A leading global enterprise SaaS provider specializing in hiring, learning, and talent technology was preparing for the large-scale expansion of its new AI-driven hiring assistant product. However, the organization lacked a unified, data-driven method to identify which customer accounts were most likely to adopt AI features and generate meaningful usage.

Despite having extensive signals across its ecosystem of talent, learning, recruiting, and analytics tools, the company had no consolidated framework to measure:

• Which organizations demonstrated strong AI adoption potential
• What behaviors or attributes meaningfully predicted AI readiness
• How readiness varied across industries, segments, and customer tiers
• Where to focus marketing and sales efforts ahead of the product’s launch

The absence of a systematic approach made it difficult for Marketing, Sales, and Product teams to align on targeting, sequencing, or forecasting AI-related adoption. With an upcoming product expansion deadline approaching, the company needed a robust, interpretable model to drive precision targeting and customer engagement.

Steven led the design and development of an end-to-end AI Readiness Model, a machine-learning-based scoring framework that quantified an organization’s propensity to adopt AI-driven hiring features. The project required extensive collaboration across Marketing, Data Science, Product, and Sales Operations.

Steven first aligned cross-functional teams on a single operational definition of readiness. This included:

• Historical usage of AI-powered product features
• Engagement intensity across core recruiting and learning workflows
• Organizational hiring sophistication (size, segment, industry, seat mix)
• Activation patterns signaling openness to automation and augmented decision-making

Prior to this project, each team used different signals; this alignment was foundational to all downstream modeling decisions.

At the individual user level, Steven engineered a unified Talent Product Usage Score, aggregating dozens of behavioral signals, including:

• Recruiter search depth and workflow engagement
• Usage of job posting, pipeline management, and sourcing tools
• Learning engagement, especially with AI-related content
• Analytics and insights tool usage
• Early interaction with newly released AI capabilities

This transformed raw event logs into an interpretable score capturing behavioral maturity across the entire talent suite.

Using the member-level score as a foundation, Steven developed an account-level AI Readiness Score composed of two transparent components:

• Firmographic Score – based on size, industry, hiring sophistication, seat composition, and org structure
• Usage Score – based on aggregated activity patterns across the company’s talent technology platform

The model evaluated thousands of customer organizations monthly and assigned each one a readiness tier to guide go-to-market initiatives.

Steven partnered with data engineering teams to build an automated pipeline that refreshed monthly and fed directly into datasets used by marketing and field teams. The outputs powered dashboards that showcased:

• AI readiness distribution across all customer segments
• Heatmaps of usage vs. firmographic sophistication
• Tier-based targeting recommendations
• Priority accounts for marketing and sales activation

These dashboards became the primary tool used by marketing teams for campaign targeting and by sales teams for account prioritization.

Beyond modeling, Steven delivered strategic insights to guide the company’s product launch and customer engagement strategy, including:

• Prioritizing customers with strong behavioral usage but moderate firmographic maturity
• Targeting industries showing rapid acceleration toward AI adoption
• Sequencing outreach based on readiness tiers to maximize adoption lift
• Aligning upsell and renewal strategies to accounts with highest predicted impact

This work bridged analytics with concrete go-to-market execution.

The AI Readiness Model became a core component of the company’s product expansion playbook. Key outcomes included:

• Significant improvement in targeting accuracy, enabling marketing to shift from broad outreach to precision AI-readiness segmentation
• Enhanced sales prioritization, allowing field teams to focus on the highest-propensity accounts
• A unified measurement framework, giving leadership visibility into AI readiness across all customer segments
• Full pipeline automation, scoring millions of users and thousands of accounts on a continuous refresh cycle

Most importantly, the model equipped the organization with the clarity and precision required to successfully launch its AI-driven hiring assistant product and accelerate AI adoption across its customer base.