Pilot to production
- Before
- One person can make the AI demo work, but the team cannot rely on it.
- After
- The workflow has owners, review rules, training, logs, and operating review.
AI Transformation
AI IMPLEMENTATION
AI Implementation Consultant
An AI implementation consultant should help a company move from a chosen use case to a working workflow. That means current-state mapping, data and source rules, prototype or automation design, human review, permissions, training, rollout, and a measurement cadence that proves whether the work improved.
USE THIS WHEN
When this is the right inquiry path.
These criteria help qualify whether the next move should be a score, audit, blueprint, sprint, or a direct no-fit answer.
Use this when
- A workflow has enough value and urgency to improve in the next quarter.
- The team can provide a process owner, users, source material, and system access.
- Leadership wants production adoption, not a disconnected demo.
- The workflow needs review rules, training, and value measurement before scale.
Do not hire us if
- The use case is not chosen and leadership still needs discovery.
- The team cannot support process-owner interviews or user testing.
- The goal is fully autonomous decisions in sensitive workflows.
- The company wants a tool reseller rather than vendor-agnostic implementation.
PRICE AND TIMELINE
Match the engagement size to the operating question.
The first paid step should be large enough to answer the real workflow question and small enough to avoid a tool-first overbuild.
- BLUEPRINT FIRST
- $15,000-$25,000: Use when the workflow, vendor, or governance path is not yet clear.
- IMPLEMENTATION SPRINT
- $35,000-$90,000+: Use when one or two workflows are ready to build and launch.
- WORKFLOW AUTOMATION
- $20,000-$60,000: Use for a narrower automation with clear owners and lower complexity.
- MANAGED SUPPORT
- $3,000-$12,000/mo: Use after launch to monitor quality, adoption, cost, and vendor changes.
SAMPLE WORKFLOWS
The inquiry should become a named workflow.
Vague AI interest becomes useful when it is tied to a recurring job, an owner, a review standard, and a measurable before state.
Internal copilot
- Before
- Employees ask public tools questions without source or access control.
- After
- The assistant is bounded by approved sources, permissions, and review standards.
Operations reporting
- Before
- Managers copy updates into decks and miss exceptions until review meetings.
- After
- Inputs are summarized, exceptions are flagged, and owners review the result weekly.
COMPARE THE OPTIONS
Choose the path that matches readiness.
A sprint does not push AI into production until human review, source rules, permissions, rollback, training, and measurement are explicit.
Option
Best when
Watch for
Next path
Strategy workshop
Leadership needs education and alignment.
No production owner, no data review, and no implementation cadence.
AI Transformation Blueprint
Prototype build
A narrow test needs to prove technical feasibility.
A prototype can create false confidence if adoption and governance are ignored.
AI Pilot vs. Production Workflow
Implementation sprint
A workflow is valuable, feasible, and ready for operating launch.
Sensitive decisions still need explicit human approval and specialist review.
90-Day AI Implementation Sprint
RELATED AI PATHS
Choose the next relevant path.
Use these role, function, industry, and service pages to move from a general AI question to the specific workflow in front of you.
RELATED INTELLIGENCE
Three articles behind this inquiry path.
Use these pages to move from a commercial question into workflow selection, cost, readiness, and implementation detail.
The implementation cadence for one workflow over a quarter.
What changes before an AI workflow becomes reliable enough to use.
How to scope source-backed internal knowledge retrieval.
FAQ
Questions leaders usually ask.
What should an AI implementation consultant deliver?
They should deliver a working workflow, not just a recommendation: owner map, source rules, build or configuration, review path, training, rollout plan, and measurement cadence.
How long should AI implementation take?
A focused first workflow usually fits a 30- to 90-day sprint. Broader programs should be sequenced into smaller launches.
What comes before implementation?
If the use case is unclear, start with an AI Opportunity Score, QuickStart Audit, or AI Transformation Blueprint before building.
What makes implementation fail?
Most failures come from tool-first scope, unclear owners, weak source material, missing review rules, or no adoption cadence after launch.