Why The Great Reallocation Matters For CFOs

As a CFO, you sit right at the center of what Alistair Croll calls The Great Reallocation. This is not just a technology story, it is a capital allocation story. Money that once went to people, hardware, and big software platforms is quietly shifting into AI agents, automation, and very focused digital services. If you do not understand this shift, your budgets, headcount plans, and even your valuation models can drift badly off course without you noticing until it is too late.

The idea of a shadow workforce of AI agents sounds like a buzzword at first, but for finance leaders it is really a new category of operating expense. People across your company can now put a credit card into a software product and instantly create what looks like a new department. That has huge implications for cost control, productivity measurement, risk, and long term planning.

The Four Great Reallocations Of Tech In Plain Finance Terms

To understand what is happening to your P&L today, it helps to see the pattern of the last twenty five years. Each technology shift created a big misallocation at first, then a fast rebalancing. Companies that understood the rebalancing early captured a lot of value. Those that did not, paid for legacy structures that no longer made sense.

1. Desktop To SaaS

Two decades ago, we moved from buying software on discs to paying for SaaS subscriptions. From a finance point of view, you shifted from large upfront capital purchases and annual upgrades to recurring operating expenses. Revenue recognition, cash flow timing, and vendor dependency all changed. The winners were the firms that redesigned their sales and support around recurring revenue, and the buyers who consolidated licenses and controlled seat growth.

2. Hardware To Cloud

Next came the move from owning servers to renting cloud infrastructure. Instead of big one time hardware investments, you began to see variable monthly cloud bills. CapEx became OpEx, cost visibility improved but so did the risk of sprawl. Firms that treated cloud as a strategic utility with proper tagging, showback and governance saw lower unit costs and higher flexibility. Those that treated it like a bottomless data center ended up with runaway bills and poor unit economics.

3. Traditional Media To Social

Marketing budgets used to go mostly into TV, print and paid media. Then value shifted into content, community and social platforms. Finance teams had to learn how to fund activities where the link between spend and outcome was indirect, but extremely powerful when it worked. A well timed viral post could create more brand impact than a paid national campaign, but it was hard to forecast and hard to fit into old ROI models.

4. Human To AI Agents

We are now entering the fourth reallocation. Instead of assuming a task requires a human worker or a traditional SaaS seat, you can often use an AI agent that runs inside a SaaS console. The same budget line that used to fund ten sales reps can, in some situations, fund one sales leader and an AI powered inside sales department. For a CFO this is not theoretical, it is already showing up as lower cost per opportunity, faster project delivery, and strange looking vendor invoices that actually represent whole workflows.

From Shadow IT To Shadow Workforce

You probably remember the old shadow IT problem. A sales manager would put Salesforce on a corporate card, a team would spin up its own AWS account, and suddenly your company had dozens of untracked systems, all with different security and licensing issues. Finance and IT had to pull everything back under control and standardize.

Shadow workforce is the same pattern, but much faster and more powerful. Today a manager can swipe a card and get a full AI outbound motion, or an AI project delivery team. You might only see a 900 dollar a month invoice, while in reality that tool is doing the work of several FTEs, with almost no HR visibility. HR does not know if they should hire more people in that area, or fewer. You do not know whether to treat that product as software, as labor, or as something in between.

A Simple Shadow Workforce Example

Imagine your head of sales operations signs up for an AI powered outbound platform. The tool connects to your CRM, email, and calendar. Within a week it is writing outreach messages, sequencing follow ups, booking meetings, and updating records automatically.

On your ledger it looks like a 1,500 dollar monthly software fee. In practice, it is doing work that previously required two sales development reps with a total loaded cost of, for example, 14,000 dollars per month. If you are still budgeting assuming you need the same number of SDRs as last year, you are misallocating capital. If you cut the reps without a clear plan, you might also create risk if the AI setup breaks or behaves badly, for instance spamming the wrong prospects.

From CRMs To AI Departments

The key mental shift for a CFO is this. Many SaaS products used to be tools for humans. Increasingly, they are consoles where AI agents do the work. The invoice looks similar, but what you are actually “buying” has changed.

AI Inside Sales Instead Of Just A CRM

Previously you might approve a CRM subscription at 80 dollars per user per month, and then budget for a 6 person inside sales team to use it. Now a modern AI enhanced platform can automatically generate leads, qualify them, send outreach, and schedule calls. The human team becomes smaller and more senior, focusing on strategy and complex deals. Your HR cost per qualified opportunity may fall dramatically, but your software per user cost might go up. If you only look at the software line, you might feel costs are rising, when in reality unit economics have improved.

