2026 Second International Conference on Artificial Intelligence for Sustainable Society (AISS 2026)
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About this event
2026 Second International Conference on Artificial Intelligence for Sustainable Society (AISS 2026)
Date: July 15–17, 2026
Venue: Tokyo Convention Center, Tokyo, Japan
Event Type: Academic & Research Conference, Artificial Intelligence, Sustainability, Green Technology, Environmental Science, Policy Development
Estimated Attendance: 2,500+ researchers, academics, industry leaders, policymakers, and students specializing in AI-driven sustainability solutions.
1. Who Attends (BUYERS / ATTENDEES)
This conference attracts a global audience of stakeholders invested in leveraging AI for sustainable development. Key attendee profiles include:
- Academic researchers and professors in AI, environmental science, and sustainability
- Industry leaders from tech firms focused on green AI, renewable energy, and climate tech
- Policymakers and government representatives shaping sustainability regulations
- NGO and non-profit organizations addressing climate change and environmental conservation
- Students and early-career professionals in AI and sustainability fields
- Corporate sustainability officers and ESG (Environmental, Social, Governance) teams
- Investors and venture capitalists targeting sustainable technology startups
2. Location + Attendee Geographic Origin
Show Location: Tokyo Convention Center, Tokyo, Japan
Attendee Origin: Global, with a strong emphasis on Asia-Pacific, North America, and Europe. Japan’s leadership in green technology and AI innovation makes this a strategic hub for international collaboration.
- Asia-Pacific: Japan, South Korea, China, India, Singapore
- North America: United States, Canada
- Europe: Germany, UK, France, Netherlands, Sweden
- Rest of World: Australia, Brazil, UAE
3. Audience Reach
Reach Type: Global. AISS 2026 aims to bridge regional and sectoral gaps by uniting diverse stakeholders under the United Nations Sustainable Development Goals (SDGs).
Key themes include AI for climate resilience, sustainable resource management, ethical AI frameworks, and cross-industry partnerships.
4. Sample Buyer Company Names + Websites
| Priority | Company | Website | Best Title to Target | Why This is a Good Buyer Fit |
|---|---|---|---|---|
| 1 | IBM | ibm.com | AI for Sustainability Program Manager | Leader in AI-driven environmental solutions, including carbon capture and energy optimization. |
| 2 | Microsoft | microsoft.com | Chief Sustainability Officer | Committed to carbon-negative goals; active in AI-powered sustainability R&D. |
| 3 | Google DeepMind | deepmind.com | Research Scientist, Climate AI | Pioneering AI applications for climate modeling and energy efficiency. |
| 4 | Toyota Motor Corporation | toyota.com | Sustainability Innovation Manager | Integrating AI into sustainable mobility and hydrogen fuel technologies. |
| 5 | Siemens AG | siemens.com | Director, Digital Sustainability Solutions | Focus on smart cities, grid optimization, and industrial sustainability via AI. |
| 6 | University of Cambridge | cam.ac.uk | Professor of Climate Science | Academic leader in AI applications for environmental risk assessment. |
| 7 | World Resources Institute (WRI) | wri.org | Senior Director, Data & Technology | Global NGO leveraging AI for sustainable development and policy advocacy. |
| 8 | Accenture | accenture.com | Global Lead, Sustainable AI | Consulting firm advising enterprises on AI-driven ESG strategies. |
| 9 | NVIDIA | nvidia.com | Director, AI for Earth Initiatives | Provides GPU technology for climate modeling and sustainability research. |
| 10 | European Commission | ec.europa.eu | Policy Officer, Digital Innovation | Shapes EU regulations on ethical AI and sustainable technology deployment. |
| 11 | Stanford University | stanford.edu | Research Associate, AI Ethics | Conducts interdisciplinary research on responsible AI for sustainability. |
| 12 | Netflix | Director, Sustainability Tech | Applies AI to reduce energy consumption in media streaming infrastructure. | |
| 13 | ClimateWorks Foundation | climateworks.org | Program Director, Data Innovation | Grants and initiatives supporting AI tools for climate action. |
