The Turning Point: A New Service That Turns Data Assets into AI
In March 2025, x3d announced the launch of its “AI Model, Digital Twin, and Persona AI Development Support Service.” This news carries significance beyond a simple new service release. The concept of transforming dormant corporate data assets into AI fundamentally challenges conventional wisdom about AI utilization.
Traditionally, corporate AI adoption has centered on “operational efficiency.” However, this service takes a more essential approach: “turning data itself into an asset through AI.” What business leaders need to grasp is the “value shift of data assets” that this trend represents.
Personally, during legal negotiations in Malaysia, I analyzed and reconstructed all emails, ordinances, and laws using AI. At the time, it was a near-manual process, but today, leveraging such services enables faster, more accurate data asset creation.
The Core Difference Between Digital Twins and Persona AI
This service introduces two concepts: “digital twins” and “persona AI.” Business leaders must understand the precise distinction.
A digital twin is a technology that replicates real-world business processes or equipment in data. In manufacturing, it’s used for factory line simulations, but recently, there’s a growing trend of creating “twins of business processes” for back-office operations.
In contrast, persona AI recreates specific individuals or customer profiles using AI. For example, by training AI on your top salesperson’s thought patterns and negotiation techniques, you can use it for new employee training. Alternatively, you can create a persona of the “average customer” from purchase behavior data to test marketing strategies.
Combining these two gives companies both an “AI that replicates past success patterns” and an “AI that simulates the future.”
When to Reassess the Value of Your Data
Many companies face the challenge of having data but not utilizing it. In over 80% of the companies I visit for consulting, past project data and customer information remain abandoned in Excel or PDF files.
The benefits of turning this data into AI models are threefold. First, it eliminates knowledge silos. Know-how known only to specific employees is preserved as AI within the organization. Second, it improves business reproducibility. AI analyzes past successes and applies them to new projects. Third, it enhances decision-making quality by enabling data-driven simulations.
Regarding implementation costs, initial setup for services like this typically ranges from several hundred to several thousand USD, with monthly fees from tens to hundreds of USD. However, costs vary significantly based on data volume and quality. The key point is that “data organization” is the most expensive part. Before AI modeling, you need to inventory your data’s current state.
How the Latest Model “Gemini 3.5 Flash” is Changing AI Reality
Around the same time, exaBase began offering the latest model, “Gemini 3.5 Flash.” Viewing this news in the context of data assetization reveals an interesting dynamic.
Gemini 3.5 Flash offers improved processing speed and cost-performance compared to previous models. Specifically, it reportedly achieves approximately 30% cost reduction and doubles response speed.
This is a crucial technological foundation for companies transitioning from the “trial” phase to “full-scale implementation” of AI. Cost and speed are critical, especially when operating data-assetized AI models in practice.
For instance, using persona AI for real-time customer support is useless if responses are slow. Similarly, processing large volumes of data becomes unviable if API costs are prohibitive. The emergence of cost-efficient models like Gemini 3.5 Flash makes the business model of data assetization more realistic.
Combining with Training Programs is Key to Success
In another development, SC Digital launched a “Generative AI Training Program.” This program focuses not just on tool usage, but on learning how to integrate AI into business processes.
From my experience, the success of AI adoption depends more on “people and organizational readiness” than on “technology.” Having implemented 93 AI use cases within my own company, the biggest challenge wasn’t tool selection, but gaining internal buy-in.
Similar challenges arise when introducing data assetization services. If business leaders themselves don’t have answers to “Why do we need to AI-ify our current data?” and “How will we use the AI-ified data?”, the team won’t move forward.
When selecting a training program, check three criteria: “Does it include case studies specific to your industry?”, “Is it a practical workshop format?”, and “Is there post-implementation support?”
First Steps for SMEs to Start Data Assetization Now
Based on the above, here’s what SME leaders can do right now.
First, inventory your data. What data exists, where is it stored, and who manages it? AI-ification cannot begin without this understanding. Second, set priorities. You don’t need to AI-ify all data at once. Start with the area promising the most impact. Candidates include sales team deal data or customer support interaction histories.
Third, choose your partner. Will you use a specialized service like x3d, or build an AI model in-house? While the barrier to in-house development has lowered, data preprocessing and model operation require expertise. I recommend starting with a specialized service.
In terms of cost, it’s realistic to start with a small-scale Proof of Concept (PoC). An investment of a few hundred USD can AI-ify a specific dataset and verify its effectiveness. Once confirmed, you can scale up gradually.
Personally, I achieved an annual reduction of approximately 1,550 work hours and an ROI of 2,989% in my own company. These numbers aren’t exceptional; with the right strategy and execution, any company can replicate them. Data assetization is a powerful tool for achieving this.
AI evolution is accelerating. Cost-efficient models like Gemini 3.5 Flash are emerging, and data assetization services are becoming more robust. Now is the time to look at the value sleeping within your company’s data.
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