Without Baseline Data, AI ROI Is Conjecture

Why measuring before you deploy AI determines whether ROI can be proven

Artificial intelligence (AI) is often positioned as the answer to slow, manual, or tedious processes. And in many cases, it can be. But across many organizations exploring AI, one issue consistently undermines the ability to assess return on investment (ROI):

Baseline data does not exist.

Instead of quantified evidence, teams rely on a general sense that a process is inefficient:

• “This process takes too long.”

• “The team spends too much time on it.”

• “There has to be a better way now with AI.”

Those instincts may be valid, but they are not metrics. Without baseline data, it becomes nearly impossible to determine whether an AI solution is truly delivering value.

A Common Pattern in Early AI Conversations

During early conversations with companies interested in running AI proofs of concept, we ask foundational questions:

• “What problem are you trying to solve?”

• “Why do you believe AI is the right solution?”

• “What data do you have to show this problem exists today?”

In many cases, the answer to the last question is "None."

The pain is real, but unmeasured.

The process feels slow, tedious, or inefficient, yet there is no quantified cycle time, quality metrics, or workload data to describe how the process performs today.

Why the Lack of Baseline Data Creates ROI Blind Spots

When baseline data is missing:

• Improvements can’t be quantified

• Efficiency gains can’t be attributed to AI versus normal variation

• Quality improvements can’t be validated

• ROI becomes subjective rather than defensible

Even if the AI genuinely improves the process, the lack of baseline data makes the results difficult to explain, justify, or trust. There is a significant difference between saying “this feels faster” and “cycle time decreased by 35% while first-time yield improved by 12%.” Only one of those statements stands up in executive reviews, funding decisions, and scaling discussions.

When Baseline Data Exists, the Conversation Changes

When baseline data is collected upfront, AI conversations become objective instead of speculative.

In a proof-of-concept project we conducted, baseline data was available for:

• End-to-end process cycle time

• First-time yield (FTY) quality performance

Because these metrics were already defined and measured, the impact of AI could be assessed objectively. The results were not based on perception or anecdote; they were based on evidence, which made it possible to:

• Accurately quantify efficiency gains

• Measure improvements in FTY scores

• Demonstrate clear, defensible ROI tied to operational and quality outcomes

Baseline data removed ambiguity. The value of the AI solution was measurable, credible, and easy to communicate.

The Benefits of Establishing Baseline Data

Establishing baseline data upfront enables:

• Accurate ROI measurement

• Stronger leadership confidence

• Clearer scaling decisions

• Reduced risk of overstating or understating AI value

Baseline measurement does not slow AI adoption—it enables smarter decisions about where and how AI should be applied.

What to Measure Before Starting an AI Initiative

Baseline data does not need to be perfect, but it does need to be relevant. Before assessing AI as a potential solution to a perceived problem, organizations should consider capturing:

• Process cycle times

• Manual effort or FTE hours

• Rework or error rates

• First-time yield or quality scores

• Output variability or inconsistency

• Backlog volume or throughput

Even a short baseline period can provide enough insight to establish a meaningful reference point for comparison.

The Real Challenges of Establishing Baseline Data

Baseline measurement is often avoided—not because it lacks value, but because it exposes reality. Common challenges include:

• Data spread across multiple systems that were never designed to work together

• Upfront effort required for time studies, manual sampling, or retrospective reviews

• Inconsistent processes, workarounds, or unclear ownership

• Uncomfortable truths such as excessive rework, poor quality outcomes, inefficient handoffs, or underutilized systems

Baseline data reveals how work actually happens, not how it’s assumed to happen.

That visibility is precisely what makes AI investments measurable, scalable, and defensible. For organizations serious about AI delivering measurable outcomes, this is a worthwhile trade-off.

AI ROI Starts at AI Strategy Development

AI does not create ROI on its own. Measurement does.

If leaders cannot clearly articulate how a process performs today, they cannot credibly claim improvement tomorrow. Without baseline data, AI initiatives remain experiments that are difficult to defend, scale, or fund.

The most successful organizations treat baseline measurement as part of the AI strategy, not an optional step. They pause long enough to quantify reality, define success, and establish the metrics that matter.

If you’re developing an AI strategy or seeking to deploy AI, optiQMS Solutions helps you define the right AI use cases and baseline metrics, then validates ROI through a targeted proof-of-concept on the myQMS.ai platform—before broader investment or scale.