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About this event
AI Con USA 2026
Event type: Artificial Intelligence, Machine Learning, Data Science, Enterprise Technology, Automation, Digital Transformation, Software & Innovation
Estimated attendance: Likely several thousand attendees, with a strong mix of enterprise buyers, technical decision-makers, founders, product leaders, data teams, solution providers, and AI ecosystem stakeholders.
Audience profile: This is typically a high-value B2B technology event, especially attractive for companies selling AI tools, analytics platforms, cloud services, automation software, data infrastructure, cybersecurity, developer tools, consulting, and enterprise transformation solutions.
1️⃣ Who attends: Buyers / Attendees
AI Con USA 2026 is best understood as a business and technology decision-maker event rather than a general consumer exhibition. The strongest buying audience usually comes from organizations that are actively exploring, implementing, scaling, or monetizing artificial intelligence across their operations.
The main attendee groups you should expect include:
- C-suite and executive leadership such as CEOs, CTOs, CIOs, Chief Data Officers, Chief AI Officers, and innovation leaders.
- Technology and product teams including engineering managers, AI architects, ML engineers, data scientists, platform leads, and solution architects.
- Business transformation leaders responsible for automation, digital strategy, operations optimization, and enterprise modernization.
- Data and analytics teams focused on predictive modeling, reporting, governance, business intelligence, and AI adoption.
- Security, compliance, and risk teams concerned with responsible AI, privacy, governance, model safety, and enterprise controls.
- Consulting and systems integration firms that advise clients on AI strategy, implementation, and change management.
- Startups, venture-backed companies, and product innovators seeking partnerships, investment, tools, and enterprise visibility.
- Enterprise buyers from vertical industries such as healthcare, finance, retail, manufacturing, logistics, telecom, education, media, government, and professional services.
For attendee-list monetization, the best buyers are not just “people interested in AI.” The highest-value targets are the ones with budget authority or active project ownership: AI transformation leads, enterprise architects, data platform managers, innovation directors, product leaders, and digital strategy executives.
2️⃣ Where the show is happening + attendee geographic origin
Location: The exact venue for AI Con USA 2026 should be confirmed from the official event page, because this title can be used for a conference format that may shift by city or venue.
Attendee origin: Based on the event positioning, the audience is likely national with global interest. AI events in the U.S. typically attract:
- Domestic attendees from major U.S. technology hubs such as California, New York, Texas, Washington, Massachusetts, Illinois, Georgia, and Florida.
- Traveling corporate buyers from across North America.
- International attendees from Europe, India, the Middle East, Canada, and Asia if the event has a strong thought-leadership and enterprise-technology profile.
If the conference is held in a major city like San Francisco, New York, Las Vegas, Austin, Chicago, or Boston, the local draw will likely be strong, but the real value will come from national and global travel buyers attending for AI strategy, product discovery, and partnership development.
3️⃣ Audience reach: Local / National / Global
Reach type: National with global relevance
AI is a globally relevant subject, but the buyer mix usually depends on the event’s scale and speaker lineup. For an event named AI Con USA 2026, the strongest reach classification is generally:
- Local: Yes, if the event is in a major metro area with a dense tech ecosystem.
- National: Definitely. This is the primary reach category.
- Global: Also possible, especially if the event covers enterprise AI, generative AI, model deployment, governance, and real-world commercial use cases.
That makes this a strong event for selling buyer/attendee data to vendors serving enterprise innovation, SaaS, cloud, analytics, automation, and digital transformation markets.
4️⃣ Sample buyer company names + websites
Below is a practical sample buyer list of companies that would likely be strong matches for AI Con USA 2026. These are buyer-style targets only, chosen for their likely interest in AI, enterprise tech, data infrastructure, automation, or digital transformation.
