Go To Market: How to Bridge the Gap Between Market and Data
🎯 TL;DR
Amid the fantasy that AI will automate everything, many companies are falling into the trap of paying for AI tools without generating revenue. According to MIT research, 95% of generative AI pilot projects in companies are failing, and the failure rate of AI adoption in Korean companies reaches 80%. The core of GTM (Go-to-Market) strategy in 2025 is not about flashy tools. It is the capability to identify the gap between the actual voice of the market and data, and to determine 'where' and 'how' to use AI. Especially for companies in growth stagnation or early stages, this capability becomes a decisive factor for survival.
📉 2025, Korean Startups Walking on Thin Ice
In the first quarter of 2025, the chill in the Korean startup investment market is fierce.
The number of investments decreased by 24% compared to the previous year, and the investment amount also decreased by 4%. 64.8% of founders and 58.9% of investors diagnose that the situation has worsened compared to last year. Expressions like "cold season" and "investment winter" are heard daily.
In this environment, many companies are focusing on AI-based GTM automation as a breakthrough. "If you automate sales with AI, you can grow 10 times with fewer people" is a story heard everywhere.
But what is the reality?
🚨 Shocking Truth: 95% of AI Adoptions Fail
Recent MIT research reveals a shocking fact. 95% of companies' generative AI pilot projects are failing. The situation in Korea is even more serious. According to the RAND Corporation, a US think tank, the failure rate of AI adoption in Korean companies reaches 80%. This means that if 10 companies adopt AI, 8 of them fail.
Why does this happen? The core of the problem is simple.
Buying AI tools does not automatically generate profit.
💸 A Situation Where Only AI Companies Make Money
Let's look at the situation many companies actually face.
Situation 1: The Company Where Tools Just Pile Up
This is a story of a B2B SaaS startup I met.
- Data collection tools, email automation tools, CRM integration tools... Monthly subscription fees exceed 5 million won (partly due to exchange rates).
- AI generates 1,000 leads a day.
- The sales team sends emails to these leads saying "AI personalized this".
- The result? Response rate 0.3%, 2 contracts closed in 6 months.
The contract unit price is 10 million won, so the revenue for 6 months is 20 million won. Since they spent 30 million won on AI tools alone, it's a loss. If you add employee labor costs here... In the end, only the AI tool company made money.
Situation 2: The Company Drowning in Data
A domestic startup approached it more actively.
- They collected potential customer data using the latest AI engine.
- The dashboard was filled with flashy graphs.
- "Series B investment attracted companies", "5 or more job postings in the last 3 months", "Search history of our product keywords"...
It looked perfect. But when the sales team started making calls, problems were revealed.
- It was true that the company received Series B investment, but they were pouring all that money into product development.
- External solution purchases were postponed for a year.
- Many job postings meant they were busy now, not that they had the leeway to buy our solution.
The data was accurate. But the context was completely missing.
Three months later, this company canceled the AI tool subscription and hired one experienced sales person. The result? 8 contracts closed in the next 3 months. How was this possible? Because that salesperson grasped the customer's actual situation with a single phone call.
🎯 So What is Really Important?
Hearing the voice of the market directly.
This is why GTM strategy is necessary for companies in growth stagnation or early stages. When all indicators are upward, you can maintain the existing method. But when growth stops or you are just about to enter the market, we need to know:
- What does the customer actually want?
- Does the data we are looking at reflect the real appearance of the market?
- Is the 'insight' generated by AI really the right story?
Real Examples: The Gap Between Data and Reality
AI tool says: "This company recently received Series B investment, so the purchase probability is high."
What the sales representative actually heard on the phone: "We are using all investment funds for product development. We won't buy external solutions for at least a year. In-house development is the priority now."
Another case:
AI analysis: "This company searched our product keyword 3 times. Interest is high."
Actual situation: An intern at that company searched to make a competitor analysis report. There was no intention to purchase at all.
The data is correct. But the context is missing. Bridging this gap, that is the core of the real GTM strategy.
🚶 Feet on the Ground and AI, How to Combine?
The evolution of GTM strategy in 2025 is not a dichotomy of "AI or Human". The human capability to judge where and how to use AI determines the outcome.
Common Points of Companies That Actually See Results
AI in Startup GTM Report 2025 Pt. 1: Benchmark Report
Step 1: Start from the Field
This is the case of US startup Kickfurther. Before introducing AI tools, their SDR team made over 100 calls directly.
The purpose was only one. To check the customer's actual reaction.
Then they distinguished between parts AI would automate and parts humans had to do directly.
Result?
- Response rate increased by 25%
- Click-through rate increased by 17%
- Sales productivity improved 10 times
They used the same AI tools. The difference was that humans understood the field first.
Step 2: Start with Small Experiments
The story of digital security company EasyDMARC.
They tried to introduce AI lead management automation fully. But they found something strange during the pilot test. 40% of leads coming into the website were not even being recorded in the system.
If they had introduced AI automation right away? The automation system would have processed only 60% of leads, and the remaining 40% would have disappeared forever.
EasyDMARC blocked this 'leak' first. Then they applied AI.
Order is important. Automation comes next.
Step 3: Coldly Calculate Cost vs. Effect
Honest calculation of a B2B SaaS company:
- AI sales automation tool: 5 million won/month
- 6 months use: Total 30 million won
- Contracts closed: 2 (10 million won each)
- Total revenue: 20 million won
- Loss: 10 million won
This company made a decision. They canceled the tool and hired one experienced sales person with that money.
8 contracts closed in 3 months. Total revenue 80 million won.
Sometimes humans are cheaper than AI. And much more effective.
💡 The Essence of GTM Strategy in the AI Era
So what is a truly effective GTM strategy?
