Enterprise AI
AI procurement
Also known as: buying AI tools
AI 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.
What it is
It is the buying process applied to a category where capability changes quickly, pricing is often usage based and outputs are probabilistic rather than fixed. Typical steps include defining the use case, shortlisting vendors, testing on real tasks, reviewing data handling and subprocessors, agreeing pricing and service levels, and planning rollout and training. Legal, information security, data protection and finance are usually involved alongside the team that will use the tool.
Why it matters
For marketing and search teams, tool choices shape how content is produced, how discovery data is gathered and what claims can be made, so a poor purchase creates quality and compliance risk as well as wasted budget. It also matters commercially in the other direction: vendors selling into these processes need documentation, security answers and pricing clarity that buyers and their AI assistants can find and cite. Duplicated subscriptions and shadow tool use are common, so a visible process saves money and reduces exposure.
How it works
Practitioners write down the job to be done and the success criteria, then run a time boxed pilot on representative work rather than relying on demos. They ask vendors about training data use, retention, hosting location, human review, model changes and the ability to export data on exit, and they compare total cost including usage overages and internal time. Decisions are recorded so renewals can be judged against the original criteria.
When it applies
It applies whenever a team wants to add an AI tool, expand a pilot to a larger licence, or renew an existing contract, and whenever an AI feature is switched on inside software you already own. It is also triggered by policy work, such as an internal register of approved tools.
Examples
- A content team pilots two AI writing platforms on 20 real briefs and scores output on accuracy, tone and editing time before choosing one
- A retailer's security review blocks a tool because customer data would be used for model training with no opt out
- A finance lead switches a per seat AI contract to usage based pricing after usage logs show only a third of licences are active
How it is measured
- Time from request to decision, and time from decision to first production use
- Cost per active user or per unit of output, including usage overages
- Pilot to production conversion rate and tool retirement rate at renewal
- Share of AI tools in use that have completed security and data protection review
Insights on AI procurement
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 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.
- AI triageAI triage is the use of machine learning or language models to sort, prioritise and route incoming cases so that the most urgent or most suitable ones reach the right person first. It is used in clinical settings for symptom assessment and in service operations for tickets, leads and enquiries. The system classifies and ranks, while a human usually makes the final decision.
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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