| Priority | Company | Website | Best Title to Target | Why This is a Good Buyer Fit |
|---|---|---|---|---|
| 1 | Microsoft | microsoft.com | Director, AI Strategy / Cloud Solution Architect / Enterprise Innovation Lead | Strong enterprise AI, cloud, developer, and productivity platform buyer fit. |
| 2 | Google Cloud | cloud.google.com | AI Product Marketing Manager / Industry Solutions Lead | Excellent fit for AI infrastructure, analytics, and enterprise transformation. |
| 3 | Amazon Web Services | aws.amazon.com | GenAI Specialist / Partner Solutions Manager / Enterprise Account Executive | High alignment with cloud AI, machine learning, and enterprise adoption. |
| 4 | IBM | ibm.com | AI Partnerships Manager / Data & AI Sales Leader | Longstanding enterprise AI, consulting, and governance relevance. |
| 5 | NVIDIA | nvidia.com | Developer Relations Manager / AI Ecosystem Manager | Very strong fit for AI computing, model training, and enterprise acceleration. |
| 6 | Salesforce | salesforce.com | Product Marketing Director, AI / CRM Innovation Lead | Buyer fit for generative AI, customer intelligence, and workflow automation. |
| 7 | Oracle | oracle.com | Cloud AI Solutions Director / Data Platform Sales Lead | Strong enterprise data, database, and AI platform alignment. |
| 8 | Adobe | adobe.com | AI Product Manager / Digital Experience Strategy Lead | Great fit for creative AI, marketing automation, and digital experience solutions. |
| 9 | ServiceNow | servicenow.com | Workflow Automation Strategist / AI Solutions Manager | High relevance for enterprise workflow AI and operational automation. |
| 10 | Palantir | palantir.com | Government/Enterprise AI Sales Lead / Solutions Engineer | Excellent fit for data platforms, decision intelligence, and AI operations. |
| 11 | Databricks | databricks.com | Field Marketing Manager / Data Platform Partnerships Lead | Very strong buyer for data engineering, ML, and enterprise AI stacks. |
| 12 | Snowflake | snowflake.com | AI/ML Product Marketing Manager / Strategic Alliances Lead | Good fit for AI data cloud, analytics, and enterprise data governance. |
| 13 | Accenture | accenture.com | Managing Director, AI Transformation / Technology Strategy Lead | Consulting-heavy buyer with active AI advisory and implementation work. |
| 14 | Deloitte | deloitte.com | AI Advisory Partner / Digital Transformation Director | Strong fit for enterprise AI strategy, governance, and client delivery. |
| 15 | PwC | pwc.com | Innovation Leader / AI & Data Strategy Partner | Relevant for AI consulting, audit-tech, risk, and enterprise transformation. |
| 16 | IBM Consulting | ibm.com/services | Transformation Director / AI Consulting Lead | Strong AI implementation and enterprise modernization buyer. |
| 17 | HPE | hpe.com | AI Infrastructure Product Manager / Enterprise Sales Director | Good fit for hybrid cloud, compute, storage, and AI infrastructure buying. |
| 18 | Cisco | cisco.com | AI Networking Strategy Lead / Product Marketing Manager | Relevant for AI-enabled infrastructure, networking, security, and collaboration. |
Top 5 best sample buyers to show first: Microsoft, AWS, NVIDIA, Databricks, Accenture. These cover the strongest buyer categories across cloud, compute, data, and enterprise AI services.
5️⃣ Job profiles, industries & event type
This event attracts a highly qualified audience, so the best targeting strategy is to prioritize titles connected to AI strategy, technical implementation, data governance, and business transformation.
Best job profiles to target:
- Chief AI Officer
- Chief Technology Officer
- Chief Information Officer
- Chief Data Officer
- VP, Artificial Intelligence
- VP, Data & Analytics
- Director, AI Strategy
- Director, Digital Transformation
- Director, Machine Learning
- Head of Data Science
- AI Product Manager
- Machine Learning Engineer Manager
- Solutions Architect
- Enterprise Architect
- Innovation Director
- Data Platform Manager
- Responsible AI / Governance Lead
- Automation Program Manager
- Technology Partnerships Manager
- Consulting Partner / Practice Leader
Best industries to use:
- Computer Software
- Information Technology & Services
- Internet
- Computer & Network Security
- Financial Services
- Banking
- Insurance
- Management Consulting
- Marketing & Advertising
- Telecommunications
- Retail
- Healthcare / Hospital & Health Care
- Pharmaceuticals
- Education Management
- Government Administration
- Utilities
- Transportation / Logistics
- Manufacturing-related sectors through company keywords and functions
Event type fit: Enterprise AI conference, technical summit, innovation forum, B2B networking, product showcase, solution buyer conference, thought leadership event.
6️⃣ Estimated attendance / expected footfall
Without the official event brief in hand, the safest estimate for AI Con USA 2026 is mid-size to large conference attendance. For a U.S. AI event with this positioning, expected footfall is often in the range of:
- 1,000 to 5,000+ attendees for a focused, high-quality conference
- 5,000 to 10,000+ if the event includes multiple tracks, an expo floor, sponsorship-heavy participation, and strong enterprise branding
The quality of the audience matters more than raw numbers for attendee-list sales. Even a smaller AI event can be highly valuable if it contains senior leaders, product decision-makers, technical buyers, and active implementation teams.
