AI & Big Data Expo North America
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About this event
AI & Big Data Expo North America
Date: June 25–27, 2025 |
Venue: Santa Clara Convention Center, Santa Clara, California, USA
Event Type: Artificial Intelligence, Big Data, Machine Learning, Data Analytics, IoT, Cloud Computing, Cybersecurity, and Emerging Technologies Conference & Exhibition
Estimated Attendance: 10,000+ industry professionals, including 2,500+ C-level executives, 1,200+ data scientists, and 300+ exhibitors from leading technology firms and startups.
1. Who Attends (Buyers / Attendees)
This event attracts a cross-section of decision-makers and technical professionals focused on AI and Big Data adoption. Key buyer profiles include:
- Chief Information Officers (CIOs) and Chief Technology Officers (CTOs)
- Data Scientists, Machine Learning Engineers, and Big Data Architects
- IT Directors, Enterprise Architects, and Infrastructure Managers
- Business Intelligence and Analytics Managers
- Procurement Officers for AI/ML platforms and data infrastructure
- Startup Founders and Investors in AI/Big Data space
- Academic Researchers and University representatives
- Government and Public Sector Technology Officers
2. Location + Attendee Geographic Origin
Show Location: Santa Clara Convention Center, Silicon Valley, California, USA – a global hub for technology innovation.
Attendee Origin: Primarily North American (85%+ from the U.S. and Canada), with 10–15% international delegates from Europe, Asia-Pacific, and Latin America. Strong representation from Silicon Valley tech giants and East Coast financial institutions.
3. Audience Reach
Reach Type: Global, with a core North American focus. The event draws multinational corporations, global startups, and international academia, but the majority of attendees are based in the U.S.
4. Sample Buyer Company Names (BUYERS ONLY) + Websites
| Priority | Company | Website | Best Title to Target | Why This is a Good Buyer Fit |
|---|---|---|---|---|
| 1 | Google Cloud | cloud.google.com | Head of AI Platform Sales / Director of Big Data Solutions | Major sponsor; actively seeks partnerships for cloud-based AI/ML infrastructure |
| 2 | Microsoft | microsoft.com | AI Solutions Architect / Azure Data Engineer Lead | Exhibitor with heavy focus on enterprise AI adoption and data analytics tools |
| 3 | NVIDIA | nvidia.com | Director of AI Hardware Sales / Data Center GPU Solutions Manager | Key player in AI infrastructure; targets enterprises upgrading compute capabilities |
| 4 | IBM | ibm.com | AI Ethics and Governance Lead / Watson Health Data Manager | Focuses on enterprise-grade AI governance and healthcare data analytics |
| 5 | Amazon Web Services (AWS) | aws.amazon.com | Principal Solutions Architect (AI/ML) / Big Data Account Manager | Gold sponsor; drives cloud migration and AI/ML workload adoption |
| 6 | SAS Institute | sas.com | VP of Data Science Platforms / Chief Data Officer | Longtime leader in analytics; targets financial services and healthcare sectors |
| 7 | Accenture | accenture.com | AI Consulting Director / Data Strategy Lead | Consulting partner for Fortune 500 companies implementing AI transformation |
| 8 | Intel | intel.com | AI Hardware Solutions Manager / Data Center Sales Director | Focuses on AI chipsets and infrastructure for enterprise data centers |
| 9 | Palantir Technologies | palantir.com | Director of Enterprise Data Integration / Government Contracts Lead | Targets defense, intelligence, and large-scale enterprise data platforms |
| 10 | Snowflake | snowflake.com | VP of Data Cloud Sales / Cloud Data Platform Engineer | Exhibitor with strong focus on cloud data warehousing and analytics |
