AI ROI Misallocation and the Human-Centric Correction Opportunity: Strategic Rebalancing for Long-Term Value Creation

The artificial intelligence (AI) investment landscape in 2025 is marked by a paradox: while corporate spending on AI has surged, tangible returns remain elusive for the majority of projects. According to a Deloitte report, 85% of organizations increased their AI investments in the past year, yet only 5% of corporate AI projects deliver measurable financial value, with 95% failing to achieve ROI. This "GenAI Divide" highlights a critical misalignment between capital allocation and outcomes, driven by challenges in integrating AI into existing workflows, isolating its impact from broader organizational changes, and prioritizing high-ROI use cases.

The Roots of AI ROI Misallocation

The disconnect between investment and returns stems from structural inefficiencies. MIT's 2025 study reveals that most industries lack the foundational disruption required to unlock AI's potential, with companies disproportionately allocating resources to sales and marketing (areas with lower ROI) rather than operations and finance (higher-impact domains). Additionally, agentic AI-systems capable of end-to-end automation-remains complex and slow to yield returns, with only 10% of organizations currently realizing significant value from such technologies.

A key factor exacerbating this misallocation is the overreliance on internal AI development. Enterprises that adopt vendor-built solutions, however, tend to succeed at a higher rate. For instance, back-office automation powered by AI has demonstrated millions in annual savings through document processing, procurement, and risk monitoring. This underscores the importance of aligning AI strategies with existing workflows and leveraging external expertise to accelerate value realization.

The Human-Centric Correction: Rebalancing for Sustainable ROI

To address AI ROI misallocation, organizations are pivoting toward human-centric strategies that prioritize measurable outcomes, employee engagement, and long-term value creation. A 2025 Deloitte survey highlights that successful AI adoption hinges on embedding systems into workflows and measuring human-centered metrics such as productivity, employee satisfaction, and adoption rates. Companies that focus on these outcomes are twice as likely to scale AI effectively compared to those limiting AI to isolated teams according to Deloitte.

One framework gaining traction is the NIST AI Risk Management Framework (RMF), which emphasizes four core functions: Govern, Map, Measure, and Manage according to Intervision. The Govern function ensures AI initiatives align with organizational goals and ethical standards, while the Measure function tracks both quantitative (e.g., cost savings) and qualitative (e.g., employee trust) benefits. This structured approach enables enterprises to phase AI integration, optimize value, and mitigate risks systematically.

Case Studies in Strategic Rebalancing

Real-world examples illustrate the efficacy of human-centric AI strategies. In e-commerce, a mid-size fashion brand deployed an AI-powered chatbot, reducing customer service tickets requiring human intervention by 40% and achieving a 400% ROI within six months. Similarly, a Fortune 500 manufacturer implemented AI for predictive maintenance, cutting unplanned downtime by 62% and delivering an 823% ROI over 18 months according to the same case study. These cases highlight the importance of aligning AI with high-impact use cases and measuring both hard (e.g., cost savings) and soft (e.g., employee productivity) ROI.

Agentic AI, though still nascent, offers a glimpse of future potential. When governed properly, these systems can enhance decision-making in sectors like biotechnology and law according to Intervision. However, their deployment requires robust ethical safeguards to ensure they complement human capabilities rather than replace them.

The Path Forward: Integrating Human-Centric Principles

To optimize AI ROI, organizations must adopt actionable methodologies that prioritize human agency and collaboration. This includes:
1. Ethical AI Design: Embedding fairness, transparency, and accountability into AI systems to build trust and ensure alignment with societal values.
2. Human-AI Collaboration Metrics: Tracking indicators such as task handoff efficiency and decision quality to evaluate how effectively humans and AI work together.
3. Strategic Workforce Integration: Reframing AI as a tool for augmentation rather than replacement, with a focus on upskilling employees to leverage AI for higher-value tasks.

For instance, developers using AI coding assistants have shifted focus toward core coding tasks, boosting productivity and skill development over time. This dual ROI framework-combining immediate efficiency gains with long-term workforce transformation-demonstrates the value of human-centric strategies.

Conclusion

The AI ROI misallocation crisis demands a strategic rebalancing that prioritizes human-centric principles, measurable outcomes, and long-term value creation. By aligning AI with business workflows, adopting governance frameworks like NIST RMF, and focusing on high-impact use cases, organizations can bridge the GenAI Divide. As AI evolves from generative to agentic systems, the key to sustainable ROI lies not in technological prowess alone but in harmonizing AI with human capabilities, ethics, and organizational goals.