7️⃣ Key focus areas & buyer engagement
AI Con USA 2026 will likely center on practical business outcomes rather than abstract theory. The strongest focus areas generally include:
- Generative AI and large language model adoption
- Machine learning model development and deployment
- Data engineering, data quality, and data governance
- AI infrastructure, cloud, compute, and platform scaling
- Responsible AI, compliance, ethics, and risk management
- AI in enterprise workflows and automation
- AI product strategy and product management
- Customer experience, marketing, and sales enablement through AI
- AI for cybersecurity and threat detection
- Vertical AI use cases in healthcare, finance, retail, logistics, and manufacturing
Buyer engagement angle: This event is ideal for positioning solutions around measurable business outcomes. Buyers at AI conferences respond best to clear messaging such as:
- How your solution reduces cost or manual effort
- How it accelerates deployment of AI models
- How it improves governance, trust, and compliance
- How it integrates with existing data and cloud stacks
- How it supports enterprise use cases rather than just demos
The most engaged buyers are usually those with active budgets, current proof-of-concept projects, or clear mandates to implement AI within 6–12 months.
8️⃣ Client-product fit note
Before finalizing the best buyers for this event, I should review your client’s website and product positioning. The right buyer list changes a lot depending on what your client actually sells.
Examples:
- If your client sells AI software or automation tools: target AI leaders, CTOs, data platform managers, and innovation executives.
- If your client sells cloud or infrastructure services: target platform architects, cloud engineering leaders, and enterprise IT decision-makers.
- If your client sells consulting or implementation services: target transformation leaders, strategy teams, and operations executives.
- If your client sells cybersecurity: target security leaders, compliance officers, and infrastructure decision-makers.
- If your client sells analytics or data tools: target CDOs, data science leads, BI leaders, and engineering managers.
Best next step: Share the client website, and I can narrow this into the most relevant buyer companies, job titles, and industries for your exact product.
9️⃣ Final recommendation
Overall assessment: AI Con USA 2026 looks like a strong event for high-value B2B attendee-list sales, especially if your database includes enterprise technology leaders, AI decision-makers, data teams, and consulting buyers.
Best buyer segments to collect:
- Chief AI / Technology / Data officers
- AI strategy and innovation leaders
- Machine learning and data science managers
- Cloud and platform architects
- Digital transformation executives
- Consulting firms and systems integrators
- Enterprise buyers from finance, healthcare, retail, manufacturing, and telecom
Quality rating for attendee-list sales: 9/10
This is the kind of event that can produce very strong lead quality if the audience is properly segmented by title, function, seniority, and buying intent. The key is not just collecting “AI interest” contacts, but identifying people tied to real enterprise budgets and implementation needs.
Recommendation: If you share your client website, I can refine this into a sharper buyer shortlist and tell you which industries and titles are the best match for your product.
Data sheet
| Event Name | AI Con USA 2026 |
| Event Date | 7 June 2026 – 12 June 2026 |
| Event Status | Completed |
| Venue | Hyatt Regency Seattle |
| City | Seattle |
| State / Region | Washington |
| Country | United States |
| Organizer | Organizer not publicly confirmed in the source set reviewed for this brief. |
| Official Event Website | Official event website not publicly confirmed in the source set reviewed for this brief. |
| Event Type | B2B conference / summit focused on artificial intelligence, machine learning, data science, enterprise technology, automation, digital transformation, software, and innovation. |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Business Services |
| Audience Reach | Likely national, with potential international participation from the AI, cloud, software, data, and enterprise technology ecosystem. |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Unconfirmed for the 2026 edition. Available assessment is based on event positioning, venue scale, and typical enterprise AI conference patterns. |
| Main Purpose of Event | To convene enterprise technology leaders, AI practitioners, product teams, data stakeholders, solution providers, and innovation decision-makers around AI adoption, deployment, governance, tooling, and business transformation. |
AI Con USA 2026 appears to be a business-focused artificial intelligence event positioned around enterprise AI adoption, machine learning, analytics, automation, software innovation, and digital transformation. Based on the event title, venue, schedule, and the descriptive brief supplied for this report, the event is most relevant to senior technology leaders, AI and data teams, product and engineering functions, and commercial providers selling into enterprise modernization programs.
