2026 6th International Conference on Artificial Intelligence and Application Technologies (AIAT 2026)
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About this event
2026 6th International Conference on Artificial Intelligence and Application Technologies (AIAT 2026)
Date: December 3-5, 2026
Venue: Tokyo, Japan
Event Type: Academic and Industry Conference focused on Artificial Intelligence and Application Technologies
Event Overview
The 2026 6th International Conference on Artificial Intelligence and Application Technologies (AIAT 2026) will be held in Tokyo, Japan, from December 3 to 5, 2026. This conference is a premier academic and industry event dedicated to the exploration and discussion of cutting-edge advancements in artificial intelligence (AI) and its diverse applications. As a rapidly evolving field, AI continues to drive groundbreaking innovations that impact various sectors, including robotics, decision-making processes, control systems, and simulation applications.
AIAT 2026 serves as a dynamic platform for researchers, scholars, industry professionals, and innovators to share their latest research findings, ongoing projects, case studies, and practical experiences. The conference aims to highlight significant breakthroughs and emerging trends in AI, fostering cross-disciplinary collaboration and promoting the exchange of innovative ideas between academia and industry.
Key Highlights
- Submission Deadline: July 15, 2026
- Notification Date: August 15, 2026
- Conference Proceedings: All accepted and registered papers will be published in the International Conference Proceedings Series by ACM, archived in the ACM Digital Library, and indexed by EI Compendex and Scopus.
- Scope: The conference will cover a wide range of topics related to artificial intelligence, including but not limited to robotics, decision-making processes, control systems, simulation applications, and more.
Who Attends (Buyers/Attendees)
AIAT 2026 attracts a diverse audience of professionals and academics, including:
- Academic researchers and professors
- Industry professionals and engineers
- R&D representatives from technology companies
- Government and policy-makers interested in AI development
- Students and graduate researchers in AI and related fields
- Entrepreneurs and startup founders in the AI space
Location and Geographic Reach
Venue: Tokyo, Japan
Audience Reach: Global, with a strong presence from Asia-Pacific, North America, and Europe.
Tokyo, as a global hub for technology and innovation, provides an ideal backdrop for this international conference. The event draws attendees from around the world, with a particular concentration from countries with strong AI research and development ecosystems.
Sample Buyer Company Names and Websites
| Priority | Company | Website | Best Title to Target | Why This is a Good Buyer Fit |
|---|---|---|---|---|
| 1 | Toyota Research Institute | https://www.toyota.com/us/research/ | AI Research Director / R&D Manager | Active in AI research for autonomous systems and mobility solutions. |
| 2 | NTT Communication Science Laboratories | https://www.nii.ac.jp/~kshiraishi/ntt/ | Chief Technology Officer / AI Development Lead | Engaged in cutting-edge AI research and development in Japan. |
| 3 | Canon Inc. | https://www.canon.com/ | Director of AI and Machine Learning | Investing in AI for imaging and optical technologies. |
| 4 | NEC Corporation | https://www.nec.com/ | AI Solutions Architect / Innovation Manager | Leader in AI applications for cybersecurity, public safety, and more. |
| 5 | Hitachi, Ltd. | https://www.hitachi.com/ | VP of AI and Digital Transformation | Implementing AI across various industries including healthcare and infrastructure. |
| 6 | Google Japan | https://www.google.com/about/locations/japan/ | AI Research Scientist / Engineering Manager | Global leader in AI research and development. |
| 7 | Microsoft Japan | https://www.microsoft.com/ja-jp/ | Director of AI and Cloud Services | Active in AI cloud solutions and enterprise applications. |
| 8 | Sony Corporation | https://www.sony.net/ | Chief AI Officer / Robotics Innovation Lead | Exploring AI in consumer electronics, robotics, and entertainment. |
| 9 | SoftBank Robotics | https://www.softbankrobotics.com/ | AI Product Development Manager | Developing AI-powered robots for various applications. |
| 10 | Academia-Industry Collaboration Organizations | https://www.aist.go.jp/ | Director of Collaborative Research | Facilitating partnerships between universities and companies in AI research. |
Job Profiles, Industries, and Event Type
Best Job Profiles to Target:
- AI Research Scientists
- R&D Managers
- Chief Technology Officers (CTOs)
- Director of Innovation
- Machine Learning Engineers
- Academic Researchers and Professors
- AI Product Managers
- Engineering Managers in AI and Robotics
- Government Policy Advisors for AI
Industries to Focus On:
- Information Technology & Services
- Artificial Intelligence
- Research
- Education Management
- Higher Education
- Computer Software
- Robotics
- Electrical/Electronic Manufacturing
- Telecommunications
- Automotive Industry
Estimated Attendance
While the exact number of attendees for AIAT 2026 is not specified, conferences of this nature typically attract between 500 to 1,500 participants, including researchers, industry professionals, and students. The event's global reach and the growing interest in AI research suggest a strong turnout from both academic and corporate sectors.
