Is It Really AI or Just Intelligent Automation? How to Tell the Difference.

AI is everywhere—or at least, that’s what the marketing copy says. From workflow tools to customer service chatbots, many products promise “AI-powered” capabilities. But after reviewing dozens of solutions labeled as “AI,” I’ve come to realize that many rely more on intelligent automation than on true AI. This article unpacks the difference, provides strategies to spot real AI solutions, and offers considerations for professionals evaluating technology investments.

Understanding the Hype Around AI

According to a McKinsey report on the state of AI, adoption of AI technologies has more than doubled over the past five years, with 56% of organizations reporting some form of AI deployment. This surge in AI mention has coincided with a major spike in marketing claims, leading to confusion about what actually qualifies as AI.

Why Terminology Matters

• Investor and Executive Interest: Venture capital funding for AI startups increased from around $1.3 billion in 2010 to over $40 billion in 2020, per data from Stanford’s AI Index. This has incentivized companies to label processes as “AI” to attract capital.
• Business Stakeholder Perception: Many executives see AI as the “future of work,” so there’s pressure to adopt or appear to adopt AI-driven solutions.

Intelligent Automation vs. True AI

What Is Intelligent Automation?

Intelligent automation is a set of technologies—such as robotic process automation (RPA), business process management (BPM), or rule-based systems—that automate repetitive tasks and workflows. A Deloitte study found that RPA alone can save up to 30% in operational costs.333 However, these systems primarily follow predefined rules rather than “learning” from new data or changing circumstances.

Key Traits of Intelligent Automation

• Rule-Based Decision-Making: Automates processes based on if-this-then-that logic.
• Predictable Outcomes: Outputs remain consistent unless explicitly reprogrammed.
• Minimal Need for Data Training: Works effectively with structured data and doesn’t adapt without manual updates.

What Is True AI?

True AI generally involves machine learning, deep learning, natural language processing (NLP), or related techniques. These systems learn patterns from data and can adapt to new inputs over time. For instance, a machine learning model that reads thousands of customer emails may identify emerging trends and improve its classification accuracy without explicit human intervention.

Key Traits of True AI

• Data-Driven Learning: Improves accuracy or efficiency over time through training.
• Adaptive Decision-Making: Adjusts to changes in data patterns.
• Handles Unstructured Data: Capable of analyzing text, images, or audio beyond simple, rule-based logic.

How to Spot Intelligent Automation (and Not Real AI)

1. Overreliance on Pre-Set Rules When a system’s actions are almost entirely governed by human-defined rules or macros, you’re likely dealing with intelligent automation rather than AI.
2. Static Behavior If you don’t see evidence that the product improves accuracy or adapts to new situations over time, it’s likely just running on rigid scripts.
3. Lack of Model Transparency Organizations genuinely leveraging AI often reference terms like “deep neural networks,” “supervised learning,” or “transformer-based models” in their documentation. Vague or generic explanations can be a red flag.
4. Absence of Ongoing Model Training AI solutions typically require continuous data ingestion or periodic model retraining to remain effective. If you don’t see any process for updates or re-training, it’s a good sign the product might not be using AI at all.

Identifying a Genuine AI-Powered Product

To differentiate the marketing spin from genuine AI capabilities, consider the following steps:

1. Ask About the Underlying Technology Inquire about the specific machine learning or deep learning frameworks (e.g., TensorFlow, PyTorch). Genuine AI vendors are often transparent about their technology stack.
2. Request a Demo of Adaptive Learning Request a live demo showing how the tool adapts when exposed to new or unexpected data inputs. A real AI system should at least attempt to make sense of or learn from unfamiliar patterns.
3. Assess the Data Requirements True AI solutions generally need consistent, high-quality data. Ask about the volume and variety of data required for training or operational use.
4. Probe for Real-World Use Cases Seek references or case studies describing how the system performed over time in a dynamic environment, not just in controlled pilot projects.

When Intelligent Automation Is Good Enough

Intelligent automation isn’t inherently inferior; in many cases, it’s the perfect solution. Rule-based systems can drastically reduce manual labor, eliminate human error, and deliver a fast ROI.

For example:

• Customer Service Triage: Automatically routing tickets to the right department using keywords.
• Invoice Processing: Extracting data from invoices and entering it into a finance system.
• Basic Data Cleansing: Cleaning up data sets using simple transformation rules.

In scenarios where data patterns don’t change frequently or complex predictions aren’t required, intelligent automation may be all you need.

Why True AI Matters

AI excels in tasks that require adaptation, complex decision-making, or processing of highly unstructured data:

• Predictive Maintenance: Identifying anomalies in machinery before breakdowns occur.
• Natural Language Understanding: Powering advanced chatbots or virtual assistants that can handle nuanced customer queries.
• Advanced Analytics and Forecasting: Providing real-time, data-driven insights to guide strategic decision-making.