Where Does Your Company Stand on AI Right Now? — 10 Questions, an Answer in 2 Minutes

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Why TecAce built AX Diagnostic, and what it can tell you

Everyone already knows AI helps

Hardly anyone disputes that AI is transforming the way we work. From writing documents to forecasting inventory, quality inspection, and customer service, there are more than enough areas where it is already delivering results on the ground.

Yet this is exactly where many companies get stuck. "We get that it's good, but where on earth do we even begin?"

The places that feel most lost in front of this question are not large enterprises. They are the small and mid-sized businesses and self-employed owners who have no dedicated AI team and no data organization. Moving on to AI transformation (AX) is far easier if some degree of digital transformation (DX) is already in place, but in reality there are far more companies that have not even begun DX in earnest. This is especially true in industries that have long run on offline operations, such as manufacturing, distribution, and retail.

The 'overlooked majority' that the statistics reveal

This is not just a feeling — it is a reality confirmed by the numbers.

According to a report published by the National Information Society Agency (NIA) in May 2025, the AI adoption rate in manufacturing is 25.4%, falling short of the all-industry average (30.3%). The biggest barriers to adoption were cited side by side as a "lack of adequate information and infrastructure" (36.8%) and a "shortage of skilled personnel" (34.7%). (NIA, 2025.5) Narrowing the focus makes the picture even clearer. The AI utilization rate among small businesses and the self-employed hovers around 10% (SME News, 2025), and when limited to small and mid-sized "manufacturing" firms, the adoption rate is only about 1%. Because these numbers are so low, in November 2025 the government set a goal, through its "AI-Based Smart Manufacturing Innovation 3.0 Strategy," to roll out AI smart factories to 12,000 companies and raise the adoption rate to 10% by 2030. (Ministry of SMEs and Startups, 2025.11)

And this is not just a Korean phenomenon. In the United States too, 87% of manufacturers have yet to adopt AI in their work (automation.com, 2026), and the smaller the company, the more overwhelming the response that "AI isn't a fit for our business." Yet that response drops sharply as companies grow larger. In the end, this is less a problem of AI "not being applicable" and more one of not knowing where to start. (U.S. SBA, 2025.9)

The real problem: 'we don't know where we stand'

Here is the crux of it. It is not that many companies can't do AI — it's that they don't know where they currently stand. Are we ahead of or behind our industry peers? What is our weakest link? Where should we start to see results fastest? Without this map, the first step never gets taken.

Global research reaches the same conclusion. According to McKinsey's 2025 'State of AI' survey, 88% of companies use AI in at least one function, but only a third have scaled it company-wide, and just 6% qualify as high performers that extract meaningful value from AI. McKinsey is emphatic that what separates them is not algorithms but talent, leadership, and change management. (McKinsey, State of AI 2025) Deloitte likewise found that organizations scoring 70 or above on AI readiness are three times more likely to succeed at adoption within 12 months, yet most SMEs sit in the 35–55 range. (Deloitte AI Readiness, 2025) The point isn't that the score is low — it's that once you know the score, you can see what to fix.

So we opened it up for anyone to try, for free — AX Diagnostic

This kind of diagnosis used to require hiring a consultant. Yet the very places that need it most are the small companies for whom bringing in a consultant feels like too much. So TecAce decided to open up, for free, the 10 core questions it has refined through interviews with a wide range of clients, so that anyone can run them on their own.

No grand report, no consultant visit required. Answer 10 scenario-based questions that anyone on the ground can answer, and in just 2 minutes a one-page diagnostic report appears on your screen. Here's what it contains.

1. AI maturity level (Levels 1–5) — tells you in one line where you stand among the five levels and what it takes to reach the next one.

   • Level 1 · Pre-adoption — AI has barely been touched. Start by automating a single, highly repetitive task and you'll feel the impact quickly.

   • Level 2 · Explorer — individuals are experimenting with AI. Standardizing proven tools and tidying up your data accelerates adoption.

   • Level 3 · Implementer — multiple teams are actually using AI. Strengthening system integration and in-house capabilities makes operational efficiency leap forward.

