Widespread deployment of Artificial Intelligence is forcing companies to scrutinize their cost structures in new ways. News reports of companies laying off thousands of workers due to the impact of AI are increasingly common, such as Megan Cerullo’s CBS News report last month. To her credit, she questions whether layoffs are a “harvesting of value” produced by AI or caused by some other factor. Ben Cagle, Managing Partner at Cagle Partners, is blunter in his assessment, “The narrative? AI productivity. The reality? AI capital expenditure.” [2]
The truth is that companies are struggling to squeeze investments in AI into their already constrained operating expense budgets. Who loses that struggle? The workers whose jobs are eliminated to make room for increased operating expenses generated by “pay per use” generative AI tools such as ChatGPT or Claude.
A Common Pitfall: Cost Myopia
The approach of making space for generative AI expenses by large percentage reductions in labor spending is a common, but serious error due to its focus on cost rather than value. It is too easy for an organization’s finance team to solve a cost overrun by mandating a reduction in labor costs. Once these reductions are baked into a budget, they become non-negotiable as Operators in the business are held accountable to achieve the cost numbers articulated in the budget.
A Better Way: Value Engineering
Value Engineering, the analysis of a good or service in terms of the cost of its subcomponents and the relative value they contribute to a finished good, is a concept that has its roots in purchasing and materials management. I was introduced to this style of analysis back in the 1990s via a purchasing management class based on the book, Purchasing and Materials Management – Text and Cases, by Dobler, Burt and Lee, Jr.
The approach has value beyond purchasing and materials management. We can apply the fundamental idea to any business in two specific areas: growing a business by improving its ability to attract more customers or improving the efficiency with which customers receive products or services.
We begin with a simple definition of value: the benefits generated by a product or capability minus its costs. A key characteristic of this definition is that the benefits must be monetized so costs can be subtracted from them. The definition is important because it forces us to wrestle with ways to monetize things that many leaders consider to be “intangible,” such as functional value, aesthetic value, or environmental value. [3]
What can be “Value Engineered?”
From its inception at General Electric in the 1940s to keep products affordable without sacrificing quality during World War II, value engineering has been most frequently associated with products: finished goods that are sold to external customers. [4] However, the technique can be applied to other elements of a business and at various levels of abstraction, including:
- Value Streams
- Business capabilities and their subcomponents:
- Roles / People
- Business processes
- Assets (both physical and technology [including data])
- Organizations/business units
One of the benefits of evaluating a set of items (e.g. business capabilities, business processes or assets) is that it enables the organization to understand the relative value being created by various elements in its business.
Sometimes this type of assessment unlocks “surprises” that are hidden in the financial and usage data. I once led a value engineering analysis of a suite of e-commerce applications for a large technology firm. We discovered a pattern that the centralized infrastructure function routinely purchased 3x the hardware needed to run an application. Our average utilization was 12%, and workload rarely exceeded 25% of capacity.
Purchase of unnecessary capacity inflated costs allocated to other business units in multiple ways because the centralized infrastructure department allocated cost to business units by number of servers deployed to a business unit. Once discovered, our value engineering team was able to immediately save scores of millions of dollars in annual infrastructure costs by shutting down the unnecessary servers.
Value Engineering in Eight (Not So) Easy Steps
At the highest level of abstraction, value engineering is a combination of eight steps. The first two are challenging because they set the guardrails for the remaining work. The last one is challenging because it forces the organization to convert the benefits generated into cash that can be used to fund subsequent improvements.
- Identify the unit of analysis,
- Quantify how the unit of analysis generates benefits,
- Identify subcomponents,
- Calculate one time and ongoing costs for subcomponents,
- Find improvement opportunities,
- Break opportunities into discrete units of value,
- Prioritize and begin working the list, and
- Monetize the results.
Identify the Unit of Analysis
Identify the “thing” to be analyzed: value stream, business capability, process, or set of assets. Effort to conduct the analysis is largely correlated to the number of things being analyzed. For example, a mid-sized to large organization typically has 8 – 15 “level one” capabilities, the highest-level abstraction in a business capability map. Each capability has multiple business processes, roles and supporting assets. To prevent scope from quickly expanding to an unreasonable amount, it’s often useful to select a unit of analysis based on the degree to which it is underperforming, either financially or against its planned cycle time and quality service levels.
Quantify Benefits
Quantifying the benefits a unit of analysis generates is often the most challenging step in the process, particularly for items classified as “strategic and governance” capabilities. Generation of value must be quantifiable and convertible to cash. Value must also be generated as the unit of analysis operates its business processes or work is processed by an asset.
The measurable benefits are going to serve an important role in the rest of the analysis, not only for benchmarking current performance, but also placing an upper limit on investments in improvements. For example, an organization would not spend $5 million improving something that only generates $100,000 a year in value.
Identify Subcomponents
Having identified a unit of analysis and its source(s) of benefits, the next step is to identify its subcomponents. For a value stream, its subcomponents are the value stream stages. For a business capability, its subcomponents can be either a more granular business capability, or the combination of the people (roles), business processes, and assets that instantiate the capability. For an asset, its subcomponents are the labor and non-labor elements needed to operate and sustain the asset.
