Why Clarity Matters More Than the Hype
I see this again and again, automation being confused with AI.
No, you’re not implementing AI just because you’re automating workflows. It’s not wrong, it’s just not AI. Yes, you can use AI with automation – and more and more this combination comes with the tools used – but making certain actions in spreadsheets trigger certain other actions is not, necessarily, AI.
I’m all for optimizing work, and a lot of work can be optimized with automation, but this is something we could do for a long time. Just because we somehow discover the option now does not mean that we’re becoming “AI first.” It just means that we’ve begun to implement automations, and while that definitely is good and can help us deal with some repetitive work, it lacks the AI.
Why This Confusion Matters
This isn’t just semantic hair-splitting. When organizations misidentify automation as AI, they make flawed strategic decisions about resource allocation, skill development, and capability building. I’ve seen this pattern repeatedly in my studies on AI adaptation and organizational change, companies invest in “AI initiatives” that are really just workflow automation projects, then wonder why they’re not seeing the transformative results they expected.
The distinction matters because automation and AI solve fundamentally different problems. Automation excels at repetitive, rule-based tasks with predictable inputs and outputs. AI handles interpretation, pattern recognition, and decision-making in contexts where rules alone aren’t sufficient. When we blur these categories, we lose the ability to match the right tool to the right problem.[1] [2]
Consider this, if you’re automating email responses with templates triggered by keywords, that’s automation. If you’re using a system that understands the intent behind varied customer queries and generates contextually appropriate responses, that’s AI. Both are valuable. Both save time. But they’re not the same thing, and pretending they are leads to confused expectations and poor implementation strategies.
The Semantic Drift
To be fair, words change meaning. For example, in 2020, “AI” meant machine learning models. In 2025, it is getting blurred with automation. This isn’t necessarily bad, it shows AI is normalizing, but we should name things clearly so we know what capabilities we’re actually building.
This semantic drift has real-world consequences. Research shows that many organizations are stuck in what some call “pilot hell,” where AI projects never scale beyond initial experiments.[3] Part of this failure stems from fundamental confusion about what they’re actually building. When you call automation “AI,” you set expectations for adaptive, learning systems – then deliver rule-based processes that do exactly what they’re programmed to do, nothing more.
The enterprise AI landscape reflects this confusion. Studies indicate that while 84% of executives believe AI is essential for growth, most large organizations are “merely dipping their toe in the water” with actual AI adoption.[4][5] Many have “AI-embedded” SaaS tools and helper chatbots, but these often amount to sophisticated automation rather than genuinely intelligent systems.
A Framework for Clarity: Workflow Maturity Levels
Or, maybe we should put it like this – think of workflows, that is, workflow maturity levels:
Level 1: Manual workflows (you do everything)
Level 2: Automated workflows (rules do predictable tasks)
Level 3: Augmented workflows (AI handles interpretation, you decide)
Level 4: Autonomous workflows (AI decides, you audit)
Most “AI implementations” I see are Level 2 being called Level 3. Both are valuable, but let’s be precise about where we are.
This framework isn’t just theoretical categorization. It’s a practical tool for honest assessment of organizational capabilities. When I work with teams on AI adoption, the first step is always determining where they actually are, not where they wish they were or where the marketing materials claim they are.
Understanding the Levels
Level 2 automation follows deterministic logic: if X happens, do Y. It’s fast, consistent, and predictable. It eliminates human error in repetitive tasks. But it cannot adapt to situations outside its programmed rules. It doesn’t learn. It doesn’t interpret context.[6]
Level 3 augmentation involves systems that can handle ambiguity and variation. They recognize patterns, interpret intent, and surface insights, but human judgment remains central to decision-making. The AI doesn’t replace the human; it amplifies human capability by handling the cognitive heavy lifting of processing and pattern recognition.
Level 4 autonomy represents systems that make decisions independently within defined parameters, with humans providing oversight and intervention when needed. This is where AI’s decision-making capabilities are trusted enough to act without constant human approval, though humans remain accountable for outcomes.
