Stockmark Adds 'Solution Comparison & Evaluation Beta' to Manufacturing AI Agent 'Aconnect' to Break Down Siloed Decision-Making
Stockmark Inc. has integrated a new 'Solution Comparison & Evaluation Beta' feature into its 'Aconnect' AI agent for the manufacturing sector. This feature structures technical selection processes to resolve the issues of siloed expertise and ambiguous criteria in R&D, facilitating faster and more transparent organizational decision-making.
📋 Article Processing Timeline
- 📰 Published: May 20, 2026 at 20:00
- 🔍 Collected: May 20, 2026 at 11:31
- 🤖 AI Analyzed: May 20, 2026 at 12:10 (38 min after Collected)
Stockmark Inc., a developer of domestic generative AI platforms and business-focused AI services, has announced the integration of a new 'Solution Comparison & Evaluation Beta' feature into the technology-scouting agent of its manufacturing-focused AI agent, 'Aconnect.'
This feature is designed to resolve issues in research and development (R&D) such as 'siloed technical selection processes' and 'ambiguous criteria,' supporting faster and more transparent decision-making across the entire organization.
## Background: 'Black-Boxed Technical Evaluation' in R&D
Selecting solutions is a critical process in manufacturing R&D departments. However, many sites face constant challenges such as criteria relying on personal experience (tacit knowledge), superficial comparison tables, discussions losing direction as evaluation axes shift, and the loss of decision processes due to a lack of documentation. This 'siloing of decision-making' has become a major factor in slowing down organizational speed.
## Overview of the New 'Solution Comparison & Evaluation Beta'
The new feature added to the 'Aconnect' technology-scouting agent displays the decision-making process by structuring 'which criteria were used' and 'how they were evaluated' for each idea.
## Key Features of the New Function
- 'Standardization' of evaluation: By organizing perspectives such as technology, cost, feasibility, and risk, it converts decision-making into a reproducible process.
- 'Visualization' for Transparency: It clearly defines the reasons for selecting or rejecting options based on objective indicators, enabling smoother consensus among stakeholders.
- 'Knowledge-ization' of Decision-Making: The process of comparative study is accumulated as data, preventing the 'reinvention of the wheel' or repetition of the same discussions.
- Elevating Organizational Skills: Clear evaluation criteria enable even inexperienced members to make high-level decisions and contributions.
## Impact on Meetings and Organization
With this feature, meetings evolve from 'explanation forums' to 'decision-making sessions.' Discussions shift from subjective 'feelings' to structured analysis, preparation time is streamlined, and consideration processes are archived as organizational assets. It helps build a robust decision-making structure that does not rely on a few experts.
'Aconnect' leverages AI that understands your business to find information, deliver insights, and detect risks and opportunities. For more details, please visit the official website (https://aconnect.stockmark.co.jp).
This feature is designed to resolve issues in research and development (R&D) such as 'siloed technical selection processes' and 'ambiguous criteria,' supporting faster and more transparent decision-making across the entire organization.
## Background: 'Black-Boxed Technical Evaluation' in R&D
Selecting solutions is a critical process in manufacturing R&D departments. However, many sites face constant challenges such as criteria relying on personal experience (tacit knowledge), superficial comparison tables, discussions losing direction as evaluation axes shift, and the loss of decision processes due to a lack of documentation. This 'siloing of decision-making' has become a major factor in slowing down organizational speed.
## Overview of the New 'Solution Comparison & Evaluation Beta'
The new feature added to the 'Aconnect' technology-scouting agent displays the decision-making process by structuring 'which criteria were used' and 'how they were evaluated' for each idea.
## Key Features of the New Function
- 'Standardization' of evaluation: By organizing perspectives such as technology, cost, feasibility, and risk, it converts decision-making into a reproducible process.
- 'Visualization' for Transparency: It clearly defines the reasons for selecting or rejecting options based on objective indicators, enabling smoother consensus among stakeholders.
- 'Knowledge-ization' of Decision-Making: The process of comparative study is accumulated as data, preventing the 'reinvention of the wheel' or repetition of the same discussions.
- Elevating Organizational Skills: Clear evaluation criteria enable even inexperienced members to make high-level decisions and contributions.
## Impact on Meetings and Organization
With this feature, meetings evolve from 'explanation forums' to 'decision-making sessions.' Discussions shift from subjective 'feelings' to structured analysis, preparation time is streamlined, and consideration processes are archived as organizational assets. It helps build a robust decision-making structure that does not rely on a few experts.
'Aconnect' leverages AI that understands your business to find information, deliver insights, and detect risks and opportunities. For more details, please visit the official website (https://aconnect.stockmark.co.jp).
FAQ
What changes with the 'Solution Comparison & Evaluation Beta'?
Evaluation criteria previously based on individual experience are standardized by AI, allowing anyone to conduct structured comparisons and discussions, thereby improving organizational decision-making quality.
Why is this feature important?
Technical selection in R&D is a source of corporate competitiveness but tends to be individual-dependent. This feature turns the decision process into an asset, preventing reinvention and upgrading organizational capability.
What is the benefit for junior staff?
By leveraging the standardized AI evaluation framework, junior staff can make logical judgments and contribute effectively even in non-specialized areas, promoting their growth and involvement.