Neuronify
Free AI Readiness Assessment

Your Team Has AI Tools.Are They Actually Using Them?

Most companies have licensed AI and seen little change. Get a free assessment that shows exactly where your workforce stands, where the gaps are, and how to close them.

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Takes 2 minutes. Free, no obligation.

Score your team in 2 minutes

Five quick questions. Get an instant maturity score across the dimensions that determine whether AI actually sticks — plus where to start.

Leadership & strategy1 / 5

How clear is your company's AI direction?

Buying AI Tools Isn't the Same as Using Them

The gap isn't access — it's adoption. Industry research tells the story:

87%

of companies report an AI skills gap in their workforce

82% / 59%

provide some AI training, yet most still report a gap — tools bought, adoption stuck

$1,200

average spend per employee per year on AI upskilling

Assess. Train. Adopt.

The assessment is step one of a measured path to real adoption.

1

Assess

A short readiness assessment scores your team across five dimensions and pinpoints exactly where the gaps are.

2

Train

Role-based training on the AI that matters for your people's actual jobs — delivered by vetted training partners we match you with, not a video library.

3

Adopt

We measure real adoption — usage, confidence, and output — so you can show leadership a result, not a completion certificate.

What the Assessment Covers

Five dimensions, one clear scorecard, your top gaps ranked.

Leadership & strategy

Is there a clear AI mandate, an owner, and a budget behind it?

Current tool usage

What's licensed versus actually used — and by whom?

Skills by role

Which functions are furthest behind and need help first?

Governance & policy

Are the data and security guardrails in place to scale safely?

Adoption barriers

What's really blocking usage — fear, time, workflows, incentives?

A real program, not a course catalog

Nobody should hand you videos and hope. The assessment finds the gaps, a vetted training partner runs role-based programs against them, and adoption gets measured — so AI lands where slide decks and seat licenses stall.

For Mid-Market Teams That Need Results

If you're a 50-500 person company whose leadership wants AI adoption — but it's stalled, and you don't have an internal AI training function — this is built for you.

What the hiring market says about readiness

Most readiness advice is written from opinion. We can check it against behaviour instead. Every morning we read the job boards of 47 New York AI companies — the ones that have already crossed the readiness gap you are measuring — and count what they are actually hiring for. As of September 9, 2026, 1,566 roles were open. Three patterns in that data should shape how you read your own score.

They hire go-to-market faster than engineering

537 open go-to-market roles against 484 in engineering. Companies whose entire product is AI are recruiting more people to sell and deploy it than to build it. If your readiness plan treats AI as a purely technical programme and stops at the engineering team, you are organised differently from the companies furthest along — and the commercial and delivery functions are where their constraint has moved.

There is no junior bench to hire from

47 entry-level roles open against 177 at staff and principal level — senior demand running roughly 4× ahead of junior. The market is not producing the people you would hire to fill a capability gap, which means the realistic path for most teams is raising the people already inside the building. That is a training answer, not a recruiting one.

Buying the capability is expensive and slow

Posted engineering salaries in New York run to a midpoint of $215,000, with the middle half between $180,500 and $250,650. Add benefits and overhead and a single hire clears a quarter of a million a year before they have shipped anything. Against that, upskilling an existing team is not the cheap option because it is second-best — it is frequently the only option that moves within the budget cycle you are actually working in.

Figures from the NYC AI Hiring Index, read daily from company applicant-tracking systems. Salary ranges come from the postings themselves, which New York’s pay transparency law requires employers to publish for roles performed in the city.

Three things that look like readiness and aren’t

Most organisations score themselves too high, and almost always for the same three reasons. Each is a measurement that feels rigorous and answers the wrong question.

Licences issued

Procurement counts seats because seats are what it can count. Seats tell you what finance approved, not what anyone does on a Tuesday. The only version of this number worth reporting is weekly active use per person, broken out by team — and the gap between the two is usually the finding.

