From RPA to real autonomy (Agentic): How to choose the right automation and prove its value

Sep 12, 2025

artificial intelligence & RPA

Not all automation is created equal, and choosing the right level of automation and measuring its value effectively can make the difference between a successful transformation and a costly misfire. This document explores the three core levels of automation, Traditional Automation (RPA), AI-Assisted (Copilot), and Agentic AI (Autonomous Agents), highlighting their strengths, limitations, and key value metrics.

Key differences

Traditional Automation (RPA) is best suited for repetitive, rule-based tasks and offers predictable and auditable processes. However, it can be brittle with exceptions and requires ongoing rule maintenance.

AI-Assisted automation integrates AI to draft, suggest, and analyze while keeping humans in control, fitting seamlessly into existing workflows. It boosts productivity without major disruptions but is limited by human-in-the-loop throughput.

Agentic AI, the most complex but also cutting-edge level, involves goal-driven agents that act end-to-end, coordinate across systems, and learn continuously. It offers maximum efficiency and scalability but requires high data quality, robust governance, and careful change management.

Traditional Automation (RPA) is best suited for repetitive, rule-based tasks and offers predictable and auditable processes. However, it can be brittle with exceptions and requires ongoing rule maintenance. AI-Assisted automation integrates AI to draft, suggest, and analyze while keeping humans in control, fitting seamlessly into existing workflows. It boosts productivity without major disruptions but is limited by human-in-the-loop throughput. Agentic AI, the most complex but also cutting-edge level, involves goal-driven agents that act end-to-end, coordinate across systems, and learn continuously. It offers maximum efficiency and scalability but requires high data quality, robust governance, and careful change management.

Let’s be clear: not all automation is created equal, and choosing the right flavor can make the difference between a game-changing transformation and a costly misfire. The difference between a successful AI initiative and a costly experiment often comes down to two things: choosing the right level of automation and measuring value relentlessly.

At delaware, we’ve seen organizations leap into “AI” without a clear framework, only to hit the wall when leadership asks the inevitable: “What’s the ROI?”.

The 3 levels of automation and where they pay off

Before diving into bold claims or ambitious investments, it is crucial to map the spectrum of automation—from traditional, rule-based systems to advanced, agentic autonomy. Each level brings unique capabilities, strengths, and potential pitfalls. To navigate this complex terrain and maximize returns, let us break down the three core levels of automation, exploring how they operate, where they shine, and what to watch out for.

Going Agentic: the 3 big challenges and how to turn them into value drivers

Once upon a fiscal quarter, a bold organization unveiled its latest innovation: a suite of agentic AIs set to revolutionize core business processes. With anticipation running high, the leadership celebrated the promise of staggering time savings, cost reductions, and unprecedented customer satisfaction. Their vision was clear: automation would slash cycle times, optimize journal postings, and empower agents to handle claims and customer queries with superhuman precision.

Yet, the journey from vision to value proved far more treacherous than expected:

First pitfall: data quality, agents fed by inconsistent and incomplete information began making questionable decisions, triggering a spike in human overrides.
Second challenge: opaque monitoring ,a “set-and-forget” deployment with little scrutiny of real-world behaviors allowed minor glitches to snowball into compliance risks and customer trust issues.
Third hurdle: user adoption & change management ,despite robust tech, the organization failed to engage and train users, causing resistance to change, unclear communication, and gaps in upskilling.

The result? Instead of the seamless transformation they had envisioned, the organization found itself tangled in a web of inefficiencies, lost revenue, and eroding customer satisfaction.

It became clear that success depended not just on deploying smart agents, but on navigating these three perilous pitfalls: data quality, robust monitoring, and effective change management to foster genuine user adoption.

Data quality & availability: the fuel for autonomy

Agentic AI thrives on accurate, timely, and complete data. Without it, even the smartest agent will make poor decisions or stall. Many organizations underestimate this step, but data readiness is Phase 0 of any agentic AI journey.

Why it matters: Bad data = bad decisions. Inconsistent or siloed data leads to errors, compliance risks, and loss of trust.

What to do:

Audit critical data sources and identify gaps.
Fix the top five quality issues before launch.
Enable near real-time access through APIs or streaming pipelines.

Value link: Clean data improves Task Success Rate, reduces human overrides, and accelerates ROI.

Monitoring & AgentOps: from “deploy and ray” to continuous control

Deploying an agent is not a one-off event; it’s the start of an ongoing relationship. Traditional MLOps focuses on model accuracy, but AgentOps goes further: it monitors behavior, outcomes, and system health in real time.

Why it matters: Without visibility, you can’t detect drift, explain decisions, or prove compliance.

