Skyrit Studio / 02 / AI & Workflow Automation
AI automation, built around the work.
Explore AI and automation for repetitive tasks, clearer handoffs, and practical internal workflows.
01 / The approach
Understand the need before choosing the solution.
Automation is most useful when the process already has a clear purpose. Before choosing an AI model or connecting applications, look at the task, the information it needs, the decisions involved, and the person responsible for the result.
Some workflows suit straightforward rules. Others may benefit from AI-assisted drafting, categorisation, or summarisation with a person reviewing the output. The aim of an initial discussion is to identify a suitable use case and decide how a small prototype could test its usefulness.
02 / Problems to explore
Where this could help.
The same information is handled repeatedly
Copying information between tools or preparing routine summaries can create avoidable work. Map the inputs and outputs to see whether a simpler process or a carefully scoped integration is appropriate.
Handoffs depend on someone remembering
A workflow can stall when a request, status change, or follow-up has no explicit owner. Clear triggers and responsibilities come before automated notifications or routing.
AI experiments have no clear operating role
A promising demonstration is different from a dependable workflow. Define where AI contributes, what a person checks, and what happens when the input is incomplete or the output is wrong.
03 / Possible deliverables
A scope we can make specific.
These are possible parts of a project. The selected deliverables and responsibilities depend on the problem, requirements, and agreed scope.
Workflow and opportunity mapping
A view of the existing steps, inputs, owners, exceptions, and possible improvements. This may identify a process change that is more suitable than new software.
A focused automation prototype
An agreed prototype for one bounded task, using suitable rules, integrations, or AI assistance. The tools depend on available systems, access, data, and the agreed requirements.
Review and exception handling
An explicit plan for human checks, incomplete information, failures, and stopping or reversing an action where the workflow requires it.
Evaluation and operating guidance
Representative test examples, useful review criteria, and guidance on ownership and maintenance. External service costs and access requirements are discussed before choosing an approach.
04 / Working process
From the question to the next step.
- 01
Understand the actual task
Walk through the current workflow with examples. Identify the repeated work, the business rule behind it, and the decisions that should remain with a person.
- 02
Explore the simplest intervention
Compare a process change, conventional automation, and AI assistance. Select a small use case with clear inputs and a result that can be reviewed.
- 03
Build a bounded prototype
Implement the selected approach with agreed access and test data. Include the important exceptions and review points rather than evaluating only the easiest example.
- 04
Review before expanding
Check the output, effort, reliability, and operating requirements against the initial problem. Use those findings to decide whether to refine, expand, or stop the experiment.
05 / Planning the scope
What to consider together.
AI output needs an appropriate review
AI-generated content can be incomplete or inaccurate. The workflow needs review criteria proportionate to the decision being made; a prototype should not quietly become an unattended business process.
Access and data shape feasibility
Existing application capabilities, data permissions, and external service terms affect what can be built. Share the structure of a process first; sensitive source material can wait until an appropriate working arrangement is clear.
Related work and thinking
Explore the reasoning.
Read the questions behind the work, explore related system approaches, or try a small tool.
Notes
- Before Automating a Process, Ask Whether the Process Should Exist Automation can make a sound process faster. It can also make a confusing one repeat its failures at scale. The first job is to understand the process, not to mechanise it.
- Why CRM Should React to Behavior, Not Just Schedules A journey that sends messages on fixed days may be automated, but that does not make it responsive. Useful CRM changes the next action when customer behavior or context changes.
- Why Predictive Analytics Must End in an Action A predictive score is interesting only when it helps someone make a better decision. The useful output is not a number on its own, but a clear change in prioritisation, workflow, or experiment design.
Work
- Designing Customer Lifecycle Systems Using customer behavior to inform structured lifecycle decisions—from activation and retention to reactivation, segmentation, journey logic, and measurement.
- Turning Data Into Operational Signals Designing analytics workflows that move beyond dashboards by turning behavioral and operational data into signals that support clearer decisions.
Start a conversation
Tell me what you are trying to achieve.
Describe one task that repeats, the tools involved, and what a useful result would look like. Include the part that still needs a person's judgement.