Five Considerations When Adopting AI: An Executive’s SOLID Compass
As a business leader, you’re no stranger to the relentless pace of technological advancements. The surge of headlines and pitches all compete for your attention, each claiming to herald the next big thing in AI.
In this deluge, it’s crucial to discern which innovations truly matter and how to effectively navigate them without getting lost in jargon or distracted by passing trends. What you need is clarity, a guide to discern the bigger picture.
In this article, I will share the SOLID framework, a guide for navigating AI decisions, which I’ve forged from practical experience and developed through real-world AI endeavors. It’s your AI compass, designed to streamline the complex terrain of AI adoption and keep you on course.
Here are five considerations to explore as you navigate the AI landscape:
[s/trillion-dollar model/trillion-parameter model/g]
Imagine a global semiconductor equipment maker considering their fabrication facility customers’ strict security needs. When they face a choice between small and large AI models, the need for precision in a security-focused industry will be crucial.
Smaller AI models are the optimal choice in this context. Agile and precise, these models fit well within the secure, confined computational environments of on-site, PC-sized computers, adhering to the high-security standards essential in semiconductor fabrication.
However, in scenarios where expansive knowledge and broader data processing capabilities are essential, large language models (LLMs) may be more suitable. Their vast knowledge base and comprehensive understanding make them ideal for tasks requiring a wide-ranging grasp of various subjects.
The decision, thus, hinges on matching the AI model’s capabilities with the specific operational and security needs of the business environment.
Owning or renting AI models resembles the real estate choice of buying versus renting.
Using proprietary models from vendors, akin to apartment renting, offers quick solutions. This route is particularly appealing for businesses seeking immediate AI deployment without the complexities and resources required for development. It may involve limitations and dependencies on the vendor’s offerings and terms.
Conversely, developing an AI system based on open-source foundations mirrors the benefits of homeownership. It allows businesses to tailor their AI systems, embedding them with domain-specific knowledge and proprietary insights. This not only enhances the AI’s effectiveness in meeting organizational goals but also provides greater control over the technology.
Ultimately, renting offers ease and quick access, and owning provides customization and independence.
The distinction between reflexive System-1 AI and deliberative System-2 AI—inspired by Daniel Kahneman’s idea—is key in AI applications.
System-1, like a library encyclopedia, excels in quick question-and-answer scenarios.
In contrast, System-2 AI is deliberative, suited for long-range planning and reasoning. Its strength lies in dealing with workflows that require in-depth analysis and thought. By applying this AI model’s analytical and strategic capabilities, companies can overhaul operations and achieve significant time reductions and operational efficiencies.
Choosing between System-1 and System-2 AI hinges on the task’s nature and the required depth of engagement. System-1’s reflexive response is perfect for direct, routine challenges. System-2’s deliberative approach is indispensable for situations that demand careful, long-range planning and complex problem-solving.
Tech giants such as Google and Microsoft demonstrate the prowess of AI in sifting through and extracting valuable insights from vast seas of structured digital data. Their success is largely due to AI’s proficiency in processing and analyzing these extensive, homogeneous data sets, which are characteristic of digital domains.
In physical industries like manufacturing, AI faces a distinct challenge due to the heterogeneous and sparse nature of data. Despite the abundance of IoT data, usable data for machine learning becomes limited once it has been stratified by equipment type, model year and workload. This is particularly problematic in predictive maintenance, where few failure instances hinder AI’s learning capabilities.
For example, a global commercial refrigeration manufacturer with sparse failure data would have to meld human expertise with AI algorithms. This approach would allow them to navigate data limitations and more likely lead to effective prediction of equipment failures and improved maintenance planning.
This scenario underscores the importance of human-AI collaboration in physical sectors. Here, AI must handle less uniform, more nuanced data. Merging human insight with AI analysis is crucial in these complex scenarios.
Choosing between specialist and generalist models is strategic, akin to selecting either a seasoned architect or a skilled craftsman.
Generalist AI models, similar to architects with broad skills, are adept at handling a diverse array of tasks across various domains. They excel in environments where a comprehensive, versatile approach is needed, capable of processing a wide range of data and tackling multiple types of problems.
In sectors requiring precision and specific expertise, specialist AI models are crucial. Like master craftsmen, they’re fine-tuned for particular areas, addressing challenges with high accuracy. Their focused expertise allows for exceptional performance in their specific domains.
This choice between specialist and generalist AI models hinges on the specific needs of the scenario. While generalist models offer breadth and adaptability, specialist models bring depth and precision. The decision should align with the nature of the tasks at hand and the level of specialization required to address them effectively.
Steering your organization through the AI landscape involves more than embracing new technology; it requires strategic and informed decision-making. The SOLID framework, distilled from extensive real-world experiences, is intended to guide you in making choices that align with your organization’s needs and goals.
The point of the framework is to emphasize the importance of considering Size, Ownership, Level of AI, Industry application and Domain knowledge in your AI strategy. In navigating the vast and diverse world of AI, understanding these key dimensions enables you not only to adopt AI but to effectively leverage it, driving your business toward greater success and innovation.