The progression isn’t just about adding more technology. It’s about fundamentally different relationships between humans and systems, different trust levels, different skill requirements, and different organizational readiness factors.
What Real AI Augmentation Looks Like
What could be an example of a Level 3 AI workflow? Take Google Meet’s transcription and note assistant and create an automation – we shouldn’t dismiss automation as a crucial part of good AI implementation – that transfers the transcript to a dedicated Google Doc, which is connected to NotebookLM. There you are, you have a meeting knowledge management assistant that can help with knowledge capture. When you need to recall decisions or action items, you query NotebookLM and it surfaces relevant context. You interpret and act on what it finds. Conceptually easy and an actual AI implementation that augments workflows.
Notice the combination here: automation handles the mechanical transfer of data, AI handles the interpretation and retrieval. The automation ensures consistency and eliminates manual steps. The AI provides intelligence, understanding context and surfacing relevant information based on semantic meaning rather than keyword matching.
This is what I mean by complementary functions rather than redundant capabilities. The automation does what automation does best: reliable, repeatable execution. The AI does what AI does best: pattern recognition, semantic understanding, and adaptive response to varied queries. Together, they create something more valuable than either could alone.
Considerations from the research support this approach. Organizations that successfully implement AI don’t just bolt intelligence onto existing processes. They redesign workflows to leverage both automation for efficiency and AI for adaptation. They invest in data quality, governance, and the human skills needed to work effectively with augmented systems.
Why Getting This Right Matters for Implementation
The confusion between automation and AI has practical consequences for implementation success. Research on why AI projects fail consistently points to a few core issues: weak change management, unclear roles, skill gaps, and fragmented ownership between technical and business teams. These problems are exacerbated when organizations don’t clearly understand what they’re actually implementing.
If you think you’re deploying AI but you’re really deploying automation, you’ll make the wrong investments. You’ll hire the wrong skills. You’ll set the wrong expectations with stakeholders. You’ll measure the wrong outcomes. And when the “AI” doesn’t deliver the adaptive, learning capabilities you expected, you’ll conclude that AI doesn’t work, when really, you never implemented AI in the first place.
I’ve seen this pattern in multiple contexts. Teams build sophisticated workflow automation, complete with complex conditional logic and multiple integration points. It works beautifully for its intended purpose. But then they’re disappointed that it doesn’t “get smarter over time” or “learn from user behavior” or “adapt to new situations,” because those aren’t characteristics of automation. They’re characteristics of AI.
The reverse is also true. Teams that try to use AI where automation would suffice introduce unnecessary complexity, cost, and unpredictability. Not every problem requires machine learning. Sometimes, a well-designed set of rules is exactly what you need, more reliable, easier to maintain, and far less expensive to operate.
Moving Forward with Clarity
The path forward requires honest assessment and clear language. Before embarking on any “AI initiative,” organizations should ask:
- Does this problem require interpretation and pattern recognition, or is it rule-based?
- Do we need a system that learns and adapts, or one that executes consistently?
- Are we building Level 2 automation, Level 3 augmentation, or Level 4 autonomy?
- Do we have the data quality, governance, and skills for actual AI, or should we start with robust automation?
These aren’t just technical questions. They’re strategic questions about capability building, resource allocation, and organizational readiness. Getting them right requires moving past the hype and the semantic confusion to understand what we’re actually building and why.
The good news is that both automation and AI have enormous value. The confusion between them doesn’t negate that value. But clarity about what we’re implementing, what capabilities we’re building, and what outcomes we can realistically expect, that clarity is essential for success.
So let’s stop calling automation “AI.” Let’s be precise about workflow maturity levels. Let’s match tools to problems based on actual requirements rather than marketing buzzwords. And let’s build systems that leverage both automation and AI for their distinct strengths, creating genuinely augmented workflows that make us more effective without pretending we’ve achieved capabilities we haven’t.
This isn’t just about pedantic people being obsessed with semantic precision. Not at all, this is about how to understand the foundation for successful AI adoption in the real world.