Table of Contents
1. Understanding the Unique Nature of AI Creative Projects
1-1. Differences from Traditional Creative Production
AI creative projects possess fundamentally different characteristics compared to traditional design or video production.
The most important difference is the unpredictability of deliverables.
Unpredictability of Deliverables and Quality Variations
In traditional creative production, experienced designers could predict the final deliverables to some extent based on required specifications. However, with AI creative work, even with the same prompts or conditions, there can be significant variations in the generated results.
When requesting “corporate logo design,” traditional methods would allow selection from several pattern proposals. With AI, hundreds of patterns can be generated, but each varies slightly, and finding one that perfectly matches client requirements may take considerable time.
Balancing Technical Constraints with Creative Requirements
AI tools have clear technical constraints. For example, they may excel at certain styles but struggle with others, have difficulty generating detailed text, or show low accuracy in specific compositions.
It’s necessary to balance these constraints with creative requirements.
Production Process Based on Iterative Improvement
While traditional creative production often reached completion through initial proposals and several revisions, AI creative work typically involves dozens or sometimes hundreds of generation and adjustment cycles to approach the ideal deliverable.
1-2. Common Elements of Successful Projects
Clear Goal Setting and Stakeholder Expectation Management
In AI creative projects, defining “what constitutes success” is particularly important. Due to the high subjectivity of quality, having all stakeholders share the same goal image is key to success.
- Clarifying quality standards such as “commercial-use level” or “prototype level”
- Presenting multiple reference cases and concrete examples beforehand to specify target quality ranges (confirming reproducibility through portfolios, etc.)
- Clarifying the choice between “80-point deliverables in large quantities quickly” vs “110-point deliverables with time investment”
Risk Management Through Phased Approach
Rather than jumping directly into full-scale production, a phased approach starting with small-scale verification and gradually expanding is effective.
This allows early detection of gaps between technical feasibility, speed, budget, resource allocation, and business requirements, preventing major setbacks.
Importance of Continuous Communication
In AI creative production, alignment during intermediate stages is particularly important.
Art direction communication is essential as course corrections occur while viewing generated results.
2. Pre-Project Preparation: 5 Points Clients Should Organize
It’s no exaggeration to say that pre-order preparation accounts for 80% of AI creative project success. Organizing the following 5 points in advance can significantly improve project success probability.
2-1. Business Objective Clarification
Specific Problem Definition
Rather than vague requirements like “wanting to create good things using AI,” it’s important to articulate specific business challenges in writing.
- “Existing product images make differentiation difficult. Want to improve purchase rates by 20% with more attractive visuals”
- “Video advertisement production costs 5 million yen monthly. Want to reduce costs by 30% with AI while maintaining equivalent effectiveness”
- “Want to automate customized design proposals and improve sales efficiency by 3x”
Success Indicator (KPI) Setting Methods
Measuring AI creative effectiveness requires considering AI-specific indicators in addition to traditional metrics.
- Business Outcome Indicators: Sales improvement, cost reduction, efficiency enhancement, etc.
- Quality Indicators: User satisfaction, brand image compatibility, etc.
- Operational Efficiency Indicators: Production time reduction, revision count reduction, variation and production speed improvement/efficiency, etc.
Realistic Budget and Schedule Setting
For first-time transactions, AI creative work is more difficult to predict than traditional production, so setting larger buffers is important.
Generally, allowing 1.5-2x the period of traditional production is recommended (not simply production time, but including production flow construction).
2-2. Production Requirement Level Definition
Quality Standard Setting (Commercial Level vs Prototype Level)
Clearly defining the quality level of AI-generated deliverables is important. Whether it’s “commercial-use capable level” or “proof-of-concept level” significantly changes required man-hours and technical approach.
- Level 1 (Concept Verification): Idea visualization, internal review use
- Level 2 (Prototype Quality): Stakeholder presentation, initial testing use
- Level 3 (Semi-Commercial): Limited external use, A/B testing use
- Level 4 (Commercial): Full commercial use, directly related to brand value
Clear Definition of Expected Use Scenarios and Target Users
Specifically define where, by whom, and how the deliverables will be used. This clarifies technical requirement specifications.
