BLS says AI will reshape, not erase, tech jobs

The real BLS takeaway for monday.com teams

AI is reshaping tasks faster than it is erasing whole occupations, and that distinction matters inside a company like monday.com. The Bureau of Labor Statistics says its AI case studies for the 2023 to 2033 projections cycle focus on computer, legal, business and financial, and architecture and engineering jobs, with AI expected to hit the tasks that generative tools can most easily replicate in their current form.

The bigger labor-market picture is not panic, either. BLS projects total U.S. employment will rise 4.0% and add 6.7 million jobs from 2023 to 2033, climbing from 167.8 million to 174.6 million. Its message is blunt: technological change usually works through gradual restructuring, not instant mass displacement, and its projections framework is built to reflect historical patterns rather than sudden technological leaps.

That is the right lens for monday.com employees. The question is not whether AI arrives in the product stack, but which work gets absorbed by it and which work becomes more valuable because of it.

What BLS says AI will absorb first

BLS is not describing a blanket replacement story. It is describing a task-level shift, where AI takes on repeatable work inside jobs that still need human oversight, context, and accountability. The agency also notes that ChatGPT, released in November 2022, made millions of Americans directly aware of what large language models can do, which accelerated the way workers and employers started thinking about AI.

For software developers, the applications are concrete. BLS says AI can help develop, test, and document code, improve data quality, and build user stories that explain feature value. That is a useful reality check for engineers at monday.com: the first wave of AI does not eliminate the need for strong developers, it changes where their time is spent.

A practical way to read the BLS analysis is this:

• If the work is repetitive, text-heavy, and rule-driven, AI can often handle a first pass.
• If the work depends on judgment, tradeoffs, trust, or coordination, human value rises.
• If the work connects product output to real customer outcomes, the human side becomes harder to replace, not easier.

That is why AI adoption inside software teams should be measured less by headcount fear and more by which tasks are being redesigned.

What that means for engineers, product managers, and sales

For engineers, the shift is toward architecture, quality, tooling, and judgment. If AI can draft code, test flows, or clean up data faster, the highest-value engineer is the one who can decide what should be automated, what should be reviewed, and what should never be left to a model alone. In a work-OS company, that also means understanding the product layer well enough to turn AI into something reliable inside real workflows.

Product managers need a similar reset. Discovery does not disappear, but it becomes more important to know which customer pain points are best solved by AI, which require a workflow change, and which still need a human-in-the-loop design. Validation becomes sharper too, because AI features can look impressive while failing on edge cases, compliance, or customer trust.

Sales teams face a different but equally immediate shift. Customers are increasingly asking whether a platform helps them do more with fewer manual steps, and that makes augmentation the real value proposition. The conversation is less about replacing people and more about compressing the time between work intake, decision, and output.

For monday.com, that matters because the company sells workflow transformation, not just software seats. If AI makes routine coordination cheaper, the sales story has to show how the platform preserves control while removing drag.

How monday.com is positioning its AI bet

monday.com has already put a clear frame around its AI direction. On February 10, 2025, it said its AI Vision would center on three pillars: AI Blocks, Product Power-ups, and the Digital Workforce. The company described AI Blocks as modular, customizable actions, including “Categorize” and “Extract,” meant to sit inside the workflows customers already use.

That approach says a lot about where monday.com sees value. Instead of asking non-technical users to become AI experts, the company wants them to become builders of AI inside the product. It has also said the goal is to help SMBs, mid-market companies, enterprise customers, and Fortune 500 clients scale without increasing resources.

The use cases it pointed to are exactly the kind of operational friction that AI can remove first:

• Resource management
• Predictive risk management
• CRM data automation
• Real-time service ticket resolution

Those are not abstract AI slogans. They are the kinds of daily workflow problems that create visible bottlenecks when teams are understaffed or moving too slowly. If monday.com gets these right, AI becomes a layer that shortens the distance between intake and action.

Why the company’s growth makes the AI shift more concrete

The timing of monday.com’s AI push matters because the company is now operating at a scale where workflow efficiency is no longer a nice-to-have. It said it reached $1 billion in annual recurring revenue on August 26, 2024, then reported $268.0 million in fourth-quarter 2024 revenue and $972.0 million in full-year 2024 revenue. It later reported full-year 2025 revenue of $1.232 billion.

That growth gives the AI strategy more weight. monday.com also said larger customers are increasingly standardizing on the platform for mission-critical workflows, which means AI features are no longer about novelty. They are about helping bigger accounts run core processes with fewer handoffs, less manual entry, and better decision support.

For workers inside the company, that changes the internal bar. Product teams are not just shipping features, they are deciding which jobs AI should absorb in a customer workflow and which human roles should become more strategic because of that automation.

How managers should redesign work now

The BLS view is useful because it points managers toward a redesign problem, not a layoff panic. If AI is strongest at replicable tasks, then teams should reorganize around where humans remain essential.

Start with role mapping. Break each job into three buckets: tasks AI can handle, tasks AI can draft or support, and tasks that still require human judgment. That exercise usually reveals that the biggest gains come from removing low-value prep work, not from deleting entire roles.

Then redesign training. Engineers need fluency in AI-assisted coding, testing, and documentation, but they also need stronger review habits and a sharper sense of data quality. Product teams need to learn how to validate AI outputs against customer intent, not just internal specs. Sales teams need training that turns AI into a customer story about fewer manual steps and faster outcomes.

Finally, redesign workflows. The best test is simple: if AI can create a faster first draft, where does the human checkpoint belong? If the answer is nowhere, the workflow is too risky. If the answer is everywhere, the workflow is too slow.

That is the part BLS gets right and monday.com has already bet on. AI will not erase the need for skilled people in software, but it will shift the premium toward those who can combine domain knowledge, product judgment, and AI fluency. For a company built around work management, that is not a threat. It is the next operating model.