| 11 | Deloitte | deloitte.com | AI Transformation Consultant / Data & Analytics Director | Advisory firm guiding enterprises through AI and data strategy |
| 12 | Stanford University | stanford.edu | Director of AI Research Lab / Industry Partnerships Manager | Academic partner seeking industry collaborations and talent recruitment |
| 13 | JPMorgan Chase | chase.com | Head of AI for Risk Management / Big Data Engineering Lead | Financial services leader investing in AI-driven fraud detection and analytics |
| 14 | Johnson & Johnson | jnj.com | AI in Healthcare Director / Clinical Data Science Manager | Targets AI applications in pharmaceutical R&D and patient care |
| 15 | Uber Technologies | uber.com | Senior Machine Learning Engineer / Data Science Manager | High-volume data user; seeks tools for real-time analytics and AI optimization |
5. Job Profiles, Industries & Event Type
Best Job Profiles to Target
- Chief Data Officer (CDO)
- Machine Learning Engineer
- Big Data Architect
- AI Product Manager
- Data Privacy and Compliance Officer
- Enterprise Solutions Architect
- Director of Data Science
- IT Infrastructure Manager
- AI Research Scientist
Key Industries
- Information Technology
- Computer Software
- Financial Services
- Healthcare & Life Sciences
- Telecommunications
- Manufacturing & Industrial
- Government & Defense
- Academia & Research
- Retail & E-commerce
Event Type
Conference & Exhibition focused on B2B knowledge sharing, product demos, and networking for AI and Big Data stakeholders.
6. Estimated Attendance
Total Expected Footfall: 10,000+ registered attendees
- 2,500+ C-level executives and procurement decision-makers
- 1,200+ data scientists and ML engineers
- 300+ exhibitors and sponsors
- 200+ speakers across 100+ conference sessions
Note: Buyer density is highest in the expo hall, keynote theaters, and networking lounges.
7. Key Focus Areas & Buyer Engagement
Key Themes: Enterprise AI adoption, ethical AI, real-time data processing, AI-driven automation, cloud and edge computing, cybersecurity for AI systems, and industry-specific applications (healthcare, finance, logistics).
Buyer Engagement Angle: Position your client’s product as a solution for scalable AI infrastructure, advanced analytics, or industry-specific data challenges. Emphasize ROI metrics like cost reduction, efficiency gains, or compliance support.
8. Client-Product Fit Note
To refine the buyer list, please share your client’s website and product offerings. For example:
- If your client sells AI/ML platforms or cloud infrastructure: Prioritize Google Cloud, AWS, Microsoft, and NVIDIA contacts.
- If your client offers data analytics tools: Target SAS, Snowflake, and financial/healthcare companies like JPMorgan Chase or Johnson & Johnson.
- If your client focuses on AI consulting or training: Highlight Accenture, Deloitte, and academic institutions like Stanford.
9. Final Recommendation & Industry Suggestion
Recommendation: This event is highly effective for B2B lead generation, especially for companies offering AI infrastructure, data analytics, or industry-specific solutions. The combination of C-level executives and technical buyers creates multiple sales entry points.
Industry Suggestion: Based on the event’s focus, prioritize these industries from the Apollo list:
- Information Technology
- Computer Software
- Financial Services
- Healthcare & Life Sciences
- Telecommunications
- Government Administration
- Education Management
Quality Rating for B2B List Sales: 9/10 – High buyer density, global brand participation, and clear decision-maker profiles. Filter carefully to exclude non-buyer roles (e.g., students, media).
