Thought leadership · Reference

The AI Glossary for enterprise leaders — A to Z.

The AI industry moves fast — and so does the language used to describe it. This glossary gives business and IT leaders plain-English definitions of the terms that show up in board decks, RFPs, vendor pitches and architecture reviews. Curated by our practice leads and updated as the market evolves.

Showing 1–30 of 168 terms

A17 terms on this page

After-Call Work (ACW): Post-interaction tasks (wrap codes, notes, dispositions). Auto-summary and auto-disposition are the highest-ROI early CX AI wins.
Agent Assist: Real-time AI suggestions surfaced to a human agent during a customer interaction — answers, next-best-action, sentiment cues, after-call summary.
Agentic AI: AI systems that pursue goals autonomously by planning multi-step actions, calling tools and APIs, and revising their approach based on feedback — rather than only responding to a single prompt.
AI Agent: A software entity built around an LLM that perceives context, reasons over a task, invokes tools, and produces an outcome — often on behalf of a user or another agent.
AI Engineer: A software engineer who ships production LLM systems — prompt design, RAG, tool integration, evals, cost/latency tuning. Distinct from ML engineer.
AI Gateway: A managed proxy that routes model calls, enforces quotas, redacts PII, logs prompts, and applies policy across multiple LLM providers.
AI Product Manager: Owns discovery, scoping and lifecycle of AI features — writing evals as acceptance criteria, managing model/prompt versions, and quantifying business impact.
AI Solutions Architect: Designs end-to-end AI systems: models, retrieval, tools, integration, security, cost. The senior engineering role that bridges enterprise architecture and applied AI.
AIOps: Applying machine learning to IT operations data (logs, metrics, traces) to detect anomalies, correlate incidents and automate remediation.
Alignment: The discipline of making an AI system's behavior match human intent, values and organizational policy — both at training time and at runtime.
Anomaly Detection: Identifying data points or events that deviate from expected patterns — fraud, outages, churn signals, quality defects.
API Gateway (LLM): A managed proxy that routes model calls, enforces quotas, redacts PII, logs prompts, and applies policy across multiple LLM providers.
Artificial General Intelligence (AGI): A hypothetical AI capable of performing any intellectual task a human can, across domains. Not a shipping product category today; treat vendor claims with scrutiny.
Artificial Intelligence (AI): Software systems that perform tasks normally requiring human intelligence — perception, reasoning, language, decision-making — by learning patterns from data.
AUC (Area Under Curve): A single-number summary of a classifier's ability to separate classes across all thresholds; useful for imbalanced problems like fraud or churn.
Autonomy Level: A taxonomy (advisor → co-pilot → supervised agent → autonomous agent) describing how much human oversight a workflow requires.
Average Handle Time (AHT): Mean duration of a customer interaction including hold and wrap. A primary CX KPI reshaped by agent-assist and automation.

B7 terms on this page

Batch Inference: Running many model calls asynchronously at lower cost — used for backfills, document processing and offline analytics.
Benchmark: A standardized task suite (e.g., MMLU, GSM8K, HELM) used to compare model quality on reasoning, coding, safety or domain knowledge.
Bias (Model Bias): Systematic error in model output that disadvantages a group or skews decisions — introduced through data, labeling, objectives or deployment context.
BPO (Business Process Outsourcing): Delegating end-to-end business processes (contact center, claims, finance) to a third-party operator; increasingly restructured around AI-augmented agents.
Bring Your Own Model (BYOM): A platform pattern that lets enterprises deploy fine-tuned or proprietary models inside a vendor's runtime instead of using only the vendor's default model.
Build vs. Buy vs. Fine-tune: The core AI portfolio decision: adopt a vendor product, build on foundation-model APIs, or invest in fine-tuned/proprietary models. Rarely a single answer per enterprise.
Business Case (AI Business Case): A structured argument tying an AI initiative to measurable value, cost, risk and time-to-impact — the artifact that clears funding gates.

C6 terms on this page

Caching (Prompt/Response): Storing repeat prompts or key/value tensors to cut latency and cost; supported natively by most frontier providers.
CCaaS (Contact Center as a Service): Cloud-native contact center platform — routing, IVR, WFM, analytics, digital channels — delivered as a subscription (e.g., Amazon Connect, Genesys Cloud, NICE CXone, Five9, Google CCAI).
Chain-of-Thought (CoT): A prompting or training pattern where a model produces intermediate reasoning steps before its final answer, improving accuracy on math, planning and multi-hop tasks.
Chief AI Officer (CAIO): Executive accountable for enterprise-wide AI strategy, portfolio, governance and value realization. Increasingly a peer to CIO, CDO and CISO.
Chunking: Splitting source documents into passages sized for retrieval and the model's context window. Chunk size and overlap are top-3 RAG quality levers.
Citation / Attribution: Linking each generated claim back to the source passage that supports it — non-negotiable for regulated and knowledge-worker use cases.

All 168 terms, A–Z

Every entry has its own page with a plain-English definition, why it matters to enterprise buyers, and where it fits in a delivery program.

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The AI Glossary for enterprise leaders — A to Z.

All 168 terms, A–Z

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