How big is your AI opportunity?
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Know and Grow the ROI of Your AI®

Workhelix Nucleus shows you where your AI investment can create the most value, whether your people are actually capturing it, and what to do next to get more.

trusted by enterprise ai teams
why roi matters

Knowing that your AI is working is your competitive advantage.

Most companies can tell you how many people have an AI license. Dashboards show who logged in and how many prompts were sent. They don't show how much usage there should be, whether it's any good, or whether it's moving a number the business cares about.

total ai visibility
One number for AI ROI across every tool.
ChatGPT and Claude, GitHub Copilot and the rest of the engineering stack: Workhelix reconciles usage and value across all of them into a single view, so you never stitch five dashboards together to answer one question.
proprietary benchmarks
Know where you should be, not just where you are.
Two benchmarks built by our researchers (the AI Assessment, grounded in what your roles could be capturing, and the Power-User benchmark, set by what your best people already achieve) turn raw usage into a clear read on how far along you are and how much opportunity is still on the table.
coverage & gaps
See where AI is winning and where it's being missed.
Workhelix maps usage against every use case, so you can spot the work that should be running on AI but isn't and surface the quiet wins no one flagged. Missed coverage becomes your roadmap.
Agentic ai
Track agents, skills, and custom GPTs and who's using them.
Adoption isn't just chat anymore. Workhelix shows where autonomous agents and custom assistants are being used across the company, by which teams, and to what measurable effect.
Actionable insights
Answer any question and know what to do next.
Slice usage by team, tool, or use case in seconds, compare against the benchmarks, and turn what you find into concrete recommendations for where to push adoption next.
“Workhelix has helped Nasdaq evaluate and quantify AI's impact across the company, leveraging deep, role‑level analytics to identify impact, capture value, and unlock new growth opportunities."
Michael O'Rourke SVP of AI and Machine Learning Engineering
10x

faster than traditional automation rollouts

Built for the people who own AI outcomes.

Prove what your AI investment is returning, and where the next dollar should go.

Get a defensible, transparent number for your board and CFO, grounded in peer-reviewed research and independent of internal politics.

See the size of the prize by role and function, and where the largest gaps remain.

Get more done with the team you have. Use the data to decide where to redeploy capacity and where to hire versus automate.

Show leading indicators to stakeholders now, and build toward causal ROI over time.

Measure AI usage without exposing your people or your data.

No PII required. By default, we collect only interaction volume by job category.

Prompt content is redacted and classified with PII detection and named entity recognition before it leaves your environment. A private VPC with no outbound internet is available.

Anonymized data with minimum group sizes means no individual is identifiable.

Role-based access controls keep analytics with the people who should have them, such as your AI Center of Excellence and select leadership.

Roll out AI based on defensible data.

See real usage across your AI tools in one place. Start with one broad-based tool and build measurement that covers most of your usage.

Map high-value versus low-usage custom GPTs and get a prioritization framework for curation.

Compare tools head to head, such as ChatGPT versus Copilot, for the job families that matter most.

Start with flat files or platform metadata, and move to automated pipelines when you're ready.

Grounded in research.

250,000+
tasks mapped across the workforce
500M+
public job records behind the taxonomy
Peer-reviewed
methodology published in Science
First randomized controlled study
of generative AI at a Fortune 500 company
Founded by leading economists
Erik Brynjolfsson (Stanford) and Andrew McAfee (MIT)

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