4th Data Science & AI Summit 2026 - London
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About this event
4th Data Science & AI Summit 2026 - London
The 4th Data Science & AI Summit 2026 in London brings together global leaders, innovators, and practitioners at the forefront of artificial intelligence and data science. This two-day conference, held on October 14-15, 2026, at the London Convention Centre, will explore cutting-edge advancements, real-world applications, and future trends shaping industries worldwide. Whether you're a data scientist, AI engineer, business leader, or tech enthusiast, this event offers unparalleled insights and networking opportunities to drive innovation and growth.
Key Focus Areas
- Artificial Intelligence & Machine Learning Innovations
- Big Data Analytics and Business Intelligence
- AI Ethics, Governance, and Responsible AI Practices
- Industry Applications: Healthcare, Finance, Retail, and Manufacturing
- Emerging Technologies: Generative AI, Quantum Computing, and Edge AI
Who Should Attend
- Data Scientists and Analysts
- AI and Machine Learning Engineers
- Chief Technology Officers (CTOs) and IT Leaders
- Business Decision-Makers and Innovation Heads
- Academics and Researchers
- Startup Founders and Tech Entrepreneurs
Why Attend?
- Learn from 100+ expert speakers sharing actionable insights.
- Network with 5,000+ professionals from 40+ countries.
- Explore 50+ exhibitors showcasing the latest tools and platforms.
- Participate in hands-on workshops and interactive sessions.
- Discover investment and collaboration opportunities.
Event Details
Date: October 14-15, 2026
Venue: London Convention Centre, 100 Exhibition Way, London, UK
Registration: Register Now
Contact: info@dsa-summit.com | +44 20 7946 0000
Secure Your Place Today
Join the global conversation and shape the future of data science and AI. Limited early-bird tickets available!
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Data sheet
4th Data Science & AI Summit 2026 - London – Event Attendee & Buyer Profile Analysis
Event date: 01 Oct 2026 - 02 Oct 2026
Location: Radisson Blu Hotel, London Canary Wharf, London, United Kingdom
Event status: Upcoming
Research date: 30 Jun 2026
Event Overview
| Event Name | 4th Data Science & AI Summit 2026 - London |
| Event Date | 01 Oct 2026 - 02 Oct 2026 (user-supplied known details) |
| Event Status | Upcoming |
| Venue | Radisson Blu Hotel, London Canary Wharf |
| City | London |
| State / Region | England |
| Country | United Kingdom |
| Organizer | Organizer not publicly verified in the supplied materials. |
| Official Event Website | Official current-year event page not supplied for verification. |
| Event Type | B2B conference / summit |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Science & Research; Business Services |
| Audience Reach | Likely international business and technical audience, subject to official confirmation. |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low to Medium. User-supplied event brief references promotional scale claims, but it also conflicts with the supplied date and venue details. |
| Main Purpose of Event | To connect AI, machine learning, analytics, and enterprise innovation stakeholders around applied data science use cases, tools, strategy, partnerships, and solution sourcing. |
About the Event
The 4th Data Science & AI Summit 2026 - London appears positioned as a specialist B2B technology summit focused on artificial intelligence, machine learning, data engineering, analytics, governance, and enterprise use cases. Based on the supplied event description, the summit is intended to bring together technical practitioners, digital transformation leaders, research stakeholders, startup founders, and solution providers operating across AI-led business initiatives.