Principle 1: Find the Gap
Things to do first:
- Find the difference between what the data says and what the customer actually says.
- Directly call customers classified as "good leads" by AI tools.
- Ask customers who didn't convert why they didn't purchase.
The core is not numbers, but stories.
Data says "Conversion rate 2.3%". But the customer says "Price is not the problem, it doesn't fit our team's tech stack".
The moment you understand this difference, the strategy changes.
Principle 2: Use AI as a Tool, Do Not Make It a Strategy
What AI is good at:
- Repetitive data collection and organization
- Finding clear patterns (However, humans must verify)
- Generating personalized messages to a certain level
What AI is bad at:
- Understanding customer thinking context
- Identifying customer's actual priorities
- Identifying political/organizational factors affecting purchase decisions
Shall we take an example?
AI tells you "The CFO of this company visited our website 3 times".
But AI doesn't know:
- The fact that the CFO has already allocated all budgets for this year.
- The fact that the introduction of new solutions is frozen because the company is currently restructuring.
- The fact that the CFO only came in to benchmark our product after seeing a competitor's investment announcement.
It is the human's job to figure this out.
Principle 3: Start Small, Experiment Fast
Before paying tens of millions of won to an external marketing agency, try this:
Week 1: Problem Definition
- Where is the biggest bottleneck right now?
- Are leads insufficient, is the conversion rate low, or is there a lot of churn after contract?
Week 2: Hypothesis Establishment
- Why do you think this problem occurs?
- Ask 10 customers directly.
Weeks 3-4: Small Experiment
- Apply only one change.
- Whether using AI tools or humans, just one thing.
Week 5: Measurement and Learning
- Did the numbers actually change?
- If they changed, why? If not, why?
A Korean B2B startup achieved a 30% increase in monthly revenue in 3 months with this method.
Without spending big money, with fast experiments and learning.
🔮 2025, Who is the Real Winner?
Tech trend reports paint a flashy future.
"AI automates everything."
"One GTM engineer replaces 10 SDRs."
"Data makes all decisions."
But reality is a bit different.
More serious is that most of these accidents stemmed from blind trust in AI systems.
🏁 Conclusion: Back to Basics
GTM Engineering, RevOps, Growth Ops, AI Ops...
Terms keep changing. Trends keep emerging.
But the essence does not change.
Identifying what customers want, delivering value to them, and generating revenue in a sustainable way.
AI can make this process faster. It can make it efficient. It can increase the scale.
But AI cannot replace this process.
In 2025, successful companies do not boast of flashy tools. Instead, they say:
"We listen to the customer's voice. Verify with data. Expand with AI. And go back to the customer."
This is the real GTM strategy.
Not a trendy job title, but the ceaseless effort to bridge the gap between market and data.
And the wisdom to use AI as a tool.
That is all.
📊 Checklist for Practitioners
Essential Questions Before Adoption:
1. Problem Definition
- What is the specific problem we are trying to solve?
- How much revenue are we actually losing because of this problem?
- Do we really know the cause of the problem, or is it a guess?
2. Try Without AI First
- Have we ever tried to solve this problem without AI?
- Did it work when done manually?
- Why is AI needed? (Faster? Bigger? Other reasons?)
3. Cost vs. Effect
- What is the monthly cost of this tool?
- Is the expected revenue increase at least 3 times the cost?
- Can we afford the loss if it fails?
4. Feasibility
- Is there someone who will actually use this tool?
- Does that person have time to learn how to use the tool?
- Is there a possibility of continuing to use it after 6 months?
3-Month Evaluation After Adoption
Quantitative Indicators
- Did the actual contract closing rate increase?
- Did the sales cycle shorten?
- Is the Return on Investment (ROI) positive?
- Does the sales team actually use this tool every day?
Qualitative Indicators
- Did customer satisfaction not drop?
- Does the sales team like this tool?
- Are customers not reporting us as spam?
- Is the team moving faster, or just looking busier?
Moment of Decision
- If 3 or more out of 4 are "No": Cancel the tool and find another way.
- If 2 are "No": Change the usage method or readjust the goal.
- If 1 or less is "No": Keep going, but find points for improvement.
📌 Glossary
GTM (Go-to-Market): A strategy to launch a new product or service in the market and reach customers. It covers target customer definition, pricing, sales channels, marketing messages, etc.
ICP (Ideal Customer Profile): A definition of the characteristics of the customer best suited for our product/service. It includes industry, size, budget, decision-making structure, tech stack, etc.
ABM (Account-Based Marketing): A strategy of conducting customized marketing targeting specific client companies. It is a method of focusing on high-value customers rather than mass marketing.
CAC (Customer Acquisition Cost): The average cost to acquire one customer. It is the value obtained by dividing marketing and sales costs by the number of new customers.
SDR (Sales Development Representative): A sales role responsible for discovering potential customers and initial contact. Mainly creates leads through outbound calls or emails.
RevOps (Revenue Operations): An organization and strategy that integrates marketing, sales, and customer success teams to optimize the revenue generation process. It unifies data, tools, and processes to increase efficiency.
PMF (Product-Market Fit): The state where the product meets the actual demand of the market. It means the stage where customers are satisfied with the product, recommend it voluntarily, and repurchase.
ROI (Return on Investment): The ratio of profit obtained compared to the invested amount. Calculated as (Revenue - Cost) / Cost × 100, it is a key indicator measuring the efficiency of investment.
💬 Conclusion
I would like to ask a question to those reading this article.
How does your company distinguish between the roles of AI and humans?
If you invested in AI tools, are you actually seeing results?
Or are expenses just going out?
Please share your experiences in the comments. Failure experiences are also welcome. Because we can learn more from failure.
If this article was helpful, please share it with colleagues who might be having the same concerns.