Agentic AI is an advancement over traditional RPA
Robotic Process Automation (RPA) operates within strict, predefined tracks, performing predictable tasks on repeat. Think of it like a train on rails—fast, efficient, but limited to a specific path. Agentic AI, on the other hand, is like a self-driving car on a city street. While it’s automating, it’s also assessing, deciding, adapting in real time.
Agentic AI taps into data streams, predicts outcomes, and learns from every interaction, opening up entirely new possibilities for enterprises to tackle tasks that have historically required human oversight. We’re talking about work that changes moment-to-moment, decisions that need dynamic assessment. It’s a level of automation that brings intelligence and adaptability into the picture in ways that RPA simply can’t touch.
Leading platforms like Salesforce, Microsoft, and Google are already pushing forward with agentic AI solutions, laying the groundwork for this transformative shift.
But there’s still a catch—it’s early days. The promise is huge, but widespread adoption remains a challenge. Experts point out that agentic AI still isn’t refined enough for seamless, enterprise-level integration. For instance, Cameron Marsh at Nucleus Research estimates that agentic AI will handle up to 15% of daily decision-making tasks by 2028, but we’re still in the “learning to walk” phase with this tech.
Implementing agentic AI requires major technical overhaul
Despite what vendors claim about quick-and-easy deployment, implementing agentic AI isn’t a plug-and-play operation. Bringing agentic AI into an existing RPA environment is a technical undertaking that requires major adjustments. We’re not talking about minor tweaks here—enterprises will need to rethink and re-engineer a lot of their workflow infrastructure.
For agentic AI to truly “think” and “decide,” it needs direct access to services, APIs, and a well-orchestrated data handling system. This gives the AI the right contextual knowledge to complete tasks autonomously. Without this foundation, even the smartest AI will falter.
What’s more, the trust factor can’t be overlooked. These autonomous systems require comprehensive governance and oversight. Gartner analyst Tom Coshow has forecasted that only one-third of enterprise applications will include agentic AI by 2028, and that’s with major governance and trust-building efforts.
Enterprises need to create a secure, transparent environment where the AI’s decisions can be trusted and validated. The key here is to build a system that executives, stakeholders, and frontline workers can count on in their daily workflows.
Customizing and integrating agentic AI is more challenging than it seems
Low-code and no-code tools sound appealing, but while they simplify the creation of basic AI agents, the reality is that true enterprise-level implementation is more complex. To develop an agent capable of making nuanced decisions, companies need a broad understanding of data flows, machine learning models, and API integration.
The expertise required to design these decision-capable agents often goes beyond what low-code/no-code solutions can handle. If an organization wants to take full advantage of agentic AI, a user-friendly interface is nowhere near enough—it needs technical depth.
And if you’re dealing with legacy systems, get ready. Older, entrenched software environments typically present compatibility challenges. A cloud-native startup will probably find it easier to roll out agentic AI, but established firms with older infrastructures face an uphill battle.
Imagine trying to integrate a 21st-century AI system into software built decades ago—it’s not impossible, but it’s a tough, intricate task. As noted by Nucleus Research, the learning curve for these enterprises is steep, especially when they aim to scale and customize agentic AI functionality.
Key takeaway: For those businesses with legacy systems, it’s key to evaluate whether the investment in agentic AI integration will be worth the effort in the near term.
Take a cautious, phased approach to implementing agentic AI
For many enterprises, rushing straight into full-scale agentic AI adoption might not be the smartest move right now. Instead, a measured, phased adoption could provide a smarter path forward. Experts recommend leveraging agentic AI gradually, so that organizations can continue using RPA for predictable, repetitive tasks while agentic AI is tested on more dynamic, complex processes.
Industry leaders like Sanjeev Mohan from SanjMo are advising against the temptation to dive headfirst into agentic AI solely for experimental purposes. If RPA is working well, there’s no need to rush.
Instead, businesses might find value in observing and assessing as agentic AI technology matures. And here’s where things get really interesting: RPA vendors like UiPath are already moving toward agentic capabilities. As such, companies might soon have access to agentic AI through familiar platforms.
As established RPA providers evolve, businesses may find a smoother, more cost-effective path to agentic AI—one that minimizes disruption and maximizes their current investments.
Final thoughts
Are you preparing your business to leverage the next wave of autonomous technology, or are you waiting on the sidelines as others take the lead? Do you have the systems, the mindset, and the people ready to make agentic AI work for you, or will your competitors claim that edge? Consider where this technology could take your business—not only in the short-term, but in a future that’s moving faster than any of us may anticipate.
