Opportunity shortlist
A ranked list of opportunities, each tied to a real task, the person who owns it, and the data available to support it.
AI strategy and readiness
We review proposed AI projects against your team’s actual work, available data, and likely costs, then recommend which one, if any, to pursue first.
ScopeA focused review or a staged program
Delivery timingUsually 1 to 3 weeks for a focused review; larger programs run in stages
How we check itEach recommendation states what we observed and what we assumed, so you can check the reasoning.
Decide where AI is worth the investment
Before you commit budget to an AI idea, you need to know whether it holds up against the real work. An engineer walks through recent tasks with your team and tests the assumptions behind each proposal. The recommendation may be an AI application, an integration, or a change to the existing process.
Good fit
- Leadership teams choosing between competing AI ideas
- Operations teams with a workflow that costs too much time or money
- Product teams deciding which AI features belong on the roadmap
- Organizations that want a technical plan before committing budget
Not the right fit
- Projects with no one who can act on the recommendation
- Teams that have already chosen a vendor and only want the choice confirmed
- Teams that want a generic list of AI use cases rather than a review of their own work
What you get
Feasibility findings
The problems that could stop each option from working: missing data, difficult integrations, security limits, or reasons staff may not use it.
First-project brief
A recommended first project, with what it needs before work starts, how to judge success, and when to delay or stop.
How it works
Follow the task
We walk through real examples with the people doing the job, including the exceptions and workarounds they handle.
Test the assumptions
We check whether AI, ordinary software, or a change to the process would fix the actual problem.
Decide what to fund
We compare the effort, risk, and likely benefit of each option. You decide what to fund before any build starts.
Scope, cost, and ownership
What we need from you
- Access to the people who own each workflow, and real examples of the work
- The decision you need to make and the budget limits around it
What affects cost
- Number of departments and ideas to review
- Availability of data and system access
- Depth of testing needed before a recommendation
Technical scope
- Workflow mapping
- Data review
- Model and vendor evaluation
- Security and access constraints
- Prototype design
- Operating-cost estimation
Support and maintenance
The recommendation names who makes each decision and what new information would justify another review.
Common questions
How do you choose the first project?
We compare how often each task occurs, the effort it takes today, and the likely cost of changing it. Tests of data access and integrations show which ideas are feasible.
Can you assess a prototype?
Yes. We review its user experience, architecture, data flow, test results, running costs, and the ways it can fail.
Is this an assessment or an implementation commitment?
An assessment. Deciding to build is a separate step. The assessment may recommend software, better data, a process change, an AI pilot, or no project. You keep the findings and priorities whether or not Permadyn builds the next stage.