I recently spoke with a Fortune 500 CIO about AI in their organization. As it often does now, the conversation turned to generative AI and jobs.
His board was convinced that AI would hollow out entry-level work and shrink his engineering organization. At the same time, his teams were quietly using AI to ship more software, close more deals, and resolve more customer tickets.
“AI will kill all jobs” and “AI will eviscerate entry-level positions” are common narratives I hear repeatedly. My view aligns more with Goldman Sachs CEO David Solomon, who, at Cisco’s AI Summit, stated that AI, like all other big tech transitions, will do three things — eliminate some jobs, increase productivity, and create far more jobs, resulting in a net positive.
This is consistent with data from the World Economic Forum, which also predicts a net increase in jobs. However, this has been hard to reconcile with the data. A new study by Ramp and Revelio Labs, provided to TechRepublic and other outlets, debunks some of the ongoing fear.
By linking anonymized corporate spending on AI tools to detailed workforce records for 21,559 US firms, the authors provide one of the first large-scale, firm-level views of how jobs change when companies adopt generative AI.
The headline is unambiguous: firms that invest heavily and consistently in AI are hiring more people, especially entry-level talent and workers in AI-exposed functions such as engineering, sales, customer service, and finance.
For IT pros, it’s important to understand the implications of this data.
Inside the study: who they looked at and how
The researchers focus on firms that cross a meaningful AI threshold: at least $100 per month in AI vendor spend for three consecutive months. This filters out one-off experiments and captures sustained, organization-level adoption. It also highlights companies that are successful with AI, which is critical because these early adopters are a leading indicator of where the industry will head.
The report split adopters into two groups. Low-intensity adopters spent only a few dollars on AI per employee per month during the first three months. High-intensity adopters — roughly an order of magnitude more AI dollars per employee during the same early period.
That “AI spend per employee” metric aligns with deeper AI integration, such as coding agents, APIs, and inference services, rather than merely handing out chat licenses. Because adopters are already larger, more technical, and faster-growing than non-adopters, the study doesn’t simply compare “AI firms” to “never-adopters.” Instead, it compares earlier adopters to later adopters within the same intensity band and sector, with the latter not yet having adopted.
That yields a cleaner read on how AI changes trajectories among firms that all eventually head down the AI path.
Noteworthy numbers
Three findings stand out.
- High-intensity AI adopters increase headcount by about 10% in the first two years.
Firms in the top AI-intensity band increase total employment by roughly 10.2% over the first 24 months after adoption, compared with otherwise similar firms that had not yet adopted. Low-intensity adopters show no statistically detectable change in total headcount. - Entry-level jobs are growing even faster.
For high-intensity adopters, entry-level headcount is up about 12%. That directly contradicts the idea that generative AI is primarily a substitute for junior roles. The firms leaning hardest into AI are pairing junior staff with AI tools to accelerate their work, not using AI as an excuse to remove them. - Gains are broad across AI-exposed functions.
Among high-intensity adopters, employment rises across engineering, sales, customer service, finance, administrative roles, and scientific positions. Engineering teams expand by more than 7%; sales and customer service by mid- to high single digits; and finance and administrative roles by similar margins. This is what augmentation looks like at scale: AI helps people do more, and the organization responds by hiring more people to capitalize on that leverage.
Importantly, these gains compound over time. In the month of adoption, the headcount difference is negligible. Six months in, it starts to matter. By twelve to eighteen months, the gap becomes much larger. That matches what many of you are experiencing: year one is about experimentation and integration; the payoff shows up after you institutionalize AI-driven ways of working.
By contrast, companies that only dabble in AI with small, light-touch spending don’t show broad employment gains or losses. If your AI program stays in “pilot mode,” you should not expect it to change the shape of the business.
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What this means for IT leaders
From an analyst’s perspective, based on dozens of executive conversations over the past year, here’s how I’d translate the findings into action.