Autonomous Project Delivery Instead Of Just Project Management Tools

In IT or operations, you are used to paying for project management tools that help teams track tasks. Today, tools can go further and assign tasks, chase updates, write documentation, generate code snippets, and even test them. The result is a blended delivery team of humans and AI where the marginal cost of adding another project is much lower. That changes your capacity planning and your assumptions about external consulting spend.

AI Data Science Teams Instead Of Analytics Licenses

Analytics platforms used to be mainly dashboards and reporting. With current AI capabilities, those same platforms can clean data, suggest KPIs, build forecasts, and create narrative reports. In some firms, a small central analytics group is now supported by AI agents that give every department near real time analysis, without hiring dozens of new analysts. If you keep authorizing analyst hires using last year staffing ratios, you risk over staffing while also under investing in the AI layer that drives most of the marginal value.

Millions Of Tiny Horses And What It Means For Your Vendor Strategy

Venture capital used to push for big, complex platforms that would become unicorns. In your world that meant large enterprise contracts with one vendor, and lots of customisation. With AI, it is now cheap to create very targeted solutions for narrow verticals, these are the “tiny horses”.

For example, an AI built CRM that serves only high end restaurants taking prepaid reservations can be profitable, even though that niche is small. Another product can focus entirely on tattoo studios that need privacy aware scheduling. For you, this means your teams will increasingly find perfect fit tools for very specific problems. The benefit is better process fit and often higher productivity. The risk is vendor sprawl and integration complexity, and sometimes weak vendors that may not survive a downturn.

As CFO, you need a practical vendor strategy that admits this reality. Standardising on one huge platform for everything might not give you the best economics any more. On the other hand, letting every team adopt niche AI agents freely can create a future integration mess, and governance holes.

Concrete Financial Impacts You Can Expect

The reason this matters so much for you is that the numbers are now radically different. The article talked about a 2.5 million dollar project being built in forty five minutes with AI. That is not a normal case, but even a ten times improvement is enough to shift your whole planning approach.

Imagine you had a budget for a 1 million dollar custom software project with a two year delivery timeline. You expected to capitalise part of it, depreciate the asset, and carry some vendor risk. Now suppose an AI powered build can deliver a usable version in two weeks, with a 60,000 dollar blended cost using a mix of consulting time and AI agents, plus an ongoing small subscription. Your upfront investment falls sharply, the payback period shrinks, but your OpEx grows a little over time. If you keep using your old hurdle rates and investment committee process, you will be too slow and overly cautious on valuable projects.

What Capital Markets And Perfect Efficiency Mean For You

The quote about AI making trading look like a machine that extracts every market inefficiency matters to CFOs for two reasons. First, if your company earns profits mainly because markets are inefficient, AI will compress your margins. Second, investors analysing your business will also use AI, giving them a much clearer view of where your advantages are temporary.

If your pricing power depends on customers not noticing cheaper alternatives, that is fragile. AI will make price comparisons and feature checks trivial. If your internal processes depend on slow manual reviews, smart competitors using AI agents can undercut you. A good exercise is to list where your profits rely on friction or opacity, then assume that friction can disappear in a few milliseconds. It is not a fun exercise, but it helps you see where to invest in true differentiation instead of hidden inefficiency.

The Three Orders Of AI Impact For CFOs

The article described three levels of AI impact. It is helpful to map them directly to finance work, because you probably see all three levels at once inside your company.

First Order: Doing Your Current Job Better

This is where most teams are. They use AI to draft emails, summarise documents, assist with coding, or speed up reviews. For the finance function that might be AI generating first drafts of board reports, cleaning up spreadsheets, or summarising contract terms. Productivity goes up, but the underlying role is the same. From a budgeting point of view, this is modest. You see some time savings and maybe lower need for overtime or temp staff.

Second Order: Replacing Or Reshaping Roles

At the second level you start asking if a vacancy really needs another human, or if an AI agent can take most of the work. In finance this could mean automating parts of AP, AR, reconciliations, or forecast generation. Maybe you keep the same headcount now, but as people leave you do not backfill every role. Over a few years your department cost profile changes a lot. Your total FTE count might flatten or even fall, while your software and AI spend rises. An honest workforce plan will include both trajectories, not just headcount.

Third Order: Does This Business Still Make Sense

The hard question for you and the CEO is whether some lines of business still make economic sense once AI is fully applied. If you run a BPO that relies on low cost human labor answering routine queries, AI can hollow out your core product. If you sell manual data entry services, AI tools can destroy your margin within a year. At this level, the tasks are not just to save cost, but to ask if the business model itself needs to be replaced.

This is uncomfortable, because it can lead to decisions that write down assets, close units, or change how you talk to investors. But ignoring it does not make the risk go away. You would rather run that analysis early, while you still have capital and time to pivot.

