Automation and artificial intelligence (AI) are no longer futuristic concepts—they’re actively reshaping business processes, streamlining workflows, reducing manual effort, and improving decision-making. Understanding how these technologies work and how they interconnect is crucial for organizations looking to maximize efficiency and stay competitive.

Understanding Automation

At its core, automation is the process of performing tasks or controlling systems with minimal human intervention. Every automation solution relies on inputs (data, materials, actions) and outputs (results, decisions, products). While 100% automation may not always be realistic or cost-effective, even partial automation can create substantial efficiencies.

Automation exists in both nature and business:

In nature: Processes like the water cycle or human physiology operate automatically. Water moves through evaporation, condensation, and precipitation, while the body converts food, water, and oxygen into energy and waste—without conscious effort.

In industry: Raw materials enter a production system, and finished goods emerge efficiently. Modern factories leverage robotics, sensors, and intelligent systems to increase speed, accuracy, and consistency.

Automation is not about eliminating humans entirely but about freeing them from repetitive or mundane tasks so they can focus on higher-value work.

Business Process Automation (BPA)

Business Process Automation (BPA) takes automation a step further by integrating separate technology platforms to automate entire workflows. BPA connects systems, standardizes processes, and ensures tasks are executed efficiently, accurately, and consistently.

Key Components of BPA

Data! Data! Data!

It should come as no surprise, a high volume of clean well-structured data and solid arithmetic are essential ingredients during any BPA engagement, but how that data is leveraged will vary based on the type of automation tool and the desired outcome.

ETL (Extract, Transform, Load):
ETL is the backbone of data-driven automation. This subset of automation tools extracts raw data from multiple sources, transforms it into a consistent format, and loads it into a structured database or reporting tool. A basic ETL example would be data from a data warehouse, Excel spreadsheets, SharePoint folders, or SQL server being extracted, transformed, and loaded into Power BI dashboards for analysis.

ETL tools are an essential steppingstone to more advanced automation tools such as RPA & AI. Without clean structured data generated via ETL, AI would undermine decision making with incorrect information based on bad data, ultimately doing more harm than good.

RPA (Robotic Process Automation):
Have you noticed in recent years, every homepage you visit has a comment box in the lower right screen offering support? This is the most common example of an RPA bot. RPA uses software “bots” to automate a defined workflow and perform repetitive tasks.   In many ways, RPA tools operate like an Excel Macro but are more dynamic & robust. In this basic example, the bot has been programmed to help guide you to a desired destination within a particular website.

What makes RPA tools unique is they rely heavily on user interface (UI) data. User interface (UI) data is captured behind-the-scenes by every software application and website you have ever visited. It essentially logs all the different ways you interact with that software application or website (keystrokes & mouse clicks). An RPA bot can then leverage that UI data to “automate” or “mimic” your behavior across a wide range of applications.

RPA Example: Automating project assignments, updating tickets, or populating forms in enterprise systems.

AI (Artificial Intelligence):

What is AI?

In a single line:
AI is a sophisticated mathematical function that predicts what word comes next for any piece of text.

AI has existed in theory for decades, but recent advances have made it commercially viable. Two major enablers are:

• Microchips and computing power: Modern processors contain billions of transistors, allowing high-volume & complex computations necessary for AI.
• Data storage and accessibility: Vast, inexpensive storage enables AI to process massive datasets efficiently.

AI: How It Works

Instead of predicting a word with certainty, it assigns a probability to all possible words. Once it picks a word, it uses that word to help decide the next one, and keeps going like that i.e., it builds a sentence brick by brick, only the next brick depends on the one before it.

AI language models are designed in a way that they do not always pick the most likely word. Occasionally, it selects words that are less obvious–but still contextually appropriate.

Consider this example:

• We’ll talk about this tomorrow.
• We’ll discuss this further tomorrow.

