Everyone is implementing AI right now. But very few teams can clearly answer one simple question:

Is this actually working?

Many organizations launch AI tools with good intentions, then look back months later relying on gut feel instead of real evidence. They sense improvement, but cannot explain where the value came from, how big it is, or what should happen next.

That gap is exactly why the CI Clarity Framework exists .

Clarity is not about choosing better tools. It is about knowing before you start how success will be defined, measured, and expanded over time.

Start With One Leverage Point

The biggest mistake teams make with AI is trying to improve everything at once.

Instead, Clarity starts with one leverage point. That is a workflow that is:

• High volume
• High pain
• High turnover
• Or highly visible to the business

This could be patient onboarding, customer onboarding, document review, ticket triage, or another process the organization already feels every day .

By focusing on one leverage point, teams create a clear signal when things improve, instead of noise across many systems.

Define a Single Unit of Value

Once the starting point is chosen, the next step is agreeing on a unit of value.

This means deciding upfront what will be measured and sticking to it consistently. If throughput is the most important factor, then everything is measured in throughput. If cycle time matters most, then cycle time becomes the shared language.

Without a single unit of value, teams talk past each other. Clarity ensures everyone measures success the same way .

Set a Clear Time Horizon

AI impact does not need years to show results.

Clarity uses a defined time horizon, typically around six weeks, give or take. This window is long enough to see real change, but short enough to keep teams focused and accountable.

In some high-volume cases, like onboarding or ticket processing, impact may appear even faster, sometimes within three to four weeks .

The key is deciding this before implementation begins.

Be Clear About What You Are Enabling

Not all AI does the same job.

Clarity requires teams to explicitly define whether they are enabling:

• An assistant
• True automation
• Or analysis and advice

This matters because the type of capability directly affects how fast value can be measured. Full automation in a high-volume workflow will show results faster than advisory tools in lower-volume areas .

Measure What Actually Changed

Once implementation starts, Clarity focuses on five measurement categories.

Capacity and Throughput

Are teams doing more work, or doing the same work faster? Is backlog shrinking? Are ticket volumes being handled more efficiently?

Leverage and Cycle Time

Are fewer steps required? Are there fewer questions, fewer handoffs, and less friction in processes like onboarding?

Agility

How quickly can teams respond to new requests or unplanned changes? This “request to release” speed is often one of the biggest hidden wins.

Replicability

Can this success be repeated in other workflows? How much customization is needed to scale it?

Yield

Is there a real business outcome? Lower operating costs, faster time to revenue, or higher throughput without added headcount ?

Together, these measurements show whether AI is creating real operational change, not just activity.

Determine What Success Actually Means

Measuring data is not enough. Clarity defines how success is judged.

Success includes:

• Business impact: revenue, OpEx reduction, throughput gains
• People impact: satisfaction, adoption, and usage
• Control: auditability, security, privacy, and guardrails
• Scale: the ability to replicate impact across the organization

If any one of these is missing, long-term success is at risk .

Why Clarity Comes Before AI

Clarity is not something you add after an AI pilot.

It is something you define before implementation starts, so both the organization and the implementation team know exactly what success looks like, how it will be measured, and what comes next.

This is how teams avoid failed pilots, unclear ROI, and stalled AI initiatives.

If you want to see how this approach connects to broader operating models, you can read our related article on What Is Agentic Marketing Operations?.

And if you want help applying Clarity to your own AI initiatives, you can connect with CI Digital to discuss where to start, what to measure, and how to scale impact with confidence.