Organizing Necessary Rights and Licenses
It’s important to organize in advance copyright and commercial use rights for AI-generated content.
License terms of AI tools used also require verification. Real-time legal verification is essential for law establishment by country and license agreement revisions.
2-3. Internal Structure Construction
Project Promotion Team Formation
AI creative projects require different roles compared to traditional creative production.
- Business Manager: Investment, project decisions, outcome evaluation
- Project Manager: Project oversight, schedule/budget management, requirement definition
- Art Director: Aesthetic quality, brand compatibility assessment
- Generative AI Specialist: Understanding AI technology constraints, technical communication with AI creators
- Legal/Compliance Officer: Contract matters, rights relations, risk management (including reputation risks)
Decision-Maker and Review Flow Establishment
Since frequent course corrections occur in AI creative work, establishing a structure for rapid decision-making is important.
Minimize review hierarchies and clarify decision-makers.
Securing Members with Technical Knowledge
When there are no AI technology experts internally, securing external advisors or establishing partnership structures with technical partners is important.
3. Contractor Selection: How to Evaluate AI Creators
The AI creative market is growing rapidly, with creators of various skill levels entering.
Selecting appropriate partners is an important factor in project success.
3-1. Technical Competency Evaluation Points
Track Record and Portfolio in Similar Projects
Verify not just the ability to use AI tools, but proven track records of creating deliverables that meet business requirements.
- Production experience in similar industries/applications
- Production track record at required quality levels
- Project scale and duration compatibility
- Client satisfaction and continued business relationships
Available AI Tools and Technology Stack Verification
AI creative tools evolve daily, and appropriate tool selection significantly affects quality. AI creators who can use multiple tools interchangeably are desirable.
- Basic Skills: AI creation, AI technology constraint understanding, quality assessment, design/video production skills
- Workflow Construction: Efficient production process design capability, revision/alternative flow decisions, prompt design skills
- Image Generation: Proficiency with Midjourney, DALL-E, Stable Diffusion, etc.
- Video Generation: Experience with RunwayML, KLING AI, Veo, local systems
- Music/Audio Generation: Knowledge and experience with music/sound effect generation tools
- Post-Processing Technology: Quality improvement techniques using Photoshop, After Effects, etc.
Quality Management and Workflow Maturity
In AI creative work, systems for selecting optimal outputs from large quantities of generated content and continuously improving quality are important.
3-2. Communication Ability Assessment
Clear Explanation of Technical Constraints
The ability to explain AI technical constraints in an understandable way to non-technical personnel is an important evaluation point.
Choose AI creators who can clearly communicate “what cannot be done”.
- Claims like “AI can create anything” without mentioning constraints
(claiming capability for workflows that don’t actually exist) - Technical explanations that are abstract and lack specificity, no released track record
- Reluctance to actively discuss risks or challenges
Understanding Business Requirements and Proposal Capability
Beyond technical skills, the ability to understand client business challenges and propose technical solutions is also important.
Project Management and Progress Reporting Transparency
AI creative work has different man-hour requirements compared to traditional creative work, and progress can be difficult to see when applied to existing production workflows. Selecting AI creators who can provide transparent reporting and explanations is also important.
3-3. Contract, Transaction, and Budget Condition Design
AI creative projects require significantly different contract structures and budget designs compared to traditional creative production.
It’s important to properly distribute technical uncertainty and business risks, constructing contract conditions that are fair and executable for both parties.
Utilizing Phased Contracts (Proposal → PoC → Full Production)
We recommend contract structures that minimize risk and allow phased investment decisions.
- Proposal Phase (1-2 weeks): Requirement organization and technical research
- PoC Phase (2-4 weeks): Feasibility verification
- Full Production/Development Phase (4-8 weeks): Final deliverable production
Intellectual Property Rights and Copyright Handling
Rights relationships for AI-generated content are complex. The following points should be clarified in contracts.
