The Anatomy of an AI Agent - Part 5
Industrial Revolution Style Worker Displacement is Real
Picture this: you’re an IT or business leader staring down rising costs, shrinking margins, and clients who want more for less. You’ve got a team of junior analysts burning the midnight oil, but the clock’s ticking faster than they can type.
Imagine slashing costs by 99%, doubling output, and adding $3.6 million a year to your revenue with smart AI use. It might sound ambitious, but it’s within reach with the right approach.
Stick with me, because by the end of this article, you’ll see exactly how to turn your operation into a lean, mean, profit-generating machine—and why waiting could mean your competitors get there first.
In Part 4, we broke down the four pillars of AI agent economics—Sovereignty, Consumption, Subscriptions, and Labour Costs. I hinted at something big: this tech could shake up white-collar jobs in a way we haven’t seen since the Industrial Revolution turned manual labour upside down.
Today, we’re diving into a fictional case study that’s less hypothetical than you might think. I’ll put my cost model to work and we’ll discover if AI agents really might be driving an Industrial Revolution moment for knowledge workers, or just creating more noise?
Let’s get to it.
Parallels to the Industrial Revolution
Being a British northerner, I can’t help but see echoes of the Industrial Revolution in all this. Back in the late 18th century, machines like the spinning jenny and steam engine changed everything. Textile workers and artisans watched their livelihoods vanish as factories took over. It wasn’t pretty—Luddites smashed machines in protest—but society eventually adapted. New roles emerged, like engineers and managers, and living standards rose over time.
Fast-forward to today, and most GenAI systems feel a bit like those machines. An “augmented human” using this new technology can get a lot more done than those that don’t. AI automates a lot of what we do—research, writing, format outputs—tasks that humans could spend hours on.
Just like the factory workers of the past, roles in direct competition with modern machines could shrink or vanish, pushing grads and white-collar professionals to upskill or pivot.
The parallel isn’t perfect—AI isn’t replacing everything that goes on in an office, just the repetitive bits—but the pattern feels familiar.
To see how this plays out in today’s world, let’s look at what a cutting-edge AI agent can do—and what it means for businesses.
Why This Matters: A Quick Look at My Agent’s Capabilities
Before we dig into the numbers, let me set the stage with what my agent can do.
Built with a mix of ReAct and Chain-of-Thought cognition patterns (and soon a graph data structure for even better reasoning), I can use it as co-pilot+. I give it an objective, it can scrape the web, whip up a 33-page Word doc with flowcharts, and send it off via email or Slack—all for about £0.086 per document in token costs.
The output’s solid—good enough for initial drafts or personal use—and once I add that graph upgrade, it’ll need even less human oversight.
When we use the lens of the marginal cost of producing something (like a document) as the focal point, it’s easy to see where the discussion about AI replacing certain types of human roles comes from. For those entry-level folks, it’s a real threat. If firms automate their work, where do they cut their teeth? The traditional ladder might be starting to crumble.
Crunching the Numbers: Can AI Outperform Humans?
What is Lifecycle Costing?
To make sense of all this, I’m using a financial tool called Lifecycle Costing—or LCC for short. Don’t worry, I won’t bore you with the details (and I’ve ditched the bullet points to keep things snappy).
Essentially, LCC looks at the full cost of something over its entire life—not just the upfront price, but everything it takes to run, maintain, and eventually retire it. For an AI agent, that means the cost to build it, the monthly cloud bills, the tweaks it needs to stay sharp, and even the price of switching it off one day. It’s a way to see the big picture so you’re not blindsided by hidden expenses.
Why does this matter?
Because it helps us answer the real question: is an AI agent actually cheaper than human labour in the long run?
Without a proper model, you’re just guessing—and guessing gets expensive fast.
With this framework in mind, let’s apply it to a fictional yet realistic scenario.
The Case Study: Replacing 10 Analysts with One AI Agent
To make this a bit more relatable I’m going to tackle this as a case study, albeit a fictional one.
I’ll focus on junior consultants here, but this could apply to many of us—myself included—as AI reshapes the job landscape. It’s a common example worth exploring.
Like many consulting firms, StratEdge Consulting relies on junior research analysts—fresh grads who spend their days scanning industry reports, extracting insights, compiling briefings for senior consultants, preparing slide decks, and summarizing competitive findings. The job is labour-intensive and slow, with each junior costing $70,000 annually, $42,000 (60% of their workload) is spent on automatable tasks.
To address this, one partner launches an experiment, deploying a custom-built AI agent. This autonomous assistant scrapes and analyzes industry data, writes high-quality content, generates formatted Word documents, and emails deliverables—all with the ability to seek help when needed, accessible via a Slack channel where colleagues assign objectives.