From a commercial perspective, this event matters because it sits at the intersection of technology adoption and executive decision-making. Likely participant groups include enterprise IT leaders, data science teams, AI product owners, innovation departments, consultancies, cloud and platform buyers, and research or academic participants. That mix makes the summit potentially relevant for lead generation, strategic partnerships, enterprise software outreach, thought-leadership positioning, and high-value B2B targeting. However, current-year attendance, sponsor, exhibitor, and speaker lists should be officially verified before any event-list sales claim is made.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| Chief technology and digital leaders | Large enterprises, banks, insurers, retailers, telecoms, public-sector bodies | Budget owners for AI strategy, transformation, platform selection | High-value targets for AI software, infrastructure, consulting, and managed services |
| Data science and machine learning teams | Enterprise analytics groups, AI labs, product teams, digital units | Technical evaluators, solution champions, pilot sponsors | Relevant for MLOps, data platforms, model monitoring, notebooks, and tooling |
| Data engineering and analytics leaders | Cloud-first firms, digital-native companies, regulated enterprises | Influence architecture, integration, governance, and vendor selection | Strong fit for warehousing, ETL, lakehouse, observability, and BI vendors |
| AI product managers and innovation heads | Software firms, digital transformation teams, R&D functions | Define use cases, prioritize pilots, evaluate partnerships | Good targets for applied AI tools, data partnerships, and integration services |
| Cybersecurity, governance, and risk leaders | Banks, healthcare groups, government-related entities, critical infrastructure firms | Review compliance, responsible AI, data access, and model risk | Relevant for privacy, governance, AI assurance, and security vendors |
| Procurement and strategic sourcing teams | Enterprise procurement departments, public-sector procurement teams | Commercial gatekeepers for software, cloud, and consulting purchases | Important for enterprise contract qualification and supplier onboarding |
| Consultants, systems integrators, and implementation partners | Management consultancies, digital agencies, cloud integrators | Channel partners, specifiers, and multipliers of vendor solutions | High relevance for co-selling, referral, and implementation alliances |
| Startups, founders, and venture ecosystem participants | AI startups, incubators, investors, accelerators | Early-stage buyers, technology adopters, partnership seekers | Relevant for developer tools, cloud credits, hiring, and technical services |
| Academic and research stakeholders | Universities, labs, institutes, applied research centers | Influence research partnerships and talent pipelines | Useful for collaboration, grants, innovation scouting, and recruiting |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| London | High probability of attendance from local enterprises, fintechs, consultancies, and startups | Very high | Canary Wharf location strengthens relevance for banking, financial services, consulting, and enterprise technology audiences |
| South East England | Likely draw from nearby business and university hubs | High | Accessible for day-trip and short-stay corporate attendees |
| Wider United Kingdom | Likely participation from national enterprise and public-sector technology teams | High | London remains the main national pull for AI and digital leadership events |
| Europe | Possible attendance from EU technology vendors, buyers, and research communities | Medium | International reach is plausible, but current-year country list was not verified |
| Global | Potential inbound audience from North America, Middle East, and Asia-Pacific | Medium | The supplied brief references 40+ countries, but that figure is not independently verified for the current edition |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification: Global | The summit theme, London venue, and user-supplied positioning suggest cross-border appeal to enterprise AI stakeholders. Current-year attendee geography still requires official confirmation. |
| Secondary reach description | National / European strength likely | For practical targeting, the UK and nearby European markets are likely to be the strongest near-term buyer pools. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Current-year buyer company list not publicly verified | Data availability note | No official current-year attendee, sponsor, exhibitor, or speaker organization list was supplied for validation. | N/A | N/A | Confirmed data unavailable |
| Enterprise financial services organizations in Canary Wharf and wider London | Corporate buyers | Likely buyer class for AI, analytics, governance, and decision intelligence solutions due to venue geography and event topic. | N/A in absence of official participant list | CTO, Head of Data Science, Director of AI, Chief Data Officer | Likely attendee profile |
| Large consultancies and systems integrators serving UK enterprise AI programs | Channel / implementation buyers | Relevant because these firms influence vendor selection and implementation roadmaps. | N/A in absence of official participant list | Partner, AI Practice Lead, Data & Analytics Director | Likely attendee profile |
| UK public-sector digital and innovation teams | Government / public buyers | Potential relevance for responsible AI, analytics modernization, and data governance. | N/A in absence of official participant list | Head of Data, Digital Transformation Director, Procurement Lead | Likely attendee profile |
| Research institutions and universities with AI programs | Research / academic stakeholders | Relevant for applied research partnerships, talent pipeline, and model development discussions. | N/A in absence of official participant list | Professor, Research Director, AI Lab Lead | Likely attendee profile |
Note: Prior-year participation evidence was not supplied. This section therefore reflects verified data limitations and likely buyer-side participation classes rather than a confirmed current-year attendee list.
