Frequent and bounded
The task happens often enough to matter and has a clear beginning, output, reviewer, and exception path.
Back to consulting
Applied AI & workflow automation
Find the AI use case that earns the right to scale.
Move from AI enthusiasm to a useful workflow: evaluate the opportunity, test the risks, and integrate what earns a place in production.
- A baseline worth comparing
- Measurable value
- Review and ownership stay clear
- Human control
- Designed for the real workflow
- Production fit
AI where it pays
A convincing demo is not a business case.
The useful question is not where a model can produce output. It is where the entire workflow becomes faster, better, safer, or less expensive after review, exceptions, integration, and adoption are included.
We select a bounded workflow, establish the current baseline, prototype the highest-risk part, and measure accepted outcomes—not prompt volume. The final recommendation can be to scale, narrow the scope, redesign the workflow, or stop.
What makes a viable workflow
Measurable
Cycle time, acceptance, correctness, cost, conversion, or another business outcome can be baselined before the prototype.
Context available
The workflow can access the data, policies, permissions, and business context required to produce a useful result.
Human control is explicit
The operating model states who reviews, overrides, escalates, and owns the consequence when the system is wrong.
The operating path
A disciplined path to a useful decision
- Select
Select the workflow
Rank candidate use cases by value, feasibility, risk, frequency, and how easily the result can be verified.
- Baseline
Baseline the work
Measure the current end-to-end process, including waiting, review, rework, exceptions, and fully loaded cost.
- Prototype
Prototype the risk
Test the part most likely to invalidate the business case—not the part most likely to produce an impressive demonstration.
- Measure
Measure accepted outcomes
Compare quality, time, cost, and human effort against the baseline with explicit evaluation criteria.
- Decide
Choose the operating path
Deliver a go, reshape, or stop recommendation with architecture, governance, economics, and the next proof required.
Practical deliverables
Typical deliverables, agreed with scope
The right outputs depend on the work. Together we agree the scope, deliverables, fees, and timing, then keep assumptions and changes visible.
- Ranked workflow opportunity map
- Current-state value and cost baseline
- Working critical-path prototype
- Task-specific quality evaluation
- Risk, data, and governance requirements
- Scale / reshape / stop recommendation
Straight answers
Before we start.
Do we need to have selected a model or platform?
No. Tool selection follows the workflow, data, risk, and operating requirements. Starting with a preferred platform often narrows the problem too early.
Is this only for generative AI?
No. We can evaluate classification, extraction, search, decision support, agents, or other automation approaches when they fit the work better.
What if the result is to stop?
That can be the right result when evidence shows the workflow cannot clear its value or risk threshold. Stopping with clarity is better than scaling hope.
The next move
Make AI prove its value early.
Start with one real workflow, one measurable baseline, and a decision your team can defend.