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AI workflow ROI sprint

Find the AI use case that earns the right to scale.

A two-week, evidence-first sprint that selects one valuable workflow, tests the riskiest assumptions, and produces a go, reshape, or stop decision.

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10 days
From workflow to evidence
$12.5k
Starting investment
Go / no-go
Decision at the end

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

01

Frequent and bounded

The task happens often enough to matter and has a clear beginning, output, reviewer, and exception path.

02

Measurable

Cycle time, acceptance, correctness, cost, conversion, or another business outcome can be baselined before the prototype.

03

Context available

The workflow can access the data, policies, permissions, and business context required to produce a useful result.

04

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 real decision

  1. 01

    Select the workflow

    Rank candidate use cases by value, feasibility, risk, frequency, and how easily the result can be verified.

  2. 02

    Baseline the work

    Measure the current end-to-end process, including waiting, review, rework, exceptions, and fully loaded cost.

  3. 03

    Prototype the risk

    Test the part most likely to invalidate the business case—not the part most likely to produce an impressive demonstration.

  4. 04

    Measure accepted outcomes

    Compare quality, time, cost, and human effort against the baseline with explicit evaluation criteria.

  5. 05

    Choose the operating path

    Deliver a go, reshape, or stop recommendation with architecture, governance, economics, and the next proof required.

Fixed scope

What the sprint delivers

Two working weeksStarting at $12,500
  • 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. The sprint 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 is a successful result when the evidence shows the workflow cannot clear its value or risk threshold. A bounded stop is cheaper 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.

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