The Experiment: Replacing 10 Analysts with a Machine
StratEdge deploys a custom-built AI agent—an autonomous research assistant designed to:
✅ Scrape and analyse industry data from internal and external sources
✅ Write high quality content from the context it’s sourced
✅ Generate fully formatted Word documents
✅ Email the final deliverable autonomously to the person who requested it
It can even ask for help when it’s stuck or needs clarification.
The agent’s architecture makes it accessible to all human colleagues who join its Slack channel. It can recall and reflect on its previous tasks, it’s guardrailed appropriately to make sure it behaves and has just the right amount of tools available to do the job. A human colleague assigns it an objective, and it sets to work efficiently.
The Impact: What the Numbers Say
When they took this project on, the partners at StratEdge knew they were making a long term change. As a result, their Lifecycle Cost model time horizon was 10 years. Since they’re a management consulting firm, they also needed to show they were “eating their own dog food” as we say, and wanted to attach a Discounted Cash Flow analysis to their financial projections to ensure the board would get behind it.
This resulted in three cost pools being calculated:
Labour Costs
AI Consumption Costs
Cloud Costs
#1 - The Labour Cost Pool
The partners had their off-shore IT contacts on speed dial for the human resources. They had to get some advice from an AI agent expert to scope out the project, but after that, the rest was handled in Manila.
Between the design and build phases, their agent was delivered for $110,000. The team from the Philippines got the work done in just over four months with a group consisting of a project manager, an AI engineer, back end developer, a QA and a part-time DevOps specialist.
It was known that some operational human resource would be needed. They opted to secure a DevOps engineer on a retainer for 25% of their time. At $30,000 a year per full-time resource, the annual cost was $7,500. It would turn out this was very much needed as periodic releases were indeed required to update and refine the agent’s Guardrails over time and to refine and adjust prompt templates to get the best results.
They also allocated $20,000 for two, AI Ops personnel offering 20 hours a week of support. This new role of an AI Ops Specialist was essential for managing, optimising, and quality-checking the AI-driven processes. The role would sit at the intersection of AI automation, consulting, and quality assurance—ensuring that our AI agents deliver high-quality, error-free reports, analyses, and business intelligence.
All of this meant that the baseline Lifecyle Cost model for the 10 year term showed the following numbers:
Design and Build (CapEX) = $110,000 one off.
Support (OpEx) - ($7,500 + $20,000) x 10 years = $275,000
Total Labour = $385,000
#2 - The AI Consumption Cost Pool
With 60% of their time spent on report production (about 8 per month) the production cost was $437 each. The AI consumption costs for a single run of the agent to produce a similar Word document was a “massive” $0.1436.
Purely from the AI perspective, 99% cheaper than a human.
These costs were by far the most important to measure and were prototyped in advance to ensure that StratEdge understood the most variable and cost of production. The prototype used the Templonix framework and produced the trace data below from API, LLM and storage calls during its runtime.
It was now confirmed beyond speculation and hype that the marginal cost of production by the machine was essentially zero. Even if the agent had to revise its work following interaction with the AI Ops team, the cost of production was orders of magnitude lower than what the juniors could achieve.
#3 - The Cloud Cost Pool
The preference for the operational environment for the agent was the Azure cloud, largely due to the OpenAI Enterprise Promise since the agent was built using Typescript and ran the GPT-4o LLM via the OpenAI API. The use of such lieghtweght development technology resulted in very low infrastructure costs, with some of the tools even qualifying for the free tier.
The outcome was a monthly bill of $288.80. $3,465 a year, or $34,656 over the term.
The main advantage for deploying everything onto a cloud environment was the pricing model - since everything is charged by the hour based on consumption, this made it easy to understand how scaling the agent would impact running cost, but also, what the profit margins would be with higher throughput over time.
With these cost pools in hand, let’s turn to the outcomes. The results, visualized in two dashboards, reveal both the immediate savings and the long-term potential of this AI investment.
The Results
The Cost Elimination Model (Dashboard 1)
The first analysis was simple—we cut out the cost of junior analysts based on the 60% of their time being allocated to research and document writing and the firm saves $420,000 in the first year. Using the following basic variables, this made for an impressive return - the experiment would pay for itself in just over two years.
With AI now taking over the research-heavy, document production tasks that junior consultants typically handle, management has several strategic options for reallocating those resources. The decisions will largely depend on firm strategy, demand for human expertise, and cost considerations.