Calculate Subcomponent Costs
Calculate the one time and ongoing costs for each subcomponent, taking care to ensure that the sum of the costs is a valid representation of the value stream, process or capability. One way to do this is to count the frequency with which a process is executed and then calculate the cost per execution. Once the cost per execution is known, then it can be extrapolated based on the daily, weekly, or monthly frequency with which the component is utilized.
Find Improvement Opportunities
Having determined the costs and sources of benefits for the subcomponents of our unit of analysis, we can identify outliers (high cost + low benefits, or where benefits – costs is smaller than the organization’s expected values). Identify potential improvements, structuring them as discrete changes that generate measurable value and can be implemented in less than 90 days.
Define how success will be measured, as well as the mechanism(s) that will be used to monetize the improvements. Develop a high-level cost estimate and expected improvement quantified in cash for each item. Sometimes this means adding an incremental sales goal to a sales plan when the benefit is an increased conversion rate, and at other times it may mean booking headcount reductions to monetize improvements in efficiency.
Prioritize the Opportunity List and Implement Improvements
Use a technique such as weighted shortest job first to prioritize the opportunity list. The idea here is to solve problems in small chunks and quickly generate value so that subsequent improvements are funded by the value of the improvements we have already deployed to customers and end users.
Monetize the Results
As improvements are delivered to customers and end users, review the success measures relative to their planned values, and act on the results. Aside from establishing the unit of analysis and determining sources of benefits, this is the toughest step. Many companies plan to generate benefits from improvement initiatives, but they fail to convert improvements in productivity to cash, with the unintended consequence of increasing overall operating expenses rather than decreasing them. For example, customer experience improvements can be monetized in one or more of the following ways:
- Increases in a conversion funnel times an average order value,
- Increased purchase frequency per unit of time,
- Increases in the number of product categories purchased per unit of time,
A/B and Multivariate testing are important tools organizations can use to test actual customer response to changes in capabilities or processes. Successful tests increase an organization’s confidence that the planned improvements are perceived as such by customers and end users.
Case Study: A Focus on Facts Leads to Wise Investments
One of the key benefits of the value engineering technique is that through the assembly of relevant facts, a leadership team makes decisions based on empirical evidence rather than gut feel or personal persuasiveness. For example, I once participated in a decision about whether to spend $20 million in capital to upgrade an e-commerce capability because the line of business owner asserted a system replacement was needed to achieve a 30% sales growth rate.
Another executive didn’t want to spend the capital on e-commerce and pushed back against the proposal. Both executives were looking for facts to support their intuition. A team was assembled to value engineer the e-commerce capability, including its ability to handle a 30% compound annual growth rate in sales over three years.
The team discovered that while the e-commerce software could support 30% annual growth, the customer service center would need to hire 60 additional people to handle the resulting backorders. Instead of spending $20 million on a systems replacement, the organization redirected funds to fix supply chain backorders, generating $2.5 million in annual productivity gains.
Common Objections
Value Engineering of things beyond finished goods has a lot to recommend it. Why do so few companies use it as a tool to accelerate profitable growth?
Objection 1: We don’t have time/data/skills, etc.
It’s true that organizations rarely have idle capacity to analyze the effectiveness and efficiency of their value streams and capabilities. It usually takes pain in the form of serious underperformance in a business area to justify an investment in value engineering. Scoping this form of analysis to one to three underperforming areas will give an organization some quick wins as well as developing experience with the approach.
Objection 2: Value Engineering is old-school Manufacturing – doesn’t apply to SaaS / Digital
Ironically, changes in corporate cost structures have been significantly impacted by technological advancements over the past 30 years, including cloud computing, subscription-based Software as a Service, and now pay-per-use operating expenses associated with the use of Generative AI tools.
Many of these expenses are either out of direct control of the end user departments to which the expenses are allocated, or only indirectly controlled. Therefore, knowledge of the details of sources of costs, as well as the entities who actually control the costs, are increasingly important for line of business executives to maintain predictability in their P&L statements. As “old-school” as value engineering may be, it is a straightforward way to isolate sources of benefits and cost, and clarify who directly controls the costs.
Objection 3: This is too academic for our organization to adopt
The ability to scale the technique up or down as needed allows teams to value engineer their own areas before making a larger push for department or organization-wide. Value engineering is a much easier sell when a leader presents it to colleagues in a package that includes actual results generated within the organization’s specific context. If it can be proven to improve results, the degree to which the technique is rooted in academia is irrelevant.
Conclusions
Value Engineering is an important technique for leaders to dust off and add to their toolkits to generate profitable growth in companies of all sizes. Its focus on value versus cost, combined with simplicity of implementation, enable it to be implemented at a small enough scale to produce results quickly. Value Engineering is also sufficiently scalable to generate scores of millions in annual value.
Start small this quarter: Pick one underperforming capability or process, assemble a small cross-functional team, and run a focused Value Engineering workshop. The data-driven insights you uncover will quickly pay for the effort – and may fundamentally change how your organization approaches AI-related cost pressures.
References
[1] Cerullo, Megan – AI Emerges as Top Cause of Layoffs, Accounting for 26% of April’s Job Cuts, CBS News website, retrieved May 17, 2026.
[2] Cagle, Ben – Search for AI ROI. Focus on the Business Impact, LinkedIn article, retrieved May 17, 2026.
[3] Rahman, M. Abdur – A Comprehensive Guide to Value Analysis and Value Engineering, Kindle Edition, self-published, 2025, p. 45.
[4] Ibid., p. 46.
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