AI is repricing leadership – and organizations are cutting what appreciates.
That paradox is a split into two asset classes: the execution AI absorbs is repricing down, while the capacities it cannot hold are repricing up. What appreciates is the four capacities people demand from their leaders – trust, compassion, stability, and hope. They compound through deliberate investment, and the lever is organizational – talent sets the multiplier, allocation decides the return. Most organizations are funding the wrong side of this repricing – their cuts to the appreciating side are financing the depreciating one. So which side is your budget on? The correction pays categorically where it happens.
Here is the map of the repricing and the case for holding what appreciates.
The Repricing Runs in Two Directions at Once
The falling price is the half every budget already sees. Execution, the repeatable, codifiable work that once filled role descriptions, is what AI absorbs first, and what a market can absorb it stops paying a premium for. Gallup's workforce research tracks the split running through organizations: rising AI adoption is restructuring work by tier, concentrating the automatable in some roles and the irreducibly human in others [1].
The price for human qualities is rising every quarter. AI can produce a warm phrase and a confident forecast while holding none of the capacity behind them. This is why the labor market now prices the two sides separately. PwC's global AI Jobs Barometer reads the market as two distinct paths, with the one rewarding human skills pulling away from the one AI absorbs [2].
One market, two prices – and every budget line is already a position on one of them.
So, what is actually placed on the appreciating path?
Gallup's research across 52 countries names the outcomes of the appreciating leadership capacities: trust, compassion, stability, and hope – four needs of teams that hold across every culture studied [3]. The same research program clearly shows the upside: hope is the attribute people name most often when they describe leaders who move them, at 56 percent of the attributes that define influential leadership [3]. These are load-bearing requirements of any team that has to perform – and the capacities that meet them are ones an algorithm can imitate in phrasing while holding none of the substance. AI can fake the language of care well; it cannot really replace emotional intelligence, curiosity, or judgment.
The capacities behind the four needs – integrity, vulnerability, conviction, vision – are held by people for structural reasons, and that is what makes the asset durable. A 23-year longitudinal study found leadership talents essentially stable across adult careers, which means the appreciating side of the split is a fixed inventory rather than a renewable supply. The demand side is equally fixed: people in every market studied keep demanding the same four things from leadership.
The appreciating side of the split is scarce by construction – the demand is universal, and the supply cannot be generated by anything that is doing the repricing.
That is the existence proof for the asset. The next question is what governs its value – and that question has an answer precise enough to put in a formula.
Talent Sets the Multiplier, Allocation Decides the Return
Naming the asset is only useful once you know what moves its value, and the value of these capacities follows the oldest logic in capital allocation. Talent × Investment = Strength reads as a formula because it is one: talent is the multiplier, investment is the variable an organization actually controls, and strength is the return. Gallup's 25 million assessments establish the multiplier side – talent is distributed in every population, stable over careers, and worthless until allocated.
The controlled variable is where most organizations misread the instrument. Investment in this asset means structured practice with named strengths and deliberate allocation of work toward them – assessment alone produces only modest gains, which is why Gallup ties the returns to strengths-based development rather than assessment alone [4]. The formula's unforgiving property is that talent without investment still depreciates: the multiplier sits idle while the demand for what it could produce goes unmet.
In this formula, investment is the only term leadership controls – which makes allocation the whole job.
The multiplier's stability is what makes the variable so consequential. Because the talent base does not erode between decisions, every allocation choice compounds from the same standing inventory – and the returns arrive in a form that surprises people expecting gradual improvement.
Categorical Returns: The Asset Either Compounds or Depreciates
The formula produces returns in a shape conventional programs never deliver – categorical, not incremental. Where leaders focus on what people do well, disengagement sits at one percent; where they focus on weaknesses, it reaches 40 percent, with indifference in between at 22 percent. Those are the poles of a switch, and the mechanism is the compounding the formula predicts: investment in the strength and it builds on itself, so the return arrives as a different category of workforce rather than a better version of the same one.
The categorical pattern repeats wherever the investment is real. Employees who feel seen for their strengths are 15 percent less likely to quit [5]. Across 11,441 teams, performance amplifies when strengths are deliberately composed into the team rather than left to chance [6]. And the compounding runs at the level of the whole culture: organizations that embed strengths across how they hire, manage, and develop report 23 percent higher engagement, 72 percent lower turnover, 19 percent higher sales, and 29 percent higher profitability [4].