These subtle differences like – “discuss” instead of “talk”, or an additional word like “further” in the second sentence – reflects natural language in humans. AI models learn patterns like this and may occasionally favor other versions (like one in the above), even though the first one might be more common. These subtle variations are what makes AI-generated language more authentic and less robotic.

How does AI learn these patterns?

Computers do not understand text, like humans do – computers only understand numbers. Within an AI model, each word is represented by a continuous set of values. These values capture the word’s meaning, and how it relates to other words.  

Consider each word to be a unique “flavor profile” but made up of numbers.  When these flavor profiles – words combine, they create a dish – a sentence. The model looks at how these flavors blend, helping it interpret nuance and meaning based on context. 

But it doesn’t stop at one iteration. The AI model processes the text through multiple layers, each focusing on different relationships between words. This repeated refinement builds a richer understanding of what’s being said—similar to a chef tasting and adjusting a dish until it’s just right.

Types of AI:

• GenerativeAI (Weak): Trained for a specific task, such as image recognition, language translation, or chatbots.
• Agentic AI (Stronger): Capable of autonomously making decisions, learning, and taking actions toward defined goals.

Real-World Applications of Automation and AI

AI adds intelligence to automation, analyzing data, identifying patterns, and predicting outcomes.

Example: AI can summarize reports, flag anomalies, or recommend next steps in workflows.

• Manufacturing: Robotic arms automate repetitive tasks, while AI monitors quality and predicts maintenance needs.
• Finance and Accounting: Bots handle data entry, reconciliation, and report generation, while AI identifies anomalies and trends.
• Marketing and Sales: AI analyzes customer behavior, predicts trends, and automates personalized communications.
• Healthcare: Automation streamlines administrative tasks, while AI assists in diagnostics and treatment planning.

These technologies work best when integrated across systems, creating seamless workflows where inputs, processes, and outputs are efficiently aligned.

Benefits of Automation and AI

By combining ETL, RPA, and AI, organizations can create end-to-end automation solutions: clean data flows through automated processes, bots handle repetitive tasks, and AI enhances decision-making with predictive insights.

• Efficiency: Tasks are completed faster, with fewer manual steps.
• Accuracy and consistency: Reduced human error ensures reliable outcomes.
• Cost savings: Optimizes resource use and reduces operational expenses.
• Strategic focus: Employees can dedicate time to complex, creative, or high-value work.
• Enhanced customer experience: Automation and AI improve speed, reliability, and personalization of service.

Challenges and Considerations

While automation and AI offer many benefits, implementing them requires careful planning:

• Integration: Different systems and tools must communicate effectively.
• Data quality: Automation relies on accurate, well structured data.
• Change management: Employees must embrace automation & AI but need training and support to adopt new technologies.
• Cost and ROI: Investments in automation must be balanced against expected efficiency gains.

Organizations that carefully plan, test, and scale their automation and AI strategies are most likely to realize the full potential of these technologies.

The Future of Automation and AI

Automation and AI are evolving rapidly, and their impact will only increase. Interoperability, scalability, and intelligent decision-making will define the next wave of innovation. By embracing these technologies, businesses can improve accuracy, efficiency, and strategic agility, preparing for a future where AI and automation are central to success.

Automation and AI are not just buzzwords—they are transforming how work gets done. From structured ETL workflows to intelligent, autonomous AI agents, these technologies streamline processes, reduce manual effort, and enable smarter decision-making. By freeing employees from repetitive tasks, organizations can focus on higher-value work, enhance customer experiences, and drive innovation. Meaden & Moore’s Advisory Services team has the expertise to help businesses navigate these technologies and apply them effectively.

Ready to transform your business with automation and AI? Reach out today to explore how your organization can optimize processes, drive efficiency, and unlock new growth opportunities.

This article was co-authored by Steve Luc and Saranjeet Saluja.

3Blue1Brown. “Large Language Models explained briefly.” YouTube, uploaded by 3Blue1brown, 20 November 2024, https://youtu.be/LPZh9BOjkQs?si=orxBVQxa-TISe1fY