What to do:

Define goal-outcome metrics (e.g., success rate, override frequency).
Instrument every decision for traceability.
Build dashboards for executives, operations, and compliance.

Value link: Strong monitoring builds trust, prevents costly errors, and provides the evidence you need to scale confidently.

Change management & human impact: winning hearts and mind

Agentic AI doesn’t just change processes, it changes roles. Employees move from “doing the work” to supervising, validating, and improving the system. Without a clear change story, adoption will stall, even if the tech works perfectly.

Why it matters: Resistance or fear can derail projects faster than any technical issue.

What to do:

Communicate the “why” early and often: focus on empowerment, not replacement.
Start in copilot mode to build trust, then graduate to autonomy for low-risk cases.
Provide training and create feedback loops so employees feel ownership.

Value link: Engaged teams accelerate adoption, reduce errors, and unlock innovation.

For a real-world parallel, Klarna’s AI Experiment which illustrates both the promise and the risks of scaling agentic automation in customer-facing operations.

discover Klarna’s AI Experiment

Strategy & Readiness

Align your organization around clear outcomes, establish effective governance structures, and select use cases with reliable data and well-defined KPIs. This stage also involves engaging stakeholders early to ensure shared vision and accountability.

At this stage, we also focus on operational gains and financial stewardship, avoiding surprises and enabling confident scaling.

Pilot & Prove

Develop a resilient, safeguarded agent, thoroughly test edge cases, and launch with a human-in-the-loop setup to ensure risk is minimal.

Scale & Integrate

Redesign standard operating procedures to accommodate autonomous agents, support evolving employee roles, and introduce AgentOps. Begin expanding AI capabilities into related processes to maximize impact, while maintaining transparency.

Optimize & Transform

Gradually increase agent autonomy where automation is safest, introduce multi-agent orchestration for more complex workflows, and embed AI-specific KPIs into regular business performance reviews. Keep compliance teams involved throughout, ensuring ethical and legal alignment as you progress.

The delaware touch

What makes this framework effective in practice:

Data first, visibly: We dedicate a focused “Phase 0” to quickly fixing the minimum data issues necessary to unlock a meaningful pilot, preventing endless preparation cycles.
AgentOps from day one: Key metrics such as success rate, override rate, and cycle time are tracked and displayed on a real-time wallboard, ensuring everyone from leadership to front-line staff stays informed.
People in the loop: Front-line teams co-design pilots and process changes, starting in a copilot mode to build trust. Over time, the system transitions to greater autonomy for routine tasks, keeping human expertise where it matters most.

This modular, transparent approach ensures your agentic AI journey is grounded in real business value, continuous learning, and sustainable adoption.

How to start (or accelerate) your agentic AI journey, a 90day plan

Cost considerations: transparency over precision

While it’s impossible to provide a universal cost estimate for agentic AI deployment, given the variability in scope, data readiness, and integration complexity, what matters most is transparency. From pilot to scale, organisations should expect a mix of upfront investment (setup, integration, training) and ongoing costs (monitoring, governance, maintenance). The key is to align these costs with expected operational gains and to make them visible early. By tracking metrics like Total Cost of Ownership (TCO), Time to Value, and Payback Period, leaders can ensure that financial stewardship matches technical ambition. This approach builds trust, avoids budget surprises, and supports confident decision-making throughout the automation journey.

Setting the stage for scalable agentic AI success

To ensure this momentum is sustained, leaders should set an ambitious yet practical roadmap for expansion. Begin by identifying the next set of high-impact use cases, those where agentic AI can deliver outsized returns or address persistent operational pain points. Prioritize initiatives where measurable business outcomes can be demonstrated quickly, creating proof points that build organizational confidence.

Establish cross-functional "AI value squads" that pair domain experts with technical leads. These teams can rapidly prototype, test, and refine agent-driven workflows, embedding feedback loops that surface insights and surface challenges early. Don't wait for perfection, move forward with agile pilots, using transparent metrics to decide when to scale or pivot.

Double down on change management: invest in targeted training that empowers business users to interact with, supervise, and even improve agent behaviors. Celebrate quick wins and lessons learned, making successes visible across the organization. Use storytelling and real-world data to demystify AI, reinforcing the message that agentic systems are partners, not replacements.

Finally, revisit your governance structures as adoption grows. Regularly update risk assessments and compliance checklists to reflect new capabilities and workflows. As agents become more autonomous, expand your monitoring to include not just technical KPIs but also user satisfaction and unexpected downstream effects.

By moving deliberately, balancing speed with stewardship, you create a culture where AI is not a bolt-on but an integrated force for progress.