The CFO and the Blind Spot in Technology
The challenge of linking engineering investment to business outcomes.
Bernardo Hernández
Co-founder
Apr 21, 2026
Why financial clarity is no longer enough
The role of the CFO has expanded significantly over the last decade.
What used to be a function focused on reporting, control, and capital allocation is now deeply embedded in strategic decision-making. CFOs are expected to understand where the company is going, what bets are being made, and how resources translate into outcomes.
And yet, there is one area where that clarity often breaks down: technology.
In most companies, engineering represents one of the largest cost centers. It is not unusual for it to account for 30% to 40% of total spend. Despite that, many CFOs still depend on layers of interpretation to understand what is actually happening.
Data exists, but it is fragmented across systems. Access depends on technical teams. Interpretation depends on engineering leadership. And by the time insights reach the CFO, they are already filtered.
This creates a structural dependency that should not exist. Financial leaders should not need to rely on IT to understand where millions in investment are going. They need direct, reliable visibility into how that investment translates into execution. Because without that, financial transparency is incomplete.
The critical role of the CFO
In a previous article, I wrote about how board conversations around engineering are often difficult to answer.
The same dynamic applies to the CFO, with an added layer of responsibility to answer questions about costs and investment returns.
How much are we investing in technology is the easy question to answer, but what about:
Is that investment translating into faster delivery?
Are we improving efficiency, or just increasing cost?
Where are we seeing returns, and where are we not?
These are not abstract questions. They are fundamental to how companies allocate capital, resources and define the overall company strategy.
And yet again, in many cases, the answers rely on proxies given by technical teams: Story points, team velocity, tickets closed. Metrics that were designed to help teams coordinate work, but were never meant to support financial decision-making.
If you are responsible for the financial health of the company, you cannot operate on signals that do not map clearly to outcomes.
CFOs need to understand, at a deep level, what engineering teams are working on, how complex that work is, and how it connects to business impact and metrics.
Only then can cost tracking evolve into something more meaningful: a clear view of ROI.
The AI question every CFO is already being asked
This challenge becomes even more critical with the rise of AI. In a very short period of time, companies have started to invest heavily in AI tools. Coding assistants, automation layers, new infrastructure. The promise is clear: faster delivery, higher productivity and better outcomes.
But the financial reality is less clear.
Costs scale quickly while tooling expands and usage grows across teams.
And at some point, every CFO will face the same question:
Is this investment actually working?
Are teams delivering more?
Are they delivering faster?
Is quality improving, or are we introducing more rework?
Can we ship more with less?
If the answer is unclear, then the investment is not under control. This is where the lack of direct visibility becomes a real risk.
AI changes how work is produced, but it does not automatically make it more valuable. Without the ability to measure its impact on execution, companies risk optimizing for activity instead of outcomes.
For a CFO, that is a dangerous place to be.
The real question is whether as a company you can prove that the AI investment is generating returns.
From cost center to capital asset
There is another dimension where this lack of visibility has very specific consequences: the tax treatment of R&D. I wrote a separate, detailed blog about this.
Over the past few years, changes in U.S. tax law have made this significantly more complex. Following amendments to Section 174 of the Internal Revenue Code, companies have been required to capitalize and amortize R&D expenses, including most software development, generally over several years instead of deducting them immediately. For many, what used to be a current‑year deduction suddenly became a long‑term asset on the balance sheet.
The impact was not theoretical: taxable income increased, cash taxes increased, and for companies with large engineering teams, the effect was material.
The recent introduction of Section 174A partially reverses this for domestic R&D, generally allowing immediate expensing again for U.S.‑based R&E (including software development) for tax years beginning after December 31, 2024, while maintaining stricter capitalization and amortization rules for foreign development. What this creates is not simplification, but a more nuanced, location‑sensitive system.
Now, where the work happens and how it is classified directly affect financial outcomes. And this is where the real problem appears, because most finance teams are not working with data that is structured to support this level of precision. They rely on surveys, estimates, and high‑level allocations to determine how engineering costs should be treated.
However, tax authorities do not accept unsupported guesses. They expect traceability, consistency, and a clear connection between cost and the actual work performed. Without that, companies either leave money on the table or expose themselves to unnecessary risk. If you cannot reliably map engineering work to cost, geography, and purpose, you cannot make defensible decisions about capitalization, deductions, or R&D tax strategy. For a CFO, that is a structural limitation—one that a platform like Pensero is specifically designed to address by turning engineering activity into auditable, tax‑ready cost attribution.
Closing the gap
Throughout my career, I have seen how difficult it is for non-technical executives to fully understand what happens inside engineering teams just because the system was never designed to give them that visibility.
For CFOs, this gap is particularly critical: They are responsible for allocating capital, answering to the board, and ensuring that investments translate into outcomes. And yet, one of the largest areas of investment often remains partially opaque.
That is no longer sustainable.
As technology becomes central to every company, financial leadership needs to evolve with it.
Not by becoming technical.
But by having access to a shared, objective view of reality.
Because at the end of the day, the question is simple:
Can you clearly explain where your engineering investment is going, and what it is producing?
