Proposal Preparation Time of Three Days Quietly Comes to an End

Freelancers submit proposals to new clients. It’s a common story.

They conduct interviews, research the industry, think about the structure, design the layout, and attach a quote. If done meticulously, it takes 2 to 3 days. Outsourcing it costs between 50,000 to 150,000 yen. That was considered “normal.”

However, that “normal” is beginning to break down.

The AI proposal generation tool “Proposly” can produce a professional proposal in just a few minutes after entering client information. The only cost involved is a subscription fee of a few thousand yen per month. When calculated per proposal, it amounts to just a few hundred yen.

What used to cost 50,000 yen is now 500 yen. That’s one-hundredth of the original cost. This is no longer just “cheaper”; it’s practically free.

The same phenomenon is occurring in market research. The question is simple.

“Is your company still paying 300,000 yen for research?”

Market Research Costs Could Drop by Up to 80%

Recent studies have confirmed that using large language models (LLMs) for data generation and analysis can reduce traditional market research costs by 24.9% to 79.8%.

To be specific:

The typical cost structure for traditional market research is as follows:

• Survey design and implementation: 100,000 to 300,000 yen
• Data aggregation and analysis: 100,000 to 200,000 yen
• Report preparation: 50,000 to 150,000 yen
• Total: 250,000 to 650,000 yen

By utilizing LLMs, AI can create initial drafts for survey design, handle primary data analysis, and generate report drafts. The only tasks left for humans are “final checks on questions,” “interpretation of analysis results,” and “decision-making.”

If costs are reduced by 79.8%, a 650,000 yen study becomes 130,000 yen. Even with a 24.9% reduction, 490,000 yen drops to 370,000 yen. In either case, the saved funds can be allocated to other initiatives.

For small and medium-sized enterprises, this difference is the very tipping point of “to do or not to do.” Companies that previously thought, “We can’t afford to spend that much on research,” will now be able to make data-driven decisions. As costs decrease, the quality of decision-making improves. This is the essence.

The Story of AI Calling Pubs Nationwide

There is an interesting case. In Ireland, an AI agent called pubs across the country to survey the price of Guinness beer.

Traditionally, researchers would be hired, a list would be created, and calls would be made one by one. For 100 pubs, this would take several days; for 1,000 pubs, it could take weeks. Just the labor costs would run into the hundreds of thousands of yen.

The AI agent automated this process, making calls, asking for prices, and organizing the data. Humans only needed to look at the results and make judgments like, “This area is expensive” or “There’s a business opportunity here.”

This structure applies directly to local small and medium-sized enterprises.

For instance, if a local food manufacturer wants to investigate competitors’ retail prices, they would traditionally send a salesperson to visit stores or hire a research company. Both options are time-consuming and costly. With AI agents, it’s now possible to automatically collect price information from the web and even automate phone surveys.

What happens when the cost of “researching” approaches zero?

The answer is clear. “Making decisions based on research” will become the norm. Areas that previously relied on intuition due to the “cost of researching” as a bottleneck will all shift to being data-driven.

The Definition of ‘Outsourcing’ Changes

Here, we want to consider the change in the very concept of “outsourcing.”

Small and medium-sized enterprises typically outsource for two main reasons:

1. Lack of skills in-house (design, specialized analysis, etc.)
2. Lack of resources (time) in-house (unable to handle volume)

AI is beginning to solve both of these issues simultaneously.

Even without design skills for proposals, AI can output in a well-structured format. Even without specialized knowledge in market analysis, AI can structure the data. Even with time constraints, AI can complete tasks in a matter of minutes.

In other words, “lack of skills” and “lack of time” will no longer be valid reasons for outsourcing.

So, will outsourcing become unnecessary? Not at all. The value of outsourcing will shift from “task execution” to “decision support.”

Creating proposals can now be efficiently handled by AI. However, determining “what angle to propose to this client” or “how to interpret this market data and what the next steps should be”—these are human judgments. It is precisely for external partners who can assist in those judgments that companies will find value in paying.

The era of paying 50,000 yen for tasks is over; the era of paying 50,000 yen for judgments has arrived.

The ‘Reversal Structure’ for Small and Medium-Sized Enterprises

Now, we come to a truly important point for local small and medium-sized enterprises.

Large corporations typically have dedicated research departments, specialized teams for creating proposals, and abundant outsourcing options. Therefore, the benefits of cost reduction through AI are merely “nice to have.”

On the other hand, small and medium-sized enterprises are different. What they couldn’t do before due to lack of funds can now be accomplished with AI.

• They can now submit proposals for projects they previously couldn’t.
• They can back up pricing decisions that were previously based on intuition with data.
• They can now conduct research that they couldn’t outsource before in-house.

The benefits of reduced costs are greater for those who have less. This is the reversal structure.

If a large company has 100 weapons and a small company has only 10, when AI equips everyone with 90 weapons, the gap shrinks from “100 to 10” to “100 to 90.” Relatively speaking, small and medium-sized enterprises stand to gain significantly.

Further Cost Reductions Through ‘Task Differentiation’ with LLMs

There is one more practically important point. The cost-conscious model orchestration of LLMs—essentially, the method of “differentiating AI models based on task difficulty.”

For simple tasks (standard text generation, data organization), inexpensive lightweight models are used, while complex tasks (strategic analysis, nuanced writing) employ high-performance models. This differentiation has been shown to improve accuracy by 0.90% to 11.92% while significantly reducing costs.

In practical terms, this means:

• Standard sections of proposals (company overview, list of achievements) → Automatically generated using lightweight models (almost free)
• Core sections of proposals (problem analysis, proposal content) → Generated using high-performance models (a few dozen yen)
• Final judgment (direction of the proposal, pricing) → Human

By systematizing this “light, medium, heavy” differentiation, costs can be minimized without sacrificing quality.

So, What Should We Do?

There are three things you can start doing tomorrow.

1. Have AI create the ‘template’ for proposals
Try instructing ChatGPT or Claude right now to “create a structure for a proposal for the XX industry.” You’ll be surprised at how reasonable the output is. Using dedicated tools like Proposly will further enhance the quality. Start by creating one proposal with AI.

2. Delegate primary information gathering for market research to AI
Price surveys of competitors, organizing industry trends, designing customer surveys—these “researching” tasks can be handed over to AI. Using research-focused AIs like Perplexity can reduce information gathering time from half a day to just 30 minutes.

3. Separate your work into ‘tasks’ and ‘judgments’
This is the most important part. Take stock of your current tasks and categorize them as “Is this a task that can be delegated to AI?” or “Is this something I need to decide?” The more tasks you delegate to AI, the more time you will have to focus on decision-making.

The Remaining Work Is Only ‘Judgment’

The cost of creating proposals has dropped to one-hundredth, and market research costs could decrease by up to 80%. This change is no longer a “future story.” It is a reality that can be realized today with tools that are already available.

The era of creation is over. The era of selection has arrived.

In a world where AI can create proposals and conduct research for almost nothing, what remains for humans is the work of deciding which proposal to submit, how to interpret this data, and what to do next.

For local small and medium-sized enterprises, this is nothing short of an opportunity. What was once only possible for large corporations can now be done for a monthly fee of just a few thousand yen. The tools are now available. The question is whether to use them.

While you say, “It’s still too early for us,” the neighboring company is already submitting proposals using AI. This is the era we live in.