This is the hidden reason your AI investments are failing, according to Brené Brown

BetterUp survey data cited in the article shows managers with high AI usage in high-trust, high-development cultures saw team performance rise 6%, while similarly high AI usage in low-trust, low-development cultures saw performance fall 9%. Leaders who paired AI investment with stronger human engagement delivered 17% better productivity, work quality, and effectiveness than those focused mainly on AI. The piece argues that trust, coaching, and employee development are key to realizing AI productivity gains.

Analysis

The key second-order read-through is that AI ROI is becoming a function of organizational slack, not model quality. Firms with strong culture and management bandwidth will translate automation into throughput gains, while burnout-heavy organizations likely experience a near-term productivity dip as AI adds coordination burden without increasing trust or discretionary effort. That creates a widening gap between “AI adopters” and true “AI beneficiaries,” with the latter disproportionately in companies that already have higher employee engagement and better manager quality.

From a market perspective, this is mildly negative for the broad “AI = instant margin expansion” trade, because it implies that labor savings are not mechanically accruing to P&L this year. The bigger winners are firms selling manager enablement, workflow software, and employee development tools rather than pure model exposure. It also suggests a hidden beneficiary set in consulting, HR tech, and internal communications platforms that help companies operationalize change management, especially over the next 4–8 quarters as AI deployment moves from pilot to scale.

The contrarian point: consensus is likely overestimating how much AI capex alone will lift earnings in the next 12 months. If performance gains require trust and coaching, then the returns curve is slower and more uneven, which argues for underwriting dispersion rather than beta. The main reversal risk is that once a few large firms publicize real productivity wins, management teams may rapidly reallocate time toward human capital, causing a catch-up re-rating in the more operationally disciplined AI beneficiaries.

Tail risk runs the other way too: if burnout worsens or middle management gets cut too aggressively, adoption could stall and produce a 1–2 quarter digestion period where AI spending rises but output does not. That would pressure multiples on names priced for quick monetization and favor cash-generative software with proven retention/engagement uplift.

Key Decisions for Investors

• Long HUBS / long ring-fenced employee-engagement software basket vs short a high-beta AI infrastructure basket over 6-12 months: expressed as long DOCU or NOW calls financed by short-term puts on an overowned AI capex proxy; thesis is faster monetization in workflow adoption than in raw model spend.
• Pair trade long MSFT vs short a basket of AI infrastructure beneficiaries with weaker application-level monetization, sized for 3-6 months: MSFT is better positioned to convert AI into enterprise workflow gains and should capture the trust/workflow layer, while hardware-sensitive names face slower ROI realization.
• Initiate a small long in TEAM or SNOW on 6-9 month horizon if management signals AI-driven productivity programs tied to retention/coaching metrics; upside comes from becoming the operating layer for organizational change, not just data/engineering spend.
• Avoid chasing near-term upside in pure-play AI capex beneficiaries after earnings; sell covered calls or use call spreads on the strongest momentum names, because the article implies a lag between spend and measurable performance, which can compress multiples if guidance doesn’t catch up.
• Watch for a long/call structure in HR tech names like WDAY over the next 1-2 quarters if customers begin explicitly budgeting for manager training and employee development as part of AI rollout; this is a better second-order beneficiary than generic AI hardware.