-reducing instructor review time ~40%.

Copilot Workflow Transformation: Eliminated assessment creation bottlenecks through AI-assisted item generation, maintaining quality standards while accelerating delivery by 25%.

The Business Case for AI Enablement

Organizations adopting AI face a capability gap: teams need to both understand AI concepts and know how to apply them effectively. Traditional training addresses the first; my work solves the second.

Faster capability validation: AI simulations enable practice at scale without scheduling facilitators or coordinating role-play partners.

Reduced operational friction: AI-assisted workflows eliminate bottlenecks that previously required specialized skills or manual handoffs.

Measurable quality improvements: Automated evaluation frameworks ensure consistency while freeing human reviewers to focus on strategic feedback.

Impact at a Glance

• 62,000+ global learners enabled through customer engagement methodology training
• 25% efficiency improvement in assessment creation through AI-assisted workflows
• 20% reduction in time-to-productivity for security new hire onboarding
• 11,000+ learners reached through short-form video enablement
• Pioneered generative AI role-play for technical validation at enterprise scale

What's Next

The intersection of AI and learning is moving fast. I'm actively exploring applications in:

• Adaptive learning paths: LLMs that adjust content difficulty based on real-time comprehension signals
• Capability transfer acceleration: Using AI to identify skill gaps and recommend targeted interventions
• Multi-modal simulation environments: Combining conversational AI with task-based scenarios for richer practice contexts

If you're building AI-enhanced learning experiences, pioneering new enablement applications, or figuring out how to measure AI's actual impact—let's connect.