Cutting Through the AI Hype: A 4‑layer Framework for Meaningful Innovation
Artificial Intelligence feels like the new electricity these days. Everywhere you turn, there’s a flashy AI demo, a headline about the latest chatbot, or a startup claiming their AI will change the world. It’s exciting – AI does have the potential to transform almost every industry, much as electrification did in the early 1900s. (AI visionary Andrew Ng once noted that “just as electricity transformed almost everything 100 years ago, today I have a hard time thinking of an industry that I don’t think AI will transform in the next several years” .) But alongside that excitement comes hype. Business leaders hear so much noise that it’s hard to tell which innovations will truly matter for their company and which are just science fair experiments. In fact, a recent survey found 65% of U.S. business leaders are “sick of the hype” and just want practical AI solutions.
How can you, as a business leader, cut through this frenzy? One approach is to view the AI landscape as a progression of four layer – from basic awareness to the futuristic vision of true general AI. This framework can serve as a map, helping you focus on meaningful innovations at each stage and avoid chasing every shiny object. Let’s break down these four levels (starting at layer 0 for groundwork and going up to 4 layer for the frontier) and see how you can navigate them in a way that uniquely serves your business.
Implicit and Explicit AI
You’re Already Using It (Even If You Don’t Know It)
Before diving into layers, it helps to realize that AI isn’t always something you “use” the way you might open an app or talk to a chatbot. Some AI is explicit—like ChatGPT or Midjourney, where you’re clearly interacting with an AI system. But much of the AI powering today’s business world is implicit—working invisibly behind the scenes. It’s what routes your Uber, filters spam from your inbox, optimizes your ad spend, or prioritizes tickets in your support software. Businesses would commonly promote Implicit AI as AI Powered.
And here’s the key insight: as a business leader, you don’t need to “build AI” to benefit from it.
You can start by choosing platforms that already embed AI capabilities in useful, targeted ways.
For example:
- CRM and sales tools like HubSpot or Salesforce use AI to score leads, predict conversions, and recommend next-best actions.
- Marketing platforms like Meta Ads or Google Ads use AI to automatically test creatives, target audiences, and optimize campaign spend.
- Customer support systems like Intercom or Zendesk use AI to classify tickets, suggest responses, and route queries to the right team.
- Inventory and logistics platforms often use AI for demand forecasting and route optimization.
- Even email marketing tools like Mailchimp or Klaviyo use AI to personalize send times, subject lines, and content blocks.
By selecting and integrating these kinds of platforms, your business can unlock meaningful AI benefits without needing a single data scientist on staff. You’re not “falling behind” if you haven’t built a custom chatbot—chances are, you’re already riding the AI wave. The real question is: are you picking tools that turn invisible AI into tangible business value?
Beyond the Magic Trick
AI’s Real Capabilities and Limits
AI today can feel like magic. But it’s important to distinguish between what it seems to do and what’s actually happening under the hood. Large language models like GPT don’t truly “understand” or “think”—they’re guessing the next word based on patterns in massive datasets. They don’t reason like a human; they simulate reasoning based on statistical associations. This doesn’t mean they’re useless—far from it. But it does mean we need to be precise when evaluating what tasks AI is genuinely good at versus where it still struggles.
Experts knows this and that is why they clearly separate today’s AI systems from Artificial General Intelligence (AGI)—a hypothetical form of AI that can understand, learn, and reason across any domain like a human. What we have now is narrow AI: powerful, but task-specific. For the purpose of this post, we’ll focus only on current, real-world AI. That’s where meaningful innovation and practical business value lie today.
Here’s a breakdown:
- Current AI (e.g., GPT models) excels at pattern recognition and data recall, but is not true intelligence or AGI. For instance, it can’t actually cook an egg—it can only generate instructions.
- GPT models work by predicting the next word, operating in an abstract space of 12,288 dimensions. Words are embedded and transformed based on context using billions of parameters.Because of this GPT models do really well in most standardised tests, which is how we measure our own intelligence, but at the same time this is also why it don’t do better than us in Math.
- AI models don’t actually “do math” but repeat learned patterns, which is why they often fail at tasks requiring logic or calculation. This is also why it learns language without needing to know the language “rules” just a lot of correct example use of the language.
