Six Steps to Turn a Business Goal Into an AI Pilot

This week I delivered a 60-minute workshop to CFOs, CHROs, and CLOs teaching them how to go from desired outcome to designed AI pilot in 6 steps.

Here are the 6 steps. If you want the deck and worksheet I used for the workshop, check the link at the end.

Start With the Outcome, Not the Technology

Transformation isn't about AI. It's about driving outcomes and using tools like AI to solve problems that stand in the way. Before deciding what work or product to transform, you need to decide the outcome.

To do that, fill in the blanks:

Improve [measure] from [baseline] to [target] by [date].

Examples:

• Overdue invoices 18% to 12%
• Month-end close 10 days to 6
• Cash-forecast updates 2 days to 2 hours
• Offer acceptance 40% to 60%

Find the Workflow That Passes the Pain Test

Every business is plagued with inefficiency, even the most AI-native ones. Make a list of all the workflows within your function or company that sit close enough to your outcome to impact it.

Shorten the list to the workflows that pass the PAIN IN THE ASS TEST, then circle one workflow that you believe, if made maximally efficient, would have the greatest impact on driving your outcome.

One exercise for brainstorming workflows is forcing yourself to answer two questions:

If you imagine work 12 months from now...

• What is one specific way work happens differently?
• What measurable result does that change create?

How to Redesign the Work for AI

First, create a process diagram of the workflow you chose as it stands today. It should look like 5-7 steps connected by lines.

Second, draw a new process diagram of the workflow in its future, most efficient form. Next to each step put a label for what drives the work. Three labels to choose from: AI-led, AI-assisted, Human-only.

Example: Hiring outreach process

Current: Define hiring criteria → Search for prospects → Research fit and contact history → Write outreach messages → Approve, send, and log in ATS → Copy activity into a separate tracker → Handle replies and hand off

New:

• Define criteria and permitted sources (Human)
• Find prospects using approved sources (AI-led)
• Review AI research; choose contacts (AI-assisted)
• Draft outreach from approved context (AI-led)
• Approve (Human)
• Send, and log in ATS (AI-led)
• Handle replies and hand off (AI-assisted)

Eight Readiness Checks Before You Press Go

Once you've reimagined the work, you still can't press go. There's a list of 8 items that must be checked (GREEN/YELLOW/RED) before proceeding. You don't necessarily need all items to be GREEN ahead of a pilot, but you definitely do ahead of production.

• Value at stake - You can name the specific benefit to your business (you've built out the ROI case) and a plausible path from this workflow to it
• Process clarity and measurability - You know the new and old steps, start and finish, exceptions, and how to establish a baseline
• Data and context readiness - The required context exists, is fit for the test, and can be accessed within agreed boundaries
• Deployment capability - Someone internally or externally can configure, connect, test, support, and roll back the pilot
• User adoption - Intended users help design it, understand their role, and will try it in real work
• Change capacity - The team can spare time for training, review, feedback, and workflow changes
• Ownership and decision rights - A business owner owns the result. People know who approves changes, resolves issues, and stops the test
• Legal risk and controls - Required approvals, access limits, human review, logging, and stop rules are in place for the pilot

What Every AI Pilot Document Needs

Every pilot should be documented and pitched internally with the following considerations included:

Scope: Who's included, what's the work they're doing, for how long?

Example: Two recruiters. One engineering role family. Four weeks, after permissions and controls are cleared.

Success: What's the business result you're looking to drive and what are early signals you need to see?

Example: Compare with the manual baseline: ≥25% less net prospecting time, no drop in prospect relevance, and saved time used for candidate care. Track offer acceptance longer.

Economics: Which benefit and cost assumptions need testing?

Example: Measure setup and running costs, including recruiter review time. Use observed time savings and prospect quality to update the business-case assumptions. Track actual agency fees avoided and additional delivery contribution over a longer period.

Guardrails: What must not happen?

Example: No unauthorized outreach or material opt-out breach.

Decision: What must be true to expand, revise, or stop the pilot?

Example: At week 4: do the early results justify continuing a bounded test? Continue if early gates pass; revise if fixes are viable; stop for a material breach or no credible path to value. Full ROI is not yet proven.

Scale Scope and Autonomy Based on Results

Based on performance of the pilot and state of readiness criteria from step 4, progressive productionizing of the workflow, rollout to the org, and autonomy for AI occurs.

This is where the real transformation happens. But it only works if you've done the first five steps right. If you're looking for a deeper breakdown, I've previously shared the 10 steps to AI transformation, where only one of the steps is actually about AI itself.

The Outcome Comes First, the Tool Comes Second

The framework is simple: identify the outcome, find the painful workflow, redesign it with clear AI and human handoffs, assess your readiness, document the pilot, then scale based on what you learn.

Most organizations jump straight to the technology. They pick an AI tool and look for places to use it. This framework flips that. You start with the business result you need, then work backward to figure out where AI actually helps.

Grab the workshop deck and worksheet at the link I shared above.