value stream–driven AI ROI statistics

Ron Crabtree’s session at the Software Oasis™ B2B Executive AI Bootcamp centered on a simple idea: most organizations do not have an AI problem, they have a value stream problem. He argued that when AI is “spread like peanut butter” across an organization—lightly applied everywhere without focus—adoption stalls, ROI disappoints, and teams conclude that AI “doesn’t work here.” His answer is a value stream–driven AI ROI approach that combines value stream mapping with activity‑based costing (ABC) to pinpoint exactly where AI can remove cost, delays, and errors in measurable ways.

That approach aligns closely with the Software Oasis Experts article Value Stream–Driven AI ROI, which argues that AI investments should follow clearly quantified value streams rather than abstract innovation roadmaps. It also fits with patterns seen in Consulting Statistics: AI & Automation Benchmarks, where many firms report high experimentation rates but relatively few can tie AI spend directly to changes in cost, throughput, or customer experience.

The AI Failure Statistics Ron Is Solving For

Up To 85% Of Initiatives Miss Their ROI

Ron opened by citing data from large consulting firms and industry events suggesting that up to 85% of AI and digital transformation efforts fail to deliver their promised results. Across his podcast conversations and client work, he sees three recurring causes:

Unfocused rollout: organizations “spray and pray,” buying tools, running training, and hoping teams will figure out how to use AI effectively in their day‑to‑day work.
Adoption headwinds: fear, generational differences, and lack of understanding create resistance, especially when people feel AI is being done to them rather than with them.
Inappropriate selection: teams digitize or automate weak processes and pick tools that do not fit the real work, effectively doing “extremely well something that should not be done at all.”

These patterns mirror broader AI and transformation research, where meta‑analyses and industry surveys regularly report high failure rates and highlight poor process understanding and misaligned use cases as primary culprits. Ron’s core point is that value stream mapping plus ABC gives leaders a way to attack those root causes directly instead of relying on generic best practices.

“If You Can’t Describe The Process, You Don’t Know What You’re Doing”

How Value Stream Mapping And ABC Make AI ROI Measurable

Quoting W. Edwards Deming, Ron reminded the audience that “if you can’t describe what you’re doing as a process, you don’t know what you’re doing.” Value stream mapping, in his view, is the discipline of making those descriptions explicit—across functions, systems, and handoffs—so AI can be applied where it actually changes outcomes rather than where it happens to be trendy. This focus on end‑to‑end process visibility is consistent with operational‑excellence and lean literature, where value stream mapping is repeatedly shown to reveal hidden delays, rework, and cost drivers that are invisible in siloed reports.

Mapping Every Step, Cost, Delay, And Quality Hit

Ron’s method layers activity‑based costing on top of value stream maps to build a quantitative picture of the work. For each step in a process, his teams capture:

Who is involved (functions and roles)
How often the step happens
Resource time spent and its cost
Delay time (inboxes, waiting for inputs, approvals)
First‑time quality—the percentage of work that is “one and done” versus requiring rework or hidden “factory” loops
Data and system touches: which IT systems, spreadsheets, emails, and external tools are used, and how often

With this data in hand, they run Pareto analysis—the classic 80/20 rule—to identify the small percentage of steps that drive the vast majority of cost, delay, and quality problems. This evidence‑based prioritization is a core element of value stream–driven AI ROI, because it keeps teams from automating low‑impact steps while ignoring high‑impact bottlenecks.

Case Study: 23% Of Steps Driving 93% Of Cost

In one live case study Ron shared, a billion‑dollar‑plus supplier serving an industrial OEM was considering a roughly $2 million investment in a product‑lifecycle management and AI toolset. The leadership team asked an essential question: against roughly $25 million in overhead tied to quoting, engineering, and getting products into production, where exactly would that money pay off?

Using value stream mapping and ABC, Ron’s team documented 215 process steps from RFP receipt to full‑rate production, including more than 20 functional lanes and 518 primary IT touches across those steps. The analysis revealed that:

Just 49 steps (23% of the total) accounted for over 93% of the process cost.
Those same steps were associated with a high density of system touches and pain points, making them prime candidates for process redesign and targeted AI support.

