AI Operations

AI Workflow Mapping & Operating Design

AI is not a separate strategy. It is one layer of Mantle OS™ — the operating system founder-led companies need as complexity increases. I map where work actually happens, find where agents can help, and design the handoffs so humans stay in control.

How I design AI into operations

A five-step loop that connects AI pilots to the way your company actually works.

Map the workflow

Where does work actually happen today? I shadow the real workflow — inbox, docs, meetings, tools — and record each step, owner, input, and output. Most teams map the process they think they run; I map the one they actually run.

Identify friction

Where are people repeating, waiting, searching, copying, summarizing, routing, or manually coordinating? These friction points are the leverage points. AI can't fix a workflow with no owner — I fix the ownership first.

Identify AI insertion points

Where can AI prepare, synthesize, automate, recommend, or execute? Every step gets scored on repeatability, volume, data availability, and consequence of error. High-repeatability, low-consequence steps go first.

Define human-agent handoffs

Where must humans review, decide, approve, or escalate? Every handoff gets a trigger, an owner, and a quality gate. The line between agent and human is written down, not assumed.

Pilot and measure

Hours saved • cycle time • quality • adoption • capacity released. I sequence a 90-day pilot so adoption sticks, then expand use cases quarter by quarter.

What you get

Current-state workflow map with owners, inputs, outputs, and cycle time
AI leverage score for every step: automate / assist / keep human
Target-state map with named tools, prompt/context sources, and human checkpoints
Human–agent RACI and escalation rules tied to consequence level
90-day pilot sequence with measurable success criteria

When this fits

Most teams don't have an AI problem — they have an operating-model problem. These are the signals that you're ready to move from scattered tools to a coherent AI operating design.

Tool sprawl

Signal: The team has more AI subscriptions than clear use cases.

Founder still the router

Signal: Agents generate drafts, but everything still funnels back to the CEO for approval.

Repeatable work eating growth time

Signal: High-leverage people are stuck copying, summarizing, routing, or reconciling.

Inconsistent output

Signal: AI results vary wildly because prompts and context are unmanaged.

Data anxiety

Signal: No clear policy on what can enter which AI tool.

Map your workflows before you automate them.

Book a free consultation. I'll find where AI can reduce drag, where humans must stay in control, and what the first 90 days look like.

Book a Free Consultation