Leaders In AI Summit Los Angeles 2026

Attendees, exhibitors & solutions

Who this event is for

Industry trends

Key trends shaping this market and its wider industry.

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Frequently asked

Glossary

1

Agentic AI

Systems in which a model plans and executes multi-step tasks with tools and memory rather than answering a single prompt; the recurring governance question at leadership level is how much autonomy to grant before human review.

2

Retrieval-augmented generation (RAG)

Grounding a model's answer in documents pulled from an enterprise index at query time. Common architecture for making internal knowledge searchable without retraining the model.

3

Foundation model

A large pre-trained model (text, image or multimodal) adapted to many downstream tasks. Build-versus-buy decisions on foundation models are a standard board-level agenda item for AI leaders.

4

Fine-tuning

Further training of a pre-trained model on domain data so it follows sector vocabulary and formats. Weighed against prompting and RAG on cost, latency and maintenance effort.

5

Inference economics

The cost and latency of running a model in production, measured per request or per token. Usually the difference between a successful pilot and a scalable deployment.

6

MLOps

The engineering discipline around deploying, monitoring and versioning models in production: pipelines, drift detection, rollback and audit trails.

7

AI governance

Policies, controls and accountability structures for AI use: risk classification, data provenance, model documentation, vendor due diligence and internal approval gates.

8

Guardrails

Runtime controls that constrain model behaviour, from content filters and policy prompts to output validators and escalation to a human reviewer.

9

Evaluation (evals)

Structured test sets and scoring methods used to compare models and prompts before and after release. Enterprise buyers increasingly ask vendors for eval results, not demos.

10

Hallucination

A confident but incorrect model output. Mitigations discussed by practitioners include retrieval grounding, citation requirements and human-in-the-loop review.

11

Human-in-the-loop

A workflow where a person reviews or approves model output before it triggers a business action; the usual compromise in regulated or high-value processes.

12

Pilot-to-production gap

The stretch between a working proof of concept and a deployed system with owners, budgets, monitoring and change management. A recurring theme for executives attending AI leadership summits.

13

Multimodal model

A model that handles more than one input type, typically text plus images, audio or video; relevant to document processing, inspection and customer service use cases.

14

Model context window

The amount of text or data a model can consider in a single request. Drives architecture choices for long-document and agent memory use cases.