The AI Workflow Library is a practical reference for organisations exploring where AI can reduce workload, improve consistency, or surface information that is currently getting lost. It is organised around problems, not technology. Find the description that matches your situation and follow it through to the relevant use cases.
If you recognise your organisation's problem in one of the six groups below, click through to the relevant page. Each page contains the full workflow patterns for that problem group: what triggers them, what they produce, and where a human must stay in the loop. You do not need to read all six. Find the problem that matches yours and start there.
A note on connected systems
Every workflow pattern in this library assumes that AI workers can reach the systems they need: the CRM, the ERP, the document repository, the ticketing platform, the calendar. That connectivity does not happen automatically. Data pipeline and integration work, which covers the extraction, transformation, and routing of data between systems, is the infrastructure layer that makes these patterns possible. It is not a worker use case. It is a precondition for all of them. If your organisation is asking "how would AI actually connect to our systems," that is an integration design question, and it should be answered before any workflow pattern is deployed. The Use Cases also assume that the data reaching those systems is sufficiently clean and complete to be usable. Where data quality is uncertain, that should be resolved before workflow deployment, not after.
The six problem groups
Too Much Coming In
Every organisation has inboxes, queues, and intake channels that produce more incoming material than people can process at human speed. Invoices arrive faster than AP teams can code them. Customer enquiries pile up faster than support agents can triage them. Documents arrive in formats no system was built to read. The AI workflow patterns in this group all address the same underlying problem: volume at the front door. They do not replace the people handling intake. They ensure that by the time a human looks at something, the classification, extraction, and routing work is already done.
Includes: Email Triage and Response · Finance Intake and AP · Translation and Localisation · Proactive Monitoring and Alerting
Output Needs to Meet Standards
Drafting is not the hard part. Drafting something that conforms to brand guidelines, legal language, pricing rules, regulatory requirements, and institutional tone, every time, without variation, is the hard part. The AI workflow patterns in this group produce structured outputs that must meet defined standards before they leave the organisation. A proposal that quotes the wrong price, a policy document that uses unapproved language, or a codebase that violates security conventions all carry real consequences. These use cases describe how AI handles the generation layer while humans retain ownership of the standards and the final approval.
Includes: Quote and Proposal Drafting · Sales Prep and Deal Support · HR Hiring and Onboarding · Corporate Communications and Institutional Drafting · Operations, Compliance, and Frontline Execution · Software Development and Code Assistance
Knowledge Exists But Cannot Be Found
Most organisations do not have a knowledge creation problem. They have a knowledge recovery problem. The research was done but filed somewhere nobody searches. The meeting produced a decision but the notes were never written up. The competitor analysis exists but the person who wrote it left eighteen months ago. The AI workflow patterns in this group address institutional memory: finding what already exists, reconstructing it into a usable form, and putting it in front of the person who needs it at the moment they need it. The goal is not to generate new knowledge. It is to stop organisations paying to rebuild what they already own.
Includes:
Customer Service Case Resolution · Research, Review and Synthesis · Knowledge Reuse and Memory Reconstruction · Meeting Intelligence and Action Capture
Things Fall Between the Gaps
The work that gets lost in organisations is rarely the work anyone owns clearly. It lives in the space between meetings and action items, between one team finishing and another starting, between a file being created and anyone being able to find it again. Coordination overhead covers status updates, handoff summaries, project tracking, and file organisation. It consumes significant time without producing anything directly valuable. The AI workflow patterns in this group handle the connective tissue of organisational work: the summaries, the trackers, the status reports, and the organised repositories that keep work moving without requiring a person to spend their day maintaining them.
Includes: Calendar, Project and Status Coordination · File and Repository Organisation · Meeting Intelligence and Action Capture
Data Needs to Mean Something
Data is not the problem. Most organisations have more data than they know what to do with. The problem is that raw data, whether supplier proposals, contract terms, performance metrics, or financial results, does not make decisions by itself. Someone has to read it, compare it, interpret it, and translate it into a recommendation a decision-maker can act on. The AI workflow patterns in this group handle the analytical middle layer: extracting the comparable elements from complex documents, identifying what the numbers are actually saying, and producing decision-ready outputs that tell a human where their attention is needed and why.
Includes: Procurement and Supplier Comparison · Contract and Document Intelligence · Marketing and Campaign Content · Data Interpretation and Decision Support
People Need Guided Support
Not every AI workflow is about processing documents or generating outputs. Some of the most valuable patterns are the ones that sit alongside a person while they work through something difficult: a complex policy question, a new skill they are developing, a process they have never navigated before. The AI workflow patterns in this group support people directly through conversation, guided pathways, and contextual assistance. They cover the help desk agent answering the same policy question for the hundredth time, the new employee trying to understand their benefits, and the professional developing a capability through structured practice. The common thread is not efficiency. It is support at the moment someone needs it.
Includes: Benefits, HR Policy and Employee Q&A · Guided Support, Coaching and Learning Assistance
Use case examples are drawn from publicly available industry sources including vendor documentation, practitioner case studies, and deployment reports. The framework, problem groupings, and human-in-the-loop structure are original to this library.
The AI Workflow Library is maintained by Dan Kerson. Last updated March 2026.