Knowledge, provenance and access.
Independent artificial intelligence research
London AI Research™
Global research on artificial intelligence and enterprise systems.
Independent research on how artificial intelligence is adopted, governed, secured, and integrated across institutions, industries, and global markets.
Research mandate
Understanding the systems that turn AI capability into institutional capacity.
London AI Research examines the operating conditions required for responsible AI adoption: architecture, governance, identity, risk, security, organizational readiness, and accountability. Our work is designed for decision-makers working across enterprise, government, capital, and technology.
Interactive research system
Follow AI from capability to institutional control.
Select a layer to see how our research connects technology, adoption, governance, and operating evidence.
Capability
What can the system reliably do?
We begin with model and system capability: reasoning, retrieval, tool use, autonomy, multimodality, latency, cost, and failure modes in real operating contexts.
State of AI researchAdoption
Can the institution absorb it?
Enterprise value depends on workflow fit, data readiness, architecture, ownership, operating models, workforce design, and measurable outcomes—not model access alone.
Enterprise AIIdentity
Who or what is acting?
As AI agents acquire tools and permissions, identity, delegated authority, provenance, access boundaries, and revocation become core infrastructure.
AI identityGovernance
Who owns the decision and control?
We study inventories, risk classification, decision rights, controls, approvals, monitoring, escalation, and accountability across the AI lifecycle.
AI governanceAssurance
Can the institution prove what happened?
Evidence, testing, logs, review artifacts, exceptions, remediation, and versioned reassessment make governance observable and challengeable.
Responsible AI frameworkResearch agenda
Four domains. One institutional system.
Our agenda connects technical capability with the operating systems required to deploy AI safely, productively, and accountably.
01
Enterprise AI adoption
How organizations move from isolated pilots to repeatable, measurable, governed AI operating capability.
02AI governance
Decision rights, evidence, controls, assurance, oversight, and practical governance architectures.
03AI identity management
Identity, authorization, provenance, audit, and lifecycle control for increasingly autonomous AI agents.
04State of AI 2026
Signals across models, enterprise use, infrastructure, governance, policy, and agentic systems.
Institutional operating map
Research beyond the model layer.
High-performing AI systems sit inside a larger environment of people, data, permissions, policy, infrastructure, and evidence.
Latest research
Research insights for 2026.
AI IDENTITY MANAGEMENT / 2026
Identity for AI Agents: The Missing Layer in Enterprise Access Control
Why AI agents require first-class identity, scoped authorization, provenance, audit trails, and lifecycle management as they gain enterprise access.
Read researchAI GOVERNANCE / 2026
Governing Agentic AI: Control Models for Systems That Can Act
A governance model for AI agents that can plan, call tools, access data, and execute actions across enterprise systems.
Read researchAI GOVERNANCE / 2026
AI Governance in Practice: From Principles to Evidence
A practical model for converting responsible AI principles into decision rights, controls, evidence, review, and continuous assurance.
Read researchENTERPRISE AI ADOPTION / 2026
The Enterprise AI Adoption Gap: Capability Is Advancing Faster Than Institutions
Why the gap between frontier AI capability and institutional deployment capacity may become one of the defining enterprise technology problems of 2026.
Read researchENTERPRISE AI ADOPTION / 2026
Enterprise AI Adoption 2026: The Shift from Pilots to Operating Systems
A research note on why enterprise AI adoption is moving from isolated use cases toward governed operating systems spanning models, data, workflow, controls, and measurement.
Read researchSTATE OF AI 2026 / 2026
The State of AI 2026: From Model Capability to Institutional Capacity
A 2026 field view of the shift from model-centric competition toward deployment capacity, agentic workflows, governance, and institution-level operating capability.
Read researchMethod
Evidence first. Interpretation clearly separated.
01
Source
Prioritize primary data, standards, technical documentation, regulatory materials, and reproducible evidence.
02
Interpret
Separate observed facts from analyst interpretation, assumptions, scenarios, and forward-looking judgments.
03
Connect
Translate technical developments into implications for enterprise architecture, governance, security, and operating models.
04
Update
Treat fast-moving AI findings as versioned research that can evolve as evidence and capabilities change.
Global perspective
Research from London. Built for global institutions.
London provides a vantage point across science, finance, public policy, cybersecurity, enterprise technology, and global capital. Our research looks beyond a single market to understand how AI systems are being deployed across institutions, sectors, and jurisdictions.
About London AI ResearchResearch & collaboration
Research for institutions navigating the AI transition.
For institutional research, enterprise studies, briefings, and collaboration.
Contact London AI Research