Why Field Service Businesses Are the Safe Zone in the AI Displacement Economy
On February 23, 2026, two things happened that will be cited in business school case studies for the next decade.
First, a Substack post from Citrini Research - a hypothetical scenario set in 2028 called "The Global Intelligence Crisis" - went viral enough to pull the Dow down 800+ points. The software ETF IGV hit a 52-week low. ServiceNow, Salesforce, MongoDB all dropped 4–6%. Michael Burry shared it. Bloomberg, Fortune, and the WSJ all covered it by noon.
Then, in the same news cycle, Anthropic launched Claude Code for COBOL modernization. IBM dropped 10% - roughly $20 billion in market cap - in a single session. Accenture and Cognizant followed. The tool automates what consulting firms charge millions of dollars and multiple years to do: mapping legacy code dependencies, documenting workflows, identifying modernization risks across hundreds of thousands of lines of 60-year-old code.
Same day. Same thesis. One was a hypothetical scenario. The other was a product launch. Together, they constituted the clearest public proof yet of what the AI economy actually looks like - and who wins inside it.
If you run an HVAC company, a plumbing outfit, a propane fleet, or any field service operation, here is what you need to understand: you are on the right side of this shift. Not accidentally. Structurally.
The Citrini Thesis, Explained Simply
The Citrini report is built on one core idea: modern economic value was priced assuming that human intelligence is scarce and expensive. Decades of business models were built on that scarcity - consulting firms that charge $400/hour because expertise is rare, SaaS companies that charge per seat because skilled operators are limited, staffing agencies that take 20% because matching talent to jobs requires human judgment.
What happens when AI removes the scarcity? Every business model built on friction - on the fact that doing something complex requires expensive human time - collapses. Not slowly. Rapidly, because AI adoption compounds.
The COBOL announcement was the thesis manifesting in real time. IBM's entire enterprise consulting business exists because legacy code modernization is hard, slow, and requires rare expertise. Anthropic shipped a tool that does the hard part automatically. IBM drops 10% the same day the market is already panicking about exactly this scenario. The timing was not coincidental - it was the market recognizing that the scenario stopped being hypothetical.
Who Is Actually in the Danger Zone
The Citrini scenario is not about all businesses. It is about a specific category: companies whose value is built on being the necessary intermediary between a problem and a solution - and whose intermediation is only necessary because the solution is complex or requires rare expertise.
The dividing line is not "does this business use intelligence." It is "can AI replace the scarce input that creates the business's value." For IBM, the scarce input was COBOL expertise. Anthropic just made that abundant. For your HVAC company, the scarce input is a licensed technician who can physically replace a condenser at 10pm on a Friday. No language model is doing that.
Why Field Services Is Different: The Labor Shortage Angle
This is the piece the Citrini scenario does not fully account for: field service businesses were not using AI to replace workers. They were already failing to hire enough of them.
The HVAC industry has a median age of 43. The plumbing industry turns away roughly 25% of demand because there are not enough licensed journeymen to fill the schedule. Propane operators are running paper-based dispatch not because they chose to, but because the office staff they need to run digital systems either does not exist in their labor market or costs more than they can justify.
The automation we build for these businesses does not displace workers. It fills roles that were already empty. It handles the 2am emergency call that was going to voicemail because no one was awake. It manages the dispatch board that the owner was manually updating at 6am before anyone else got in. It captures the after-hours lead that was going to a competitor not by choice, but because there was no human available to answer.
AI replaces 10 analysts who were each doing $120K of work. Company saves $1.2M, 10 people lose jobs. Citrini's displacement spiral begins.
AI handles after-hours calls, dispatch coordination, and customer communication that no one was doing. Company captures $60K in previously lost revenue. No one is displaced.
The Consulting Model Is Dead. The Implementation Model Wins.
The COBOL announcement illustrates something beyond just IBM's bad day. It exposes the structural flaw in the consulting model: charging for time and expertise when AI can compress both.
IBM would charge a regional bank $3–5M and 2–3 years to modernize a legacy COBOL system. That pricing existed because the work was genuinely hard, required rare expertise, and had serious risk. Anthropic's tool does not eliminate all of that - but it compresses the timeline and reduces the expertise barrier enough that the old pricing model stops making sense. The bank no longer needs to pay for 2 years of consulting to get the outcome they wanted.
The same dynamic is playing out in small business automation. The old model was: hire a development agency, spend $80–150K on a custom build over 6–12 months, then pay an ongoing retainer to maintain it. That model existed because building workflow automation from scratch was genuinely hard. The expertise was scarce.
