It is undeniable that AI is one of the most disruptive and transformative technologies of our time, with the potential to impact every sector and industry in the world. However, despite the growing awareness and fascination with AI, many organizations are still struggling to adopt AI effectively and realize its value. What are the reasons for this gap between AI hype and AI execution? How can organizations overcome the challenges and seize the opportunities of AI adoption? According to Deloitte, there has been some speculation that the shortfall in AI realization is due to an inability to translate theory into practice.
Two years ago, I shared my thoughts on the rise of AI and its position in the S-Curve. I argued that AI was in the growth phase, where the early majority began to use it for various purposes and realized its value and potential. They also learned what AI is and how it can be applied.
However, earlier this year, we witnessed a dramatic shift in AI that made it skyrocket exponentially. This was due to the launch of ChatGPT and other generative AI models that simplified the use of AI for both companies and individuals. They made AI accessible to anyone with an internet-connected device, democratizing AI for everyone. This increased the awareness and the fear of AI, so people from non-technical backgrounds started to study and comprehend AI. They also used it in different ways to achieve their goals, such as writing emails, solving problems, reviewing, and creating various documents and research papers that enhanced our productivity in an unparalleled way.
I think that the late majority, especially in the enterprise sector, are still in the fascination phase. They are getting to know AI, its value, and some basic use cases that can enhance their daily operations and productivity. But they are not yet pursuing the execution and value realization to start investing in this radical change. According to Accenture, 63% of companies are AI Experimenters.
This is the most exciting topic and trend, but I also notice that the majority are in the peak of illusion that AI and generative AI are the solution for everything. This is what Gartner described in its hype cycle for AI 2023, that generative AI is at the peak of inflated expectations.
In this article, I will explore these questions and provide some insights and recommendations for organizations that want to accelerate their AI adoption journey. I will use the Organization Skill/Capability vs Will/Motivation matrix as a framework to categorize the organizations based on their skill/capability and will/motivation to adopt AI.
This matrix can help identify the state of AI adoption in organizations and the potential challenges and opportunities for each type of organization.
The matrix consists of four quadrants, each representing a different type of organization:
- Laggard (Low Will/Low Skill): Organizations that have a low motivation and a low skill to adopt AI. They are either unaware of the benefits of AI, or resistant to change their status quo.
- Hesitant (Low Will/High Skill): Organizations that have a high skill but a low motivation to adopt AI. They are either concerned about the ethical, legal, or social implications of AI, or reluctant to share their data and expertise with others.
- Aspirationals (High Will/Low Skill): Organizations that have a high motivation to adopt AI but lack the necessary skills or capabilities to do so effectively. They are either eager to learn from others, or willing to outsource or partner with experts.
- Exemplars (High Will/High Skill): Organizations that have a high motivation and a high skill to adopt AI. They are either visionary leaders, or agile innovators, who leverage AI to create value and competitive advantage.
I will discuss each type in detail and provide some examples of challenges and strategies for each quadrant. I will also share some best practices and suggestions for taking action for each type. By reading this article, you will learn how to assess your organization’s AI adoption status and how to improve it.
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