Get Your AI Engineering Capability Snapshot
Can your team build production AI? Take this enterprise AI benchmarking and capability snapshot with you.
Our 12-question assessment of findings from our AI Makerspace enterprise work is designed to build better teams and AI capability. Work at your pace to find the gaps and what discussion you should have next.
Most firms don't have an AI ambition problem. They have a capability problem. With our AI Capability Snapshot, you can now score your team overall and by category:
- AI engineering skills
- Production readiness
- Problem-to-product alignment
- Systems
- Internal capability
Eight Common Signs Of an Enterprise AI Capability Gap
Why AI Makerspace?
A clear AI capability snapshot is a starting point, not the finish line. Our goal with this is to help you understand where your teams are today, see where the gaps are, and decide what building stronger AI capability could look like.
Where are we weak? What is it costing us? And then, how do we build it?
AI Makerspace helps you take that understanding into action, with practical learning programs designed around the skills, systems, and people your organization can build with.
See the capability. Understand the gaps. Build what comes next.
Banking Industry: The AI Challenge Lab
Through enterprise engagements with organizations including a global bank, AI Makerspace helps teams bridge the gap between AI awareness and production AI engineering. The focus is practical: building the skills, systems, and internal champions needed to deploy AI safely and effectively in complex enterprise environments.
Business Consulting: AI Titanium Engineer Program
Brought together by enterprise leaders in a global business consulting firm, AI Makerspace helps locations across the world develop the practical skills needed to move from AI strategy to AI execution. Executive vision only matters when teams know how to build it.
BEYOND TECHNICAL AND AI ENGINEERING SKILLS: THE CATEGORIES YOU'LL SCORE
Production Readiness
Can you take an AI solution from a working prototype to something people can reliably use?
Includes: deployment, testing, evaluation, observability, reliability, security, performance, and maintaining systems as they change.
What it looks like: An engineer can identify what needs to happen before an AI application is trusted in production, and can help make it happen.
Problem-to-Product Alignment
This is about making sure the team is building something that solves a meaningful problem, for the right users, in a way that creates value.
Includes: problem framing, understanding users and workflows, defining outcomes, choosing appropriate AI approaches, prioritizing, and validating whether the solution is useful.
What it looks like: An engineer can move from “We should use AI” to “Here’s the problem, here’s why AI helps, and here’s how we’ll know it worked.”
Systems
Can you design and build the technical systems that make agentic AI applications work?
This is the engineering foundation: understanding how models, data, software, and infrastructure fit together.
Includes: software architecture, data pipelines, APIs, model integration, retrieval and RAG, agent engineering, tooling, orchestration, infrastructure, and the interfaces between components.
What it looks like: An engineer can build beyond a single model call and understand how the pieces work together as a system.
Internal Capability
Can your organization build and sustain AI capability without depending entirely on outside help?
This is about developing the people, practices, and shared knowledge that allow AI work to continue and improve internally.
Includes: technical skill development, internal champions, knowledge sharing, documentation, reusable patterns, collaboration, mentoring, and the ability to evaluate and adopt new tools.
What it looks like: Teams become more confident and capable of building, shipping, evaluating, and improving AI systems themselves.
Why This Enterprise AI Benchmarking is Different and Works
Moving Teams Beyond Pilots Starts with Engineering
Address the changes in your current operations and processes from running fragmented vendor pilots to establishing durable internal AI systems.
Gain Confidence in AI Governance and Regulatory Systems
We emphasize the importance of robust evaluation and governance frameworks necessary for high-compliance environments.
Upskilling vs. Outsourcing Production AI
This assessment highlights the value of training existing engineering and business teams rather than relying indefinitely on external vendors.
See the Capability. Find the Gap. Build What Comes Next.
For the best results of your AI capability, don't guess the unknowns or work on your own. Complete the AI Capability Snapshot as a team.
You may have access to the tools everyone else does, use cases, and even talented engineers. But you may still struggle with one of the five dimensions. The tally sheet gives you a simple way to collect your score overall and by category.
Easily score your AI engineering skills, production readiness, problem-to-product alignment, systems, and internal capability with this resource.