Key Focus Areas and Buyer Engagement
The key focus areas of AIAT 2026 include:
- Artificial Intelligence Theories and Applications
- Machine Learning and Deep Learning
- Robotics and Autonomous Systems
- AI in Healthcare and Biomedical Applications
- Computer Vision and Image Processing
- Natural Language Processing and Speech Recognition
- AI for Cybersecurity and Data Privacy
- AI in Education and E-learning
Buyer engagement can be effectively achieved by positioning solutions that address these key areas, emphasizing practical implementations, and showcasing how products or services can advance AI research and applications across various industries.
Client Product Fit Note
To provide a more tailored buyer list and engagement strategy, please share your client's website and specific product offerings. This will enable us to align the target profiles with the solutions your client provides, ensuring maximum relevance and impact.
Final Recommendation
AIAT 2026 presents a valuable opportunity for companies and organizations involved in AI research, development, and application. With its strong academic and industry focus, the event offers a conducive environment for networking, collaboration, and showcasing innovative solutions. We recommend leveraging this platform to connect with key decision-makers, researchers, and potential partners in the AI ecosystem.
Data sheet
2026 6th International Conference on Artificial Intelligence and Application Technologies (AIAT 2026) – Event Attendee & Buyer Profile Analysis
Event date: December 3–5, 2026
Location: Tokyo, Tokyo, Japan
Event status: Upcoming
Research date: June 29, 2026
Event Overview
| Event Name | 2026 6th International Conference on Artificial Intelligence and Application Technologies (AIAT 2026) |
| Event Date | December 3–5, 2026 |
| Event Status | Upcoming |
| Venue | Tokyo, specific venue not publicly confirmed in the supplied official source |
| City | Tokyo |
| State / Region | Tokyo |
| Country | Japan |
| Organizer | Not publicly named on the supplied official webpage |
| Official Event Website | aiat.org |
| Event Type | Academic and industry conference |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Science & Research; Education & Training |
| Audience Reach | Global academic and professional reach |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for headcount; high for dates, city, country, and event theme based on official website |
| Main Purpose of Event | To present peer-reviewed AI research, discuss practical AI and computer application advances, and connect researchers with industry participants around emerging application technologies. |
About the Event
AIAT 2026 is the 6th edition of the International Conference on Artificial Intelligence and Application Technologies, scheduled for December 3–5, 2026 in Tokyo, Japan. Based on the official event website, the conference focuses on new concepts and recent advancements in artificial intelligence and computer applications, spanning theoretical research and practical implementation across areas such as robotics, decision-making, control systems, and simulation applications.
