Navigating the Elusive ROI of Agentic AI

Agentic AI represents a transformative leap in artificial intelligence, where systems don't simply respond to queries but autonomously plan, reason, and execute multi-step tasks with minimal human oversight. From streamlining customer service to optimizing revenue operations, these AI agents promise to boost productivity, reduce costs, and drive innovation across industries. Yet, despite the hype, realizing a strong return on investment (ROI) remains challenging for many organizations. As adoption grows, surveys and analyst reports highlight a stark reality: while investments in agentic AI are surging, measurable value often lags behind expectations.

The core challenges in achieving ROI with agentic AI

The path to ROI in agentic AI can be challenging and fraught with hurdles stemming from the technology's inherent complexity, organizational readiness, and the broader ecosystem required for success. These issues are not unique to any single vendor or implementation; they are systemic challenges that affect generic agentic AI deployments across the board.

1. High upfront and hidden costs with uncertain returns: Agentic AI demands significant initial investments in infrastructure, data preparation, and development, often leading to budget overruns. Beyond obvious expenses like licensing large language models (LLMs) and integrations, hidden costs—such as ongoing data quality improvements, employee upskilling, and compliance audits—can erode projected savings. Traditional ROI models, which emphasize short-term cost reductions, struggle to capture long-term benefits like enhanced decision-making or strategic agility, resulting in misaligned expectations. For instance, many projects are judged on immediate metrics like headcount reduction, overlooking broader productivity gains, which can delay perceived value for 1-5 years.
    
2. Data quality and integration difficulties: Agentic AI thrives on high-quality, real-time data, but fragmented systems, silos, and poor data governance often lead to unreliable outputs. Integrating agents with existing ERP, CRM, or third-party cloud platforms can cause compatibility issues and disruptions, amplifying risks like inaccurate decisions or "hallucinations" where agents generate flawed responses. Without a solid data foundation, agents cannot reason effectively, undermining trust and scalability.
    
3. Lack of trust, governance, and observability: The opaque nature of agentic AI—where decision-making processes remain hidden—can breed skepticism among business process owners, executive leadership, and end users. Security vulnerabilities, regulatory compliance risks, and ethical concerns (e.g., bias in autonomous actions) further complicate adoption. Without robust governance frameworks, organizations face internal resistance, including fears of job displacement or unreliable performance. Moreover, limited visibility into agent activities makes it hard to monitor, optimize, or prove value, contributing to high failure rates—analysts predict over 40% of projects could be scrapped by 2027 due to unproven ROI and governance gaps.
    
4. Skill gaps and organizational resistance: Deploying agentic AI requires expertise in AI and data engineering, prompt design, and workflow redesign, but many teams lack these skills. This leads to a tech-first approach that prioritizes tools over business problems, resulting in pilots that never scale or gain enterprise-level adoption. Cultural pushback, such as reluctance to redesign processes or concerns over AI replacing human roles, exacerbates the issue, stalling adoption and limiting ROI to isolated experiments rather than enterprise-wide impact.
    
5. Scalability and reliability hurdles: While agentic AI excels in controlled pilots, scaling to handle complex, real-world workflows introduces unpredictability. Agents may falter in dynamic environments, requiring constant tuning to maintain accuracy and efficiency. This, combined with overhype from vendors, sometimes referred to as agent washing", sets unrealistic expectations, where projects focus on flashy capabilities rather than measurable business outcomes.

These challenges explain why, despite widespread experimentation, only a small fraction of organizations report significant ROI today. The good news? Purpose-built platforms are emerging to address these pain points, offering structured pathways to value without reinventing the wheel.

How Salesforce Agentforce smooths the path to ROI

While generic agentic AI deployments often stumble on the above issues, integrated platforms like Salesforce Agentforce 360 provide a more grounded approach. Built natively on the Salesforce ecosystem, Agentforce isn't a standalone tool—it is an extensible platform that embeds AI agents into existing workflows, data, and applications, helping organizations overcome common hurdles while delivering tangible results.

• Tackling costs and measurement with built-in efficiency: Agentforce minimizes upfront friction through low-code/no-code builders, enabling rapid deployment without massive custom development. It focuses on productivity gains—like automation of repetitive tasks and providing 24/7 availability—leading to cost savings and faster value realization. For example, by handling routine inquiries and escalating complex ones seamlessly, it reduces operational burdens while allowing teams to track ROI through metrics like reduced handle times and improved retention.
• Enhancing data integration and quality: When combined with Data 360 (formerly Data Cloud), Agentforce unifies structured and unstructured data in real-time, eliminating silos and ensuring agents have accurate context for decisions. Advanced retrieval-augmented generation (RAG) via the Atlas Reasoning Engine pulls relevant information dynamically, reducing errors and improving reliability without extensive data migration.
• Building trust through governance and visibility: Agentforce prioritizes enterprise-grade security with features like the Trust Layer, which encrypts data, audits actions, and enforces guardrails to prevent biases or unauthorized decisions. The Command Center offers full observability, letting leaders monitor agent performance, latency, and accuracy in real-time—addressing the opacity issue and fostering confidence. This has enabled deployments where agents autonomously resolve up to 70% of cases while maintaining compliance.
• Bridging skill gaps and resistance: With more than 100 pre-built AI capabilities tailored to industries, Agentforce lowers the expertise barrier, allowing non-technical users to customize agents via natural language. It supports human-AI collaboration through intelligent handoffs, easing fears of displacement by augmenting rather than replacing roles, and integrates seamlessly with tools like Slack for broader adoption.
• Ensuring scalability and reliability Agentforce's autonomous AI agents reason adaptively with the Atlas Engine, handling complex workflows while learning on-the-job. This reduces tuning needs and supports scaling across sales, service, and marketing, with proven outcomes like 15-22% improvements in efficiency metrics.

In essence, Agentforce 360 transforms the elusive ROI of agentic AI into something achievable by embedding solutions directly into the platform. It's not about overpromising—it's about delivering grounded, measurable impact within trusted boundaries. As we explore further in subsequent parts, real-world implementations show how this approach turns challenges into competitive advantages.

Ready to transform your Agentforce 360 initiatives from stalled projects into proven ROI drivers in just 4 weeks? Argano's Agentforce 360 Value Realization Program is designed to assess, triage, stabilize, and accelerate value realization, tackling under-adoption, usage bottlenecks, cost overruns, data readiness gaps, and more. Through our five core components - Usage Ownership, Stabilization Support, Consumption Forecasting, Roadmap Planning, and Data Readiness - your agentic deployment leaders gain tangible deliverables like weekly audits, root cause fixes, scenario-based forecasts, phased adoption roadmaps, and data report cards to unlock quick wins and long-term excellence.

Don't let elusive Agentic AI ROI hold you back—learn more about Argano’s Agentforce 360 Value Realization Program or contact us today for a no-obligation discussion with our Agentforce experts.