Liza developed the Tool to Teammate to Workflow progression that B2B GTM teams now use as their AI adoption model. She built a five-stage adoption scale (Ask it, Think with it, Build with it, Connect it, Scale it), four mindset shifts that move teams from treating AI as a Q&A machine to using it as a true thought partner, and an operating model called "democratized building, centralized enablement" that solves the governance problem most companies hit when AI scales. Her philosophy, "people first, AI forward," treats every AI decision through a people lens. The goal is always work that wasn't possible before, not just faster versions of old work.

Autonomy beyond automation. Liza drew the line between automation and agentic AI early. Her AI Agent Mystery Shopping framework used ChatGPT's Agent Mode to autonomously research, compare, rate, and recommend vendors, then tested how AI handles sensitive information handoffs. She taught marketing teams to see that AI doesn't just gather information anymore. It forms preferences and makes buying recommendations. That changes how every GTM team needs to think about their buyer's journey.

At a global cybersecurity company, Liza ran the full AI transformation for the marketing organization. In under six months, the team trained 75 early adopters and embedded 57 AI teammates into daily workflows across 150+ marketers. The SDR team saw open rates jump from 15% to 40% and saved 100+ hours per week. The team built a three-pillar measurement system covering business efficiency, people development, and market positioning. Marketing's proven results led the company to roll out enterprise AI access across the entire organization. Separately, Liza's early work with a B2B SaaS company helped introduce AI thinking across the GTM function, seeding an approach that the team later scaled into an AI-native growth engine delivering 80% meeting rates, 3x higher reply rates, and 20% lower cost per lead.

Daniel Henderson has built a Website Scoring Agent that runs on a monthly schedule for our known competitors. This agent evaluates a set of competitor websites by reading their page content, extracting evidence for 10 predefined rubric categories, assigning weighted scores with short rationales, and optionally comparing the results to a previous run to show score changes over time. It then generates a Markdown report summarizing the findings, including notes about missing evidence or rendering limitations, and appends both a structured summary and the raw JSON results into a Google Sheet for recordkeeping. Overall, it is a linear website-scoring workflow built for repeatable benchmarking, change tracking, and persistent reporting.

The Website Scoring Agent has significantly strengthened our website strategy at Glean. It helps our team identify content gaps by comparing our site against competitor pages and surfacing areas where key messaging or information is missing. That has increased confidence across our Sales organization, giving them greater trust in sending prospects to our website for deeper product and market context. It has also helped ensure we consistently publish content that addresses competitor claims with strong counterpoints.

In less than two quarters, with the work primarily focused on the homepage, this effort has driven a 20% increase in engaged sessions, a 10% increase in pageviews per unique visitor, 15% increase to website pipeline, and at least 5 hours saved per month in manual website competitive analysis.

Jordan's Innovation with Agentic AI & Its Marketing Impact

Jordan is doing something genuinely differentiated — rather than just using AI as a productivity tool, he's architecting an end-to-end agentic system around how Pantheon captures and converts inbound demand.

His role centers on owning the full Qualified conversational marketing platform — from live chat and "Contact Us" submissions to Support-to-Sales handoffs — with a deliberate evolution from manual V1 execution toward a fully AI-driven V2 experience that qualifies prospects and books meetings in real time.

The impact is showing up across a few meaningful dimensions:

Speed to Lead— By designing AI-driven qualification flows, Jordan has dramatically compressed the window between a prospect's first signal and a meaningful sales conversation. Where inbound leads used to sit in a queue, they're now being routed, qualified, and scheduled autonomously — in real time.

Differentiated First Touchpoints — Prospects are noticing. The initial experience Pantheon creates is more intelligent, more responsive, and more personalized than what competitors are putting in front of the same buyers. That's a direct result of Jordan's conversational design work.

Resourcefulness at Scale — Jordan is building automated workflows for abandoned cadences and warm MQL follow-ups while also collaborating with RevOps to refine routing rules and data hygiene as automation increases. He's doing more with the same headcount — not by cutting corners, but by systematizing what used to require manual judgment.

Marketing Precision — The goal of his advanced reporting work is a comprehensive dashboard reflecting conversational marketing's direct impact on revenue and pipeline, giving the marketing team cleaner signal on what's working. The result is that marketing can be more targeted — spending budget and effort where intent is actually showing up, rather than spraying broadly.

Jordan's approach is a strong proof point that agentic AI isn't just an efficiency play — when deployed thoughtfully, it becomes a competitive differentiator at the very top of the funnel.

Competitive Intelligence & Enablement
‍A competitive news agent continuously monitors competitor blogs, product releases, press coverage, and industry publications. When a meaningful signal is detected, it pushes a digest to a dedicated Slack channel so every revenue-facing team member stays current, and simultaneously writes updates directly into the relevant competitive documentation, battle cards, and customer-facing collateral.

A built-in change log surfaces exactly what changed and when, so a rep opening a battle card before a call sees the current picture, not something accurate six months ago. The system also pulls from Gong call recordings, analyzing transcripts to identify objection patterns and competitive deal dynamics, then feeds those signals back into collateral. The field informs the content, automatically, in a continuous loop.Indicative metrics:

Battle card refresh cycle:

• Quarterly → continuous
• Estimated 75% reduction in time reps spend searching for or questioning competitive materials
• Competitive objection handling scores in Gong within the first 30 days as reps learned the system. 

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Designed and deployed a system of six interconnected AI agents to orchestrate end-to-end GTM strategy, spanning ICP definition, persona development, messaging, content audits, website analysis, and channel planning. Each agent applies structured frameworks to analyze inputs and generate strategic recommendations, while a shared memory layer enables continuity, cross-agent collaboration, and cumulative learning across engagements—transforming AI from point solutions into a coordinated system for strategy development and execution.

By embedding these agents into consulting workflows, Payal and Tom have:

• Increased speed to insight and strategy development
• Reduced time required to produce high-quality deliverables
• Improved consistency and depth of strategic recommendations
• Enabled consultants to focus more time on higher-value thinking and client impact

This directly improves delivery efficiency while enhancing the quality of client outcomes.