Data sheet
AI & Big Data Expo North America – Event Attendee & Buyer Profile Analysis
Event date: May 24–25, 2027
Location: San Jose McEnery Convention Center, San Jose, California, United States
Event status: Upcoming
Research date: June 30, 2026
Event Overview
| Event Name | AI & Big Data Expo North America |
| Event Date | May 24–25, 2027 |
| Event Status | Upcoming |
| Venue | San Jose McEnery Convention Center |
| City | San Jose |
| State / Region | California |
| Country | United States |
| Organizer | TechEx Events / official event organizer branding used by the AI & Big Data Expo series. Current 2027 organizer entity should be reconfirmed on the official event page. |
| Official Event Website | AI & Big Data Expo North America official event website |
| Event Type | B2B conference and exhibition focused on artificial intelligence, big data, machine learning, analytics, enterprise technology adoption, and adjacent digital transformation topics. |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Business Services; Telecommunication |
| Audience Reach | National to international technology audience, with strong West Coast and Silicon Valley concentration. |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer for 2027. Historical / prior-year reference supplied indicates 10,000+ professionals and 300+ exhibitors for the 2025 edition. |
| Attendance Data Reliability | Historical / prior-year evidence for scale; current-year 2027 attendance not confirmed. |
| Main Purpose of Event | To connect enterprise technology buyers, AI practitioners, data leaders, solution providers, startups, and ecosystem partners around AI deployment, analytics, automation, infrastructure, security, and digital transformation buying priorities. |
About the Event
AI & Big Data Expo North America is a business-focused technology conference and exhibition serving enterprise, public sector, startup, and solution-provider audiences working across artificial intelligence, machine learning, data engineering, analytics, cloud, cybersecurity, IoT, and emerging digital infrastructure. Based on the event series positioning and the user-supplied prior-edition description, the program is designed to combine thought leadership, product discovery, implementation insight, and vendor-buyer networking in one venue.
The event matters commercially because it attracts both strategic decision-makers and hands-on technical stakeholders involved in evaluating, specifying, recommending, procuring, and deploying AI and data platforms. For lead generation teams, it is most relevant for enterprise software sales, data infrastructure vendors, cloud and security providers, consulting firms, systems integrators, and B2B service companies targeting innovation, analytics, IT modernization, and digital transformation budgets.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| CIOs, CTOs, Chief Digital Officers | Large enterprises, scale-ups, public sector agencies, digital-native firms | Executive budget owners and platform approval authorities | High-value targets for AI platforms, cloud, cybersecurity, consulting, and transformation programs |
| Data and analytics leaders | Enterprises building analytics, BI, data science, and ML capabilities | Evaluate data architecture, governance, analytics tools, model deployment, and vendor fit | Core audience for data platforms, observability, governance, MLOps, and visualization solutions |
| Data scientists, ML engineers, AI architects | Tech firms, financial services, healthcare, manufacturing, retail, telecom, government contractors | Technical influencers and solution evaluators | Useful for technical validation, product trials, pilots, and proof-of-concept conversations |
| IT directors and enterprise architects | Mid-market and enterprise organizations modernizing infrastructure | Influence vendor selection, integration strategy, architecture standards, and interoperability requirements | Strong fit for cloud, edge, platform integration, security, and infrastructure vendors |
| Procurement and strategic sourcing teams | Enterprises and public sector-related technology buyers | Commercial review, vendor onboarding, contracting, pricing negotiations | Important for enterprise sales conversion after technical interest is established |
| Operations and digital transformation leaders | Manufacturing, logistics, retail, healthcare, utilities, financial institutions | Drive AI business cases tied to productivity, automation, and process efficiency | Good targets for ROI-led outreach and use-case selling |
| Cybersecurity and risk leaders | Enterprises securing AI, cloud, and data environments | Influence governance, compliance, data protection, and secure deployment choices | Relevant for security vendors, identity platforms, compliance services, and managed security |