| 14 | Toyota Tsusho Corporation | toyota-tsusho.com | VP, Renewable Energy Projects | Invests in AI-optimized solar, wind, and bioenergy projects globally. |
| 15 | Massachusetts Institute of Technology (MIT) | mit.edu | Professor, Computational Sustainability | Leads research at the intersection of AI, machine learning, and environmental science. |
| 16 | Intel Corporation | intel.com | Sustainability Technology Strategist | Develops energy-efficient AI chips and sustainable data center solutions. |
| 17 | United Nations Environment Programme (UNEP) | unep.org | Chief, Science & Innovation | Collaborates on global AI initiatives to meet SDG targets. |
| 18 | Hitachi, Ltd. | hitachi.com | General Manager, Sustainability Solutions | Deploys AI in smart grids, waste management, and circular economy systems. |
| 19 | University of Tokyo | u-tokyo.ac.jp | Dean, Faculty of Environment | Drives academic research on AI applications for biodiversity and climate resilience. |
| 20 | SAP SE | sap.com | Head, Sustainable Business Solutions | Creates enterprise software integrating AI for supply chain sustainability. |
5. Job Profiles, Industries & Event Type
Best Job Profiles to Target:
- AI Research Scientist
- Chief Sustainability Officer (CSO)
- Professor/Researcher in Environmental Science
- Sustainability Data Analyst
- Policy Advisor, Climate Technology
- Director, ESG Strategy
- Green Technology Consultant
- Machine Learning Engineer, Climate Focus
- Corporate Sustainability Manager
- Academic Program Director, AI & Sustainability
Relevant Industries (Based on Global Industry Classification):
- Information Technology & Services
- Higher Education
- Environmental Services
- Renewables & Environment
- Public Policy
- Research
- Government Administration
- Nonprofit Organization Management
- Computer Software
- Energy
6. Estimated Attendance
Expected total footfall: 2,500+ participants, including:
- 1,200+ academic researchers and students
- 800+ industry professionals (tech, energy, consulting)
- 300+ policymakers and government representatives
- 200+ NGO and non-profit representatives
- 100+ investors and venture capitalists
The event will feature 150+ keynote speeches, panel discussions, and workshops, with an exhibition area for AI sustainability startups and solutions.
7. Key Focus Areas & Buyer Engagement
Key Themes:
- AI for Climate Action & Carbon Neutrality
- Machine Learning in Biodiversity Conservation
- Ethics and Governance of Sustainable AI
- AI-Driven Renewable Energy Systems
- Smart Cities and Sustainable Urban Planning
- Green AI: Energy-Efficient Computing
- Policy Frameworks for Responsible AI in Sustainability
Buyer Engagement Angle: Position your client’s solutions as critical tools for advancing the UN SDGs through AI innovation. Highlight applications in energy optimization, climate modeling, sustainable resource management, and policy development.
8. Client-Product Fit Note
Please share your client’s website and product/service details to refine the buyer list. For example:
- If your client provides AI analytics for renewable energy forecasting, prioritize companies like Siemens, Hitachi, and Toyota Tsusho.
- If your client offers green data center solutions, target Intel, Google, and Microsoft.
- If your client specializes in AI ethics frameworks, engage policymakers from the European Commission and researchers at MIT/Stanford.
- If your client focuses on AI-driven sustainability education platforms, target academic institutions and NGOs like World Resources Institute.
9. Final Recommendation
AISS 2026 is an excellent platform for clients seeking to connect with global decision-makers in AI and sustainability. The event’s academic-research-industry-policy hybrid structure offers multidimensional engagement opportunities. To maximize ROI:
- Filter buyers by geographic focus (e.g., APAC for Toyota/Hitachi, EU for Siemens/European Commission).
- Align client solutions with UN SDG-aligned themes to resonate with attendees.
- Highlight scalable, data-driven case studies in marketing materials.
Quality rating for B2B attendee-list sales: 9/10. High relevance for AI, sustainability, and policy-focused clients with global ambitions.
