Enterprise AI
Workflow automation
Also known as: business process automation, automated workflows
Workflow automation is the use of software to run a sequence of tasks, approvals and data transfers without someone doing each step by hand. It covers simple rule-based triggers, such as moving a form submission into a CRM, as well as longer chains that involve multiple systems and people. In marketing teams it is often used to keep content, data and reporting moving between tools on a fixed set of rules.
What it is
A workflow is a defined series of steps that turns an input into an outcome, such as a draft brief becoming a published page. Workflow automation encodes those steps as triggers, conditions and actions so the software carries them out in order. Some steps run entirely without people, while others pause for a human check before continuing.
Why it matters
Discovery work involves a lot of repeatable production and maintenance: publishing, tagging, internal linking, schema updates, refreshing stale pages and pulling data into reports. Automating the mechanical parts frees specialists to spend time on judgement calls such as positioning, topic choice and quality review. It also makes execution more consistent, which matters when search engines and AI assistants reward well structured, well maintained sites.
How it works
Practitioners map the current process, identify the steps that are rule based and repeatable, then build them in an automation platform, a CMS workflow feature or custom scripts connected by APIs. Each automation has a trigger, such as a new record or a scheduled time, followed by conditions and actions, often with an approval gate where accuracy matters. Teams usually start with one narrow process, add logging and error alerts, then expand once the automation proves reliable.
When it applies
It applies when a process runs often, follows clear rules and passes data between systems or people. It is a poor fit for one-off tasks, processes that change constantly, or decisions that need editorial judgement without review.
Examples
- A new blog post publishes, which automatically updates the XML sitemap, pings the CMS cache and posts the link to a team channel for promotion.
- A form submission creates a CRM record, assigns an owner by region and triggers a follow-up email sequence.
- A scheduled job pulls Search Console and analytics data each Monday into a shared dashboard and flags pages whose clicks fell week on week.
How it is measured
- Hours of manual work removed per month for the automated process
- Cycle time from trigger to completed outcome, such as brief to published page
- Error or rework rate compared with the manual process
- Automation failure rate, covering runs that error, stall or need manual intervention
Insights on Workflow automation
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 cost managementAI cost management is the practice of tracking, forecasting and controlling what an organisation spends on AI systems, including model API calls, inference compute, vector storage and the tooling around them. It applies familiar financial discipline to usage that is variable, token based and easy to scale without noticing. The goal is predictable spend at an acceptable level of quality and latency, rather than the lowest possible bill.
- 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.
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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