| Startup founders and innovation teams | AI startups, software ventures, innovation labs, venture-backed firms | Fast-moving buyers of infrastructure, data tools, partnerships, and GTM services | Useful for partnership, channel, API, and platform ecosystem selling |
| Investors and corporate venture teams | VC firms, strategic investors, corporate innovation groups | Assess technology trends, market timing, and partnership opportunities | Relevant for ecosystem access rather than direct procurement volume |
| Government and public sector technology officers | Agencies, civic innovation offices, public institutions, research organizations | Evaluate responsible AI, analytics, cybersecurity, and modernization programs | Relevant where suppliers sell secure, compliant, public-sector-ready solutions |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| San Jose | Strong local attendance from Silicon Valley companies, startups, and tech service firms | Very high | Host city is a core U.S. technology cluster with dense concentration of enterprise and startup decision-makers |
| California | Bay Area, San Francisco, Oakland, Santa Clara, Sunnyvale, Mountain View, Palo Alto, Los Angeles, San Diego, Sacramento | Very high | California offers strong enterprise software, semiconductor, cloud, defense-adjacent, university, and VC ecosystems |
| U.S. West Coast | Washington, Oregon, Arizona, Nevada, Utah, Colorado, Texas technology corridors | High | Accessible for enterprise IT teams, cloud vendors, startups, and systems integrators |
| United States national | Decision-makers from major tech-buying metros including New York, Boston, Austin, Chicago, Atlanta, Washington DC, Seattle | High | North America positioning supports broad national draw for AI and data transformation themes |
| Canada and North America | Likely attendance from Toronto, Vancouver, Montreal, and cross-border tech buyers | Medium | Likely due to event branding, but current-year country mix not publicly confirmed |
| International | Selected attendance from Europe and Asia-Pacific technology ecosystems | Medium | Likely international participation given AI category and Silicon Valley location, but not confirmed for 2027 |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| National | Primary classification | The event is branded for North America and is positioned to attract enterprise and technology participants from across the United States and selected Canadian markets. |
| Secondary: Global | Possible supplementary reach | AI and big data categories, combined with a Silicon Valley venue, create international relevance; however, the organizer’s 2027 country-level audience breakdown is not publicly confirmed in the data available here. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Enterprise technology buyer | Major AI, cloud, analytics, and infrastructure buyer with strong regional presence | google.com | Director of AI, Data Engineering Director, Procurement Manager, Cloud Architecture Lead | Strong Market Fit, Attendance Not Confirmed | |
| Microsoft | Enterprise technology buyer | Relevant for AI tooling, cloud infrastructure, data platforms, and strategic partnerships | microsoft.com | Principal PM, Director Data Platform, AI Program Manager, Strategic Sourcing Manager | Strong Market Fit, Attendance Not Confirmed |
| Amazon Web Services | Cloud and enterprise buyer / partner ecosystem target | Active in AI infrastructure, partnerships, developer tools, and enterprise transformation | aws.amazon.com | Partner Development Manager, AI/ML Product Lead, Procurement Manager, Solutions Architect Director | Strong Market Fit, Attendance Not Confirmed |
| Cisco | Enterprise technology buyer | Regional tech leader relevant for AI operations, networking, security, and enterprise integration | cisco.com | VP Engineering, Director Data Science, Security Director, Category Manager IT | Strong Market Fit, Attendance Not Confirmed |
| NVIDIA | AI ecosystem buyer / partner | High relevance in AI infrastructure, compute, software ecosystem, and developer enablement | nvidia.com | AI Solutions Director, Ecosystem Partnerships Lead, Infrastructure Procurement Manager | Strong Market Fit, Attendance Not Confirmed |
| Intel | Enterprise and innovation buyer | Relevant for AI acceleration, data infrastructure, semiconductors, and enterprise ecosystem partnerships | intel.com | Director AI Products, Data Platform Lead, Procurement Director, Innovation Partnerships Manager | Strong Market Fit, Attendance Not Confirmed |