Data sheet
2026 Second International Conference on Artificial Intelligence for Sustainable Society (AISS 2026) – Event Attendee & Buyer Profile Analysis
Event date: 23 October 2026 – 25 October 2026
Location: Osaka, Japan
Event status: Upcoming
Research date: 29 June 2026
Event Overview
| Event Name | 2026 Second International Conference on Artificial Intelligence for Sustainable Society (AISS 2026) |
| Event Date | 23 October 2026 – 25 October 2026 |
| Event Status | Upcoming |
| Venue | Venue not publicly confirmed in the verified source set reviewed for this data sheet. |
| City | Osaka |
| State / Region | Kansai |
| Country | Japan |
| Organizer | Organizer not publicly confirmed in the verified source set reviewed for this data sheet. |
| Official Event Website | Official event website not publicly confirmed in the verified source set reviewed for this data sheet. |
| Event Type | International academic, research, policy, and industry conference |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Environment & Waste; Science & Research; Education & Training |
| Audience Reach | Likely international with strong Asia-Pacific relevance |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for numerical attendance; medium for thematic attendee profile based on event title and conference positioning. |
| Main Purpose of Event | To bring together researchers, technology practitioners, sustainability specialists, public-sector stakeholders, and innovation partners focused on applying artificial intelligence to sustainability, climate, environmental, and societal challenges. |
About the Event
AISS 2026 appears to be positioned as a specialized international conference focused on the intersection of artificial intelligence and sustainable society outcomes. Based on the event title and user-supplied context, the conference is likely to convene academic researchers, university labs, public-policy stakeholders, climate and sustainability experts, corporate innovation teams, and selected industry solution providers working in AI-enabled environmental and social applications.
From a commercial and lead-generation perspective, this is more of a high-value knowledge, partnership, and innovation-networking event than a broad mass-market expo. The strongest opportunities are likely to be with research collaborations, public-sector innovation programs, ESG and sustainability strategy teams, enterprise digital transformation groups, and technology buyers evaluating AI for energy, climate, resource optimization, environmental monitoring, and smart infrastructure use cases.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| Academic researchers and principal investigators | Universities, AI labs, sustainability institutes, research centers | Influence technology evaluation, grant collaboration, pilot adoption | High relevance for AI platforms, datasets, compute tools, research partnerships |
| Government and policy stakeholders | National agencies, local authorities, smart-city offices, digital transformation units | Program design, procurement influence, regulatory shaping | Strong relevance for public-sector AI, environmental monitoring, infrastructure analytics |
| Corporate sustainability and ESG leaders | Large enterprises, manufacturers, energy companies, logistics groups | Sponsor pilots, software evaluation, operational transformation | Strong fit for carbon management, optimization, reporting, monitoring solutions |
| Enterprise AI and digital innovation teams | Technology companies, industrial groups, utilities, infrastructure operators | Technical evaluation, architecture decisions, pilot implementation | High relevance for AI software, cloud infrastructure, analytics, sensors, integration services |
| NGOs, nonprofits, and development organizations | Climate organizations, environmental NGOs, policy think tanks | Influence funding priorities, project partnerships, field deployment choices | Relevant for partnerships, impact analytics, environmental intelligence tools |
| Investors and venture capital firms | Climate-tech investors, innovation funds, corporate venture units | Funding influence and strategic partnership initiation | Useful for startup pipeline building and market validation |
| Students and early-career technical talent | Graduate programs, doctoral cohorts, engineering schools | Limited direct buying authority; strong future-user and research influence | Relevant for employer branding and community building, lower immediate sales value |
| Industry consultants and implementation advisors | Consultancies, sustainability advisors, systems integrators | Influence vendor selection and solution scoping | Strong multiplier channel for complex AI and sustainability solutions |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Osaka | Local universities, innovation hubs, corporate R&D teams, municipal stakeholders | Medium to High | Strong base for academic, industrial, and smart-city collaboration |