From a lead-generation perspective, the event matters because AI budgets increasingly span multiple buying centers, including IT, data, product, operations, cybersecurity, cloud, and transformation leadership. Even where public attendee verification is limited, an event of this profile is typically valuable for B2B outreach, partnership development, account-based marketing, and buyer mapping across enterprise software, consulting, infrastructure, analytics, and automation categories.
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| C-suite technology leadership | Enterprise software firms, large corporates, financial institutions, healthcare systems, manufacturers, retailers, public sector technology teams | Budget ownership, transformation sponsorship, platform approval | High relevance for strategic software, infrastructure, AI transformation, and consulting deals |
| Chief data, analytics, and AI leaders | Data-driven enterprises, digital-native companies, regulated industries, advanced analytics teams | Model strategy, data stack decisions, governance, AI program ownership | Key buyers for data platforms, MLOps, observability, governance, and data engineering solutions |
| Product and engineering leaders | SaaS companies, enterprise platforms, application development organizations, innovation teams | Evaluation of AI-enabled product features, developer tools, APIs, and deployment platforms | Strong relevance for AI tooling, developer infrastructure, and product acceleration vendors |
| Machine learning and data science practitioners | Internal data science teams, research groups, applied AI teams, innovation labs | Technical recommendation, proof-of-concept validation, stack influence | Important influencers for pilot adoption and technical shortlisting |
| IT infrastructure and cloud teams | Enterprises operating hybrid cloud, security, storage, and enterprise architecture functions | Platform integration, hosting decisions, architecture and scalability approval | Relevant to cloud, compute, orchestration, storage, security, and networking suppliers |
| Operations and transformation leaders | Large enterprises seeking workflow automation and process optimization | Use-case prioritization, ROI evaluation, cross-functional sponsorship | High relevance for automation, process intelligence, and change-management offerings |
| Cybersecurity and risk leaders | Security-conscious enterprises, financial services, healthcare, government-adjacent organizations | AI risk controls, data protection, compliance, vendor due diligence | Relevant to AI governance, cybersecurity, privacy, and compliance vendors |
| Procurement and sourcing stakeholders | Mid-market and enterprise buying organizations | Commercial review, supplier onboarding, contract negotiation | Useful for converting technical interest into approved vendor status |
| Consulting, systems integrator, and advisory firms | Management consulting, cloud consultancies, implementation partners, digital transformation advisors | Influence technology selection and implementation scope | Strong partner and channel relevance |
| Investors, founders, and innovation ecosystem participants | VCs, startups, incubators, venture studios, strategic innovation units | Partnership, funding, early technology scouting | Relevant for ecosystem mapping, alliances, and market visibility |
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Seattle | Local enterprise technology, cloud, software, startup, and consulting ecosystem | High | Seattle is a strong base for cloud, software engineering, AI talent, and enterprise digital transformation buyers. |
| Washington State | Regional attendees from technology, healthcare, higher education, manufacturing, and public sector-adjacent organizations | Medium to High | Likely draw from wider state business and innovation communities. |
| Pacific Northwest | Oregon, Idaho, British Columbia, and nearby innovation corridors | Medium | Good regional access for technology and enterprise modernization buyers. |
| United States national market | Enterprise buyers, software vendors, AI startups, consultants, and practitioners from major U.S. business hubs | High | Likely strongest participation from West Coast, Texas, Northeast, and major data/AI business centers. |
| International | Possible participation from Canada, Europe, and Asia-Pacific technology organizations | Medium | International reach is plausible for an AI event in Seattle, but the 2026 attendee mix is not publicly confirmed. |
| Reach Level | Assessment | Explanation |
|---|---|---|
| National | Primary classification | The event profile, Seattle venue, and AI enterprise subject matter indicate a likely U.S.-wide audience of technology buyers, practitioners, and solution providers. |
| Secondary description | Regional plus selective international spillover | Seattle’s technology ecosystem may increase Pacific Northwest attendance and attract some cross-border participation, but this is not confirmed by a published attendee breakdown. |
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| No official current-year attendee, buyer, exhibitor, sponsor, or speaker-organization list was publicly confirmed in the source set reviewed for this brief. To avoid unsupported attendance claims, no company-level attendee table is asserted here. This limits current-edition attendee list building accuracy. If an official participant directory, agenda, sponsor page, or exhibitor list is shared, this section can be upgraded immediately. | |||||
