A New Era of AI Tool Procurement Has Begun
Anthropic, a leading generative AI company, has announced the launch of the “Claude Marketplace.” This is a platform where businesses can centrally procure and implement various AI tools provided by Anthropic’s partner companies. Following ChatGPT’s “GPT Store,” the emergence of marketplaces from major model providers is set to significantly impact corporate AI adoption strategies.
Until now, companies had two main options for introducing AI into their operations. One was the “SaaS-type” approach of using general-purpose tools like ChatGPT or Claude as-is. The other was the path of “in-house development” to create AI specialized for their own data and business processes. The former is easy to start with but has limited customization, while the latter promises high effectiveness but faces barriers of development costs and technical hurdles.
The Claude Marketplace presents a “third way” beyond this dichotomy. It is the option to procure “highly polished AI tools” specialized for specific tasks from trusted vendors, within a secure environment. Executives and CTOs should view this trend not merely as “adding another tool,” but as “an opportunity to fundamentally reconsider the allocation of their internal development resources.”
The Boundary Between “Buy” and “Build” is Blurring
The core of this news lies in the rapid maturation of the AI tool “ecosystem.” Solutions like the “AI robot incorporating station staff experience” being trialed by the Nagoya City Transportation Bureau, or the “generative AI-powered product development support” service for businesses in the Hokusetsu region, are all specialized for particular domains.
As such specialized solutions become easily procurable through marketplaces, management decisions will change. The thought process used to be: “We want to automate this task → Should we develop it in-house or outsource it?” However, going forward, the first step will likely be: “We want to automate this task → First, check if an existing tool is available on a marketplace.”
This change relativizes the significance of in-house development. There is no longer a need to internalize everything. Development resources can be concentrated only on areas related to core competitiveness or on differentiating elements that simply cannot be achieved with existing tools. In other words, the classic management question of “what to buy and what to make in-house” has emerged as a top priority in the realm of AI as well.
Three Conditions for Choosing In-House Development
So, under what circumstances should you choose the path of in-house development? Based on our experience of implementing 93 AI use cases in-house and achieving an annual reduction of 1,550 work hours, we present three criteria for judgment.
1. When it is the “Core” of Your Competitive Advantage
Business processes directly tied to your company’s proprietary know-how, customer data, or brand should not be dependent on external tools. For example, while we use AI to automate the review and amendment suggestions for contracts that vary per customer, the prompts and workflow for this are a source of our competitiveness, and we have no intention of externalizing this service.
2. When “Differentiation” Unattainable by Existing Tools is Required
Tools on marketplaces are designed to meet general needs. If you have unique requirements specific to your industry or operations (e.g., full compliance with specific industry regulations, integration with proprietary data formats), in-house development is the answer. For the integration of AI with traditional tasks faced by long-established Kyoto companies, for instance, there will always be aspects that off-the-shelf tools cannot address.
3. When Continuous Customization and Evolution are Essential
Procured tools depend on the vendor’s roadmap. If the pace and nature of your business change rapidly, requiring daily improvements to the tool itself, you should consider internal development. We chose to develop our SNS auto-posting pipeline in-house so it can immediately respond to algorithm changes or shifts in our own content strategy.
Concrete Steps for Utilizing Marketplaces
Now that a new option has emerged, how should executives and CTOs act? We propose four concrete steps.
Step 1: Create an “Automation Potential” Map of Your Operations
First, list all your company’s tasks and evaluate each for “difficulty of AI automation” and “operational importance.” On this map, tasks with “high importance and medium difficulty” become the initial candidates for seeking solutions on a marketplace.
Step 2: Use Marketplaces as a “Specification Sheet”
Use the Claude Marketplace or GPT Store not just as a purchasing venue, but as a source of information on “what kind of AI solutions companies worldwide are seeking and providing.” By investigating the features and price ranges of tools handling similar tasks, you gain clarity on the market rate and necessary features if you were to develop in-house.
Step 3: Conduct PoC (Proof of Concept) “Short-Term & Low-Cost”
If you find a tool of interest, test it on a minimal scale first. Many cloud-based AI tools operate on a pay-as-you-go model starting from a few thousand to tens of thousands of yen per month. For example, trying a tool costing ~$125/month for three months totals ~$375. If this allows you to assess the potential for operational efficiency, it’s a major advantage to determine direction before investing hundreds of thousands of dollars in development.
Step 4: Design Integration Assuming Potential “Lock-in”
The biggest risk when introducing an external tool is vendor lock-in (becoming dependent on a specific vendor and unable to extricate yourself). From the implementation stage, check the tool’s data export functionality and whether key APIs are publicly available. Design the integration points with your own systems to be as controllable in-house as possible.
Often Overlooked Costs and Risks
Procurement via marketplaces is convenient, but it comes with hidden costs and risks.
Integration Costs: While the monthly fee for the tool itself may be clear, development costs for integrating it with existing CRM or core systems are separate. Even for simple API integration, you should anticipate several dozen hours of engineering effort.
Training & Change Management Costs: Introducing a new tool necessitates employee training and a review of the business processes themselves. Underestimating this human cost leads to tools becoming “shelfware”—implemented but unused.
Security and Governance Risks: You must rigorously verify how external tools handle your company’s data and whether audit trails are available. Especially in heavily regulated industries like finance or healthcare, vendor security certifications (SOC2, ISO27001, etc.) will be essential requirements.
The Future is a “Hybrid” Model
In the generative AI era, why do top-tier business professionals still not abandon learning English? It’s because they recognize the importance of “human foundational capabilities”—not just relying on tools, but understanding them, mastering their use, and sometimes compensating for their limitations. This applies to corporate AI strategy as well.
The winning companies of the future will build a “hybrid model” that combines “excellent off-the-shelf products” procured from marketplaces with “core proprietary AI” developed and nurtured in-house. Attempting to internalize everything is inefficient. Conversely, relying entirely on external procurement erases differentiating factors, turning a company into a mere commodity player.
Anthropic’s Claude Marketplace is merely the prologue forcing this choice. The same movement is likely to spread to other major AI companies. We recommend that executives and CTOs immediately confront their company’s operational map and begin the crucial separation between “what to buy” and “what to protect and cultivate.” Even in the world of AI, the essence of management remains unchanged.
(Cost Reference: Development for integrating a general AI tool API, even for simple cases, costs approximately $3,150 – $6,300. Monthly fees for specialized tools via marketplaces range from ~$63 to several thousand dollars per month depending on the task. For in-house development, in addition to initial development, securing ongoing engineering resources for maintenance and improvement is essential.)
Comments