   • Level 4 · Integrator — AI is integrated into core workflows. With governance and measurement systems in place, you can scale reliably.

   • Level 5 · Leader — AI is at the center of your strategy. Now it's time to widen the gap with autonomous agents and new business models.

2. Your position from two scores — how much you're using AI now (current usage, AX Position) and how solid your foundation for growth is (foundational readiness, AX Readiness), each converted to a score out of 100 and shown side by side with the industry average. Plotting the two scores as coordinates reveals at a glance which of four types your company falls into: Leading Group, High-Potential, Speed-First, or Starting Line.

3. Scores across 10 areas — culture, data, infrastructure, organization and people, budget, leadership, and performance measurement, plus concrete technical areas like Vision AI, Knowledge (RAG), and Agent/automation — all laid out in a single bar chart that shows your strengths and weak links.

4. The weak area to tackle first + one Quick-Win — for example, if "organization and people" is your weak spot, it points out that "naming a person to drive the effort is the fastest first step," while also pinpointing one starting point that delivers results without big investment, like "start with Knowledge/RAG so you can instantly search scattered manuals and know-how."

On top of this, it adds your gap to target — "N points to go until your target score" — so it's clear in numbers what to do next.

This Quick-Win matters especially. The golden rule of AI adoption that experts uniformly recommend is: "start small, create quick wins, validate, then scale." For manufacturing, that might be predictive maintenance of equipment or computer-vision defect inspection; for distribution and retail, demand forecasting and automatic inventory replenishment; and across the board, automatic categorization of customer inquiries and drafting responses — these are paths whose effectiveness is already proven. (AWS Smart Business) AX Diagnostic points you to the one that fits you.

It's a 2-minute survey, but not a casual quiz

Short doesn't mean lightweight. AX Diagnostic's 10 questions are different from the common online "AI personality tests." Each question is built on a diagnostic framework that TecAce has refined dozens of times in real consulting engagements. Your answers are mapped to the evaluation axes that NIA, McKinsey, and Deloitte all emphasize — data, people, process, and leadership — and, compared against real statistics by industry and size, are converted into a single point on a five-level maturity model. In short, it compresses the key questions a consultant asks in a first meeting into something anyone can answer on their own in 2 minutes. So while it's short, the result is closer to a real diagnosis than a casual quiz.

The diagnosis is just the beginning

A 2-minute diagnosis won't give you every answer. But it turns vagueness into direction. The pressure of "we need to do AI…" becomes a concrete starting point: "We're at level OO, we're weak in △△, so we should start with ◇◇."

What happens after the diagnosis is entirely up to you. You can take the results and move to the next steps in-house, or if you'd like to talk more, an AI Consultant is available 24/7 for a no-pressure conversation. When you need an in-depth discussion with a person, it flows naturally to a TecAce expert consultant. Whichever you choose, the diagnosis itself is always free.

The hardest part of adopting AI is the first step. Making that first step something anyone can take without pressure — that's why we've opened AX Diagnostic to everyone, for free.

Diagnose your company's AI level right now. No email needed to start, and you can see your results instantly.

https://www.tecace.com/ax-diagnostic

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Sources (all published between May 2025 and May 2026)

• National Information Society Agency (NIA), "Analysis of AI Adoption and Challenges in Enterprises: Focusing on Manufacturing," 2025.5.29 — manufacturing adoption rate 25.4%, reasons for non-adoption

• Ministry of SMEs and Startups, "AI-Based Smart Manufacturing Innovation 3.0 Strategy," 2025.11 — SME manufacturing AI adoption rate target: 1% → 10% by 2030

• SME News, AI utilization rate among SMEs and small businesses around 10%, 2025

• automation.com, state of AI adoption in US manufacturing (87% not yet adopted), 2026

• U.S. Small Business Administration, 『AI in Business: Small Firms Closing In』, 2025.9

• McKinsey, 『The State of AI 2025』, 2025.11

• Deloitte AI Readiness Index, 2025