One of the options is to reallocate this new capacity to higher value work, especially where the firm sees juniors as future leaders and want to upskill them. Instead of spending 60% of their time on reports, juniors could be shifted to more client-facing, analytical, or strategic roles like client interaction & relationship building or giving them some experience in translating insights into strategic recommendations and interact directly with clients.
The second option was the one that everyone expects when AI moves onto the human patch - role elimination.
This is the harshest reality—executives are incentivised to maximise ROI. A 91.43% IRR with a payback period of just 2.3 years makes the AI agent investment an absolute no-brainer.
Some firms would shrink junior headcount if AI eliminates the need for manual research. If AI can replace 60% of a junior's workload, a firm might cut the bottom bottom 30-50% of junior consultants. The firm could operate with fewer people and increase profit margins. This is especially likely for firms with low promotion rates or those that see juniors as expendable talent rather than long-term investments.
What makes this more “appealing” to decision makers is the opening up of the new role of AI Ops off-shore. When an agent can do the bulk of the work, all it needs at that point is a chaperone.
While cutting costs is a strong starting point, the real game-changer lies in leveraging AI to scale revenue—a possibility we explore next with Dashboard 2.
The Scale & Revenue Model (Dashboard 2)
This is where things get very interesting. Scaling takes things a lot further.
In the old model, 10 junior consultants produced 80 reports a month at a production cost of $34,960. Each report takes 15 hours at $250 an hour, so clients pay $3,750 per report. That’s $360,000 in revenue per junior annually.
Now, with the AI agent, StratEdge doubles output to 160 reports a month. Consumption and labour costs scale a bit, but the marginal cost per report stays tiny. Clients still pay $3,750 each, so the extra 80 reports add $3.6 million a year to revenue.
That’s not a typo—$3.6 million and over 400% internal rate of return just from letting the agent do what it does best.
This isn’t automation—it’s a whole new way to run a consulting firm, turning it into a scalable, high-margin business.
The Final Verdict
So, what does this all mean for StratEdge—and for your own business? Let’s break it down with the numbers we’ve crunched.
From a financial perspective, the AI agent delivers undeniable value. Replacing 60% of the juniors’ workload saves StratEdge $420,000 in the first year alone. The total cost of the AI system—build, support, and cloud—pays for itself in just over two years, with a 91.43% internal rate of return (IRR). Meanwhile, scaling to 160 reports a month adds $3.6 million a year to revenue. These are the kinds of numbers that make executives sit up and take notice, and for good reason: at the end of the day businesses (non-profits notwithstanding of course) exist to generate profit and deliver shareholder value.
But here’s the reality—how you act on these numbers isn’t a one-size-fits-all decision. For some leaders, that 91.43% IRR might mean automating repetitive tasks and reducing headcount to maximize ROI, especially if junior roles are seen as expendable. Others might choose to keep their team intact, using AI to scale output while redeploying juniors to higher-value work like client relationships or strategic analysis—investing in people as much as profits. And for many, it’ll be a bit of both: leveraging AI to drive efficiency while upskilling staff to stay competitive.
Ultimately, the choice comes down to leadership and culture. It’s a hard truth, but a true one. The data gives you options—how you use it depends on the kind of company you want to be. Either way, one thing’s clear: AI agents are rewriting the rules of what’s possible, and understanding their economic impact is the first step to staying ahead.
Curious About What GenAI Could Do for You?
If this article got you thinking about the economics of AI agents and their real impact, you’re not alone. Many readers are exploring this new frontier but struggle to separate reality from hype.
That’s exactly why I built ProtoNomics™—a risk-free way to validate GenAI feasibility before you commit resources. No hype. No sales pitch. Just data-driven insights to help you make an informed decision.
If you’re interested, I now run a limited number of GenAI Readiness Assessments each month. If you'd like to see what this technology could do for your business, you can Learn More Here
Or, if you're “just here for the tech” the next article in the series is 👇
Next Time on The Anatomy of an AI Agent
Part 6: Guardrails and Bounded Agency
We’ve spent the last few weeks giving AI agents superpowers—researching, reasoning, and writing at scale. And today we closed off the money side of the equation.
But here’s the thing: just because an AI can do something doesn’t mean it should.
Left unchecked, agents can go rogue—spitting out nonsense, hallucinating insights, or worse, bankrupting you thanks to that infinite loop calling the LLM API.
That’s why next week, we’re diving into Guardrails and Bounded Agency, or as I like to call it: Defenses Against the Dark Arts for Agents.
Same time next week. Bring your wand.
Until the next one, Chris.
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This is a very practical write up to think about the economics. Thank you for sharing!