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Chief Technology Officer | Technology | C-Level | Owns AI platform strategy, modernization, and vendor direction. |
| 2 | Chief Data Officer | Data / Analytics | C-Level | Leads data governance, architecture, and analytics priorities. |
| 3 | Head of AI / Director of AI | AI / Innovation | Director | Direct owner of AI use cases, pilots, and solution comparisons. |
| 4 | VP Data & Analytics | Analytics | VP | Influences enterprise analytics stack and transformation roadmap. |
| 5 | Director of Data Science | Data Science | Director | Technical decision-maker for model lifecycle and team tooling. |
| 6 | Machine Learning Engineering Manager | Engineering | Manager | Evaluates deployment, MLOps, inference, and infrastructure products. |
| 7 | Head of Data Engineering | Data Engineering | Director / Head | Key buyer for pipelines, lakehouse, integration, and quality platforms. |
| 8 | Procurement Manager | Procurement | Manager | Commercial gatekeeper for software, cloud, and service contracts. |
| 9 | Innovation Director | Strategy / Innovation | Director | Sponsors pilots and cross-functional AI transformation programs. |
| 10 | Product Manager, AI / Data | Product | Manager | Shapes use cases and integration priorities for business adoption. |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Information Technology & Services | Core fit for AI, data, cloud, and digital transformation buyers | AI software, data platforms, consulting |
| 2 | Computer Software | High concentration of product, engineering, and AI teams | Developer tools, MLOps, APIs |
| 3 | Financial Services | Strong London market relevance and AI adoption intensity | Fraud, risk, customer intelligence, automation |
| 4 | Banking | Canary Wharf location aligns with bank technology buyers | Governed AI, analytics, compliance solutions |
| 5 | Management Consulting | Consultancies influence enterprise AI procurement | Implementation partnerships and co-selling |
| 6 | Government Administration | Relevant for data modernization and responsible AI programs | Public-sector analytics and service automation |
| 7 | Hospital & Health Care | Healthcare is commonly highlighted in AI application tracks | Decision support, workflow intelligence, predictive analytics |
| 8 | Retail | Retail AI use cases are commonly represented in enterprise events | Personalization, forecasting, customer analytics |
| 9 | Telecommunications | Large data-intensive operators are active AI adopters | Network analytics, customer retention, automation |
| 10 | Research | Academic and applied research participation is likely | Collaboration, grants, innovation programs |
| 11 | Higher Education | Universities are relevant for AI research and talent pipelines | Research partnerships, training, talent recruitment |
| 12 | Computer & Network Security | Responsible AI and data security are likely agenda themes | Model security, governance, privacy platforms |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer | Unconfirmed | No official current-year attendance page supplied | User-provided descriptive brief mentions 5,000+ professionals, but this conflicts with the supplied venue/date and is not treated as confirmed. |
| Exhibitor count | Not publicly confirmed | Unconfirmed | No official exhibitor directory supplied | Promotional reference mentions 50+ exhibitors; independent verification pending. |
| Buyer count | Not publicly confirmed | Unconfirmed | No buyer program details supplied | Likely mixed audience of technical and business participants rather than a formal hosted-buyer model. |
| Speaker count | Not publicly confirmed | Unconfirmed | No current-year agenda page supplied | Promotional reference mentions 100+ expert speakers; not independently verified. |
| Sponsor count | Not publicly confirmed | Unconfirmed | No sponsor list supplied | Sponsor quality will materially affect lead value for B2B outreach. |
| Historical attendance | Historical / prior-year evidence not supplied | Unavailable | No prior-year verified records included in the materials reviewed | Official archived event pages would improve forecasting confidence. |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Artificial Intelligence & Machine Learning | Model development, deployment, and ROI | Use-case discovery and technical demos | AI platforms, model services, MLOps |
| Big Data Analytics & BI | Data visibility, insight generation, reporting scale | Architecture review and analytics assessment | Data warehouses, BI, ETL, dashboards |
| AI Ethics, Governance & Responsible AI | Compliance, explainability, policy alignment | Executive and risk stakeholder discussions | Governance frameworks, risk tooling, audit solutions |
| Generative AI | Productivity, automation, customer experience | Pilot planning and proof-of-value conversations | LLM applications, copilots, orchestration layers |
| Cloud & Data Infrastructure | Scalable compute, storage, integration | Platform migration and optimization dialogue | Cloud services, compute optimization, connectors |
| Cybersecurity & Privacy | Secure AI adoption and protected data environments | Risk-led qualification conversations | Security controls, privacy tech, access governance |