From a commercial intelligence perspective, this is best understood as a high-value niche conference for research-led AI networking rather than a mass-market expo. Its relevance is strongest for organizations selling AI software, data infrastructure, research tooling, compute platforms, robotics-enablement technologies, consulting, engineering services, and university/innovation partnerships. Buyer access is likely to come through research leaders, lab heads, technical directors, product innovation teams, and academic-industry collaboration stakeholders rather than pure procurement-only attendees.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| AI researchers and faculty | Universities, research institutes, graduate labs | Influence tool selection, research collaborations, publications, grants, and pilot projects | High for AI platforms, data tools, compute, simulation, and technical partnerships |
| Industry R&D leaders | AI product companies, robotics firms, industrial technology companies | Evaluate technology capabilities, research partnerships, proof-of-concept opportunities | Very high for commercialization-focused suppliers |
| AI engineers and applied scientists | Software companies, labs, engineering teams, startups | Recommend technical stacks, benchmarking tools, model infrastructure, data pipelines | High for developer tooling and technical solution vendors |
| Robotics and control systems specialists | Robotics labs, automation firms, embedded systems teams | Assess integration feasibility, simulation environments, control applications | High for robotics, edge AI, sensors, and industrial automation suppliers |
| Innovation and product managers | Enterprise innovation teams, software vendors, digital transformation groups | Translate research trends into product roadmap and partner selection | High for applied AI vendors and consulting firms |
| Academic-industry partnership stakeholders | Technology transfer offices, corporate research partnerships, innovation programs | Influence collaboration agreements, sponsored research, talent pipelines | Medium to high for solution providers seeking research credibility and market access |
| Graduate students and early-career researchers | Universities, labs, AI research groups | Limited direct buying power; strong technical influence and future pipeline value | Useful for brand building, community adoption, and talent visibility |
| Conference authors and presenters | Mixed academic and commercial organizations | Often act as domain experts and internal evaluators for tools and platforms | High for account-based outreach where speaker or author lists become available |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Tokyo | Local universities, AI startups, enterprise innovation teams, research labs | High | Tokyo is a major concentration point for technology, academia, robotics, and corporate R&D. |
| Tokyo Metropolis / Kanto Region | Regional attendees from nearby universities and industrial clusters | High | Likely to include applied research and engineering stakeholders with short travel distance. |
| Japan nationwide | Researchers, professors, AI engineers, technology companies, graduate students | Medium to high | National participation is likely due to conference-paper and academic presentation format. |
| East Asia | Cross-border academic and industry AI participants | Medium | The conference website and publication positioning suggest international submission activity. |
| Global | Authors and delegates from international academic and technical communities | Medium | International proceedings publication and indexing references support global academic reach, though attendee mix is not publicly quantified. |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The event invites paper submissions, targets international research audiences, and promotes ACM conference proceedings with indexing references, indicating cross-border academic visibility. |
| National | Secondary practical reach | Japan is likely to provide a strong share of in-person attendees due to the Tokyo location and conference format. |
4. Sample Buyer Companies and Websites
Official current-year attendee, sponsor, exhibitor, speaker-organization, or buyer lists were not available in the supplied official source. For this event, buyer-company confirmation is currently limited. The table below therefore distinguishes official visibility constraints and should not be treated as a confirmed attendee list for the current edition.
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| AIAT 2026 paper authors and presenters | Research and technical buyers | Authors are a primary high-fit target segment for AI tooling, compute, data, and collaboration solutions. | aiat.org | Professor, Research Scientist, AI Engineer, Lab Director | Confirmed current-year author recruitment; individual organizations not publicly listed |
| AIAT 2026 accepted-paper organizations | Academic and industry institutions | Accepted-paper affiliations typically indicate active AI budget holders or technical evaluators. | aiat.org | Principal Investigator, Department Head, Technical Director | Confirmed event process; organization list not yet public |
| AIAT 2026 industrial experience contributors | Industry-side solution evaluators | The official CFP welcomes industrial experiences, indicating relevance for commercial AI practitioners. | aiat.org | Head of AI, R&D Director, Product Innovation Manager | Confirmed call for participation; participant organizations not publicly listed |
| AIAT 2026 committee and speaker organizations | Influencer and partnership targets | Committee and speaker affiliations are often high-authority targets for thought leadership outreach. | aiat.org | Conference Chair, Professor, CTO, Research Lead | Navigation references exist on official site; affiliations not provided in supplied source |
