Editing Idea:Task Classification for AI-Human Allocation (section)

=== Research Tasks ===

'''Definition:''' Technology exploration, feasibility analysis, competitive research, solution investigation, and knowledge acquisition for informed decision-making.

==== Task Characteristics ====

'''Information-Intensive Nature:'''
* Large-scale information gathering and synthesis requirements
* Evaluation of multiple sources with varying quality and relevance
* Trend analysis and pattern recognition across diverse data sources
* Synthesis of findings into actionable insights and recommendations

'''Typical Examples:'''
* Technology evaluation and vendor comparison analysis
* Industry trend research and competitive landscape assessment
* Academic literature review and best practice identification
* Open source tool evaluation and selection criteria development

==== Success Factor Analysis ====

'''Critical Success Factors (Weight 5/5):'''
* '''Creative Problem-Solving:''' Synthesis of diverse information sources and insight generation
* '''Technical Depth:''' Evaluation of technical merit and applicability assessment

'''High Importance Factors (Weight 4/5):'''
* '''Strategic Thinking:''' Alignment of research findings with organizational objectives
* '''Autonomous Execution:''' Self-directed information gathering and analysis

'''Moderate Importance Factors (Weight 3/5):'''
* '''Communication & Collaboration:''' Research finding presentation and stakeholder education
* '''Domain Expertise:''' Industry-specific knowledge for contextual evaluation

==== Optimal Allocation Strategy ====

'''Primary Approach: AI Information Gathering with Human Synthesis'''

'''AI Advantages:'''
* Comprehensive information gathering across multiple sources
* Systematic evaluation against predefined criteria
* Pattern recognition and trend identification
* Large-scale data processing and initial filtering

'''Human Synthesis Requirements:'''
* Strategic evaluation and business relevance assessment
* Creative insight generation and recommendation development
* Stakeholder communication and finding presentation
* Decision-making integration and implementation planning

'''Success Metrics:'''
* Research comprehensiveness and source quality assessment
* Decision-making effectiveness based on research findings
* Time savings achieved through AI-assisted information gathering
* Stakeholder satisfaction with research quality and relevance