- Copyright ownership of generated content
- Rights processing for training data
- Responsibility distribution for third-party rights infringement
- Commercial use scope and restrictions
Quality Standards and Revision Count Documentation
To avoid subjective quality evaluations, set objective standards as much as possible and clearly define revision count limits.
Alternatively, determine man-hour or work scope ranges for budget management.
Phase-by-Phase Guidelines
- Contract period: 1-2 weeks
- Deliverables: Technical feasibility study, production policy document, detailed estimate
- Payment terms: Lump sum payment upon proposal completion
- Contract termination clause: Both parties can assess next phase progression, contract termination possible without reason
- Intellectual property rights: Copyright of proposal/research materials belongs to contractor, client acquires usage rights
- Contract period: 2-4 weeks
- Deliverables: Prototype, technical verification report, full production specification proposal
- Payment terms: 50% upfront + 50% upon completion
- Quality standards: Feasibility confirmation at 70-80% commercial level quality
- Contract termination clause: Mutual cancellation rights when technical implementation proves difficult
- Intellectual property rights: PoC deliverable improvement and derivative usage rights transfer upon full production contract
- Contract period: 4-8 weeks (adjusted according to complexity)
- Deliverables: Final commercial deliverables, complete related materials
- Payment terms: 30% upfront + 40% interim + 30% upon completion
- Quality standards: Commercial-use capable level (specific indicators documented)
- Contract termination clause: Mutual consultation for condition review upon major specification changes
Contract Type Characteristics and Selection Guidelines
- Application cases: High uncertainty in requirement specifications, strong experimental/verification elements
- Benefits: Flexible course correction possible, shared quality responsibility
- Disadvantages: Difficult budget ceiling management, limited deliverable quality assurance
- Recommendation level: ⭐️⭐️⭐️ (for high uncertainty projects)
- Application cases: Clear requirement specifications, abundant similar project track record, portfolio cases
- Benefits: Fixed budget, clear schedule
- Disadvantages: Increased costs for specification changes, client bears quality risks
- Recommendation level: ⭐️⭐️☆ (for medium-risk projects)
- Application cases: Measurable business outcomes, long-term continuation premise
- Benefits: Appropriate compensation linked to results, improved contractor motivation
- Disadvantages: Difficulty setting outcome indicators, external factor risks
- Recommendation level: ⭐️☆☆ (for special cases)
Understanding AI Creative-Specific Cost Structures
Understanding cost structures significantly different from traditional creative production and implementing appropriate budget allocation.
- AI tool usage fees: 20-30% (Midjourney, GPT, specialized software, etc.)
- Personnel costs (prompt engineering): 40-50% (trial and error, quality adjustment work)
- Personnel costs (post-processing, quality improvement): 15-25% (adjustments using Photoshop, After Effects, etc.)
- Personnel costs (project management): 10-15% (progress management, communication, documentation)
- Technical experiment costs: 5-10% (new method verification, tool evaluation)
- Quality assurance costs: 10-15% (selection from mass generation, quality checking)
- Revision/redo costs: 15-20% (course correction, client requirement change responses)
- Rights processing costs: 5-10% (copyright investigation, license acquisition)
4. AI Project Implementation Practical Flow
A detailed explanation of the 4-stage practical flow for successfully leading AI creative projects. By clarifying specific work content and deliverables for each phase, efficient project implementation becomes possible.
4-1. 【Phase 1】Requirement Organization and Direction Confirmation (1-2 weeks)
This phase clarifies the client’s true needs and aligns understanding with contractors. This is the most critical phase that determines the success of subsequent phases.
Client Preparation Work
Preparation work that should be completed on the client side before project start.
- Specific description of problems to be solved
- Quantification of current issues and improvement targets
- Definition of success indicators (KPIs) and measurement methods
- Budget and schedule constraint conditions
- Quality requirement level clarification
- Reference cases showing target creative direction
- Success cases from competitors and same industry
- Brand guidelines and existing materials
- NG examples of “what should not happen”
- Sharing project purpose and expected effects
- Promoting understanding of AI creative characteristics and constraints
- Establishing decision-making flow and review cycles
- Pre-sharing risks and countermeasures
Joint Work Between Client and Contractor
Collaborative work between client and contractor. The quality of alignment in this phase determines the success or failure of the entire project.