If the answer is no, then the problem is not financial. It is structural.
Why financial clarity is no longer enough
The role of the CFO has expanded significantly over the last decade.
What used to be a function focused on reporting, control, and capital allocation is now deeply embedded in strategic decision-making. CFOs are expected to understand where the company is going, what bets are being made, and how resources translate into outcomes.
And yet, there is one area where that clarity often breaks down: technology.
In most companies, engineering represents one of the largest cost centers. It is not unusual for it to account for 30% to 40% of total spend. Despite that, many CFOs still depend on layers of interpretation to understand what is actually happening.
Data exists, but it is fragmented across systems. Access depends on technical teams. Interpretation depends on engineering leadership. And by the time insights reach the CFO, they are already filtered.
This creates a structural dependency that should not exist. Financial leaders should not need to rely on IT to understand where millions in investment are going. They need direct, reliable visibility into how that investment translates into execution. Because without that, financial transparency is incomplete.
The critical role of the CFO
In a previous article, I wrote about how board conversations around engineering are often difficult to answer.
The same dynamic applies to the CFO, with an added layer of responsibility to answer questions about costs and investment returns.
How much are we investing in technology is the easy question to answer, but what about:
Is that investment translating into faster delivery?
Are we improving efficiency, or just increasing cost?
Where are we seeing returns, and where are we not?
These are not abstract questions. They are fundamental to how companies allocate capital, resources and define the overall company strategy.
And yet again, in many cases, the answers rely on proxies given by technical teams: Story points, team velocity, tickets closed. Metrics that were designed to help teams coordinate work, but were never meant to support financial decision-making.
If you are responsible for the financial health of the company, you cannot operate on signals that do not map clearly to outcomes.
CFOs need to understand, at a deep level, what engineering teams are working on, how complex that work is, and how it connects to business impact and metrics.
Only then can cost tracking evolve into something more meaningful: a clear view of ROI.
The AI question every CFO is already being asked
This challenge becomes even more critical with the rise of AI. In a very short period of time, companies have started to invest heavily in AI tools. Coding assistants, automation layers, new infrastructure. The promise is clear: faster delivery, higher productivity and better outcomes.
But the financial reality is less clear.
Costs scale quickly while tooling expands and usage grows across teams.
And at some point, every CFO will face the same question:
Is this investment actually working?
Are teams delivering more?
Are they delivering faster?
Is quality improving, or are we introducing more rework?
Can we ship more with less?
If the answer is unclear, then the investment is not under control. This is where the lack of direct visibility becomes a real risk.
AI changes how work is produced, but it does not automatically make it more valuable. Without the ability to measure its impact on execution, companies risk optimizing for activity instead of outcomes.
For a CFO, that is a dangerous place to be.
The real question is whether as a company you can prove that the AI investment is generating returns.
From cost center to capital asset
There is another dimension where this lack of visibility has very specific consequences: the tax treatment of R&D. I wrote a separate, detailed blog about this.
Over the past few years, changes in U.S. tax law have made this significantly more complex. Following amendments to Section 174 of the Internal Revenue Code, companies have been required to capitalize and amortize R&D expenses, including most software development, generally over several years instead of deducting them immediately. For many, what used to be a current‑year deduction suddenly became a long‑term asset on the balance sheet.
The impact was not theoretical: taxable income increased, cash taxes increased, and for companies with large engineering teams, the effect was material.
The recent introduction of Section 174A partially reverses this for domestic R&D, generally allowing immediate expensing again for U.S.‑based R&E (including software development) for tax years beginning after December 31, 2024, while maintaining stricter capitalization and amortization rules for foreign development. What this creates is not simplification, but a more nuanced, location‑sensitive system.
Now, where the work happens and how it is classified directly affect financial outcomes. And this is where the real problem appears, because most finance teams are not working with data that is structured to support this level of precision. They rely on surveys, estimates, and high‑level allocations to determine how engineering costs should be treated.
However, tax authorities do not accept unsupported guesses. They expect traceability, consistency, and a clear connection between cost and the actual work performed. Without that, companies either leave money on the table or expose themselves to unnecessary risk. If you cannot reliably map engineering work to cost, geography, and purpose, you cannot make defensible decisions about capitalization, deductions, or R&D tax strategy. For a CFO, that is a structural limitation—one that a platform like Pensero is specifically designed to address by turning engineering activity into auditable, tax‑ready cost attribution.
Closing the gap
Throughout my career, I have seen how difficult it is for non-technical executives to fully understand what happens inside engineering teams just because the system was never designed to give them that visibility.
For CFOs, this gap is particularly critical: They are responsible for allocating capital, answering to the board, and ensuring that investments translate into outcomes. And yet, one of the largest areas of investment often remains partially opaque.
That is no longer sustainable.
As technology becomes central to every company, financial leadership needs to evolve with it.
Not by becoming technical.
But by having access to a shared, objective view of reality.
Because at the end of the day, the question is simple:
Can you clearly explain where your engineering investment is going, and what it is producing?
If the answer is no, then the problem is not financial. It is structural.
