An AI workflow project moves from understanding the task to designing and building a tested solution, then introducing it into everyday use. Each stage should have a clear outcome and a decision about what happens next. Deployment includes training, support and ownership, so the business can operate the system after launch.
What happens during research?
Research establishes the problem, the current workflow and the evidence needed to justify a change. It should end with a defined task and a shared understanding of what improvement would mean.
Begin with the people doing the work. Ask them to walk through a recent example, showing the documents, messages and systems involved. Identify where they copy information, wait for another team or make a judgement that depends on context.
Keep ordinary work and exceptions visible. A clean example can explain the intended process, but an incomplete request reveals what staff must do when it breaks down. That information helps define the system's limits and the handoffs that should remain with people.
Agree what is outside the first scope. An enquiry assistant might summarise requests and prepare drafts without setting prices or confirming availability. Those boundaries make the project easier to assess than a broad promise to automate customer communication.
Before moving on, record the baseline and unresolved dependencies. The information may need cleaning, permissions may need checking or a connection may need investigation. Discovering that a simpler process change is enough is a useful research outcome too.
How do design and build fit together?
Design defines how the workflow should behave, while building connects that behaviour to the systems it needs. Review the approach with staff early enough to change it before unnecessary work accumulates.
Show what users will receive and what they can do next. For a document workflow, that could be the source document beside extracted fields, a correction option and an approval step. Include missing information, failed updates and items awaiting review, not just a successful result.
Separate the parts requiring interpretation from those following rules. AI might prepare a summary, while ordinary automation routes an approved record. Anthropic's guidance on effective agents supports choosing the simplest suitable approach before adding more flexible behaviour.
Build a representative test set with expected outcomes. Include varied wording, duplicates and requests outside scope. Check the entire task through its destination system, because a correct output that never reaches the reviewer is still unfinished work.
Keep access limited to the required actions. Use separate test information where possible and store credentials in managed settings. Changes that send messages or update important records need an explicit design, rather than inheriting permissions simply because they are technically available.
What should be agreed before deployment?
Agree the release scope, acceptance criteria, operating owner and fallback before introducing the system into everyday work. The team should know both how to use it and how to stop or bypass it.
A practical release checklist includes:
- The task and supported document or request types.
- Required checks and actions needing human approval.
- Expected behaviour when information or connections are missing.
- The person receiving alerts and unresolved items.
- Staff guidance and a clear support route.
- Access to code, accounts and operating documentation.
- Measures for deciding whether the release is useful.
Use a limited launch when appropriate. One team or one intake route can reveal operating issues without changing the whole business at once. Staff may review every result initially, then assess whether a different level of review is justified for particular low-consequence steps.
Training should use realistic situations. Show how to correct an interpretation, find the original message and recover an interrupted task. Ask users to complete those steps themselves rather than only watching the successful route in a demonstration.
What happens after the system goes live?
After launch, compare actual outcomes with the baseline and respond to problems through an agreed support process. Someone must also keep reference information and operating instructions current.
Measure handling time including review, correction frequency and work that remains unresolved. If the original issue was a delayed handoff, track that waiting time separately. Avoid treating the number of generated outputs as evidence of business value on its own.
Review the costs that continue after setup: subscriptions, usage, staff checking, support and maintenance. Changes to supplier systems or business rules can affect the workflow, so assign responsibility for reviewing those changes before they cause avoidable failures.
The NIST AI Risk Management Framework is a useful voluntary reference for managing AI risks across use. In practice, a business needs named owners, documented checks and a repeatable way to investigate issues.
2410 Studio is a UK studio that builds AI into the tools a business already uses, with the client keeping custom code. Its project portfolio shows examples of its work. Agree the handover, support scope and account ownership as part of the project, including any third-party licences that remain separate.
Frequently asked questions
Can we skip research if we know what we want?
You can keep research focused, but still confirm the workflow, information and success criteria. A clear initial idea does not remove the need to test its assumptions.
Does deployment mean the project is finished?
It marks a change in how the work is operated. Support, monitoring and ownership should already be agreed, even when the initial build is complete.
Will our team need technical training?
Users need practical training for their role, including corrections and exceptions. Technical handover should match the people responsible for maintaining the system.
Bring a workflow you want to improve to a project conversation with 2410 Studio. Start with the task and the outcome, then agree how research, build and deployment should support it.
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