Riding AI Waves: Agentic AI, Open Source Tools, & AI Adoption Challenges

Historical Waves of Technology [0:50]

• The first wave, approximately 240 years ago, was the Industrial Revolution, characterized by machines and water power enabling mass production.
• The second and third waves focused on transportation, including trains, steam power, and electricity for factories, extending manufacturing and distribution capabilities.
• The fourth wave introduced computers, electronics, and airplanes, further accelerating production, distribution, and access to digital information and computation.
• The fifth wave, starting around 1990, is defined by the internet, leading to near-instantaneous global connectivity, with user numbers growing from 2.3 million to 3.4 billion by 2016.
• The sixth wave began in 2020 and is characterized by Artificial Intelligence (AI), transforming work by operating on information at an accelerated pace.

The first wave that you see and these all these waves are all about technology. So they are technology waves.

The AI Wave and Its Sub-Waves [3:37]

• The current AI wave can be broken down into four distinct sub-waves that describe its emergence.
• The first sub-wave was the "birth" of AI, conceptualized around 1950, followed by an "AI winter" due to overselling its capabilities.
• The emergence phase, starting later, utilized machine learning and data, exemplified by IBM's Deep Blue beating Garry Kasparov in chess in 1996.
• The modern wave is driven by deep learning, leading to consumer-facing applications like Alexa and Siri.
• The current surge of AI can be further divided into generative AI (recognizing patterns and trends), conversational AI (LLMs capable of natural interaction), agentic AI (performing autonomous tasks), and the upcoming physical AI.

All of this started building up into the wave that we're in now, which is a surge, a real surge of a lot of AI coming at us.

Challenges in AI Adoption [7:03]

• A primary challenge is knowing where to begin with AI adoption, similar to a surfer deciding which break to choose.
• Organizations grapple with selecting the right AI technologies, such as LLMs, chatbots, or assistive tools, analogous to a surfer choosing between a longboard or shortboard, and deciding on equipment like wetsuits.
• Ensuring readiness, including data preparation and integration capabilities with legacy systems, is crucial, much like a surfer ensuring they have the right wax and fin setup for their chosen board.
• Having the necessary skills to develop, train, and implement AI solutions is vital, akin to a surfer possessing the skills to handle different wave sizes and maneuvers.
• Timing is critical, requiring an understanding of tides, wave direction, and wind conditions in surfing, and readiness of technology and skills for AI adoption.
• Organizational resistance, stemming from the rapid pace of technological change, can pose a significant hurdle, similar to facing resistance from local surfers at a break.

The first thing that we think about when we're adopting AI is how do I start? A lot of times organizations don't even know where to begin with AI.

Lessons from Surfing for AI Adoption [11:59]

• Clearly define what you are trying to achieve (outcomes) before applying AI technologies, just as a surfer decides whether they want to ride big waves, small waves, or long rides.
• "Be ready" by having the necessary skills, technologies, and being in the right position at the right time, which for a surfer involves understanding wave breaks and paddling at the optimal moment.
• "Look two steps ahead" to avoid rushing into adopting the wrong technology or missing better opportunities, much like a surfer waiting for a larger set of waves instead of prematurely catching a smaller one.
• "Be patient" and wait for the right wave or opportunity to come, rather than impulsively acting.
• "Seize the opportunity" when the timing is right, by fully committing and taking action once prepared.
• "Embrace change and adapt" as technologies and their applications evolve, just as a surfer must adjust to different wave conditions.
• "Learn from failure," understanding that crashing and making mistakes are part of the process, leading to adaptation and continuous improvement.
• "Cheer people on" and support others, recognizing that this AI wave is a significant, generational event requiring collective encouragement.

What are you trying to do? What is it that you're trying to accomplish? We shouldn't start by saying, "Oh, AI is here. We've got to do AI, so let's just start applying all these technologies at it." No, you should really say, "What are you trying to do? What are the outcomes you're trying to accomplish?"