| IBM | Enterprise technology buyer / solution partnership target | Strong fit across AI consulting, data modernization, governance, and hybrid cloud use cases | ibm.com | Consulting Partner, Director Data & AI, Procurement Category Lead, Practice Leader | Strong Market Fit, Attendance Not Confirmed |
| Salesforce | Enterprise software buyer | Relevant for applied AI, data cloud, analytics, and customer intelligence initiatives | salesforce.com | SVP Product, Director AI Strategy, Data Operations Lead, Strategic Sourcing Manager | Strong Market Fit, Attendance Not Confirmed |
| ServiceNow | Enterprise software buyer | Relevant for workflow automation, AI operations, enterprise architecture, and data-driven service management | servicenow.com | Director AI Products, Workflow Automation Lead, Procurement Manager, Platform Architect | Strong Market Fit, Attendance Not Confirmed |
| Oracle | Enterprise technology buyer | Relevant for cloud, database, analytics, AI applications, and ecosystem partnerships | oracle.com | VP Product, Director Data Platform, Cloud Procurement Lead, AI Program Director | Strong Market Fit, Attendance Not Confirmed |
| Meta | AI and data-intensive enterprise buyer | Large-scale buyer of AI infrastructure, data tooling, security, and developer solutions | meta.com | Engineering Director, Data Infrastructure Lead, Sourcing Manager, AI Research Operations Manager | Strong Market Fit, Attendance Not Confirmed |
| Adobe | Enterprise software buyer | Relevant for applied AI, generative workflows, analytics, and customer data use cases | adobe.com | Product Director, AI Platform Lead, Data Engineering Manager, Procurement Manager | Strong Market Fit, Attendance Not Confirmed |
Note: The table above is a prospecting-oriented buyer target list based on strong market fit to the event theme and regional ecosystem. It is not a confirmed current-year attendee list for the 2027 edition.
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Chief Information Officer | IT | C-Level | Owns enterprise transformation, platform budgets, and strategic vendor decisions |
| 2 | Chief Technology Officer | Technology | C-Level | Influences architecture, AI strategy, and innovation direction |
| 3 | Chief Data Officer / Head of Data | Data & Analytics | C-Level / VP | Key buyer for governance, data quality, analytics, and monetization programs |
| 4 | VP Data & Analytics | Analytics | VP | Manages analytics roadmaps, team priorities, and vendor evaluation |
| 5 | Director of AI / Director of Machine Learning | AI / Engineering | Director | Drives use-case prioritization, tool selection, model lifecycle, and implementation decisions |
| 6 | Director of Data Engineering | Engineering | Director | Strong influence over pipelines, architecture, data platforms, and integration stack |
| 7 | Enterprise Architect | IT / Architecture | Manager / Director | Evaluates compatibility, technical fit, and integration risk |
| 8 | IT Director | IT | Director | Often involved in shortlisting, infrastructure planning, and deployment ownership |
| 9 | Cybersecurity Director / CISO Office | Security | Director / VP | Critical for secure AI deployment, governance, identity, and data protection |
| 10 | Procurement Manager / Strategic Sourcing Manager | Procurement | Manager / Director | Important for vendor onboarding, pricing, legal, and commercial closure |
| 11 | Product Manager, AI / Analytics | Product | Manager / Director | Influences functional requirements, user adoption, and product-led partnerships |
| 12 | Digital Transformation Director | Operations / Strategy | Director / VP | Connects AI buying decisions to operational outcomes and business ROI |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Information Technology & Services | Core audience for enterprise AI adoption, services, and digital infrastructure | AI platforms, analytics, consulting, cloud modernization |
| 2 | Computer Software | High concentration of software vendors and internal product teams adopting AI and data tools | MLOps, APIs, developer tools, data orchestration |
| 3 | Computer Hardware | Relevant for AI compute, edge systems, and processing infrastructure | Infrastructure, chips, hardware-software integration |
| 4 | Semiconductors | Strong fit in Silicon Valley and AI acceleration markets | AI compute procurement, design automation, supply ecosystem targeting |
| 5 | Computer & Network Security | AI governance and secure data environments are major buying themes | Security platforms, compliance, identity, model security |
| 6 | Telecommunications | Telecom operators use AI for network analytics, automation, customer intelligence, and edge use cases | Predictive analytics, automation, infrastructure optimization |