The asset obeys binary logic – invested, it compounds into a different category of organization; uninvested, it depreciates – and there is no neutral position to hold.
Binary logic makes the stakes legible: with no neutral position available, every organization is already long or short on this asset. Which raises the uncomfortable question of what the aggregate position looks like – and the answer is stranger than neglect.
The Default Pattern Is Disinvestment
With binary returns this steep, the rational move is to hold the appreciating side and invest in strengths, but the organizational record shows the opposite as the default for years.
The pattern starts in a habit that looks harmless: attention flows to what is broken, so development budgets chase the lowest grades and the weakest scores while the strongest capacities are left to run on their own. Gallup's global workplace data shows where that habit lands: manager engagement sits at 22 percent globally [7], and only 29 percent of employees believe their leader exhibits human leadership at all [8].
The AI investment shift turned a slow habit into an active position. Organizations cut the human leadership side – the trust, stability, and compassion carriers – to fund the technology, and 55 percent of businesses that made employees redundant in AI rollouts now admit those were the wrong decisions [9]. Some of the organizations that laid people off citing AI are already reversing the decisions as the human cost surfaces [10]. The repricing ran in both directions while the budgets ran in one.
The paradox: the asset with the steepest categorical returns is the asset organizations systematically sold to fund its own competition.
This is where the mispricing stops being an accounting curiosity and becomes a live risk. The disinvestment was costly on its own terms – and the technology it funded is now repricing the asset upward, which changes what the neglect costs.
The Spread Is Widening Now
The disinvestment pattern was expensive before AI – and AI is what turns expensive into compounding. Every advance in automation lifts the price of what remains on the human side of the split: the labor market already pays a premium for human skills in the most AI-exposed roles, and the organizations gaining most from AI are growing headcount rather than shrinking it [2]. The appreciating asset is being repriced upward by the very dynamic that budgets treat as a reason to cut it.
The spread widens through a second channel: the demand side is intensifying at the same time. Workers around the world report deep insecurity about the future of their roles – only 22 percent strongly agree their job is safe from AI [11] – and insecurity is precisely the condition that makes stability and hope from leadership most valuable and most scarce. Meanwhile the side that supplies those capacities is thinnest where the pressure is highest: cutting the managers who carry trust and stability to fund AI removes exactly the leadership development, institutional knowledge, and mentorship the moment demands [12]. The correction, where it happens, pays on both sides at once.
The gap between what people need and what organizations fund is widening on its own – closing it is the allocation decision of this era.
The map is now complete: an asset that exists, a formula that governs it, returns that arrive categorically, a default pattern that runs against it, and a spread that widens without intervention.
The repricing answers the budget question before leadership chooses to answer it. The four capacities people demand – trust, compassion, stability, hope – sit on the side of the split that appreciates; the formula behind them makes allocation the lever; the returns arrive categorically; and the default pattern has been funding the other side. Holding what appreciates is the position that pays when everything else is automated – and the leaders who take it early will hold an advantage the repricing itself protects.
Sources
- Andy Kemp (Gallup) — Rising AI Adoption Spurs Workforce Changes
- PwC — AI reshapes global labour market into two distinct paths, rewarding human skills: PwC 2026 Global AI Jobs Barometer
- Gallup (via LinkedIn) — Leadership Needs: Hope, Trust, Compassion, Stability in a Changing World
- Gallup — How to Create a Strengths-Based Company Culture
- Allison Schoonmaker (Crossroads Professional Coaching) — Leading with What's Strong, Not What's Wrong
- Gallup (Jim Asplund and Adam Hickman) — What We Learned From 25 Million CliftonStrengths Assessments
- Gallup — State of the Global Workplace 2026 — Global Data Summary
- Laura Antos (The CARA Group) — Leadership in the AI Era: Essential Skills and Strategies
- Orgvue — 55% of businesses admit wrong decisions in making employees redundant when bringing AI into the workforce
- Justina Lee (CNBC) — Employers who laid off workers citing AI are already starting to regret it
- Nick Lichtenberg — Workers around the world are scared. A massive new survey shows just how much
- Kara Dennison — Why Companies Cutting Middle Managers to Fund AI Is a Mistake
Please note: 51even is an AI-first organization. We embrace AI at every step of our value creation and build our processes with a deep integration of human-AI capability. Humans always have the last decision. But this text was heavily built with AI.