The Human Side of the AI Stack

Based on three waves of benchmarking data, AI doesn’t appear to be eliminating jobs at scale yet. Broad corporate use of AI does show signs of restructuring the types of jobs that exist and what those jobs are expected to produce. While entry-level technical work is showing some signs of contracting, this trend appears to be more than offset by increased hiring for AI-specific roles and investment in AI upskilling at more senior levels. Meanwhile, everyone across surveyed organizations is expected to do more. In our view, a new work paradigm is being created as AI integrates more deeply into the B2B world.

No matter the function or company size they are a part of, a majority of decision makers agree AI has increased the amount of work expected of them. Technical decision makers are 20+ points more likely than GTM decision makers to agree that AI has increased total work volume. Throughout our Wave 3 analysis, we have observed repeated suggestions that productivity gains are now running into quality, cost, and ROI output realities — how these outcomes shape human-impact benchmarks will be an important dimension to watch in Waves 4 and beyond.

Talent restructuring is happening on two tracks simultaneously. 52% of decision makers are upskilling existing staff and 48% are hiring AI-specific talent — both approaches add AI capability. But 29% of technical decision makers are also reducing entry-level technical roles — nearly 1.8× the 16% GTM rate, with enterprise moving faster than growth-stage (34% vs 24%). It’s worth noting that we do not see the entry-level reduction as a layoff story. It is, in our view, a slower-replacement story — probably linked to fewer junior slots in new headcount plans, attrition not backfilled, junior-to-senior ratios reshaped. We are likely seeing early signs of labor displacement due to AI adoption that will take years to fully form.

The prevailing narrative about AI talent focuses on scarcity — not enough qualified engineers to meet demand. Wave 3 does not support that as the primary constraint. 46% of technical decision makers cite budget as a top-2 barrier to acquiring or upskilling AI talent — ahead of limited pipeline (41%), time for upskilling (38%), and competition from Big Tech (26%). For GTM, time is the binding constraint: 55% cite a lack of time for upskilling. Both are budget-related — time and money — while pipeline ranks lower.

Two perception statements have been tracked across all three waves. “AI will revolutionize my function” climbed from 70% to 73% to 80% — a consistent rise from an already high base. “There is too much hype around AI” moved 56% → 47% → 57%, cycling with the news rather than shifting materially. Both are majority-held in Wave 3. That looks contradictory, but both can be true: the hype of revolution may be overblown, yet respondents have little doubt their function will be transformed.

The jobs creation-versus-displacement debate produces one of the sharpest segment splits in Wave 3. Enterprise Tech decision makers agree AI will create more jobs than it displaces at 40% — the only segment with net-positive agreement (+7 net). Every other segment is net-negative: Growth-stage Tech at −18, Enterprise GTM at −20, Growth-stage GTM at −25. Enterprise Tech leaders see AI creating new specialized roles that offset entry-level reductions; every other segment sees the displacement side more directly.