As a business leader, you’re no stranger to the relentless pace of technological advancements. The surge of headlines and pitches all compete for your attention, each claiming to herald the next big thing in AI.
In this deluge, it’s crucial to discern which innovations truly matter and how to effectively navigate them without getting lost in jargon or distracted by passing trends. What you need is clarity, a guide to discern the bigger picture.
In this article, I will share the SOLID framework, a guide for navigating AI decisions, which I’ve forged from practical experience and developed through real-world AI endeavors. It’s your AI compass, designed to streamline the complex terrain of AI adoption and keep you on course.
Here are five considerations to explore as you navigate the AI landscape:
[s/trillion-dollar model/trillion-parameter model/g]
Imagine a global semiconductor equipment maker considering their fabrication facility customers’ strict security needs. When they face a choice between small and large AI models, the need for precision in a security-focused industry will be crucial.
Smaller AI models are the optimal choice in this context. Agile and precise, these models fit well within the secure, confined computational environments of on-site, PC-sized computers, adhering to the high-security standards essential in semiconductor fabrication.
However, in scenarios where expansive knowledge and broader data processing capabilities are essential, large language models (LLMs) may be more suitable. Their vast knowledge base and comprehensive understanding make them ideal for tasks requiring a wide-ranging grasp of various subjects.
The decision, thus, hinges on matching the AI model’s capabilities with the specific operational and security needs of the business environment.
Owning or renting AI models resembles the real estate choice of buying versus renting.
Using proprietary models from vendors, akin to apartment renting, offers quick solutions. This route is particularly appealing for businesses seeking immediate AI deployment without the complexities and resources required for development. It may involve limitations and dependencies on the vendor’s offerings and terms.
Conversely, developing an AI system based on open-source foundations mirrors the benefits of homeownership. It allows businesses to tailor their AI systems, embedding them with domain-specific knowledge and proprietary insights. This not only enhances the AI’s effectiveness in meeting organizational goals but also provides greater control over the technology.
Ultimately, renting offers ease and quick access, and owning provides customization and independence.
The distinction between reflexive System-1 AI and deliberative System-2 AI—inspired by Daniel Kahneman’s idea—is key in AI applications.
System-1, like a library encyclopedia, excels in quick question-and-answer scenarios.
In contrast, System-2 AI is deliberative, suited for long-range planning and reasoning. Its strength lies in dealing with workflows that require in-depth analysis and thought. By applying this AI model’s analytical and strategic capabilities, companies can overhaul operations and achieve significant time reductions and operational efficiencies.
Choosing between System-1 and System-2 AI hinges on the task’s nature and the required depth of engagement. System-1’s reflexive response is perfect for direct, routine challenges. System-2’s deliberative approach is indispensable for situations that demand careful, long-range planning and complex problem-solving.
Tech giants such as Google and Microsoft demonstrate the prowess of AI in sifting through and extracting valuable insights from vast seas of structured digital data. Their success is largely due to AI’s proficiency in processing and analyzing these extensive, homogeneous data sets, which are characteristic of digital domains.
In physical industries like manufacturing, AI faces a distinct challenge due to the heterogeneous and sparse nature of data. Despite the abundance of IoT data, usable data for machine learning becomes limited once it has been stratified by equipment type, model year and workload. This is particularly problematic in predictive maintenance, where few failure instances hinder AI’s learning capabilities.
For example, a global commercial refrigeration manufacturer with sparse failure data would have to meld human expertise with AI algorithms. This approach would allow them to navigate data limitations and more likely lead to effective prediction of equipment failures and improved maintenance planning.
This scenario underscores the importance of human-AI collaboration in physical sectors. Here, AI must handle less uniform, more nuanced data. Merging human insight with AI analysis is crucial in these complex scenarios.
Choosing between specialist and generalist models is strategic, akin to selecting either a seasoned architect or a skilled craftsman.
Generalist AI models, similar to architects with broad skills, are adept at handling a diverse array of tasks across various domains. They excel in environments where a comprehensive, versatile approach is needed, capable of processing a wide range of data and tackling multiple types of problems.
In sectors requiring precision and specific expertise, specialist AI models are crucial. Like master craftsmen, they’re fine-tuned for particular areas, addressing challenges with high accuracy. Their focused expertise allows for exceptional performance in their specific domains.
This choice between specialist and generalist AI models hinges on the specific needs of the scenario. While generalist models offer breadth and adaptability, specialist models bring depth and precision. The decision should align with the nature of the tasks at hand and the level of specialization required to address them effectively.
Steering your organization through the AI landscape involves more than embracing new technology; it requires strategic and informed decision-making. The SOLID framework, distilled from extensive real-world experiences, is intended to guide you in making choices that align with your organization’s needs and goals.
The point of the framework is to emphasize the importance of considering Size, Ownership, Level of AI, Industry application and Domain knowledge in your AI strategy. In navigating the vast and diverse world of AI, understanding these key dimensions enables you not only to adopt AI but to effectively leverage it, driving your business toward greater success and innovation.
