New AI Assessments for Enablement, Productivity & Readiness

Organizations are investing heavily in AI with a clear expectation: meaningful productivity gains.

Some teams are already moving faster, reducing friction, and producing more. But for many, progress remains uneven, difficult to measure, and hard to scale beyond pockets of success.

This release introduces three new AI-focused assessments designed to help teams and leaders move beyond experimentation and gain a clear, evidence-based view of how AI is actually being used — and where it is (or isn’t) driving real impact.

Together, they help organizations operationalize AI with clarity, intent, and discipline, so productivity gains are not accidental, but repeatable and scalable.

🎯 How To Unlock Real Productivity Gains with AI

To unlock real productivity gains with AI, organizations need more than tools or experimentation — they need clarity. The path forward is operationalizing AI with purpose: embedding it into everyday work so it is effective, safe, and trusted. Not dependent on individual experts, but repeatable and scalable across teams.

These assessments provide that starting point.

They offer an objective, evidence-based view of how AI is actually being used today — not just where it exists, but how it is influencing real work.

And help leaders answer these questions:

• What progress have we actually made?

• Where is AI genuinely improving how work gets done?

• Can we scale what’s working — and make it real?

With this clarity, teams can define what effective AI use looks like, focus improvement efforts where they matter most, and align leaders around how to scale productivity gains with intent.

1.  AI Readiness Assessment 🧭

Before AI can be scaled effectively, organizations need the right foundations in place.

This assessment is designed to evaluate the capabilities, structures, and conditions required to adopt and scale AI successfully.

With this assessment, teams can:

• Understand their readiness to adopt and scale AI effectively

• Identify gaps in AI understanding, governance, and enablement

• Surface barriers that limit adoption and trust

• Align on what needs to be in place before scaling AI further

• Create a clear path from experimentation to sustainable adoption

This assessment focuses on readiness.
Not ambition. Not strategy decks.
But the real conditions required to make AI work at scale.

👉 Learn more about this assessment here

 2. AI Enablement & Productivity Assessment ⚡

AI adoption is growing across organizations, but real productivity gains are often unclear, inconsistent, or isolated.

This assessment is designed to help teams understand how AI is actually used in day-to-day work — and whether that use is effective, safe, and scalable.

With this assessment, teams can:

• See how AI is actually showing up in daily workflows

• Identify where AI is enabling productivity — and where it’s not

• Surface risks related to misuse, over-reliance, or lack of governance

• Define what effective, responsible AI use looks like across teams

• Focus improvement efforts on practices that drive real productivity gains

This assessment focuses on what matters most in AI adoption.
Not tool access. Not experimentation.
But how AI is actually used in everyday work.

It provides a practical, evidence-based view into AI enablement — designed to support clarity, alignment, and action.

👉 Learn more about this assessment here

 3. Delivery AI Enablement & Productivity Assessment 💻

AI has the potential to significantly accelerate software delivery — but only when it is applied effectively within real engineering workflows.

This assessment focuses specifically on how AI is used across software delivery activities, from coding to collaboration.

With this assessment, teams can:

• Understand how AI is applied across delivery workflows

• Identify gaps in AI-assisted coding and engineering practices

• Improve speed, quality, and consistency of delivery

• Reduce reliance on individual “power users” and enable team-wide adoption

• Focus on behaviors that directly impact flow and delivery outcomes

This assessment is grounded in delivery reality.
Not theoretical productivity gains.
But observable engineering practices that influence speed, quality, and flow.

It helps teams move from isolated AI usage to consistent, scalable delivery improvement.

👉 Learn more about this assessment here