The Future of Work[Space]

Can AI run my business?

Julian Birkinshaw’s 2018 article “What’s the Purpose of Companies in the Age of AI? “illuminates the intricate balance businesses must maintain between optimising immediate profits and ensuring long-term sustainability. 

This delicate equilibrium, often described as a classic resource allocation problem, encapsulates the challenge of investing in future-focused projects when immediate profit generation seems more appealing. 

As we delve deeper into AI’s capabilities and limitations in this context, the question arises: Can AI optimise everything?

At the heart of Birkinshaw’s exploration is the dilemma faced by businesses like Kodak, which famously prioritised short-term gains over adapting to the digital photography revolution, leading to its downfall. This example underscores the risk of neglecting long-term innovation in favour of immediate profitability. 

With its data-driven insights and predictive capabilities, AI presents a promising tool for navigating these challenges. However, the question remains: 

AI, with its potential to revolutionise how businesses approach resource allocation, offers a more nuanced understanding of market trends, customer behaviour, and technological advancements. Through machine learning algorithms and big data analytics, AI can identify patterns and predict future outcomes with a degree of accuracy previously unattainable. 

This potential could theoretically enable businesses to make optimised decisions about where to allocate their resources, balancing short-term profitability with long-term strategic goals.

Yet, the proposition of AI optimising everything is not without its complications. One of the critical challenges is the inherent uncertainty of the future. Based on historical data, AI’s predictive models can forecast trends and potential scenarios but cannot account for unforeseen events or radical market shifts. 

The COVID-19 pandemic is a stark reminder of how quickly and unpredictably the business landscape can change, challenging the limits of AI’s predictive capabilities.

Prioritising long-term sustainability over short-term profits is not merely a quantitative assessment but also a qualitative one involving values, ethics, and vision. 

For all its computational power, AI does not possess human intuition, empathy, or moral judgment. It can provide recommendations based on data but cannot assess the ethical implications of those recommendations or the impact on stakeholders beyond shareholders, such as employees, customers, and communities.

Deploying AI requires significant investment in technology, talent, and data infrastructure. This dilemma of resource allocation arises: How much should we invest in short-term profit generation versus other areas that might offer much higher returns? 

Mistakingly instructing an AI to over-invest in the short term because it is instructed to optimise everything at the expense of other critical aspects of the business is real, particularly for smaller firms with limited resources.

Integrating AI into business strategy also raises concerns about transparency and accountability. AI algorithms can be complex and opaque, making it challenging for decision-makers to understand how recommendations are generated. 

AI’s “black box” nature can lead to mistrust and reluctance to rely on AI for critical decisions, especially when those decisions involve significant risks or ethical considerations.

While AI offers powerful tools for analysing data and optimising certain aspects of business operations, it is not a panacea that can solve the fundamental challenge of balancing short-term profitability with long-term sustainability. 

The decision-making process involves analysing data and considering ethical, strategic, and human factors that AI cannot fully comprehend. 

In a sense, it requires the ability to gamble on a strategy that could create significant returns in the future.

It’s hard for humans to take risks; it’s harder for an AI.

 Questions we think are worth exploring further

• How do we decide what to optimise in a business?
  Profit, sustainability, greater good – isn’t this a philosophic question?
• AI is trained on the entire Internet, so how can it optimise anything?
  Surely it always chooses the lowest common denominator.