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Key Takeaways from My Lecture at the California Science and Technology University (CSTU)

by | Dec 19, 2024

Clément Schneider hosting a lecture on AI at CSTU.
Clément Schneider

I gave a lecture at the California Science and Technology University on artificial intelligence. My goal was to take stock of the current state of AI, its limitations, and its future.

This wasn’t about trends, but about addressing the core issues. I wanted to cut through the noise and clarify how to use AI effectively, where it truly excels, and where it falls short.

I focused specifically on multi-agent systems (MAS) and explained why businesses must move beyond narrow, disconnected solutions to adopt integrated, scalable AI systems.

Here’s a summary of the key points from my talk.

1. Experiment with What You Have Instead of Waiting for the Perfect Solution

AI solutions evolve so quickly that by the time you implement one, a more advanced option may already be available. Instead of wasting time searching for the “perfect” tool, focus on the essentials: understanding AI’s capabilities and limitations and aligning the technology with your business needs.

The real value of AI lies in identifying processes to automate—those with the highest potential impact. This requires mapping workflows, identifying bottlenecks, and prioritizing areas where AI can truly make a difference.

The most successful companies today are those that adapt to this constant evolution, not those waiting for a “miraculous” solution.

2. Vertical Solutions Will Become a Problem for Businesses

Most current AI tools are too specific. They solve one problem, such as automating customer service or optimizing ads, but they aren’t scalable for a business undergoing transformation. Managing dozens of these “vertical solutions” creates inefficiencies, fragmentation, and complications—both technologically and operationally.

The future lies in horizontal platforms: AI solutions that integrate workflows across teams and grow with the business. These aggregator systems won’t just replace individual tasks; they’ll unify processes and enable deep operational transformation.

3. Multi-Agent Systems (MAS) Will Redefine How We Work

Today, tools like ChatGPT act as single assistants: effective for specific tasks but limited in scope. The next evolution is multi-agent systems (MAS)—groups of specialized agents working together, coordinated by a “manager” or meta-agent.

MAS already enable automation on an unprecedented scale and can be implemented today. Imagine a marketing department where one agent generates content, another analyzes performance, and a third ensures campaigns align with customer data—all seamlessly coordinated.

MAS don’t just assist teams; they orchestrate entire operations. However, they aren’t plug-and-play solutions. Businesses must deeply understand their workflows, map their processes, and align these systems with their goals.

While MAS are a crucial step, they also hint at a future where a single advanced AI could autonomously manage everything.

4. AI Is Chaotic—Dive In Anyway

AI is chaotic: new tools, constant updates, and daily breakthroughs. It’s overwhelming, even for those of us in the field. But this is the reality.

The only way to truly understand AI is to experiment. You can’t just read case studies or listen to marketing pitches; you need to test tools, make mistakes, and learn through experience.

Success with AI doesn’t require expertise—it demands curiosity, resilience, and a willingness to adapt in a field that will keep changing every month for years to come.

5. The Three Types of Companies Adopting (or Avoiding) AI

I categorized companies adopting AI into three groups:

  1. Ambitious Adopters:
    These companies dive in fully, building custom solutions and experimenting aggressively. They lead innovation, testing cutting-edge approaches and pushing technological boundaries. However, they must manage high costs and operational complexity.
  2. Curious Explorers:
    These companies see AI’s potential but are unsure where to start. They recognize the opportunity but often lack clarity on priorities. With strategic guidance, they can quickly align their goals with AI capabilities and turn early experiments into lasting competitive advantages.
  3. Cautious Observers:
    These companies have heard of AI but haven’t taken the time or initiative to act. Often held back by a lack of resources or fear of the unknown, they risk falling behind competitors.

In an environment where AI adoption is accelerating, standing still means falling behind.

No matter which category you identify with, the key is to start now. AI isn’t a wave to wait for; it’s a landscape to explore. Every small action—testing a tool, automating a process, or observing your workflows—brings you closer to your goal. The earlier you start, the better prepared you’ll be to leverage the coming revolution.

Conclusion

My message was clear: the AI revolution isn’t waiting. It’s fast, messy, and full of opportunities. The winners will be those who embrace this chaos, experiment, and adapt.

AI isn’t just the future—it’s already here. The only question is: will you lead this change, or will you fall behind?