Participants: Key stakeholders of the project
Duration: 2-3 hours
Main agenda:
- Sharing project background and objectives
- Aligning understanding of expected deliverables and quality levels
- Confirming schedule, budget, and structure
- Setting communication rules
- Sharing risks and countermeasures
- Translation from business requirements to technical requirements
- Specific specification definition of deliverables
- Quantification and visualization of quality standards
- Delivery format and file specification decisions
- Technical feasibility evaluation of requirement specifications
- Selection of planned AI tools and technology stack
- Identification of anticipated technical challenges
- Consideration of alternatives and compromise solutions
4-2. 【Phase 2】Technical Verification and PoC Implementation (2-4 weeks)
Based on requirements organized in Phase 1, actual AI-based production is conducted to verify technical feasibility.
This is the most important verification phase before full-scale production.
PoC Scope Design
Scope design to achieve maximum verification effects within limited time and budget is important.
- High Priority: Technical challenges directly related to project success
- Medium Priority: Technical elements affecting quality and efficiency
- Low Priority: Value-added technical challenges
Design PoC scope with MVP (Minimum Viable Product) approach:
- Implementation focused only on core functions
- Quality set at 70-80% of commercial level
- Demonstration within 10-20% of total scope
- Additional cost estimation for quality improvement
- Production period comparison by quality level
- Scalability verification for commercialization
Implementation and Evaluation
Conduct actual PoC production with continuous evaluation and improvement.
- Week 1: Initial prototype production, basic function implementation
- Week 2: Quality improvement, function expansion, interim evaluation
- Week 3: Final adjustments, evaluation, report creation
- Weekly progress confirmation and demonstrations
- Generated content quality evaluation and direction adjustment
- Technical challenge discovery and countermeasure consideration
- Schedule and scope adjustments
- Quality evaluation from project stakeholders
- Usability feedback from expected end users
- Business requirement compatibility evaluation
- Improvement priority consensus building
- Final confirmation of technical feasibility
- Quality, cost, and schedule feasibility evaluation
- Risk factor and countermeasure organization
- Go/No-Go decision making
4-3. 【Phase 3】Requirement Definition and Specification Confirmation (1-2 weeks)
Based on PoC results, conduct detailed requirement definition and specification confirmation for full-scale production. The deliverables of this phase become the design documents for full production.
Requirement Definition Based on PoC Results
Based on knowledge gained from PoC, conduct requirement definition that balances feasibility and business requirements.
- Full implementation requirements for functions verified in PoC
- Quality standard quantification and test method definition
- Performance requirements (processing time, throughput, etc.)
- Usability requirements and user experience design
- Documentation of technical limitations discovered in PoC
- Incorporating workarounds and alternatives into requirements
- Extensibility design considering future technological progress
- Technical requirements for operation and maintenance
- Estimate refinement based on actual man-hour results discovered in PoC
- Man-hour and cost comparison by quality level
- Risk buffer setting
- Phased release plan development
Production Specification Document Confirmation
Create detailed specification documents that serve as guidelines for full-scale production. Comprehensive specification definition including AI creative-specific elements is important.
- Final deliverables: File formats, resolution, quality specifications
- Intermediate deliverables: Prototypes, progress confirmation materials
- Accompanying materials: Production process records, parameter settings
- Rights relations: Copyright, usage rights, license scope
- Objective standards for quality evaluation (resolution, color reproduction, etc.)
- Subjective evaluation methods (evaluators, evaluation items, acceptance criteria)
- Usability test implementation methods
- Revision process and count limitations for non-acceptance
- Impact analysis methods for specification changes
- Change approval flow and responsible parties
- Cost and schedule adjustment rules for changes
- Criteria for distinguishing minor and major changes
4-4.【Phase 4】Full-Scale Production and Iterative Improvement (4-8 weeks)
Based on finalized specifications, we proceed with creating the final deliverables. We advance while leveraging the characteristics of AI creative work and improving quality through iterative refinement.
