Agentic AI in Enterprise Software: A Tale of Misaligned R&D and Untapped ROI Potential

The 2025 enterprise software landscape is marked by a paradox: record-breaking R&D spending on agentic AI coexists with underwhelming returns for most organizations. While global GenAI spending is projected to hit $644 billion this year Gartner Forecasts Worldwide GenAI Spending to Reach $644 Billion^[6], enterprises allocating budgets to agentic AI face a stark divide between early adopters reaping transformative gains and latecomers stuck in experimental purgatory. This misalignment—where 58% of companies have deployed AI agents but only 14% report full-scale implementation EY Survey: AI Investments Surge, But Agentic AI Adoption Lags^[7]—reveals systemic challenges in aligning investment with value creation.

The ROI Divide: Early Adopters vs. the Rest

According to Google Cloud's 2025 ROI study, 52% of enterprises now deploy AI agents in production environments, with early adopters (those dedicating ≥50% of AI budgets to agents) achieving ROI rates 15–20% higher than peers Google Cloud Study: Early Agentic AI Adopters See Better ROI^[1]. In customer service, for instance, agentic AI reduces resolution times by 30% while boosting satisfaction scores by 22% Agentic AI Poised to Handle 68% of Customer Service^[8]. Cybersecurity applications are equally compelling: AI agents process 10,000+ alerts daily, neutralizing threats in milliseconds and predicting attack vectors with 92% accuracy Top 25 Agentic AI Use Cases Delivering Results in 2025^[9].

Yet, these successes mask a broader underperformance. Gartner warns that over 40% of agentic AI projects will be canceled by 2027 due to misapplication and cost overruns Gartner: Over 40% of Agentic AI Projects Will Be Canceled^[10]. The root cause? A mismatch between R&D priorities and business outcomes. While 58% of companies reallocated budgets to AI in 2025 BCG Survey - Agentic AI Reshaping IT Spending^[11], many are still investing in “AI for AI's sake,” prioritizing technical novelty over measurable impact.

Misallocation: Where the Money Goes vs. Where It Should

The BCG IT Spending Pulse survey reveals a critical disconnect: enterprises allocate 28% of AI R&D budgets to customer service (a high-ROI use case) but only 18% to legacy infrastructure modernization—a foundational requirement for agentic AI AI Shifts IT Budgets to Growth Investments | BCG^[12]. Meanwhile, speculative tools like AI-driven content generation receive 17% of budgets despite delivering just 3% ROI ROI of AI 2025 | Google Cloud^[13]. This imbalance is exacerbated by organizational inertia: 70% of companies still rely on legacy KPIs to evaluate AI projects, optimizing for outdated metrics like “automation of repetitive tasks” rather than strategic outcomes like customer retention or fraud prevention When Enterprise AI Prioritizes the Wrong Outcomes^[14].

Cybersecurity offers a telling case study. While 46% of AI agent deployments target security operations Google Cloud Study Reveals 52% of Executives Say Their Organizations Have Deployed AI Agents^[15], only 36% of enterprises invest in scalable data pipelines to train these agents on unstructured data (e.g., voice logs, emails) Building the Foundation for Agentic AI - Bain & Company^[16]. The result? Over-reliance on narrow-use-case models that fail to adapt to evolving threats.

The Path to Alignment: Governance, Governance, Governance

Closing the ROI gap requires a three-pronged approach:
1. Strategic Reallocation: Shift budgets toward use cases with proven ROI, such as customer service automation (43% ROI) and cybersecurity (40% ROI) Google Cloud Study: Early Agentic AI Adopters See Better ROI^[1].
2. Process Redesign: Modernize workflows to integrate AI agents into core operations, rather than treating them as isolated tools State of the Art of Agentic AI Transformation | Bain & Company^[17].
3. Governance Over Hype: Implement real-time explainability, adaptive security, and cross-functional oversight to mitigate risks like agentic errors and data silos Building the Foundation for Agentic AI - Bain & Company^[18].

For example, Bain & Company highlights how AI leaders achieve 10–25% EBITDA gains by embedding agents into end-to-end workflows State of the Art of Agentic AI Transformation | Bain & Company^[19]. These organizations prioritize “agentic constellations”—networks of specialized agents collaborating across departments—over single-function tools.

Conclusion: The $11.5 Billion Question

Enterprise R&D spending on agentic AI is set to rise 5.7% in 2025, outpacing overall IT budget growth of 1.8% Enterprise AI Spending to Rise 5.7 Percent in 2025^[20]. Yet, with 62% of companies expecting ≥100% ROI on agentic AI investments 2025 Agentic AI ROI Survey Results^[21], the stakes for strategic alignment have never been higher. The path forward lies not in chasing technical novelty but in redefining success: aligning AI systems with business outcomes, not just technical benchmarks. As one executive put it, “The future belongs to enterprises that treat agentic AI as a business transformation engine, not a cost center.”