| 7 | Financial Services | High AI adoption for fraud, risk, automation, and customer analytics | Analytics, security, compliance, decisioning tools |
| 8 | Hospital & Health Care | Healthcare systems are active buyers of AI, predictive analytics, and operational intelligence | Clinical analytics, workflow automation, data governance |
| 9 | Retail | Retailers increasingly buy AI for personalization, forecasting, supply chain, and customer insight | Recommendation engines, demand planning, customer data platforms |
| 10 | Logistics & Supply Chain | Strong AI use cases in route optimization, forecasting, automation, and visibility | Predictive operations, orchestration, warehouse analytics |
| 11 | Industrial Automation | AI is central to smart operations, quality analytics, robotics, and maintenance use cases | Computer vision, predictive maintenance, factory analytics |
| 12 | Government Administration | Public agencies increasingly evaluate AI, analytics, automation, and secure modernization tools | Gov-ready analytics, secure AI, case management modernization |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer for 2027 | Unconfirmed | No verified 2027 public attendance figure available in the provided data | Do not use a current-year attendee count in sales copy without organizer confirmation |
| Historical attendance | 10,000+ industry professionals | Historical / prior-year evidence | User-supplied prior-edition description | Prior-year participation evidence. Not a confirmed attendee list for the current edition. |
| Exhibitor count | 300+ | Historical / prior-year evidence | User-supplied prior-edition description | Useful as scale context only until 2027 exhibitor count is officially published |
| C-level executives | 2,500+ | Historical / prior-year evidence | User-supplied prior-edition description | Indicates strong executive density if prior-year profile remains directionally similar |
| Data scientists | 1,200+ | Historical / prior-year evidence | User-supplied prior-edition description | Signals strong technical practitioner audience |
| Buyer count | Not publicly confirmed | Unconfirmed | No verified buyer-only count in available data | Use role-based targeting rather than buyer count assumptions |
| Speaker count | Not publicly confirmed | Unconfirmed | No verified 2027 published count in available data | Speaker organizations should be validated from official agenda when available |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Artificial Intelligence | Deploy business-ready AI with measurable ROI | Use-case workshops, platform demos, pilot discussions | AI software, model deployment, consulting, automation services |
| Big Data & Analytics | Improve data quality, accessibility, and decision intelligence | Architecture reviews, data maturity assessments | Data lakes, warehouses, BI, data governance, observability |
| Machine Learning Operations | Scale model development and production management | Technical deep dives and proof-of-concept conversations | MLOps platforms, testing, monitoring, orchestration tools |
| Cloud & Infrastructure | Support scalable AI workloads and modern data architecture | Capacity planning and migration discussions | Cloud services, GPU infrastructure, edge platforms, integration services |
| Cybersecurity | Protect data, models, identities, and AI-enabled workflows | Risk and compliance consultations | Identity security, governance, compliance, zero trust, secure AI controls |
| Digital Transformation | Connect AI investment to process improvement and business outcomes | Executive roundtables and ROI-led meetings | Consulting, automation, integration, change management |
| IoT & Edge Data | Turn distributed data into predictive operational insight | Industry-specific use-case selling | Edge analytics, device management, streaming data solutions |
| Responsible AI & Governance | Ensure compliance, explainability, and policy alignment | Policy and governance advisory conversations | Governance tools, auditing, controls, policy consulting |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | Very High | Event themes align directly with enterprise AI, analytics, cloud, and security buying priorities. |
| Decision-maker availability | High | Historical profile suggests meaningful executive and director-level presence, though 2027 role mix is not yet confirmed. |
| Data collection potential | Medium to High | Strong if official sponsor, speaker, agenda, or networking lists become public; current confirmed participant data is limited. |
| Apollo targeting potential | Very High | The audience maps well to Apollo filters by industry, seniority, department, geography, and AI/data-related titles. |