Government, Compliance And The Risk Of Easy Bureaucracy

The article mentioned something many CFOs feel but do not always say out loud. AI can make bureaucracy dramatically easier. On the bright side, it means you can process benefits claims faster, generate legal documents at trivial cost, and comply with regulations more efficiently. A contract review that once cost 8,000 dollars in legal fees might be mostly handled by an AI assistant for a few dollars of compute, with a lawyer just doing the final check.

On the dark side, the same capabilities can be used by states and large institutions to create extremely dense, automated control systems. Perfect surveillance or automated enforcement can sound attractive when you think about fraud reduction, but very disturbing when you think about civil liberties or abuse of power. As a CFO you are involved in risk frameworks and compliance. You should at least factor in that the regulatory environment will probably tighten around AI, and that expectations of auditability, traceability and data protection will rise sharply.

Put differently, do not treat AI tools as harmless utilities. Finance processes built on AI must still be explainable and auditable. An AI that rejects a loan application, or flags a transaction for fraud, needs a clear trail that you can show to regulators or auditors.

What You Should Do This Week As A CFO

1. Audit Your Shadow Workforce

Start by asking your CIO and key business leaders to map where AI tools and agents are already in use. Look beyond official procurement. Ask managers which AI products their teams pay for on credit cards. Ask where AI is doing actual work, not just helping with writing.

You might discover that marketing is using an AI tool that writes and publishes blog posts on its own, sales is running AI assisted outreach, HR is using an AI assistant for initial CV screening, and finance itself is using AI to structure large data exports. List these tools, their cost, and the type of work they perform. You do not need to shut them down; the first step is simply visibility.

2. Identify The Biggest Inefficiencies In Your Industry

Next, look at your business model and your sector. Where do customers complain most about slow processes or high friction. Where do your internal teams spend lots of time on low judgment tasks. These are places where AI is likely to remove cost and delay very quickly.

For example, if claims processing in your insurance business takes weeks because of manual checks, that is a prime candidate. If your logistics operation still uses human schedulers for simple routing, that is another. Note down these areas and estimate the labor cost and cycle time involved. Then ask your technology leaders to explore AI based solutions in those zones first. This links AI experiments directly to measurable financial outcomes, instead of random pilots.

3. Plan For 100x Cost Shifts In Scenarios

The article talked about functions performed at one hundredth of today’s cost. Whether it ends up being ten times or fifty times, the effect on financial models is huge. As a CFO, you do not have to claim exact numbers, but you can and should run simple scenarios.

Pick a few major cost centres where work is information intensive rather than physical. For each, build three cases. A conservative case with five times productivity improvement, a medium case with ten times, and an aggressive case with fifty times. Then see what happens to your gross margin, your optimal headcount, and your ability to serve more customers with the same base team.

Use these scenarios in board discussions. This will feel speculative, but it changes the tone. Instead of asking “Should we cut ten percent of costs” you might ask “If this cost line can shrink to one fifth within three years, how do we invest now to be the leader rather than the victim”.

4. Update Your Investment And Procurement Playbook

Finally, update how you evaluate projects and software purchases. For capital projects that involve software or data workflows, expect delivery times to shorten and costs to fall. A two year, multi million dollar IT project should be challenged much harder now. Ask for prototypes delivered using AI tools in weeks, not months. Consider stage gating more tightly, with small early investments that test feasibility.

On procurement, accept that you will see more niche AI tools, the tiny horses. Build light but clear standards: data privacy requirements, basic security checks, interoperability rules. Support innovation, but make it easy to consolidate tools that prove their value, and to shut down ones that do not. A simple quarterly review of AI and automation tools, combined with finance data on actual savings or revenue uplift, can keep this manageable.

Simple Summary For Busy CFOs

To wrap this up in a way you can remember. The Great Reallocation is about money moving from humans and big generic platforms into AI agents and very focused tools. Many of the SaaS consoles your teams use are quietly becoming AI driven departments. This shadow workforce can deliver huge productivity gains, but it also hides risk if you do not see it clearly.

Your job is not to become an AI engineer. Your job is to see where labor, software, and capital are shifting, to run honest scenarios, and to steer spend into areas that will still make sense in a market where efficiency is no longer a competitive edge, but the default. Start by mapping your shadow workforce, then target obvious inefficiencies with AI pilots, and adjust your investment rules to expect faster, cheaper delivery.

If you treat this like just another small technology wave, you will probably under react. If you treat it like the financial rearchitecture it really is, you can help your company come out of this period leaner, stronger, and much better aligned with how value will be created in the next decade. And if some of your plans feel slightly rough at first, that is fine, better a few imperfections now than a perfect plan that arrives too late.