The “Production Development” Wall Beyond PoC
Many companies are engaged in PoC (Proof of Concept) projects to explore the potential of generative AI. However, it’s not uncommon for them to face the subsequent wall of “deployment to a production environment” and stall. The noteworthy point in this news is that Kobelco Systems is supporting Kobe Steel’s “Center of Excellence (CoE)” activities to promote generative AI adoption, advocating for “PoC advancement with an eye on production development.” This signifies a clear transition from merely concluding a technical verification phase to a phase of fully operationalizing AI as a management resource.
In my own experience supporting client companies with AI implementation, I have repeatedly seen patterns where PoCs succeed but subsequent rollouts stumble. The biggest barriers are not technical validation, but rather “operational design for the production environment” and “establishing a cross-organizational utilization framework.” The move by a major corporation like Kobe Steel to tackle this challenge with support from an external specialist firm (Kobelco Systems) can be seen as an important signal foreshadowing an industry-wide trend.
From “Showcasing” to “Deploying” AI Agent Platforms
Another piece of news, Allganize’s exhibition at the “10th AI & Artificial Intelligence EXPO,” can be understood in the same context. The company provides a generative AI and AI agent platform. Enhancing presence at an exhibition is not just a marketing activity; it is evidence that the market’s interest is shifting from “disposable chatbots” to “AI agents that autonomously execute business tasks.”
An AI agent goes beyond a generative AI that handles a single task (e.g., text generation). It is a program that links multiple tools and APIs, autonomously repeating judgment and action towards a given goal. My own team also utilizes AI agents for pipelines like automated social media posting and initial screening of contract reviews. The foundation that generates over 75 million JPY (approx. $47,000 USD) in annual value from an AI tool cost of about 21,000 JPY (approx. $130 USD) per month is precisely this “autonomous execution” capability.
The background to such platforms gaining attention at exhibitions is the shift in corporate needs from “wanting to know what AI can do” to “specifically how to introduce and operate it.”
Three Conditions for “Production Deployment” that Management Must Discern
So, what conditions need to be met to move from PoC to production deployment? Based on the Kobe Steel case and actual implementation support experience, the following three points are crucial.
1. Clear ROI Calculation and Visualization of Ongoing Costs
While “potential” is emphasized at the PoC stage, strict economic viability is required for production deployment. For AI agent implementation, in addition to tool licensing costs (e.g., platform usage fees for services like Allganize, or OpenAI API usage), it is necessary to calculate the development/maintenance costs for integration with in-house systems and the personnel costs for operational monitoring. Beyond simple work-hour reduction effects, evaluation metrics for indirect benefits like improved decision-making quality or prevention of missed opportunities should also be established in advance.
2. Building a Security and Governance Framework
In a production environment, how internal data interacts with AI models becomes a major concern. Especially since AI agents call external APIs, data leakage risks become more complex. A major corporation like Kobe Steel focusing on building a foundation through a CoE is to address this challenge. Even for SMEs, gradual risk management is practical, such as starting with tasks that don’t handle confidential data, or checking cloud service regions and data retention policies.
3. Planning for Role Changes and Retraining of Personnel
As AI agents execute routine tasks, employees’ roles will shift from “operators” to “instructors, monitors, and exception handlers.” A training plan to facilitate this transition smoothly is essential. A CoE also serves as a hub for knowledge within the organization and plays a role in promoting human resource development.
A Practical Approach: “Limited Production Deployment” Feasible for SMEs
Not every company can immediately establish a large-scale CoE or foundational framework like a major corporation. However, even SME managers have ways to utilize AI in a form close to production deployment while controlling risk.
What I recommend is the “Limited Production Deployment” approach. This involves not targeting all company operations, but focusing on a single task that meets the following conditions and operating an AI agent under a management system similar to a production environment.
- Does not handle confidential data (e.g., creating regular market trend reports based on public information)
- Has a clearly defined process (e.g., inputting data into CRM and invoicing systems based on order email content)
- Easy to recover from failure (e.g., retaining a human check step)
For a specific toolchain, combining no-code integration tools like Zapier or Make (formerly Integromat) with OpenAI’s GPTs or Custom GPT (for business) offers a relatively low entry barrier. Initial costs can start from around 10,000 JPY (approx. $62 USD) per month for the integration tool plus API usage fees.
For example, a task like “classifying inquiry form content, generating template text for each responsible department, and notifying via Slack” is ideal for this approach. The operational know-how and track record gained from this “limited production” deployment will fuel expansion into the next business area.
View Subsidies as an Opportunity for “Foundation Building”
The “Digitalization & AI Introduction Subsidy 2026” mentioned in the news is precisely the funding that should be used for this “foundation building for production deployment.” The important perspective is to allocate it not for one-off tool introduction, but for investments that create a foundation for sustainable use, such as building a dashboard to manage and monitor multiple AI agents or introducing an API gateway to safely connect internal data with AI.
When applying for subsidies, the key to approval is not merely listing tool names, but clearly demonstrating specific business improvement goals with production operation in mind, such as “automation of order processing using AI agents, leading to a reduction of XX hours of work per month and lower input error rates.”
Management Decisions for the Era of “Operationalizing,” Not Just “Using”
Allganize’s exhibition participation and CoE support for Kobe Steel indicate that the trend in AI utilization is evolving from “tools for individuals to use conveniently” to “infrastructure embedded in organizational business processes that continuously generates value.”
What managers and CTOs should consider next is not “which AI is interesting,” but the management decision of “which tasks, to what extent, and under what management framework should be delegated to AI agents.” This simultaneously means standardizing operations, clarifying processes, and redeploying human resources to more creative domains.
Starting with just one task is fine. Why not plan for that “Limited Production Deployment” beyond the PoC and take the first step in operationalizing AI as a management resource? That experience will define your organization’s new reproducibility and scalability in the digital age.
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