By focusing improvement and AI application on that “vital few,” the organization could avoid spreading investment thinly across all 215 steps and instead concentrate on the parts of the value stream where AI had the highest potential ROI.

From Analysis To Actionable AI ROI

Cutting System Touches And Overhead Without Cutting People

A key outcome of Ron’s case study was a dramatic reduction in system complexity. By rethinking business rules, consolidating tasks, and introducing AI to streamline specific steps, the team identified opportunities to cut IT “touches” from more than 500 to roughly 300, a 40–45% reduction in system interactions across the process. That reduction translated into:

Less time spent hunting for information across multiple systems
Lower error and rework risk as redundant data entry and manual reconciliations were eliminated
Clear proof points that AI and digital tools were addressing real problems rather than adding noise

Crucially, leadership framed these gains not as a way to reduce headcount but as a way to grow faster with the team they had. By freeing up capacity, they projected being able to do 40% more work with the same people, which mattered in a context where “we cannot hire enough engineers” was a real constraint. This “do more with the same team” framing matches broader AI and automation trends, where many firms are using automation to absorb growth and complexity rather than to shrink staff.

Laser-Focused AI, Not Peanut-Butter AI

The final step in Ron’s value stream–driven AI ROI approach is to get laser‑focused on which AI tools to deploy and where. In the case study, a simple spreadsheet view of the value stream made this visible: out of hundreds of steps, just five highlighted steps represented about 80% of the improvement opportunity. Those became the initial focus for:

Selecting specific AI tools or multi‑agent combinations
Designing leading indicators to measure impact in near‑real time
Running “fail fast” experiments to validate benefits before scaling

Ron contrasted this with the common “teach everyone AI and hope it helps” pattern, which he sees as a recipe for slow, diffuse impact. Instead, he recommended codifying successful patterns from those high‑value steps and then cascading them across similar processes elsewhere in the organization. This targeted, evidence‑driven expansion is at the heart of value stream–driven AI ROI and is consistent with best practices in continuous improvement and lean digital transformation.

How Value Stream–Driven AI ROI Fits The Bigger Picture

Linking AI To Business Outcomes, Not Just Models

Ron’s emphasis on clearly describing processes, quantifying cost and delay, and defining “what good looks like” before choosing AI tools fits the broader argument in Value Stream–Driven AI ROI: AI investments should be sequenced along value streams where the path from improvement to financial and customer outcomes is explicit, not implicit. Rather than starting with “what can this model do,” he urged teams to start with:

Which processes are most critical to customers and revenue?
Where are the biggest gaps in cost, speed, and quality today?
How will improvements be measured and validated?

This mindset echoes themes in AI ROI research and consulting surveys, which find that organizations that tie AI projects to specific, measurable business outcomes are far more likely to report positive returns than those that treat AI as a generalized capability investment.

Making AI ROI A Repeatable System

From a consulting and governance perspective, Ron’s framework offers a way to make AI ROI repeatable rather than episodic. The pattern he described—map the value stream, layer ABC, run Pareto analysis, co‑design with subject‑matter experts, focus AI on the vital few, and then measure leading indicators—can be applied to many value streams across manufacturing, supply chain, and beyond. Over time, those streams become a portfolio of AI‑enhanced processes whose impact can be tracked in:

Reduced overhead per unit of throughput
Shorter cycle times from opportunity to revenue
Higher first‑time quality and fewer hidden “factories” of rework

Seen alongside the benchmarks in Consulting Statistics: AI & Automation Benchmarks and the broader literature on digital transformation success and failure, Ron’s message is straightforward: if up to 85% of AI efforts are missing their ROI, the way out is not more pilots, but better value streams. Get the process and costing clarity right first, and AI finally has somewhere precise—and profitable—to go.

Value Stream–Driven AI ROI Statistics: How To Stop Spreading AI Like Peanut Butter