Agile Production Process
Instead of the traditional waterfall approach, we adopt an agile methodology based on short-term iterative improvements.
- Sprint 1-2: Basic functionality implementation and initial quality assurance
- Sprint 3-4: Quality improvement and feature expansion
- Sprint 5-6: Integration, optimization, and usability improvements
- Sprint 7-8: Final adjustments and acceptance preparation
- Working prototype presentation every two weeks
- Gradual implementation by functional units
- Course correction through early feedback
- Early risk detection and mitigation
- Weekly progress meetings for status sharing
- Demonstrations at sprint completion
- Evaluation and feedback collection from stakeholders
- Priority adjustment for next sprint
- Early focus on functionality, later focus on quality
- Continuous tuning of AI generation parameters
- Best selection from high-volume generation
- Manual post-processing and quality enhancement
Quality Control and Final Adjustments
In the final production phase, we improve quality to commercial-grade standards and conduct final quality assurance.
- Technical Quality Assessment: Resolution, file size, format compliance
- Aesthetic Quality Assessment: Design quality, brand alignment, creativity
- Functional Quality Assessment: Purpose suitability, usability
- Comprehensive Quality Assessment: Business requirements alignment
- Usability testing by intended end users
- Effectiveness measurement through A/B testing
- Accessibility requirements verification
- Final adjustments based on feedback
- Compliance verification based on requirements documentation
- Completeness check of deliverables
- Rights and licensing verification
- Preparation of documentation necessary for operation and maintenance
AI creative work requires different acceptance perspectives than traditional production deliverables.
- Verification of generation process reproducibility
- Final check for similar image and copyright infringement risks
- Quality degradation risk assessment for commercial use
- Confirmation of future maintenance and update methods
5. Practical Checklists for Corporate Managers and AI Creators
Success in AI creative projects requires both corporate managers and AI creators to have proper preparation and understanding. This chapter provides immediately actionable checklists and specific action guidelines for project execution.
5-1. Key Points for Corporate Managers
Pre-Project Preparation Items
📋 Business Requirements Clarification Checklist
Example: “Video ad production costs $5 million per month. We want to reduce costs by 30% with AI while maintaining the same effectiveness.”
Set across three axes: business results, quality, and operational efficiency
Understanding AI creative-specific cost structures, considering variable costs
Including reference examples and specific evaluation indicators
Also organize technical requirements by application: web ads, print materials, videos, etc.
Including usage period, geographical scope, modification rights, etc.
🏗️ Internal Structure Building Checklist
Budget and Schedule Management Considerations
- Rapid Variable Cost Increases: Tool usage fees increasing proportionally to AI generation frequency
- Quality Improvement Marginal Costs: Exponential cost increases required for 90%→95% quality improvements
- Technical Constraint Discovery: Scope changes due to infeasibility discovered mid-production
- External Environment Changes: Impact from AI tool updates and specification changes
5-2. Elements AI Creators Should Be Aware Of
Proper Communication of Technical Feasibility
🔧 Technical Communication Checklist
Explain technical limitations with specific examples
Can clients intuitively understand quality levels?
Specific explanation of why it takes time, workflow documentation
Present options by quality, cost, and schedule
Comprehensively cover technical, quality, schedule, and legal risks
- Current Analysis: Evaluate technical feasibility of requested specifications on a 5-point scale
- Constraint Explanation: Explain AI tool technical limitations with specific examples
- Proposal Presentation: Present multiple feasible alternatives (by quality, cost, and timeline)
- Risk Sharing: Clearly state anticipated risks and countermeasures in advance
- Consensus Building: Align understanding with client and make policy decisions
Creative Quality Evaluation Methods
AI creative quality evaluation tends to be subjective. It’s important to set objective evaluation axes and build common quality understanding with clients.
