Building A Learning Organization

Generated by DALL·E

𝓘n the rapidly evolving technological landscape, meta skills such as learning & assimilation are becoming more valuable than specific abilities. As companies expand, they often adopt a dominant logic or a primary way of operating. Similar to an AI model, this dominant logic is built through reinforcement of the past, and becomes deeply engrained in the companies ethos. However, it’s not just the company’s positioning that influences performance; the rapid-changing market structure also plays a significant role.

“firms had to learn about diversifying their management approach as a distinct process and skill”

Business leaders develop ‘schemas’ (Norman, 1976) throughout their development, which filter ideas based on their beliefs, theories, and frameworks. This skill is vital for making swift decisions among an overwhelming amount of information.

This works, as long as the strategy required for each business or project is similar. For instance, Procter & Gamble’s toothpaste and face cream are different products but follow similar strategies. Conversely, for a company like General Electric with dishwashers and energy grids, diverse management logic is required for success. It requires two or more parallel-running strategies.

Pattern recognition is a key distinguisher between masters and amateurs. A study by Simon in 1979 revealed that grandmaster chess players know around 50,000 patterns compared to the 1,300 known by amateurs. This ability to predict outcomes more accurately often leads to their success.

True diversity lies not in the different markets managers operate in, but in the diversity of their strategies, beliefs, and frameworks.

This approach involves cultivating a perpetual-learning culture, leveraging human-centered technology design, and understanding the transformative impact of emerging technologies on business strategy.

With technology advancing rapidly, fostering a perpetual-learning culture within organizations is crucial. Companies should encourage knowledge sharing, use communities for code review and improvement, and allocate time for experimentation with new tools or technologies. These practices support continuous improvement and innovation, a critical factor for staying competitive in the digital age (McKinsey & Company).

The trajectory of technology development is increasingly human-centric, focusing on expanding access to knowledge and enabling ongoing innovation. This approach requires not only technological innovation but also ethical considerations, ensuring AI is implemented with appropriate controls and used to enhance human intelligence (Accenture | Let There Be Change).

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