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Chief Technology Officer | Technology | C-Level | Owns AI roadmap, architecture direction, and strategic vendor selection. |
| 2 | Chief Information Officer | IT | C-Level | Controls enterprise technology budgets, security posture, and integration approvals. |
| 3 | Chief Data Officer / Chief Analytics Officer | Data & Analytics | C-Level | Leads data governance, analytics maturity, and enterprise AI readiness. |
| 4 | VP / Head of AI | AI / Innovation | VP | Critical buyer for AI platforms, model operations, deployment, and governance tools. |
| 5 | Director of Data Science | Data Science | Director | Influences technical evaluation, POC design, and team tool adoption. |
| 6 | Director of Machine Learning / MLOps | Engineering / ML Platform | Director | Key role for production AI, scalability, monitoring, and deployment infrastructure. |
| 7 | VP Product / Director of Product | Product | VP / Director | Buys or influences AI features, user experience enhancements, and embedded intelligence capabilities. |
| 8 | Enterprise Architect / Solutions Architect | Architecture | Manager to Director | Determines integration fit, interoperability, and stack compatibility. |
| 9 | Director of Digital Transformation | Strategy / Transformation | Director | Owns business case and cross-functional implementation priorities. |
| 10 | Procurement Manager / Strategic Sourcing Manager | Procurement | Manager | Important for supplier onboarding, compliance, and contract conversion after technical selection. |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Information Technology & Services | Core fit for enterprise technology adoption and AI solution procurement | AI platforms, cloud tooling, engineering services |
| 2 | Computer Software | Strong relevance for product-led and embedded AI buyers | APIs, copilots, developer tools, model integration |
| 3 | Computer & Network Security | AI governance and security are common enterprise buying themes | Model security, risk management, compliance controls |
| 4 | Financial Services | High AI spend and strong analytics maturity | Fraud detection, automation, customer intelligence |
| 5 | Hospital & Health Care | Growing demand for AI-enabled clinical and operational optimization | Workflow intelligence, predictive analytics, operational automation |
| 6 | Retail | Retailers use AI for pricing, personalization, demand forecasting, and service automation | Customer analytics, supply optimization, recommendation engines |
| 7 | Telecommunications | Large-scale data operations and automation use cases | Network intelligence, customer support AI, operations analytics |
| 8 | Manufacturing | Industrial AI use cases continue to expand | Predictive maintenance, quality intelligence, process automation |
| 9 | Government Administration | Public sector AI interest is growing in service delivery and operational efficiency | Automation, citizen service, document intelligence |
| 10 | Management Consulting | Consultancies influence enterprise AI strategy and vendor selection | Partnerships, implementation channels, referral opportunities |
| 11 | Internet | Digital-native companies are active adopters of AI tooling | Growth analytics, personalization, operational automation |
| 12 | Research | Applied research and innovation labs often evaluate leading AI tools | Experimental platforms, advanced analytics, collaboration |
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Unconfirmed | No official 2026 attendance figure located in the source set reviewed | Any market estimate would be speculative without organizer disclosure. |
| Exhibitor count | Not publicly confirmed | Unconfirmed | No official exhibitor directory verified for this brief | Important limitation for list-building use cases. |
| Buyer count | Not publicly confirmed | Unconfirmed | No official attendee segmentation data verified | Buyer composition is inferred from the event theme only. |
| Speaker count | Not publicly confirmed | Unconfirmed | No official 2026 speaker roster verified | Agenda access would materially improve stakeholder mapping. |
| Sponsor count | Not publicly confirmed | Unconfirmed | No official sponsor page verified | Sponsor data would help identify ecosystem buyers and partners. |
| Historical attendance | No prior-year official attendance figure verified in the reviewed source set | Historical / prior-year evidence unavailable | Current brief is based on supplied event details and venue verification | Can be updated if official archives are shared. |
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Enterprise AI adoption | Move from experimentation to production deployment | Executive strategy discussions, roadmap workshops, platform evaluation | AI platforms, implementation services, change management |
| Machine learning operations | Model deployment, monitoring, reproducibility, and scale | Technical demos and architecture conversations | MLOps, observability, model management, workflow orchestration |
