How to Build Process Automation That Actually Works

There’s a lot of pressure these days for businesses to automate their manual processes. Talk to any mid-level manager or director, and you’ll find the same challenges: piles of repetitive tasks, inconsistent data entry, bottlenecks in approvals, and people stuck doing work that drains time and resources. Traditional solutions—like legacy Robotic Process Automation (RPA) and business process outsourcing (BPO)—promised relief but often fell short. These systems were brittle, couldn’t easily adapt to a business’s changing needs, and ended up costing more than anticipated.

Meanwhile, the world of AI process automation has exploded onto the scene with bold promises. However, there’s a reality gap: not every AI solution is instantly transformational, and poor implementations can quickly become expensive experiments. That’s why we need a strategic approach, leveraging modern AI methods—sometimes called “agentic AI”—to build process automation that actually works.

But what do we really mean by “agentic AI”? In simple terms, it’s AI that has some capacity to make decisions on its own and adapt to new situations. It’s flexible enough to handle real-world variations yet grounded in well-defined processes so it doesn’t go rogue. When done right, intelligent process automation helps businesses solve immediate challenges and create a scalable foundation for future growth.

Starting With the Right Processes

Identify High-Impact Opportunities

The biggest mistake many organizations make is trying to automate everything at once. Instead, focus on high-impact, higher transactional processes—the ones that are repetitive, time-consuming, and prone to human error but still critical to your operation. For example, let’s say you run a mid-sized distribution company juggling multiple spreadsheets for order entry, inventory checks, and invoicing. If you target these repetitive tasks first, you’ll free up team members to concentrate on more strategic work.

Combine Repetitive Tasks With Informed Decision-Making

At first glance, automation is about reducing manual work. But effective business process automation also addresses decisions that pop up along the way. Maybe your billing department needs to flag unusual discounts or shipping anomalies. A well-designed solution doesn’t just push tasks through a pipeline; it also gathers data and applies rules (or AI-driven insights) to make smarter decisions. Even including “human in the loop” is a common AI pattern where both people and AI work in tight coordination, leveraging each’s strengths. That’s where you start shifting from “we saved a few hours” to “we’re adding real value to the process.”

Have Clear Success Metrics

Before you dive into an automation implementation strategy, define your success metrics. Are you aiming to shorten lead times, reduce data errors, or improve customer satisfaction? Metrics can be as simple as “reduce manual processing of purchase orders by 70%” or “cut invoice errors to under 1%.” Concrete goals keep your team aligned and help demonstrate the return on investment to executives and other stakeholders.

Building Intelligence Into Workflows

The Role of “Closed Domain” Knowledge

A lot of AI hype focuses on open-ended tasks—like generating fresh content or “thinking outside the box.” While that’s cutting-edge, most mid-level businesses benefit first from automating closed domain tasks where the steps are well-known. For instance, if you already know how to verify a customer’s identity or process an order return, you can use AI to accelerate that. The AI isn’t discovering a brand-new approach; it’s applying established best practices at lightning speed.

The “Knowledge Paradox”

There’s an interesting phenomenon when it comes to using AI for process automation:

• Senior employees use AI to speed up things they already know how to do well. The AI becomes an extra pair of hands (or eyes) that works quickly and consistently.
• Junior employees often rely on AI to learn what to do. Because they lack the foundational expertise, the AI’s guidance might not always be interpreted correctly, leading to mistakes or confusion.

This “knowledge paradox” highlights why human oversight remains crucial. A starting goal should be understanding how expert employees think about and perform key activities. AI can help accelerate the workflows and ensure it is done in a more consistent manner. It can also provide a starting point and guardrails for newer employees to be guided. However, giving newer employees AI to experiment, without training, can result in limited value. “CoPilot” has been branded in a number of ways. But one of the ongoing powerful interactive patterns with AI is having AI observe a business workflow, and proactively provide recommendations and guidance on how to more efficiently complete tasks. 

Moving Beyond Simple Task Automation

Intelligent automation means looking beyond standard, rule-based approaches. By integrating multiple data sources, you can give your AI processes broader context. For example, if you’re automating loan approvals, you might integrate credit scores, transaction histories, and online behavior signals all at once. But you still want human oversight for edge cases and compliance checks. This balanced approach delivers efficiency without sacrificing control.

Key Components for Success

Harnessing Large Language Models

Modern large language models (LLMs) can rapidly process unstructured data into structured data that can be more easily worked with—like emails, PDF contracts, or social media comments—that used to require manual review. The beauty of LLMs is their ability to understand context and nuances, making them perfect for tasks like scanning customer support queries or extracting data from legal documents.

Data Quality and Accessibility

As we have worked with AI and data with our clients, we have found that LLMs are giving us more power to work with and interpret messy data. Traditional data systems have struggled, and ensuring data quality has typically been a significant impediment to starting new programs or a large cost to fix or work around data challenges. With the powers of LLMs, we are able to better extract value and insights from even “messy” data that exists in various silos across organizations. We continue to advocate striving towards high-quality data. But AI might be able to work with and work around some data limitations, speeding time to implementation and tackling validating of results as part of intelligent process automation.

Integration With Existing Systems

The best AI in the world won’t help if it can’t “talk” to your customer relationship management (CRM), enterprise resource planning (ERP), or other core systems. When planning your automation implementation strategy, outline how data will flow in and out of these systems. This might involve building APIs, configuring webhooks, or deploying specialized connectors.

Compliance and Governance

As you automate more processes, you’ll likely handle sensitive data—like personal information or financial records. Whether you’re dealing with HIPAA, GDPR, or industry-specific rules, compliance can’t be an afterthought. Bake in governance from the start, and implement auditing features so you know exactly how decisions are made.

Empowering AI Agents

Agentic AI frameworks let AI “agents” autonomously take certain actions, such as escalating issues, scheduling tasks, or updating databases based on pre-approved rules. When combined with clear governance, these agents can adapt to both standard and non-standard scenarios without constantly waiting on human approval.

Implementation Approach

Start Small With Proof of Value

Begin by automating a single process that has clear ROI potential. It might be something like invoice matching or contract review. Gather metrics, demonstrate success, then use that win as a stepping stone to build organizational buy-in.

Iterate and Expand

Don’t try to automate everything right away. Once you see the initial results, expand incrementally. Maybe you automate one department’s workflows, then roll out to another. Along the way, refine your AI models and fix any data or integration issues. Focus on establishing the right AI design patterns leveraging data and new AI services, so more use cases can be tackled in a more rapid manner.

Change Management and Team Adoption

All the tech in the world won’t matter if your team doesn’t trust or use it. Communicate early and often about what’s changing, why it’s changing, and what it means for people’s day-to-day tasks. Offer training and support so everyone understands the new changes to workflows and experiences  the benefits firsthand.

Measure and Communicate Success

Track relevant KPIs—like error rates, process times, and customer satisfaction—and share these metrics with the team. Clear communication around target goals and progress builds momentum and helps justify further investments in AI process automation.

In Summary

Building process automation that delivers real, measurable results isn’t about jumping on the AI hype train. It’s about thoughtfully identifying processes that matter, designing flexible yet robust solutions, and balancing innovation with practical outcomes. Whether you’re leveraging large language models to handle unstructured data or rolling out agentic AI frameworks for dynamic decision-making, the goal is the same: streamline your business so people can spend more time doing work that matters.