How Artificial Intelligence Is Powering the Future of Automation

BLOG: News + Trends

5 Min Read | July 12, 2025 | BY Christopher Lege

Making Automation Smarter with AI

The last blog post offered a 101 on process automation. This one takes a deeper dive into the role of AI.

Before the release of ChatGPT, most AI capabilities were hidden deep inside technical organizations. AI is now accessible to everyone in an organization, hence enabling potential efficiency gains when introduced correctly. This blog post is about how to leverage AI capabilities when deploying process automations in your company.

AI can act as a thinking partner embedded within automation systems. It doesn’t just execute; it “understands” context, anticipates outcomes, and adapts in near real time. From extracting insights from large datasets to predicting workflow bottlenecks, AI now powers automation in ways that are not only faster but also smarter and more intuitive.

Yet, the goal isn’t to automate everything. The real opportunity lies in using AI to make existing automation more intelligent, flexible, and aligned with business strategy. Automating what matters least enables focus on the work that really matters and has the highest impact.

How AI Transforms Automation Workflows

AI enhances automation through three fundamental capabilities:

Discovery: Recognizing patterns and insights in unstructured or messy data.

Inference: Drawing conclusions or suggesting next steps, like AI assistants that pre-assign tasks or flag issues before they escalate.

Reasoning: Enabling complex decision-making such as scenario planning, portfolio optimization, or predictive analytics.

Deploying any of these capabilities within workflow tools like Make.com or n8n suddenly allows companies to create automations where humans were essential before. For example, before advanced AI models came along, getting a computer to respond helpfully to incomplete or unclear customer requests was nearly impossible.

Real-World Impact: From Everyday Use Cases to Smart, Scalable Design

Automation powered by AI isn’t a concept reserved for tech giants anymore; it’s increasingly accessible to anyone with a problem to solve. When designed thoughtfully, even small automations can have a measurable impact. Imagine a task that takes 5 minutes every day. Once an automation is in place, the time saved is 100 minutes a month, every month. The law of scale really kicks in when recurring tasks go away.

Everyday Use Cases:

Invoice Handling: AI automatically extracts key fields, categorizes documents, and uploads them into ERP systems.
Project Quality Checks: Intelligent assistants identify missing details or inconsistencies in workflows.
Data Categorization: AI sorts unlabelled data into categories automatically.
Personal Automations: Individuals automate personal recordkeeping, like syncing invoices into Notion or tracking car maintenance work.

None of these examples demand technical depth, just clear goals and the right tools. And a little bit of time to actually implement them.

Designing and Scaling Smart Automation Systems

Creating automation that truly transforms work requires both strong conceptualization principles and a scalable mindset. The magic happens when small, local wins grow into organization-wide systems that save time, reduce errors, and foster collaboration. This approach, from small to large, makes it easy to adopt and integrate into everyday processes.

01

Start with Meaningful Wins

Focus on processes that have a clear business impact, like repetitive approvals, reporting routines, or content reviews. Automate one part of the process, measure results, and expand step by step. All while regularly checking that AI-driven automations are performing accurately. Continuous monitoring helps maintain quality as automation scales.

02

Standardize What Works

Document every automation that proves valuable by creating internal playbooks, templates, or naming conventions. This keeps teams aligned, prevents duplication of effort as adoption grows, and helps ensure that AI-based automations aren’t rebuilt or retrained to do the same work.

03

Share and Multiply

Build a culture of knowledge sharing. Encourage teams to host brief “automation showcases”, 10-minute sessions where they demonstrate what they’ve built and the impact it had. When people see how easy automation can be, innovation spreads naturally.

04

Govern for Growth

Set clear ownership and accountability for each automation, and review for accuracy, data integrity, and security before going live. Good governance ensures that scaling doesn’t mean losing control, and ongoing monitoring helps AI automations stay accurate and adapt as data or goals change.

05

Equip with the Right Tools

The right tools make scaling much easier, especially when they integrate seamlessly. Check out our latest blog post on automation to discover my 4 go-to tools for workflow automation.

Avoiding Pitfalls: The Danger of “Workslop” and AI Slops

Even with all its promises, AI-powered automation can backfire when used carelessly. A recent Harvard Business Review piece warned about “workslop,” a flood of low-quality, AI-generated tasks and content that clutters workflows instead of improving them. This is the anti-pattern of automation. Generating more work for humans is in direct contrast to the idea of why and how AI automations should be deployed. When organizations deploy AI without oversight, they risk turning efficiency into chaos. The result? More review work, unclear responsibilities, and growing frustration. To avoid this, companies need to combine automation discipline with human judgment:

Set clear goals before deploying an automation.
Always validate AI outputs.
Treat automation as an evolving system, not a “set and forget” process.

AI should increase productivity, not dilute it.

Managing AI Accuracy: Why 95% Can Be More Than Enough

One of the most empowering questions for leaders: When is “good enough” actually more than enough? Achieving 95% accuracy with AI is no small feat. It represents a meaningful step forward for many business processes. In areas like marketing, that level of precision can already unlock major efficiency gains and enable faster decision-making. In higher-risk environments like payroll or compliance, that same 95% can still deliver value when paired with a smart, risk-based approach to oversight and governance. Forward-thinking organizations optimize for both performance and responsibility:

Weigh the upside of faster execution against the cost of rare errors.

Integrate financial, reputational, and ethical considerations early.

Define thresholds based on business sensitivity and context.

Automation success is not about chasing perfection. It is about knowing where 95% is already a win and where an extra layer of human judgment ensures balance and trust.

The Human-AI Future: Smarter Systems, Stronger Partnerships

The future of automation isn’t about doing more; it’s about doing it smarter. AI and automation together are creating systems that think, learn, and adapt, freeing humans to focus on creativity, empathy, and strategy. The most successful organizations will be those that build balance:

Automation handles the repetitive.
AI provides context and reasoning.
Humans drive direction, ethics, and innovation.

By aligning AI with purpose and process, businesses can unlock a new era of resilience, agility, and creativity, where humans and machines work not in competition, but in collaboration.

About the author

Christopher Lege

He is Senior Principal Software Engineer at CATAPULT, specializing in AI and Amazon content. Leveraging extensive experience in optimizing business processes, he focuses on data-driven strategies to enhance efficiency. With strong analytical skills and deep technical expertise, he supports our clients to drive success.