Escaping PoC purgatory: 5 steps to accelerate industrial agentic AI from pilot to impact

In 2024, less than 1% of enterprise software applications incorporated agentic AI but, according to Gartner, that number is expected to reach 33% by 2028. Understandably, industrial enterprises have shown a growing interest in agentic AI, hoping to leverage this transformative technology for real-world business applications.

While eager to adopt the next phase of AI innovation, many are facing a dreaded “Proof-of-Concept (PoC) Purgatory,” failing to scale their AI tools beyond pilot stages.

For most, the core question isn’t whether agentic AI can improve their operations but how quickly it will deliver tangible value — and PoC Purgatory is holding them back.

Let’s explore some of the key reasons for this bottleneck, the barriers to unlocking agentic AI’s full potential and strategies to overcome them.

Why are Industrial Enterprises Getting Stuck in PoC Purgatory?

The transition from PoC pilots to large-scale deployment is not a straightforward journey. Agentic AI adoption is unlike any other technological adoption before it, and several challenges are likely to arise as industrial enterprises work to take the next step toward scalable implementation, including:

1. Change Management Concerns: Industrial enterprises are often reluctant to embrace significant changes, especially when adopting technologies that drastically alter their operations. This fear stems from concerns about losing control over key business processes and the uncertainty of how these changes might impact their workflows. As a result, they may delay or abandon initiatives because of apprehension tied to unknown challenges and potential disruptions that come with relinquishing control to automated systems such as agents.

2. Lack of Clear Success Metrics: Without well-defined success metrics, it’s difficult for companies to assess the effectiveness of emerging tools. Determining how agentic AI will impact key business outcomes, such as productivity, cost reduction or operational efficiency, is not a simple task. This lack of clarity can impact decision-making and delay implementation efforts.

3. Appropriate Use Case Assessment: Identifying the right use cases for agent-driven technologies and understanding which complex processes they can effectively manage presents a significant challenge. To do this, businesses need deep domain knowledge and a clear grasp of their internal operations. Without this insight, they risk getting stuck in a testing phase, where only simple, non-representative scenarios are tried, ultimately hindering the potential to leverage agents for more impactful, complex tasks.

4. The Need for a Robust Data Framework: While 86% of organizations recognize data readiness as crucial for AI success, only 23% have built the necessary foundation to make it happen. For industrial enterprises, the challenge is even greater, as outdated technology, fragmented data and legacy systems complicate AI deployment — and scaling agents only complicates things further. Agentic AI requires a powerful framework that can support an army of agents creating vast amounts of data in near real-time, making the process more complex and resource-intensive.

5. Workforce Resistance: Since agentic AI fully automates certain tasks away from humans, employee resistance to the growing roles of agents is almost guaranteed based on early pushback seen from chatbots. While it allows people to focus on high-value items and only engage with agents on items that need approval or are lined with uncertainty, a fair amount of autonomy around work and how it’s done must be relinquished, which can be uncomfortable.

What Steps Can Industrial Enterprises Take to Reach Scaled AI Deployment?

The barriers laid out above can certainly be daunting, but overcoming them is well within reach for forward-thinking enterprises. Those looking to scale their agentic AI applications should start by following these five steps:

1. Define Clear Business Outcomes and Roles for Agents: The first step is to clearly define the business outcomes that AI agents aim to achieve, and then map these outcomes to specific types of agents. For example, a monitoring agent, which operates continuously in the background, might have a KPI focused on uptime improvements while an agent that automates an end-to-end process focuses on productivity gains. By aligning AI agents with strategic business priorities and setting clear KPIs for each, organizations can create a strong foundation for measuring success.

2. Ensure Data and Infrastructure Readiness: Agents rely not only on data quality, availability and efficient processing but also on process readiness. To move beyond PoC, companies must upgrade their data infrastructure and map out their processes. They also need to have a clear understanding of how their operations work, providing well-defined guidelines within which agents can operate. 3. Establishing AI governance frameworks ensures that implementations meet security, compliance and reliability standards while giving agents the structure needed to find effective solutions autonomously.

3. Adopt a Phased Approach to Deployment: Rather than attempting a full-scale rollout from the outset, enterprises should take a phased approach. Begin with a targeted, high-impact agent that is likely to deliver measurable results, then refine and scale the model based on feedback. Continuous iteration is key to ensuring the agents can adapt to real-world conditions and evolve alongside the business needs. Once initial success is found, additional types of agents can be deployed across other business initiatives with greater ease.

4. Drive Organizational and Workforce Alignment: With nearly half of the workforce concerned that AI may replace their jobs, leaders can’t simply introduce agentic AI and walk away. As processes become automated, employees will shift to new tasks such as overseeing outcomes and providing overall sign-off rather than executing each step manually. Companies should invest in robust onboarding initiatives, including training and upskilling programs, to ensure a smooth transition. Engaging cross-functional teams—such as operators, IT management and business leaders—early in the process will help create a sense of ownership and foster collaboration across the business.

5. Measure, Iterate and Scale with Confidence: Once agents are deployed, it’s crucial to continuously monitor their scope and performance against predefined KPIs. This includes assessing whether an agent starts with a relatively simple task and gradually gains more autonomy over time or if there are specific areas where the agent struggles. Enterprises should also evaluate if agents are trusted to go beyond the company’s internal systems, such as negotiating and purchasing from suppliers. By establishing enterprise-wide frameworks for agents, organizations can streamline future projects, improve agent performance and accelerate their ability to scale agent initiatives across the business.

Take Agentic AI from Endless Pilots to Real-World Impact

Moving beyond PoC purgatory to achieve full-scale agentic AI deployment requires overcoming several significant hurdles. Addressing common barriers such as fear of failure, siloed initiatives and infrastructure challenges will be critical for industrial enterprises to unlock the full potential of these autonomous tools.

Despite many organizations having already rolled out standard agents, the path to fully autonomous agents won’t be without challenges. By making strategic investments and taking a methodical approach to not only scaling agents but also defining their specific roles, industrial enterprises can move beyond endless trials and begin reaping the rewards of agentic AI in the real world well before Gartner’s predicted 2028 surge.

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