We charge a flat rate. We deliver in 2–3 weeks. We guarantee ROI in 60 days or we keep working for free. That pricing is possible because AI tooling compressed what used to take months into weeks, and what used to require deep custom engineering into configuration and integration work. The scarcity we removed was build time, not human expertise.
| Dimension | Old Consulting Model | Implementation Model (ResultantAI) |
|---|---|---|
| Price | $80K–$500K+ | $2,997–$4,997 flat rate |
| Timeline | 6–18 months | 2–3 weeks |
| Risk model | Client absorbs overruns | 60-day ROI guarantee |
| Expertise source | Rare human specialists | AI tooling + implementation experience |
| Ongoing cost | Retainer + maintenance team | Optional $497/mo support |
| Outcome certainty | Defined in SOW (often missed) | Fixed scope, measurable ROI |
February 23 proved that the market understands this transition. IBM's consulting model is vulnerable for the same reason the Citrini scenario targets SaaS middlemen: both are priced on scarcity that AI is making abundant. The implementation model - fixed price, fast delivery, guaranteed outcome - is what replaces it.
What This Looks Like in Practice: Three Case Studies
We have been building in this model long enough to have real numbers. Here is what the safe zone looks like when it is executed well.
AI voice qualification system handles the full inbound lead flow. Reps see pre-scored leads in HubSpot each morning. The operation scaled 10x without adding headcount. No employees displaced - roles that could not be filled due to cost constraints are now automated.
Manual client reporting process took 4 hours per client per month. AI automation collapsed it to 3 minutes. Same output quality, 98% time reduction. The analyst team now focuses on strategy rather than formatting. Zero displacement - the team grew.
After-hours emergency calls were going straight to voicemail. An AI capture system now handles them 24/7, qualifies urgency, and routes to the on-call tech. $5,200/month in previously missed revenue captured in the first 30 days. No one was replaced - the "role" of after-hours answering service never existed before.
None of these are displacement stories. They are expansion stories - businesses capturing value they were previously leaving on the table because the right system did not exist or was too expensive to build. The Citrini scenario is about an economy losing jobs. The field service AI story is about businesses finally being able to do the things they always wanted to do but couldn't staff for.
The Urgency Window: Why the Next 12 Months Matter More Than Any Year Before
The Citrini report is already reshaping how buyers think about AI adoption. The companies that were still in "wait and see" mode are watching the market price in the risk of moving too slowly. The businesses that move first in their local market - the first HVAC company in their city with 24/7 AI answering, the first propane operator with real-time digital dispatch, the first plumbing company with AI-captured discovered work sign-offs - are not building features. They are building moats.
Local service businesses compete on trust and reputation, and trust is built through availability, responsiveness, and consistent follow-through. A competitor with 24/7 AI capture and same-day follow-up automation has a structural advantage over one that still misses after-hours calls. That gap compounds. The customer who calls at 2am and gets a professional response does not shop around again.
~5% of field service businesses have real AI automation in place. First-mover advantage is real and compounding.
Awareness spreads, competitors start implementing. The gap between early and late adopters becomes visible.
24/7 capture and digital dispatch become expected, not differentiating. The window to gain advantage closes.
What to Actually Do With This Information
The Citrini report is a macro narrative. The question for any field service operator reading this is not "is the report right?" - it is "what does this mean for my operation in the next 90 days?"
The answer is straightforward: the businesses that win in an AI-displaced economy are not the ones that adopt the most AI. They are the ones that use AI to do the specific things their human team either cannot do (2am emergency calls), will not do (manual dispatch board updates at 6am), or should not be spending time on (ETA call fielding, appointment reminders, invoice follow-up).
Start with the revenue leak. Every field service business has three: after-hours calls that go unanswered, discovered work that gets done verbally and never billed, and follow-up sequences that fall apart because the owner runs out of bandwidth. An afternoon on a call with us will quantify exactly what your version of those numbers is. We have done this for plumbers, HVAC operators, propane fleets, roofers, and landscapers across the country.
The Citrini scenario is scary for a lot of businesses. It should not be scary for yours. You are in the lane where AI makes you stronger, not in the lane where AI replaces you. The only decision left is whether to move before your local competitor does.
How many calls went to voicemail last month between 6pm and 8am? What is your average job value?
Of the extra scope your techs find on-site, what percentage makes it to the final invoice? Verbal approvals are the gap.
Of the estimates you send that go unanswered, how many do you follow up with 3+ times? What is your close rate on the ones you do?
We build the systems that close all three - in 2–3 weeks, at a flat rate, with a 60-day ROI guarantee. The macro environment just handed field service businesses the clearest strategic argument for acting now that they have ever had. The only thing left to decide is whether to use it.
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Book a Free ROI Call →Chris builds revenue systems for B2B service businesses — voice AI, workflow automation, and operational systems. He's shipped systems that generated $382K in pipeline for clients in the first 12 months.