1. Reframe AI as a growth and capability play, not a layoff tool
The strongest firm-level evidence we have now shows that intensive AI adoption is linked to more jobs and faster growth. Leading with “AI will let us cut headcount” is both misaligned with the data and damaging to trust.
Instead, position AI around:
- Revenue growth and product acceleration.
- Better customer experience and responsiveness.
- Efficiency that lets teams take on more strategic work.
Restructuring decisions will still occur, but blaming AI for cuts when the data points the other way poses a reputational risk.
2. Design for sustained, high-intensity adoption, not endless pilots
The threshold effect in the study is clear: until AI is embedded in workflows, it doesn’t show up in employment or, by implication, productivity. That means your roadmap must move beyond “try a few tools” to “standardize and scale.”
Practically:
- Budget for multiyear AI integration, not just proof-of-concept line items.
- Build reference architectures that show how AI integrates with development, support, finance, and sales workflows.
- Assume AI services will become part of your operational backbone and treat platform decisions accordingly. If you stay in the low-intensity band, you shouldn’t expect the kind of business impact this study observes.
3. Invest in people and process alongside technology
The compounding nature of the effect suggests that value arrives after organizations adapt. The firms that benefit aren’t just buying models; they’re rewiring how work is done.
In your organization:
- Stand up an AI steering group that includes business, IT, security, and HR.
- Give junior staff permission and training to use AI as a co-pilot.
- Measure AI by business outcomes: resolution rates, cycle times, revenue, satisfaction.
This is where IT shifts from being a tool provider to being a design partner for AI-first workflows.
4. Start where the evidence, and your peers, are strongest
If you’re looking for quick wins that align with the study and with what I hear from CIOs:
- In engineering, deploy coding assistants, test-generation tools, and documentation co-pilots.
- In customer service, apply AI to triage, recommend responses, and retrieve knowledge.
- In sales and marketing, use AI for proposal drafting, targeting, and pipeline insights.
- In finance and administration, automate reconciliation, reporting, and document processing. These are the areas where high-intensity adopters are already creating jobs and where you can demonstrate concrete improvements to your board early.
5. Make AI usage part of your talent brand
The study ends with a simple recommendation I agree with: if you’re early in your career and choosing between similar firms, you should pick the one that uses AI. This is akin to joining a company that embraced the Internet in 1995. AI will eventually be embedded in the fabric of everything we do. Don’t hold your career back with a company that isn’t yet on board.
For IT leaders, this presents a recruiting opportunity.
- Position your AI strategy as creating new opportunities and making teams more effective.
- Make it clear that engineers and junior staff are central to your AI plans, not collateral damage.
- Back that up with training, tooling, and visible success stories.
In a market saturated with “AI will kill jobs” headlines, being able to tell candidates, “Here’s large-scale evidence that firms like ours hire more when they adopt AI,” is a powerful differentiator.
Final thoughts
Amid all the noise around AI, this study gives IT leaders something they rarely get in this debate: high-quality, firm-level data linking AI spending patterns to actual employment outcomes.
The lesson is not that AI is harmless or that displacement will never occur, as some jobs will change and some will disappear, but that the organizations embracing AI most intensively today are using it to grow, hire, and expand their capabilities.
As you build your AI roadmap, the real risk is not that you will deploy a model and immediately trigger mass layoffs; it’s that you will remain in low-intensity pilot mode while competitors integrate AI deeply and compound gains over time. The firms in this study are early indicators of where the industry is heading: AI as a core productivity layer that enables more people to do higher-value work, not a switch executives flip to empty the building.
The challenge for IT pros is to turn that potential into reality in their organizations — with the right architecture, governance, and investment in skills — while being honest about where AI will change roles and how they’ll support people through that transition.
If you can pair this data with a clear vision for AI-augmented work, you’ll be better positioned to reassure boards, regulators, and employees that your AI strategy aims to build the next generation of jobs, not eliminate them.
Also read: Microsoft layoffs could hit thousands as AI spending rises, with sales, consulting, and Xbox among the areas expected to be affected.
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