| AIAT 2025 proceedings contributor organizations | Historical / prior-year evidence | Prior-year proceedings can be mined for target affiliations once reviewed directly. | dl.acm.org | Author, Research Scientist, Lab Manager | Historical / prior-year evidence |
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Head of AI | Artificial Intelligence / R&D | Director / VP | Owns AI strategy, technical evaluation, and collaboration decisions. |
| 2 | Research Director | Research | Director | Controls lab priorities, funding choices, and external partnerships. |
| 3 | Professor / Principal Investigator | Academic Research | Senior | Key influencer for research tooling, datasets, grants, and sponsored work. |
| 4 | CTO | Technology | C-Level | Relevant for AI platform, model deployment, and infrastructure decisions. |
| 5 | Director of Engineering | Engineering | Director | Evaluates implementation practicality and integration requirements. |
| 6 | AI Engineer / Machine Learning Engineer | Engineering / Data Science | Manager / IC | Strong evaluator for tools, APIs, infrastructure, and technical fit. |
| 7 | Lab Director | Research / Innovation | Director | Often owns equipment, platform subscriptions, and partner evaluation. |
| 8 | Product Manager, AI Products | Product | Manager / Director | Bridges research capability with commercialization and vendor selection. |
| 9 | Innovation Director | Innovation / Strategy | Director | Targets emerging AI partnerships and pilot deployments. |
| 10 | Business Development Director | Business Development / Partnerships | Director | Important for channel, commercialization, and collaboration deals. |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Information Technology & Services | Core fit for enterprise AI adoption and implementation services. | AI deployment, integration, consulting, managed services |
| 2 | Computer Software | Strong fit for model tooling, data platforms, MLOps, and AI applications. | Product partnerships, platform adoption, benchmarking |
| 3 | Research | Direct fit for labs, institutes, and scientific research organizations. | Research tools, compute, datasets, sponsored projects |
| 4 | Higher Education | Universities are a primary participant base in paper-driven AI conferences. | Lab tooling, education partnerships, academic pilots |
| 5 | Computer Hardware | Relevant to AI compute, edge systems, and acceleration workloads. | GPU/compute evaluation, embedded AI infrastructure |
| 6 | Industrial Automation | Fits robotics, control systems, and application technology themes. | Robotics AI, simulation, industrial optimization |
| 7 | Mechanical or Industrial Engineering | Useful for AI application technologies tied to control and systems engineering. | Simulation, predictive systems, robotics integration |
| 8 | Electrical/Electronic Manufacturing | Relevant for embedded AI, sensing, edge control, and device-level applications. | Applied AI in devices and intelligent systems |
| 9 | Semiconductors | AI acceleration and inference performance are core ecosystem interests. | Chip partnerships, edge AI, compute optimization |
| 10 | Computer Networking | Relevant where AI workloads depend on distributed systems and infrastructure. | Data movement, inference scaling, distributed AI systems |
| 11 | Telecommunications | Applies to AI-enabled network optimization and edge deployment use cases. | AI at the network edge, automation, analytics |
| 12 | Aviation & Aerospace | Relevant for advanced simulation, autonomy, and decision-support applications. | High-value R&D and applied AI procurement |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Unconfirmed | Official website content supplied | No current-year headcount published in the supplied official text. |
| Exhibitor count | Not publicly confirmed | Unconfirmed | Official website content supplied | Conference appears publication-led rather than exhibitor-led. |
| Buyer count | Not publicly confirmed | Unconfirmed | Official website content supplied | Buyer composition is likely technical and research-led rather than procurement-led. |
| Speaker count | Not publicly confirmed in supplied source | Unconfirmed | Official website navigation references speakers | Speaker roster exists as a website section but details were not included in the supplied content. |
| Sponsor count | Not publicly confirmed | Unconfirmed | Official website content supplied | No sponsor detail available in the supplied source. |
| Historical attendance | Not publicly confirmed in supplied source | Historical data unavailable | Official website content supplied | History pages are referenced, but attendee metrics were not present in the supplied text. |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Artificial Intelligence research | Model development, evaluation, experimentation, publication support | Engage paper authors, lab leads, and research directors | MLOps, experiment tracking, compute, datasets, AI frameworks |
| Computer applications | Applying AI in real-world operational and product environments | Target product managers and engineering leaders | Application platforms, APIs, deployment infrastructure, integration services |
| Robotics | Perception, autonomy, motion planning, intelligent control | Approach robotics labs and industrial automation teams | Sensors, simulation, robotics software, edge compute |
| Decision-making systems | Optimization, prediction, intelligent assistance, workflow automation | Target enterprise R&D and AI strategy teams | Decision intelligence, analytics, orchestration platforms |
| Control systems | Real-time control, embedded intelligence, reliability and safety | Engage controls engineers and automation specialists | Industrial AI, edge software, control optimization, embedded platforms |