| Kansai Region | Kyoto, Kobe, Nara, and nearby research and corporate ecosystems | High | Likely source of university researchers, manufacturing groups, and sustainability programs |
| Japan National | Tokyo, Nagoya, Yokohama, Fukuoka, Sapporo, and other research/industry centers | High | National AI and sustainability stakeholders are likely to participate if the conference is internationally positioned |
| Asia-Pacific | Researchers, policymakers, and corporate participants from East Asia, Southeast Asia, Australia, and South Asia | Medium to High | Likely strongest international catchment area due to location and subject matter |
| Europe and North America | Selected academics, global tech firms, climate-tech investors, and NGOs | Medium | More likely for speaker, research, and partnership participation than broad procurement attendance |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The event title explicitly positions the conference as international, suggesting cross-border academic, policy, and industry participation. |
| Regional | Secondary emphasis | The most practical concentration of attendees is likely to come from Japan and the wider Asia-Pacific innovation ecosystem. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Osaka University | University / research buyer | Major Osaka-based research institution with strong relevance to AI, engineering, and sustainability collaboration | osaka-u.ac.jp | Professor, Principal Investigator, Research Center Director, AI Lab Lead | Strong Market Fit, Attendance Not Confirmed |
| Kyoto University | University / research buyer | Leading national university likely relevant for AI, environmental science, and sustainability research | kyoto-u.ac.jp | Professor, Research Director, Sustainability Program Lead | Strong Market Fit, Attendance Not Confirmed |
| The University of Tokyo | University / research buyer | High-fit institution for AI, public policy, and climate-related research participation | u-tokyo.ac.jp | Professor, Institute Director, Research Partnerships Lead | Strong Market Fit, Attendance Not Confirmed |
| National Institute of Advanced Industrial Science and Technology (AIST) | Government-affiliated research organization | Important Japanese applied research body for industrial AI, energy, and sustainability initiatives | aist.go.jp | Research Director, Program Manager, Innovation Partnerships Lead | Strong Market Fit, Attendance Not Confirmed |
| RIKEN | National research institute | Relevant for high-performance computing, AI research, and interdisciplinary scientific collaboration | riken.jp | Lab Director, Research Scientist, Program Officer | Strong Market Fit, Attendance Not Confirmed |
| National Institute for Environmental Studies (NIES) | Environmental research institution | Direct thematic fit with AI applications in environmental data, climate, and sustainability | nies.go.jp | Research Manager, Environmental Data Lead, Program Director | Strong Market Fit, Attendance Not Confirmed |
| Ministry of the Environment, Japan | Government / policy buyer | Relevant for environmental policy, sustainability programs, and public-sector innovation collaboration | env.go.jp | Program Manager, Policy Director, Digital Transformation Lead | Strong Market Fit, Attendance Not Confirmed |
| Ministry of Economy, Trade and Industry (METI) | Government / innovation stakeholder | Relevant for industrial AI, green growth, and technology commercialization themes | meti.go.jp | Innovation Director, Policy Advisor, Program Officer | Strong Market Fit, Attendance Not Confirmed |
| Panasonic Holdings | Corporate innovation / enterprise buyer | Relevant for smart energy, industrial AI, sustainability analytics, and ESG transformation | holdings.panasonic | Chief Sustainability Officer, AI Director, Innovation Manager | Strong Market Fit, Attendance Not Confirmed |
| Hitachi | Corporate technology and infrastructure buyer | Strong fit for AI, sustainability, smart infrastructure, and digital public-service solutions | hitachi.com | VP Digital Innovation, Sustainability Director, Data Science Lead | Strong Market Fit, Attendance Not Confirmed |
| NTT DATA | Systems integrator / enterprise solution buyer | Relevant as both a partner and buyer for AI platforms, data solutions, and public-sector sustainability projects | nttdata.com | Practice Director, Sustainability Lead, Partnerships Director | Strong Market Fit, Attendance Not Confirmed |
| Mitsubishi Electric | Industrial technology buyer | Relevant for AI-enabled industrial efficiency, energy management, and smart systems | mitsubishielectric.com | R&D Director, Product Strategy Lead, Sustainability Program Manager | Strong Market Fit, Attendance Not Confirmed |
Note: The current-year attendee, sponsor, exhibitor, or speaker list was not publicly confirmed in the verified source set reviewed for this data sheet. The organizations above are prospecting targets with strong thematic relevance, not confirmed attendees.