| Geographic targeting potential | Very High | San Jose and California provide an efficient regional concentration of high-fit technology buyers. |
| Best outreach approach | High | Use account-based outreach combining role-based messaging, event relevance, and use-case-driven AI business value propositions. |
| Overall lead quality | High | Strong for B2B software, cloud, data, cybersecurity, integration, consulting, and enterprise services. |
| Best use case | High-fit ABM and event-proximate prospecting | Best for building high-intent target account lists rather than claiming access to a confirmed attendee file without official evidence. |
| Limitations / risks | Medium | Current-year 2027 attendee, exhibitor, and speaker counts are not yet publicly confirmed in the data available here. |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Information Technology & Services; Computer Software; Computer Hardware; Semiconductors; Computer & Network Security; Telecommunications; Financial Services; Hospital & Health Care; Retail; Logistics & Supply Chain; Industrial Automation; Government Administration | Covers the strongest AI and data adoption segments |
| Departments | Information Technology; Engineering; Data / Analytics; Product; Operations; Procurement; Security; Innovation | Aligns with technical, operational, and commercial buyers |
| Seniority | C-Level; VP; Director; Head; Manager | Prioritizes budget holders and key influencers |
| Job titles | CIO, CTO, Chief Data Officer, VP Data & Analytics, Director of AI, Director of Machine Learning, Director of Data Engineering, Enterprise Architect, IT Director, CISO, Security Director, Digital Transformation Director, Procurement Manager, Strategic Sourcing Manager, Product Manager AI | Targets the most relevant decision profiles |
| Geography | San Jose; Santa Clara; San Francisco Bay Area; California; United States; Canada | Captures local event gravity plus North American reach |
| Employee size | 51–200; 201–500; 501–1,000; 1,001–5,000; 5,001–10,000; 10,001+ | Balances growth-stage buyers with enterprise accounts |
| Keywords | AI, artificial intelligence, machine learning, big data, analytics, data platform, data engineering, MLOps, generative AI, cloud transformation, model governance, data governance, predictive analytics | Finds companies and contacts actively aligned with event themes |
| Technologies, if relevant | Cloud platforms, data warehouses, BI tools, MLOps environments, cybersecurity stack | Improves relevance for account-based targeting |
| Revenue range, if relevant | $10M–$50M; $50M–$100M; $100M–$500M; $500M+ | Separates venture-backed growth firms from large enterprise budget owners |
| Company type | Public companies; private growth companies; enterprise technology providers; digital transformation-heavy operating companies | Refines ICP coverage by buying maturity and scale |
Suggested Apollo Search Logic: Target North American technology and data-intensive companies using title logic such as (“CIO” OR “CTO” OR “Chief Data Officer” OR “VP Data” OR “Director of AI” OR “Director of Machine Learning” OR “Director of Data Engineering” OR “Enterprise Architect” OR “IT Director” OR “Security Director” OR “Procurement Manager”) combined with company keywords like (“AI” OR “machine learning” OR “analytics” OR “data platform” OR “MLOps” OR “generative AI” OR “digital transformation”).
Client Fit Review Required
Please share the client website or product/service details. I will review the client offering and identify the highest-fit buyer companies, Apollo industries, seniority levels, departments, and job titles from this event.
Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| AI & Big Data Expo North America official website | Official event website | Event branding, theme areas, organizer identity, series positioning, and future-edition details when published | High |
| TechEx Events | Organizer website | Organizer brand and event portfolio context | High |
| San Jose McEnery Convention Center / San Jose.org | Venue source | Venue name and location context | High |
| User-provided event details | Provided input | 2027 event dates, city, state, country, and venue supplied by requester | Medium to High |
| User-supplied prior-edition description | Historical reference | Prior-year attendance indicators, audience composition, and category framing | Medium |
Verification note: Current-year 2027 attendee counts, confirmed buyer organizations, and speaker/exhibitor lists were not publicly confirmed in the provided data used for this report. Where needed, this report distinguishes between confirmed details, historical / prior-year evidence, and likely attendee profile assumptions.
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