Enterprise AI
Conversational AI
Also known as: conversational artificial intelligence, chat AI
Conversational AI is software that understands and generates natural language so people can interact with a system by talking or typing rather than clicking through menus. It covers assistants, chatbots, voice agents and support copilots, and is now commonly built on large language models. The defining feature is a back and forth exchange that keeps track of context across turns.
What it is
Conversational AI combines language understanding, dialogue management and response generation, often with retrieval from a knowledge base or connections to business systems. Earlier systems relied on fixed intents and scripted flows, while current systems generate responses from models and ground them in retrieved documents. Many production deployments mix both, using rules for regulated or transactional steps.
Why it matters
Conversational interfaces are becoming a route to discovery as well as a support channel. When customers ask an assistant for recommendations, comparisons or answers, the content and data that assistant can reach shapes which brands are mentioned. Internally, conversational AI also changes cost structures in support and sales by handling routine questions at scale.
How it works
Teams typically ground a model in their own content using retrieval, define what the assistant may and may not do, connect it to systems such as order lookup or booking, then monitor transcripts for failure. Guardrails, escalation to a human and logging are standard parts of the build. Marketers contribute by making sure product, pricing and policy content is accurate, structured and machine readable so the assistant has something reliable to retrieve.
When it applies
It applies wherever customers ask repeated questions in natural language, such as support, pre-sales, onboarding or internal knowledge search. It is also relevant whenever a brand wants to be represented accurately inside third party assistants.
Examples
- A bank's in-app assistant answers balance and payment questions and hands complex disputes to a human agent with the transcript attached.
- A retailer's site assistant answers sizing and delivery questions using the live product feed and returns policy pages.
- An internal copilot lets sales staff ask about current pricing rules and pulls answers from approved documentation rather than old slide decks.
How it is measured
- Containment rate, the share of conversations resolved without human handover
- Answer accuracy or groundedness scored on a sample of transcripts
- Escalation rate and average handling time for escalated conversations
- Conversion or task completion rate for assisted sessions versus unassisted
Insights on Conversational AI
Related terms in Enterprise AI
- AI agentsAI agents are software systems that use a language model to plan and carry out multi-step tasks, rather than simply returning a block of text. They can call tools, query APIs, browse websites and write to other systems in pursuit of a goal, with varying degrees of human oversight. The term covers everything from a scripted assistant that books a meeting to a research agent that gathers sources and drafts a report.
- AI chipsAI chips are processors designed or optimised to run machine learning workloads, especially the large matrix operations behind training and inference. The category covers GPUs, tensor and neural processing units, and custom ASICs, usually paired with high-bandwidth memory and fast interconnects. They are also called AI accelerators or AI semiconductors.
- AI drug discoveryAI drug discovery is the use of machine learning and computational models to support the early stages of finding and refining new medicines. Models are applied to tasks such as predicting protein structures, identifying candidate molecules, prioritising targets and estimating properties like toxicity or binding affinity. The aim is to narrow a very large search space before expensive laboratory and clinical work begins.
- AI infrastructureAI infrastructure is the stack of hardware, networking, storage and software needed to train, fine-tune and serve AI models at scale. It spans accelerators such as GPUs, the data centres and power that house them, and the orchestration and serving layers that turn raw compute into working model endpoints. For most marketing teams it is a cost and capacity constraint they consume through APIs rather than something they build.
- AI procurementAI procurement is the process of evaluating, buying and contracting artificial intelligence tools and services, from assistants and content platforms to models accessed through an application programming interface. It covers commercial terms alongside security, data protection, accuracy and integration questions that do not arise with ordinary software. Most organisations run it as a structured review with pilots, stakeholder sign off and defined exit conditions.
- AI securityAI security is the practice of protecting AI systems, their data and the applications built on them from misuse, manipulation and leakage. It covers threats such as prompt injection, data exfiltration through model outputs, unsafe tool use by agents and compromised supply chains. It also covers the controls that keep AI features safe once they are live.
Primary research · August 2026
How ChatGPT Shortlists Software Brands
An audit across 10 categories and 60 buying questions. I recorded what ChatGPT reads, throws away and links to when a buyer asks it which software to buy, and what that decides.
- 60
- Questions asked
- 10
- Software markets
- 2,680
- Results read
- 367
- Links shown
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