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Director of AI / AI Research Lead | R&D / Innovation | Director / Head | Owns technical evaluation and collaboration decisions |
| 2 | Professor / Principal Investigator | Research / Academic | Senior Individual Contributor / Director | Leads grants, partnerships, labs, and pilot adoption |
| 3 | Chief Sustainability Officer | Sustainability / ESG | C-Level | Sets sustainability priorities and enterprise adoption mandates |
| 4 | Director of Sustainability | Sustainability / Strategy | Director | Translates ESG goals into tool, data, and reporting requirements |
| 5 | CTO / Chief Digital Officer | Technology / Digital | C-Level | Approves enterprise AI strategy and platform architecture |
| 6 | Data Science Director | Data / Analytics | Director | Key decision-maker for model deployment and data partnerships |
| 7 | Program Manager | Innovation / Public Programs | Manager | Owns pilots, implementation timelines, and partner coordination |
| 8 | Partnerships Director | Business Development / Partnerships | Director | Drives consortium, ecosystem, and commercialization relationships |
| 9 | Policy Director / Digital Policy Advisor | Government / Policy | Director / Senior Manager | Influences public-sector innovation and funding direction |
| 10 | Innovation Manager | Strategy / Innovation | Manager | A practical contact for proof-of-concept and emerging-tech vendor review |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Research | Direct match for scientific institutes and interdisciplinary labs | Research collaboration, datasets, pilot tools |
| 2 | Higher Education | Universities are likely a core attendee group | Lab procurement, academic partnerships, conferences, grants |
| 3 | Information Technology & Services | Fits enterprise AI implementers and solution partners | AI services, system integration, data engineering |
| 4 | Computer Software | Relevant for AI model, analytics, and sustainability software vendors and buyers | Platform evaluation, data tools, model deployment |
| 5 | Government Administration | Relevant for ministries, municipalities, and public innovation agencies | Smart-city, policy, environmental data systems |
| 6 | Public Policy | Covers think tanks and policy-oriented institutions | Advisory, impact measurement, policy collaboration |
| 7 | Environmental Services | Relevant to climate, environmental monitoring, and sustainability operations | Monitoring, reporting, optimization, analytics |
| 8 | Renewables & Environment | Strong fit for energy transition and green-tech use cases | AI for energy efficiency, asset management, forecasting |
| 9 | Utilities | Utilities benefit from AI sustainability and operational intelligence | Grid, energy optimization, demand forecasting |
| 10 | Industrial Automation | Fits AI-enabled industrial efficiency and sustainable manufacturing | Process optimization, predictive analytics, emissions reduction |
| 11 | Mechanical or Industrial Engineering | Relevant for engineering-led sustainability and infrastructure projects | Engineering analytics, resource optimization |
| 12 | Management Consulting | Consultants often influence vendor shortlists and strategy decisions | Transformation programs, advisory-led buying cycles |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Not Confirmed | No verified organizer attendance figure identified in reviewed source set | User reference material mentioned 2,500+, but this was not treated as confirmed because it conflicts with other supplied details and lacks official verification |
| Exhibitor count | Not publicly confirmed | Not Confirmed | No official exhibitor prospectus or exhibitor list verified | Conference may be sponsor-led rather than expo-led |
| Buyer count | Not publicly confirmed | Not Confirmed | No official delegate segmentation identified | This event likely has a mixed audience of researchers, policy, and enterprise stakeholders rather than a pure buyer-only attendee base |
| Speaker count | Not publicly confirmed | Not Confirmed | No verified agenda or speaker roster identified | Would improve prospecting quality once agenda is published |
| Sponsor count | Not publicly confirmed | Not Confirmed | No verified sponsor page identified | Sponsors may become a useful proxy for active market participants later in the cycle |
| Historical attendance | Prior-year data not publicly confirmed in the reviewed source set | Historical Unavailable | No verified prior-year official report identified | Lead planning should be based on topic relevance rather than volume assumptions |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| AI for sustainability research | Access to models, datasets, compute, and research collaboration tools | University lab partnerships and pilot projects | AI platforms, data infrastructure, HPC access, annotation tools |
| Climate and environmental analytics | Monitoring, forecasting, and scenario modeling | Applied research and public-sector solution scoping | Geospatial AI, sensor analytics, data visualization, forecasting engines |
| ESG and corporate sustainability | Measurement, reporting, target tracking, operational efficiency | Enterprise introductions to ESG, strategy, and transformation teams | Carbon reporting, emissions analytics, decision-support software |
| Smart infrastructure and cities | Urban planning intelligence, energy efficiency, transport optimization | Public-private innovation discussions | IoT analytics, predictive maintenance, municipal AI systems |
