Agentic AI can redefine industrial operations, but that is only if it is deployed with discipline and can scale across the organization.
Agentic AI holds promise for automating industrial operations and improving efficiencies across such organizations. However, despite the promise, organizations must approach AI agents with caution.
Many organizations that implement agentic AI encounter pitfalls that hinder their success. One group or one agentic AI application might prove a huge success. So, naturally, others double (triple, quadruple) down and go all in. What quickly emerges are problems directly related to agent sprawl. Without governance, companies risk creating too many agents, leading to duplication, inconsistency, and operational confusion.
As sprawl takes over, other issues emerge. There are often what are called brittle prompts. These are poorly designed prompts that can cause agents to misinterpret data or generate unreliable recommendations. There can also be orphaned agents. These are agents without clear authority or integration into workflows that frequently become “digital shelfware.”
Taking a Step Back after Initial Agentic AI Success
For AI to add value, agents must have pathways to action and accountability.
Anushree Verma, a senior director analyst at Gartner in the Emerging Technologies and Trends group, summarized the issues in a recent Harvard Business Review article, “Why Agentic AI Projects Fail—and How to Set Yours Up for Success.” She noted that success requires more than enthusiasm. It demands focus, maturity, and strategy.
To that end, she noted that Gartner research finds that over 40% of agentic AI projects are expected to fail by 2027. The reason isn’t a lack of ambition. It is a lack of alignment between business goals, technology readiness, and disciplined execution.
A key point she raised is that agentic AI is not just another automation tool or a smarter chatbot. The technology is designed to act autonomously, managing complex tasks, making real-time decisions, and even anticipating problems before they escalate. For industrial companies, where uptime, efficiency, and safety are paramount, this level of autonomy has the potential to be game-changing.
See also: Agentic AI in Industry: The Technologies That Will Deliver Results
Why Many Projects Falter
Verma noted that the biggest mistake companies make is assuming agentic AI can be “plugged in” anywhere. “Too often, we see projects launched without a clear understanding of whether the technology fits the problem,” she said. “The result? Costly pilots that turn out to be little more than conversational interfaces bolted onto legacy workflows.”
Furthermore, she noted that integration challenges are another culprit. In industrial organizations, data often resides in silos, such as ERP systems, asset management tools, and maintenance logs, which do not communicate seamlessly. Deploying agentic AI in such environments requires rethinking processes. Without that preparation, costs balloon, timelines slip, and executives lose faith.
For structured, repetitive workflows, such as inventory reconciliation or basic report generation, traditional automation or machine learning may deliver equal or better ROI at a far lower cost.
One way to scale agentic AI and deliver value is to look for the right use cases. The technology should be reserved for problems where autonomy adds measurable value. As such, she noted that Gartner identifies three ideal scenarios:
1) Complex, dynamic environments, like supply chain optimization or predictive maintenance, where the system must continuously adapt to new data.
2) Multi-step, multi-agent collaboration, such as coordinating cross-departmental production schedules or managing quality incidents that span functions.
3) High-value, low-frequency decisions, for example, equipment replacement planning or safety-critical interventions, where decision accuracy and speed are crucial.
Making the Agentic AI Business Case
Verma’s article outlines a framework for making agentic AI investments yield a return. She suggests the steps to take include:
- Develop a strategic roadmap: Tie every AI initiative to tangible business outcomes, such as reduced downtime, faster decision-making cycles, or higher throughput.
- Embrace composite AI: Combine agentic systems with traditional automation and analytics for more resilient, transparent outcomes.
- Run a cost-benefit analysis: Evaluate not just implementation costs but integration, maintenance, and organizational change.
- Assess readiness: Ensure we have AI-quality data, mature workflows, and strong governance controls.
- Focus on enterprise impact: The real value of agentic AI lies in orchestrating work across silos, not just improving the efficiency of one team.
A Final Word
Agentic AI can redefine industrial operations by doing everything from optimizing energy use to preventing equipment failures. But that is only if it is deployed with discipline. As the article concludes, the winners won’t be those who chase the latest AI trend, but those who integrate it strategically, measure ROI relentlessly, and keep human expertise in the loop.
