Andus Labs, an organizational strategy firm founded by former Weber Shandwick executive Chris Perry in 2025, has outlined the primary structural barriers preventing enterprises from capturing full value from artificial intelligence deployments. Based on findings from its Ground Truth Index, the company reports that enterprise returns on AI investments depend far more on organizational absorption capacity and human readiness than on raw model performance.
Tracking Impact Over Basic Adoption
According to Andus Labs, many enterprises evaluate AI success using surface metrics such as seat license counts, logins, and deployed agents rather than shifts in operational output. While high tool adoption rates are frequently reported to corporate boards, underlying workflows often remain unchanged, resulting in conventional deliverables being completed faster rather than unlocking new business capabilities.
The firm observes that operational responsibility for AI performance is frequently split across IT departments, human resources, and specialized centers of excellence. This fragmentation leaves overall business impact without a single executive owner who has clear authority to modify underlying processes.
Key Adoption Obstacles in the Enterprise
The Ground Truth Index aggregates 300 documented workplace patterns collected across engagements with 595 organizations. To compile the index, five independent AI analyst agents score candidate patterns across five metrics without sharing input. Human researchers then evaluate the median scores and finalize the top ranking patterns.
The index identifies Empty Chairs as the leading barrier to enterprise AI value. This condition occurs when organizations delegate decision authority to automated systems without identifying which human leader remains accountable for outcomes or possesses the explicit authority to overturn system recommendations.
The second most prominent barrier, Learning While Drowning, describes organizations that mandate AI training programs while keeping employee workloads unchanged, leading to low adoption. Other identified obstacles include Active Inertia, where companies use AI to accelerate outdated processes instead of redesigning them, and Devaluation Anxiety among employees concerned about job security.
Redesigning Work Systems for Autonomous Agents
To achieve long-term ROI, Andus Labs frames realized AI value as capability multiplied by absorption capacity. As underlying frontier models become broadly available across industries, competitive advantage shifts toward an organization's internal decision structures, incentive alignment, and operational workflows.
The firm recommends restructuring traditional multi-department handoffs into smaller, problem-focused teams that combine human workers with AI agents. Additionally, governance framework rules must ensure human oversight remains attached to high-stakes decisions while routine, low-risk actions proceed with defined agent autonomy.
AI-generated editorial illustration; not a documentary photograph.
Source: Chris Perry, Founder and CEO of Andus Labs – Interview Series
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