Training completed

Completion rates measure attendance. A team can finish every module and change nothing about how the work gets done, because the constraint was never knowledge — it was that the workflow, the review step, or the incentive never moved. Ask what people stopped doing after the training; if nothing, it did not land.

A published AI policy

A policy document is evidence that someone considered the risk once. It is not evidence that data stays inside the building or that outputs get reviewed before they reach a customer. Governance readiness is a question about observable practice — who checked, when, and what record exists — not about whether a PDF exists on the intranet.

What your score should change

A readiness score is only worth the decision it changes. Broadly, teams land in one of three places, and the right next move is different in each.

Low score, high urgency — start with one workflow

Tools are licensed, usage is thin, nobody owns the outcome. The instinct is a company-wide training programme; the better move is a single workflow with a measurable queue, one named owner, and a baseline captured before anything changes. Breadth without a proof point is how enablement budgets get cut in the next cycle.

Mixed score — the gap is between functions, not inside them

The most common pattern we see: engineering is fluent, the commercial and operations teams are not, and the organisation reports itself as “doing AI” on the strength of one department. Role-based training matters far more than general literacy here, because the blocked functions do not need to understand transformers — they need to know which three tasks in their week are worth handing over.

High score — the constraint has moved to governance

Adoption is real and now the risk is unmanaged: data leaving the building, outputs used without review, no record of which decisions a model touched. If you hire in New York, this is also where regulation bites — Local Law 144 requires an independent bias audit, published on your own website, for automated tools used in hiring or promotion decisions.

Frequently Asked Questions

What is an AI readiness assessment?

It is a structured diagnostic that scores how prepared your workforce is to use AI productively — across leadership, current tool usage, skills by role, governance, and adoption barriers. You get a maturity score, your top gaps, and a recommended plan to close them.

Who is this for?

Mid-market companies (roughly 50-500 employees) whose leadership wants AI adoption but where it has stalled — tools are licensed but usage is low, and there is no internal AI training function.

How is this different from an AI course?

Courses sell content; this path delivers adoption. The assessment pinpoints your gaps, we match you with a vetted training partner for role-based programs, and results are measured against real usage and output — not video completion.

What does it cost?

The readiness assessment is free. If you decide to move forward, a pilot enablement program for one team is a fixed fee — far below what a consulting firm charges, because we deliver it efficiently.

How long does an AI readiness assessment take?

The scorecard itself takes about ten minutes. The useful part is what follows: two weeks of capturing a baseline on one workflow before anything changes, because you cannot demonstrate improvement against a number you never recorded.

What are the five dimensions of AI readiness?

Leadership and strategy (is there a mandate, an owner, and a budget), current tool usage (what is licensed versus actually used), skills by role (which functions are furthest behind), governance and policy (are the data and security guardrails in place), and adoption barriers (what is really blocking usage — fear, time, workflow fit, or incentives). Scoring each separately matters, because a high average routinely hides one function that is completely stuck.

Should we train our existing team or hire AI talent?

For most mid-market companies, train. Our daily index of New York AI hiring shows senior roles outnumbering entry-level ones by roughly three to one, so there is no junior bench to hire from, and posted engineering salaries clear a quarter of a million once loaded. Hiring is the slower and more expensive path to a capability your existing team can often reach inside one budget cycle.

Is AI readiness a technical problem or a management problem?

Overwhelmingly a management problem. The pattern we see most often is engineering being fluent while commercial and operations teams are not, with the company reporting itself as "doing AI" on the strength of one department. The blocked functions rarely need technical depth — they need to know which tasks in their week are worth handing over, and permission to do it.

What compliance obligations come with AI adoption in New York?

If you use automated tools in hiring or promotion decisions for roles in New York City, Local Law 144 requires an independent annual bias audit and a public summary of the results posted on your own website, plus advance notice to candidates. Enforcement sits with the Department of Consumer and Worker Protection. This is the obligation teams discover last, usually after the tool is already in use.

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