What AI automation looks like, workflow by workflow.
Six illustrative walkthroughs across industries. Each shows the manual process, the AI-assisted process, where people still approve, and an estimated range grounded in public benchmarks.
Illustrative examples, not client results. Every figure is an estimate built from public research and the manual steps each map removes. Actual savings depend on the process, volumes, data quality and scope, and are estimated for your own workflow during the assessment. No company or person in these examples is real.
At a glance
All figures are estimates. Illustrative examples, not client results.
Example
Estimated time
Estimated cost
Manufacturing: Engineering change from PLM to ERP
30 to 50% less
25 to 40% lower
Logistics: Freight invoice and proof of delivery matching
50 to 75% less
50 to 75% lower
Utilities: Field work order triage
20 to 35% less
15 to 30% lower
Healthcare: Prior authorization paperwork
30 to 55% less
30 to 55% lower
Automotive: Supplier PPAP document review
30 to 50% less
25 to 40% lower
HR (any industry): Employee onboarding
30 to 50% less
20 to 40% lower
Manual step removedAI step or human approvalPerson approves
Illustrative example, not a client result.
Manufacturing: Engineering change from PLM to ERP
An approved engineering change has to reach the ERP bill of materials, with a decision on existing stock and open orders. Today a planner bridges the two systems by hand.
Manual step removedAI step or human approvalPerson approves
Before: manual
After: AI-assisted
Estimated time
30 to 50% Estimated 30 to 50% less manual handling time per change
Estimated cost
25 to 40% Estimated 25 to 40% lower handling cost per change, after running costs
Where people approve
Engineering still approves the change in PLM. The planner approves every BOM update and decides what happens to existing stock and open orders.
Basis
Drafting and checking work. In a controlled study, professionals using a generative AI assistant on writing tasks took 40% less time. McKinsey estimates current AI and other technologies could automate activities that take 60 to 70% of employees' time. The range is set below both to allow for review time. Sources: Noy and Zhang, via MIT News. McKinsey, The economic potential of generative AI.
Illustrative example, not a client result. Actual savings depend on the process and scope.
Illustrative example, not a client result.
Logistics: Freight invoice and proof of delivery matching
Carrier invoices and proof of delivery scans arrive by email. A clerk keys them in and checks each charge against the load and the rate confirmation.
Manual step removedAI step or human approvalPerson approves
Before: manual
After: AI-assisted
Estimated time
50 to 75% Estimated 50 to 75% less manual handling time per invoice
Estimated cost
50 to 75% Estimated 50 to 75% lower processing cost per invoice
Where people approve
AP approves clean matches in batches. An AP specialist resolves every mismatch, with the reason shown. Payment release stays with finance.
Basis
Ardent Partners' State of ePayables 2025 reports $2.65 per invoice and 2.9 days for best-in-class AP teams, against $12.42 and 13.5 days for all others, about 79% lower on both. The range sits below that gap because one workflow change will not deliver the full best-in-class result. Sources: Ardent Partners, State of ePayables 2025.
Illustrative example, not a client result. Actual savings depend on the process and scope.
Illustrative example, not a client result.
Utilities: Field work order triage
Service requests arrive by phone, email and portal. A dispatcher reads each one, looks up the asset, sets priority and crew, often early in the morning.
Manual step removedAI step or human approvalPerson approves
Before: manual
After: AI-assisted
Estimated time
20 to 35% Estimated 20 to 35% less triage time per work order
Estimated cost
15 to 30% Estimated 15 to 30% lower triage labor cost
Where people approve
The dispatcher approves or edits every proposed priority and crew. The GIS record owner approves each map correction reported from the field.
Basis
A study of 5,179 support agents found an AI assistant raised issues resolved per hour by 14% on average and 34% for newer staff. Removing re-keying and manual lookups adds to that, so the range starts above the average and stays below the larger figures. Sources: Brynjolfsson, Li and Raymond, Generative AI at Work (NBER).
Illustrative example, not a client result. Actual savings depend on the process and scope.
Illustrative example, not a client result.
Healthcare: Prior authorization paperwork
Before some procedures, staff must request authorization from the health plan. Today that means phone calls, faxes and gathering clinical notes by hand. No clinical decision is automated in this example.
Manual step removedAI step or human approvalPerson approves
Before: manual
After: AI-assisted
Estimated time
30 to 55% Estimated 30 to 55% less staff time per request
Estimated cost
30 to 55% Estimated 30 to 55% lower administrative cost per request
Where people approve
Staff review every packet before it is submitted and handle every denial or appeal. Clinicians keep all clinical decisions.
Basis
The CAQH Index 2024 reports 24 minutes per manual prior authorization and 16 minutes through a portal (33% less). The 2023 Index reports 11 minutes when fully electronic (54% less than manual). The 2024 cost per transaction is $12.95 manual against $5.43 electronic. Sources: CAQH Index 2024. CAQH Index 2023.
Illustrative example, not a client result. Actual savings depend on the process and scope.
Illustrative example, not a client result.
Automotive: Supplier PPAP document review
Suppliers send Production Part Approval Process (PPAP) packages: warrant, control plan, PFMEA, dimensional results and material certificates. A quality engineer checks each file by hand.
Manual step removedAI step or human approvalPerson approves
Before: manual
After: AI-assisted
Estimated time
30 to 50% Estimated 30 to 50% less review time per package
Estimated cost
25 to 40% Estimated 25 to 40% lower review cost per package
Where people approve
The quality engineer reviews every flag, sends every note to the supplier, and makes every approval decision.
Basis
Document checking and drafting work, based on the same research as the manufacturing example: 40% less time on professional writing tasks with an AI assistant, and McKinsey's 60 to 70% technical automation potential. Set lower because each flag still needs engineering judgment. Sources: Noy and Zhang, via MIT News. McKinsey, The economic potential of generative AI.
Illustrative example, not a client result. Actual savings depend on the process and scope.
Illustrative example, not a client result.
HR (any industry): Employee onboarding
Once an offer is accepted, IT, facilities and the hiring manager each act on an emailed list. Access is copied from someone similar and the laptop waits on missing details.
Manual step removedAI step or human approvalPerson approves
Before: manual
After: AI-assisted
Estimated time
30 to 50% Estimated 30 to 50% less coordination time per new hire
Estimated cost
20 to 40% Estimated 20 to 40% lower coordination cost per new hire
Where people approve
The IT, facilities and hiring manager owners each approve their part. Any access outside the standard role bundle needs explicit approval.
Basis
Lower confidence: no direct public benchmark was found for onboarding coordination. The range is based on the steps removed and McKinsey's estimate that current AI and other technologies could automate activities that take 60 to 70% of employees' time. Sources: McKinsey, The economic potential of generative AI.
Illustrative example, not a client result. Actual savings depend on the process and scope.
Want an estimate for your own workflow?
The assessment replaces these public ranges with numbers from your volumes and your process.