| Data infrastructure | Trusted data pipelines, storage, integration, and quality | Data modernization planning and architecture mapping | Lakehouse, ETL/ELT, governance, metadata, quality platforms |
| Automation and digital transformation | Process efficiency and measurable ROI | Use-case workshops and functional buyer outreach | Workflow automation, intelligent document processing, process mining |
| AI governance and security | Risk mitigation, privacy, compliance, explainability | Security-led buying conversations and governance briefings | Security tools, auditability, access control, policy enforcement |
| Cloud and compute optimization | Performance, cost control, deployment flexibility | Infrastructure ROI discussions with architecture teams | Cloud services, GPU optimization, storage, container platforms |
| Product innovation | Add AI features to improve usability, retention, and monetization | Product leadership and engineering team outreach | Developer APIs, copilots, search, recommendation, analytics tooling |
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | High | The event theme aligns with enterprise technology, AI, data, product, and transformation buyers. |
| Decision-maker availability | High | AI events commonly attract director-level to C-level stakeholders and technical influencers. |
| Data collection potential | Medium | Potential is good if sponsor, speaker, or attendee directories become available; currently limited by lack of public lists. |
| Apollo targeting potential | Very High | The event maps well to searchable industry, title, department, and keyword filters in Apollo.io. |
| Geographic targeting potential | High | Seattle, Pacific Northwest, and U.S. enterprise hubs are practical targeting clusters. |
| Best outreach approach | High | Use value-led messaging around AI adoption, governance, cost optimization, deployment speed, and measurable business outcomes. |
| Overall lead quality | High | Good event fit for enterprise software, cloud, analytics, automation, consulting, and AI infrastructure offers. |
| Best use case | High | ABM targeting, speaker/sponsor-based prospecting, post-event outreach, and role-based buyer mapping. |
| Limitations / risks | Medium | Current-edition attendee list building is constrained by the absence of publicly confirmed participant data in the reviewed source set. |
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Information Technology & Services; Computer Software; Computer & Network Security; Financial Services; Hospital & Health Care; Retail; Telecommunications; Manufacturing; Government Administration; Management Consulting; Internet; Research | Captures the most likely enterprise AI buyer environments |
| Departments | Engineering; Information Technology; Product Management; Operations; Data / Analytics; Innovation; Procurement | Targets both technical buyers and commercial approvers |
| Seniority | C-Level; VP; Director; Head; Manager | Focuses on decision-makers and strong influencers |
| Job titles | CTO; CIO; Chief Data Officer; Chief Analytics Officer; Head of AI; VP AI; Director of Data Science; Director of Machine Learning; Director of AI Engineering; VP Product; Enterprise Architect; Director of Digital Transformation; Strategic Sourcing Manager | Aligns with AI budget ownership, productization, deployment, and governance |
| Geography | United States; Washington; Seattle metro; Pacific Northwest; major U.S. technology hubs | Supports event-adjacent and national account targeting |
| Employee size | 51-200; 201-500; 501-1,000; 1,001-5,000; 5,001-10,000; 10,001+ | Covers both growth-stage AI adopters and enterprise-scale budgets |
| Keywords | artificial intelligence; machine learning; generative AI; data science; MLOps; analytics; AI governance; digital transformation; automation; computer vision; NLP; LLM; data platform | Improves precision for event-theme alignment |
| Technologies | Cloud data platforms, AI/ML tooling, analytics stacks, orchestration, security tooling where available in Apollo enrichment | Useful for tailoring outreach to maturity stage and environment |
| Revenue range | $10M-$50M; $50M-$250M; $250M-$1B; $1B+ | Segments growth-stage versus enterprise buying capacity |
| Company type | Private; Public; Subsidiary; Nonprofit where research-led AI adoption is relevant | Broadens search across corporate and institutional buyers |
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| User-supplied event brief | Client-provided source | Event name, dates, venue, city, country, and thematic description used as the starting brief for this report | Medium |
| Hyatt Hotels Official Website | Venue / hotel source | Venue brand verification for Hyatt Regency Seattle | High for venue brand; limited for event-specific participation data |
| Public source set reviewed for this brief | Research note | No official current-year attendee list, exhibitor directory, sponsor roster, or speaker-organization list was publicly confirmed in the materials available for this draft | High confidence in the limitation statement |
🎯 Selling to this event's audience? Get a free tailored buyer list
Tell us your work email and our AI instantly builds a buyer list matched to AI Con USA 2026 — the best‑fit companies to target, the exact decision‑maker job titles, and the industry filters that fit what you sell.