📊 Quality Evaluation Axis Setting Checklist
- Stage 1: Mechanical screening from mass generation (technical quality focus)
- Stage 2: Aesthetic and functional evaluation by AI creators
- Stage 3: Comprehensive evaluation by client stakeholders
- Stage 4: Effectiveness measurement through end-user testing
6. Summary: Toward Sustainable AI Creative Projects
6-1. The Importance of Milestone Design
Milestone Design Principles for Successful Projects
In AI creative projects, milestone design is even more important than in traditional creative production. We present design principles for steadily building results while balancing technical uncertainty with creative subjectivity.
Building Trust with Stakeholders
Clear milestones play an important role in increasing project transparency and building trust with diverse stakeholders. It’s important to set progress indicators appropriate for each position—management, project teams, external partners, and end users—and maximize project value through regular communication.
- Testable Hypothesis Setting: Clarify hypotheses to be tested at each milestone
- Value Confirmation with Minimum Features: Early value creation through MVP thinking
- Risk Distribution: Divide large risks into small, testable risks
- Learning Accumulation: Mechanism to utilize learning from each stage in the next
- Technical Feasibility Confirmation → Early risk discovery
- Quality Level Achievement Confirmation → Realistic quality standards
- Business Value Hypothesis Validation → Investment continuation decision
- Scalability Confirmation → Full-scale deployment feasibility evaluation
- Operational Viability Validation → Sustainable value creation foundation
- Clear Success Criteria: Objective definition of “what constitutes success”
- Go/No-Go Decision Criteria: Clear criteria for project continuation or termination
- Learning Systematization: Mechanism to apply learning from failures to future efforts
- Stakeholder Agreement: Progress indicators that all parties can accept
6-2. Future Development and Application Possibilities
Opening New Fields Through Technology Integration
The rapid advancement of AI technology is bringing the framework of traditional creative activities into a major transformation period. The development of multimodal AI is accelerating creative fusion between different media such as voice, images, text, and video, enabling the provision of integrated creative experiences. Combined with virtual reality and augmented reality technologies, the creation of immersive content and interactive artwork production is becoming reality, with expression methods previously impossible being born one after another.
Democratized Creative Ecosystem
The proliferation of AI tools is enabling individuals without specialized technical knowledge to engage in high-quality creative activities. This democratization trend is significantly expanding participants in creative activities and promoting work creation from diverse perspectives. In educational applications, it realizes drawing out learners’ creativity and providing individualized learning experiences, bringing new possibilities to traditional educational models.
Realizing Ethical Creative Activities
As interest in sustainable and ethical AI creative activities grows, initiatives addressing challenges such as minimizing environmental impact, respecting intellectual property rights, and protecting creators’ rights are becoming increasingly important. By actively addressing challenges such as improving energy efficiency, eliminating bias, and ensuring transparency, we are required to pursue creative value while fulfilling social responsibility.
Creating New Business Models
The development of AI creative activities is transforming traditional creation and distribution models, creating new revenue opportunities. Experiments and implementations of various business models are progressing, including personalized content distribution, real-time creative experiences, and community-participatory projects. Through collaboration between creators and AI, more efficient and creative value creation is being realized, enabling new value provision that was unimaginable in traditional production processes.
Building Global Cooperation Systems
Through international expansion of AI creative projects, creative activities utilizing cultural diversity are becoming active. New cooperation models are being established, including cross-language collaboration, partnerships between creators with different cultural backgrounds, and simultaneous deployment to global markets.
6-3. Recommendations for Sustainable Project Management
Strategic Planning Based on Long-Term Vision
Realizing sustainable AI creative projects requires strategic planning that looks not only at short-term results but also at long-term value creation. It’s important to predict technological progress, market changes, and social demand fluctuations, and build organizational capabilities that can respond flexibly.
Continuous Learning and Adaptation Mechanisms
To respond to rapid AI technology advancement, improving the continuous learning and adaptation capabilities of the entire organization is necessary. It’s required to establish cycles for evaluating, introducing, and utilizing new technologies, and build systems that advance projects while constantly staying current with the latest technological trends.