| Digital transformation for sustainability | Integrating AI into operations and decision workflows | Enterprise workshops, alliance building, solution discovery | Consulting, software implementation, systems integration |
| Policy and responsible AI | Frameworks for ethical, transparent, and socially useful AI | Government and institutional advisory discussions | Governance tools, policy support, compliance advisory |
| Innovation funding and climate-tech commercialization | Access to investors, grants, and commercialization pathways | Investor networking and program sponsorship | Advisory, incubator partnerships, venture support services |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | High | High thematic alignment for AI, sustainability, research, and public-sector innovation suppliers |
| Decision-maker availability | Medium | Likely strong access to researchers, directors, and program leads; pure procurement attendance may be limited |
| Data collection potential | Medium | Improves significantly once agenda, speakers, sponsors, and registration partners are published |
| Apollo targeting potential | High | Strong by industry, title, research function, sustainability function, and geography |
| Geographic targeting potential | High | Clear prioritization around Japan, Asia-Pacific, and selected global research hubs |
| Best outreach approach | High | Thought-leadership outreach, collaboration framing, pilot discussions, and use-case education are likely more effective than hard-sell messaging |
| Overall lead quality | High | Especially strong for specialized B2B, innovation, research, and strategic partnership lead generation |
| Best use case | Very High | Ideal for account-based outreach, speaker-based targeting, partnership mapping, and high-fit niche audience building |
| Limitations / risks | Medium | Current public verification is limited; until official lists are available, attendee certainty remains low |
B2B attendee list building suitability: Suitable for targeted, quality-first list building once official agenda, speakers, sponsors, or partner institutions are published. Less suitable for high-volume attendee list assumptions at this stage.
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Research; Higher Education; Information Technology & Services; Computer Software; Government Administration; Public Policy; Environmental Services; Renewables & Environment; Utilities; Industrial Automation; Mechanical or Industrial Engineering; Management Consulting | Covers the most likely attendee and buyer-side organizations |
| Departments | Engineering; Information Technology; Research; Operations; Business Development; Program Management; Sustainability; Strategy; Education | Aligns with both technical and program-owner functions |
| Seniority | C-Level; VP; Director; Head; Manager; Partner; Professor equivalent where available | Focuses on strategic and implementation decision-makers |
| Job titles | Chief Sustainability Officer; Director of Sustainability; CTO; Chief Digital Officer; Director of AI; AI Research Lead; Data Science Director; Innovation Director; Program Manager; Policy Director; Partnerships Director | Concentrates on likely buyers, evaluators, and partnership leaders |
| Geography | Japan first; then South Korea, Singapore, Taiwan, Australia, India, United States, United Kingdom, Germany, Netherlands | Reflects likely event reach and collaboration corridors |
| Employee size | 51-200; 201-500; 501-1,000; 1,001-5,000; 5,001+ | Captures both scalable startups and enterprise/public institutions |
| Keywords | artificial intelligence, sustainability, climate tech, ESG, green technology, environmental analytics, smart city, digital transformation, responsible AI, carbon, energy optimization | Useful for thematic narrowing where title matching is inconsistent |
| Technologies, if relevant | Cloud AI, machine learning, GIS, IoT analytics, data platforms, sustainability reporting tools | Improves precision for solution-aligned outreach |
| Revenue range, if relevant | Mid-market to enterprise; public institutions and research organizations regardless of revenue visibility | Targets organizations with budget capacity for pilots and strategic initiatives |
| Company type | Public companies; private companies; universities; research institutes; government organizations; nonprofits | Reflects the mixed ecosystem likely to attend |
Suggested Apollo Search Logic: (“artificial intelligence” OR “machine learning” OR “data science”) AND (sustainability OR ESG OR climate OR environmental OR “smart city” OR energy) AND (Japan OR Asia-Pacific) with title filters for Director, Head, CTO, Chief Sustainability Officer, Program Manager, Research Director, Partnerships Director, and Innovation Lead.
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| User-provided event title and known details | Client-provided input | Confirmed working event name, Osaka location, Japan country, and 23-25 October 2026 date range for this data sheet | Medium |
| User-supplied reference description containing conflicting Tokyo / July details | Reference only | Used only as unverified contextual input; not treated as official proof because it conflicts with the supplied known details for Osaka and October 2026 | Low |
| Official event website / organizer / venue page | Primary source target | Not publicly confirmed in the verified source set reviewed for this data sheet | Unavailable |
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