AI Is Not One Thing: Where Each Type Is on the Adoption Curve
“Any sufficiently advanced technology is indistinguishable from magic.” – Arthur C. Clarke
AI seems like magic a lot of the time. You can get an answer right away by asking a chatbot a question. See a demo of an AI tool that can make an image, look at a spreadsheet, or plan a project in only a few seconds.
But for leaders, magic isn’t enough. You need to be clear: What type of AI are we talking about? How old is it? Is it a good investment or simply a lot of talk? Let’s talk the AI adoption curve for business leaders.
The Curve of Technology Adoption
There is a pattern that all technologies follow:
- Innovators: the first to try new things, often before the technology is ready.
- Early Adopters: excited to try new things and willing to take risks for possible rewards.
- Early Majority: wait until tools are useful and dependable.
- Late Majority: only use something once it is well-known and common.
- Laggards: wait until they can’t avoid it.
Think about cell phones. Only tech fans stood in line at first. Then, smart business people who got them early developed ways to employ them. By the time the early majority got involved, app shops and mobile payments had completely transformed whole industries. The position of an AI tool on this curve tells you a lot about its risk, cost, and possible reward.
The Current State of AI Technologies
There are different levels of AI. Here is a realistic view of the spectrum:
- Predictive AI
- Examples: fraud detection, demand forecasting, recommendation engines
- Adoption: Early to Late Majority
- Grown up, stable, and mostly hidden. It’s like a boring but necessary spreadsheet for AI.
- Generative AI
- Examples: ChatGPT, Copilot, Gemini, and others
- Adoption: Early Majority
- Adoption is going through the roof. Easy to get started with, but the return on investment isn’t always good. Like the early internet, it’s changing the world but still trying to figure things out.
- Several clients have told me that they were able to quickly make progress on their presentations by using a generative AI tool, but they had trouble when they tried it for more in-depth financial analysis. Use cases that look fantastic in a demo don’t necessarily work out in real life.
- AI for Certain Fields
- Examples: legal, healthcare, finance copilots
- Adoption: Late Early Adopters to Early Majority
- High value in small lanes. Adoption takes longer because accuracy and rules are important. Works best with experts in specific situations.
- Multimodal AI
- Examples: text, speech, image, and video models like GPT-4o and Gemini
- Adoption: Early Adopters
- Amazing demos, but practical integration is still behind. Like smartphones in 2008, the promise is clear, but the workflows aren’t there yet.
- Agentic AI
- Examples: autonomous task agents, orchestration across tools
- Adoption: Innovators to Very Early Adopters
- Big promises, expensive prices, and limited results at scale. Fun to watch, but still risky for most enterprises.
- Foundation Model Builders
- Examples: businesses that train their own LLMs
- Adoption: Innovators only
- Very expensive and hard to understand. Only realistic for big companies and governments. Not worth the money for most businesses.
AI Adoption Curve for Business Leaders: Questions to Ask
Cutting through the buzzwords will help you choose the best AI solution.
A vendor once demonstrated an “AI-powered system” application for me. It sounded pretty exciting, but when I looked closer, it was really just a group of employees in other countries doing data entry and coding by hand. That kind of rebranding is a big red flag.
Key questions to ask:
- What type of AI is this?
- What part of the adoption curve does it fit into?
- Are there real-world clients that have seen a return on investment, or is it just demos?
- How much of it is really automated, and how much depends on hidden human work?
- What costs occur after licensing, like setup, cleaning up data, or compliance?
- What will happen if it doesn’t work? Will it cause a small dip in productivity or a major financial or compliance risk?
Safe Bets vs. Big Risks
Safe Bets
- Predictive AI for tasks that are structured and data-heavy
- Productivity tools with built-in generative copilots
- AI that works well in specific fields
High-Stakes Plays
- Agentic AI executing important workflows
- Building your own foundation model
- Early multimodal systems where ROI is still unclear
Why This Is Important
There isn’t just one AI. It’s a set of tools at very different levels of maturity. Vendors don’t always make that clear, and some will sell an experiment as if it were a finished product.
That’s why the AI adoption curve for business leaders should be used as a map:
- Predictive AI is already part of the system.
- Generative AI is becoming more common.
- Domain-specific and multimodal AI are intriguing but not yet widespread.
- Agentic AI and foundation models remain risky bets.
Conclusion
Clarke was right: advanced technology might seem like magic. But magic can make you forget the basics.
Many leaders I talk to have seen at least one flashy demo where AI seemed to promise the world. The key is to pause and ask the tough questions:
Is this real? Is it ready? Is it right for us?
Partner with Madken Advsiors and discover how far your business can go. Contact us today!