| Industry Applications | Sector-specific AI value creation | Vertical solution alignment | Healthcare AI, fintech AI, retail AI, manufacturing analytics |
| Emerging Technologies | Future readiness and innovation scouting | Partnership and roadmap conversations | Edge AI, quantum-adjacent tooling, advanced research collaboration |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | High | Strong fit for AI, data, cloud, analytics, governance, and consulting offers. |
| Decision-maker availability | High | Likely presence of senior technology, data, and innovation leaders. |
| Data collection potential | Medium | Current-year participant lists were not publicly verified, which reduces immediate list-building confidence. |
| Apollo targeting potential | Very High | Clear title, industry, department, and geography filters are available for AI-related prospecting. |
| Geographic targeting potential | High | London, UK, and broader Europe provide strong B2B account concentration. |
| Best outreach approach | High | Use problem-led outreach tied to AI adoption, governance, deployment, and measurable business outcomes. |
| Overall lead quality | High | Commercially attractive event category, but event-list certainty depends on official publication of attendees or sponsors. |
| Best use case | High | Account-based outreach, sponsorship prospecting, exhibitor sales, and Apollo-based ICP targeting. |
| Limitations / risks | Medium | Conflicting date/venue information in supplied materials means all list-claiming activity should wait for official page validation. |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Information Technology & Services; Computer Software; Financial Services; Banking; Management Consulting; Government Administration; Hospital & Health Care; Retail; Telecommunications; Research; Higher Education; Computer & Network Security | Capture the most likely enterprise AI adopter categories. |
| Departments | Engineering; Information Technology; Data / Analytics; Product; Innovation; Procurement; Operations; Research | Focus on technical evaluators and business sponsors. |
| Seniority | C-Level; VP; Head; Director; Manager | Prioritize budget owners and implementation leads. |
| Job titles | CTO; Chief Data Officer; Chief AI Officer; VP Data & Analytics; Head of AI; Director of Data Science; Head of Data Engineering; ML Engineering Manager; Innovation Director; Product Manager AI; Procurement Manager | Narrow to roles most likely to purchase, approve, or influence AI adoption. |
| Geography | United Kingdom; London; England; Ireland; Netherlands; Germany; France; Nordics; broader Europe where relevant | Build concentric market layers around the host city and regional tech hubs. |
| Employee size | 51-200; 201-500; 501-1000; 1001-5000; 5001+ | Cover both scale-up buyers and enterprise transformation accounts. |
| Keywords | AI, artificial intelligence, machine learning, data science, analytics, MLOps, data platform, generative AI, responsible AI, governance, LLM, predictive analytics | Improve relevance where industries alone are too broad. |
| Technologies | Cloud, analytics, BI, data warehouse, AI/ML tooling, security stack where available in Apollo enrichment | Target active technology adopters rather than passive interest. |
| Revenue range | Mid-market to enterprise; adjust by client ACV | Align account size to deal economics. |
| Company type | Public companies, private growth firms, enterprise subsidiaries, government-linked entities, universities | Broaden targeting across commercial and institutional buyer types. |
Suggested Apollo Search Logic: ("artificial intelligence" OR AI OR "machine learning" OR "data science" OR analytics OR MLOps OR "generative AI" OR LLM) AND (CTO OR "Chief Data Officer" OR "Head of AI" OR "Director of Data Science" OR "VP Data" OR "Head of Data Engineering") AND (London OR United Kingdom OR Europe).
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| User-supplied known details | Client-provided event data | Event name, city, country, venue, start date, end date | Medium |
| User-supplied descriptive brief | Promotional text / reference description | Likely event themes, audience profile, and promotional scale claims | Low to Medium due to conflict with supplied date and venue |
| Radisson Hotels | Venue brand website | Venue brand existence and hospitality operator context | High for venue brand; not sufficient alone to confirm event booking |
| Canary Wharf | Location context | Business district relevance for finance and enterprise technology audiences | High for district context |
| Official event site / organizer page | Primary source expected | Still required to confirm organizer, agenda, attendee scale, speakers, sponsors, and current-year participant organizations | Not yet verified from supplied materials |
Suitability for B2B attendee list building: Conditionally suitable. The event category and buyer profile are commercially attractive, but current-year list monetization or attendance claims should only proceed after official organizer, speaker, sponsor, or attendee source validation.
🎯 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 4th Data Science & AI Summit 2026 - London — the best‑fit companies to target, the exact decision‑maker job titles, and the industry filters that fit what you sell.
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