Organizations that measure artificial intelligence returns against growth outcomes are nearly three times more likely to report strong ROI compared to those focused solely on efficiency gains, according to a new study from Wharton Human-AI Research (WHAIR) in partnership with GBK Collective. The fourth annual report, titled "The Adaptive Advantage: Who's Winning with AI and How," surveyed 1,415 US enterprise leaders and entry to mid-level employees. The research reveals a sharp divide between companies pursuing AI for cost savings versus those targeting revenue expansion, with the latter group pulling significantly ahead in terms of financial returns.

The numbers paint a stark picture of this performance gap. Thirty-five percent of companies tracking ROI against growth outcomes—such as net-new revenue, increased sales conversions, and expanded output volume—report strong returns, while just 13% of organizations measuring efficiency alone see the same results. Despite this advantage, most senior leaders remain fixated on traditional metrics: 40% judge success based on time savings versus only 27% who prioritize growth metrics like new revenue. Fifty-two percent of senior leaders name cost reduction as AI's primary objective, while a mere 22% view it as a growth engine. Meanwhile, 80% of organizations now report strong ROI on AI investments overall, and 83% of managers expect AI to fundamentally transform or majorly impact their industry within two to five years. Organizations with a designated chief AI officer see particularly strong outcomes, with 58% reporting strong positive ROI, and two-thirds of organizations now have a CAIO or equivalent role. Sixty-seven percent of companies use AI agents to complete tasks or run workflows, yet only 17% allow these agents to operate fully autonomously—10% review a sample of output after the fact, while 7% rely on real-time monitoring and systematic guardrails instead of human review.

The report identifies three markers of AI success: providing value (departments report strong positive ROI), transforming workflows, and aligning the organization around AI. Only 7% of companies demonstrate success across all three markers. The research found that 73% of managers say workflows have changed significantly because of AI, yet just 28% describe AI as fully embedded in core business processes. Among managers who use AI daily—62% of the total—only 28% report strong positive ROI. Departments leading in returns include IT at 41%, finance at 37%, HR at 35%, and legal and engineering at 19%. Tech-forward industries show 40% strong ROI, while professional services report 33%. Platform standardization also matters: 50% of organizations using a single approved AI platform report strong positive ROI, compared to 30% allowing multiple tools under formal policy and only 19% running a central hub or orchestration layer.

The report explains that workflow transformation requires three conditions: AI must be fully embedded in core business processes, the department currently uses AI in some form, and workflows have changed significantly because of AI. Organizations with strong returns didn't cut more staff than others—37% of companies report staff reductions and 33% report slowed or frozen hiring—but instead changed management structures and workflows, monitored AI use more closely, incorporated AI into performance reviews, and invested in skills training. The level of AI agent autonomy is determined by risk tolerance rather than technical capability, with barriers including data security concerns, regulatory requirements, distrust of AI reasoning, employee skill shortages, and accountability questions around agent errors. The research found that 31% of departments using agentic AI to its fullest capacity achieve transformational success, and 25% where AI also serves as an advisor. Fewer than four in ten organizations are aligned on AI, and the 2026 report marks the first time AI usage and sentiment have moved in opposite directions—usage is significantly up from 2025, yet optimistic views remain unchanged at 59%, signaling suboptimal organizational alignment.

The report offers five recommendations: fully embed AI in core workflows and move more than half of pilots to production, rebuild how work is managed rather than cutting headcount, give AI agents meaningful authority with automated monitoring instead of step-by-step approval, track growth outcomes alongside hours saved while maintaining spending visibility, and establish dedicated AI ownership through a CAIO alongside a standard platform approach. WHAIR notes that 2027 may turn less on whether AI works than on the distance between organizations that have made substantial progress toward transforming workflows with AI and those that have not. The key differentiator isn't company size but rather the ability to adapt and scale AI adoption across the organization. The question companies face isn't whether AI delivers value—it's whether they're measuring and managing it in ways that unlock its full potential. For business leaders navigating this transition, the tension between short-term efficiency plays and longer-term capability building will likely determine which organizations emerge as category leaders versus perpetual followers.