- OpenAI trained GPT-4 with 1.8 trillion parameters and massive GPU power, but performance hit a wall of diminishing returns—bigger models no longer yield proportionally better results. Worse still, there’s not enough high-quality data (words) in the world to continue scaling models effectively. We are reaching a hard resource limit in AI development.
- A promising alternative is emerging: models like DeepSeek claim similar results with fewer parameters and lower cost, hinting at a potentially more efficient path forward.
- Chain-of-Thought reasoning models break tasks into subtasks, improving logical reasoning (does well with IQ tests,decision making and programming)—yet still struggle with real-world decisions, creativity, and advanced math. They lack situational awareness, judgment, or the ability to truly grasp context beyond what they’ve been trained on.
- Not truly “creative”: AI can remix existing content impressively, but generating original thought or intuition-based leaps remains a human strength.
Layer 0: The Builders’ Toolkit
This is the foundational layer — the frameworks, infrastructure, and toolkits used to train AI models. Think of platforms like PyTorch, HuggingFace, or tools that help fine-tune models on private data.
If your organization has access to large proprietary datasets — like customer records, operational logs, or niche domain data — this layer might be where your competitive edge lies. Training your own model isn’t for everyone, but for those with data assets and tech maturity, it’s worth exploring.
TLDR: Useful if you have strong data and engineering capabilities. Otherwise, skip.
Layer 1: Pretrained Models
This is where most businesses first engage with AI. These are the models already trained by others — ready to use or adapt.
It helps to separate these by “senses”:
• Visual AI: image recognition, object detection, OCR.
• Language AI: chatbots, summarizers, translators, coding copilots.
• Audio/Voice AI: transcription, speech synthesis, voice command systems.
For many companies, this layer enables faster prototyping of digital products, internal tools, or even client-facing services. If you weren’t building software before, you still don’t have to — but now, it’s much easier if you want to.
TLDR: Boosts innovation and digital MVPs. Great for internal tools — but owning your own tech comes with long-term costs (team, maintenance, hardware).
Layer 2: Smart Tools and Automation
This is where AI becomes functionally useful — helping you improve efficiency, reduce manual work, and optimize existing systems. It includes:
• AI-enhanced CRM systems
• Process automation bots
• Recommendation systems
• Forecasting and analytics tools
This layer is a natural extension of your digital transformation efforts. If you already have a plan for improving processes or digitizing your workflows, weaving in AI will likely bring even better results.
TLDR: Don’t chase shiny tools. Plug AI into your existing business improvements.
Layer 3: Agentic AI (Emerging Frontier)
Here, we’re talking about autonomous AI agents — systems that can make decisions, train themselves, delegate tasks to other AIs, and operate with minimal human input. This is still very new and mostly experimental.
True agentic AI is still on the edge of what’s commercially usable. If you’re not in R&D or an AI-first company, you probably don’t need to worry about this yet. But it’s worth watching.
Agentic AI has a specific definition rooted in autonomy and goal-oriented behavior. However, its frequent use in marketing and diverse applications can lead to ambiguity or misuse. To ensure clarity, it is essential for developers and organizations to explicitly define what they mean by “agentic” in their context and differentiate it from related concepts like generative AI or traditional automation systems.
TLDR: Exciting but early. Pay attention if you’re innovating at the edge.
Bonus: “True AI” — A Whole Different Beast
There’s another conversation entirely around “True AI” — artificial general intelligence (AGI). This is the hypothetical future where AI could do anything a human can, across all domains. It’s fascinating, scary, and revolutionary.
But for now? Let’s keep that can of worms sealed and set aside. Focus on what’s real, useful, and actionable today.
Final Thoughts: What to Do Now
Not every business needs to train models, automate everything, or build AI agents. The real question is:
Where can AI create the most leverage for your business?
Ultimately, meaningful innovation with AI comes from understanding what the technology can do and what your organization truly needs. Treat AI as a tool in service of your strategy, not the other way around. Just as electricity eventually became a ubiquitous utility that every business harnesses in its own way, AI will likely become an ordinary yet indispensable part of business infrastructure. By cutting through the hype today and focusing on how AI can serve your unique context, you position your company to ride this revolution with clarity and success. In the end, the winners won’t be those who simply adopted AI for bragging rights, but those who integrated it wisely to power real results.