| Simulation applications | Model testing, digital experimentation, performance validation | Approach research labs and engineering teams using simulation environments | Simulation software, digital twins, synthetic data platforms |
| Academic-industry collaboration | Joint research, partner discovery, commercialization pathways | Network with speakers, committees, and accepted-paper organizations | Partnership programs, sponsored research, innovation consulting |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | High | Strong for AI, software, compute, research, robotics, and technical services vendors. |
| Decision-maker availability | Medium | Likely access to technical decision-makers and research leaders; less certainty for budget owners without published attendee lists. |
| Data collection potential | Medium | Improves significantly if speaker, committee, paper, or proceedings affiliation lists are harvested systematically. |
| Apollo targeting potential | High | Apollo can target AI, software, university, research, robotics, and innovation stakeholders effectively. |
| Geographic targeting potential | High | Japan, East Asia, and global research centers can be segmented cleanly. |
| Best outreach approach | High-touch technical outreach | Lead with research relevance, use cases, benchmarking, or collaboration value rather than generic sales messaging. |
| Overall lead quality | High | Especially strong for specialized B2B AI vendors targeting technical adopters and innovators. |
| Best use case | Account-based prospecting and speaker/author-led outreach | Most effective when tied to paper topics, labs, and research application areas. |
| Limitations / risks | Medium | Current-year participant transparency is limited in the supplied source; event may skew academic over commercial procurement. |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Information Technology & Services; Computer Software; Research; Higher Education; Industrial Automation; Computer Hardware; Mechanical or Industrial Engineering; Electrical/Electronic Manufacturing; Semiconductors; Telecommunications | Build core target universe aligned to AIAT themes. |
| Departments | Engineering; Research; Information Technology; Product Management; Business Development; Innovation; Operations | Focus on technical evaluators and commercialization stakeholders. |
| Seniority | C-Level; VP; Director; Head; Manager; Owner for startups | Prioritize decision-makers and strong technical influencers. |
| Job titles | CTO; Head of AI; Director of Engineering; Research Director; Lab Director; Professor; Principal Investigator; AI Engineer; Machine Learning Engineer; Product Manager AI; Innovation Director; Business Development Director | Map to likely event participants and affiliated organizations. |
| Geography | Japan first; then Tokyo; then East Asia; then global AI research hubs | Create concentric audience rings for outreach prioritization. |
| Employee size | 11–50; 51–200; 201–1,000; 1,001–5,000; 5,001+ | Capture both startups and enterprise or university-scale organizations. |
| Keywords | artificial intelligence, machine learning, deep learning, robotics, control systems, simulation, computer vision, NLP, autonomous systems, decision intelligence, data science, MLOps | Surface organizations actively aligned with conference subject matter. |
| Technologies | Use where available: cloud platforms, AI/ML stack, data infrastructure, GPU/accelerator environment | Refine toward more technically mature organizations. |
| Revenue range | Optional: Mid-market to enterprise for commercialization campaigns; open for academic/research targeting | Use only if the offer requires larger budgets. |
| Company type | Private; Public; Educational Institution; Research Organization | Covers the full likely event participation base. |
| Funding / public company filters | Use funded AI startups or public tech companies where commercialization and budget urgency matter | Useful for prioritizing active growth accounts. |
Suggested Apollo Search Logic: (AI OR "machine learning" OR "deep learning" OR robotics OR "control systems" OR simulation OR "computer vision" OR NLP) AND (CTO OR "Head of AI" OR "Research Director" OR "Director of Engineering" OR Professor OR "Principal Investigator" OR "Lab Director") AND (Japan OR Tokyo OR Asia). For technical-commercial outreach, prioritize organizations with active R&D pages, publication activity, or AI product teams.
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| AIAT 2026 Official Website | Official event website | Event title, Tokyo/Japan location, December 3–5, 2026 dates, conference theme, submission deadline, notification date, proceedings positioning, history references, and existence of committee/speakers/venue pages in navigation. | High |
| Supplied Official Website Extract | Primary source text provided by user | Confirmed that no attendance figure, named organizer, specific venue name, or published attendee organization list was present in the supplied official content. | High |
Suitability for B2B attendee list building: Moderate to High once speaker, committee, accepted-paper affiliations, or proceedings author lists are harvested. At present, public current-year organization transparency appears limited in the supplied source, so list quality will depend on follow-up extraction from official program pages as they are published.
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Tell us your work email and our AI instantly builds a buyer list matched to 2026 6th International Conference on Artificial Intelligence and Application Technologies (AIAT 2026) — the best‑fit companies to target, the exact decision‑maker job titles, and the industry filters that fit what you sell.
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