01
Operational discovery and workflow mapping
Clarify the current tools, people, documents, and repeated steps.
AI Systems Audit and Readiness Assessment
Review workflows, tools, data, documents, and operational pain points to identify practical AI integration opportunities.
What you get
Leave with a clear, practical roadmap instead of a vague list of AI ideas.
Built for organizations deciding where ai can responsibly help operations with a focus on clear implementation, documentation, and systems your team can actually use.
Workflow inventory
Systems map
Use-case shortlist
Risk and feasibility review
Implementation roadmap
What changes
From manual work to a system that runs.
The point isn't the technology — it's the day-to-day difference for your team.
A long list of AI ideas, no plan
A ranked, practical roadmap
Unclear what's feasible or risky
Impact, cost, and risk assessed up front
Buying tools and hoping
Build only what earns its place
Why it pays off
The case for doing this well.
Independent industry research on what this kind of work is worth — shown with sources. These are third-party figures, not Banyan's own results.
30%
of generative-AI projects are abandoned after proof of concept — often from skipping readiness work
Source: Gartner, 2024Use cases
Common use cases
See where this work can remove delay, reduce repeated handling, and give staff clearer visibility into what happens next.
- Decide where AI fits before buying more software.
- Identify the highest-value automation opportunities.
- Map systems and handoffs before a larger implementation.
How it works
How the work usually moves
Banyan starts by understanding operations, then builds around the real handoffs, systems, data, and staff review points.
02
Use-case prioritization and risk review
Prioritize opportunities by impact, feasibility, cost, and risk.
03
Implementation plan and integration build
Build the workflow, integration, assistant, or internal tool.
04
Training, refinement, and support
Train the team, refine the system, and support improvement over time.
FAQ
Frequently asked questions
Straight answers for buyers evaluating whether this is the right starting point.
What does the audit produce?
The audit produces a practical view of current workflows, integration options, AI opportunities, risks, and recommended next steps.
Is an audit required before implementation?
Not always, but it is useful when the organization has many tools, unclear priorities, or uncertainty around responsible AI use.
What does it cost?
Pricing depends on scope. After a short discovery, Banyan provides a clear estimate — typically structured by project phase or as an ongoing support plan — so there are no surprises.
How does Banyan handle security and our data?
Banyan favors security-aware, accessible, well-documented builds, keeps data access scoped to what a workflow actually needs, and keeps staff review in place for sensitive or high-impact steps.
How does a typical engagement start?
Most work starts with a short consultation, then a focused discovery or systems audit before anything is built — so scope, cost, and risk are clear before you commit to a larger project.
How long does a project take?
Smaller automations and integrations often land in a few weeks. Larger custom tools take longer, so Banyan scopes the work in phases that deliver something useful early instead of one long build.
Start a conversation
Want AI systems audit for your team?
Share the workflow or system you are trying to fix, and we will reply with practical next steps — usually within one business day.
- A practical first conversation, no obligation
- We will tell you honestly if we are not the right fit
- Sarasota-based — serving Florida on-site or remotely
Prefer email? hello@banyanaiconsulting.com
Regarding / services/ai systems auditBook a consultation
A useful first message only needs three things.
- 01What needs to work better
- 02Who needs to use it
- 03What a useful first release should accomplish