The adoption of AI is accelerating across industries to automate and improve operations, personalize customer conversations, uncover insights from data, and augment human capabilities. As AI capabilities advance, they are used at a wider scale, and AI’s boundaries are pushed in a search for new opportunities for value creation. It’s already clear that AI will be used to aid decision-making, predominately at the operational level. But the question is whether it can also be used for strategic decision-making, either as an assistant to the process, with humans in the loop, or as the sole decision-maker. There are legal and ethical implications to this, and businesses will need to follow regulatory developments regarding AI explainability, transparency, and accountability.

All of this has prompted the debate: Can AI run a company?

Leveraging the promising potential of AI in new fields of application – while balancing ethical dilemmas, legal ramifications, and the readiness of organizations to manage such change – is the biggest AI challenge for business leaders today.

Leveraging AI for business processes and operations

AI has wide-ranging applications to streamline business operations in functions such as marketing, customer service, HR, procurement, and finance. Intelligent algorithms can improve supply chains, predict sales trends, automate communications, uncover product insights from data, draft reports, and more. This improves efficiency, drives cost reductions, and enhances value delivery.

Generative AI takes this further by building on existing data to create novel content. For example, AI can generate product descriptions, draft business reports, and create data visualizations. This augments human capabilities, allowing people to focus on tasks that require creativity, experience, intuition, and higher judgment and build meaningful customer interactions. To maximize benefits, augmentative roles between humans and AI should be consciously designed.

The role of generative AI beyond business processes and operations

AI’s application expands far beyond business operations and can lead to innovations at a strategic level. Areas providing the biggest opportunities include using creative generative models to rapidly produce written content across departments and help imagine innovative products, services, and even prototypes. AI could also assist data-driven strategic planning by rapidly analyzing competitors, customer, and market data and then outlining recommendations, which gives it a crucial role in simulating business scenarios. AI can evaluate numerous possible outcomes for any given situation and predict the most likely result.

In this context, a new kind of group dynamics might emerge between AI and leaders, with leaders acting as advisors using strategic knowledge to guide AI production and AI acting as a collaborative companion extending leadership reach and augmenting decision-making.

AI can already support some aspects of leadership. Experiments of early adopter companies show that generative models can absorb aspects of an executive role – like those of a CEO – then provide suggestions in their communication style. This does not imply that AI could or should replace leadership roles in enterprises. However, these experiments show that thoughtfully designed augmentative applications can help distribute aspects of leadership by providing data and ideas to human leaders or even coaching them. As an example: an introverted leader could design their digital twin to act in a more extroverted fashion, thus learning and augmenting their style adaptively.

The extent to which intrinsic human qualities that are crucial for leadership, like emotional intelligence, might develop in AI remains debated and for now remains in the future. Further research into how human values can be used in generative models is required as AI business applications expand.

Challenges and limitations

While promising, expanding the business applications of generative AI to cover strategic decision-making has its challenges. The first is data quality and access. Training complex models requires extraordinarily large, high-quality data sets, which few companies own, particularly in the context of how strategic decisions are made. Training systems on past decisions also may not be the best logic for future decisions. Furthermore, data collection and labeling can introduce ethical questions regarding personal privacy that must be addressed. Lastly, computing infrastructure and energy demands for advanced AI currently constrain real-world implementations.

Even if these practical barriers are overcome, delegating responsibilities to AI poses risks if not managed carefully. Businesses must validate AI decisions for transparency and ensure accountability through human oversight. If stakeholders don’t trust or understand AI, they may resist its adoption. Generative models also have limitations regarding understanding context on a broad scale, which can cause issues with the quality of their results. Up-front design thinking is imperative to consciously create augmentative human-AI workflows that maximize the strengths of both.

The broader societal impacts of advancing AI automation on careers and inequality are additional considerations for businesses exploring generative applications. Responsible leaders must consider if and how roles might transform due to AI integration, and they may need to provide workforce training. As with any technology, generative AI brings promise and peril. Balanced governance and literacy efforts must align with ethical guidelines and leaders’ core values.

Future path for AI co-leadership

As AI’s role in business becomes increasingly more significant, it’s crucial for leaders to reflect on key questions to integrate AI responsibly and effectively into their strategic decision-making. By adopting a continuous learning mindset and actively exploring how AI can augment their skill sets, they can unlock its potential. This iterative process of experimentation and knowledge acquisition is key to fostering organizational readiness for AI integration.

In this context, it’s crucial for businesses to review the implications of strategic use within the business. Key considerations include cost vs. benefit analysis, ethics, governance, organizational readiness for AI adoption, and finding the right balance between AI and human intelligence.

Business leaders exploring the use of AI as a companion to their leadership should ask several strategic questions. They should first review their own decision-making – whether their choices are based on explicit or implicit knowledge and whether they can explain the logic behind every business decision. What are some decisions that could benefit from AI's ability to rapidly assess massive data sets and offer data-driven recommendations?

An honest examination of decision-making processes can reveal areas where AI can be used as a companion to enhance performance and value creation. How open are you to leveraging AI to build on your organization’s strengths and improve its weaknesses?

Finally, leaders should set a firm boundary between their intuition and the guidance of AI. What actions will you take when your gut feeling contradicts AI’s recommendations? Human decision-making involves empathy, passion, and moral judgments, which are factors that AI does not yet have.

Asking these questions up front enables leaders to develop an AI strategy that is optimized for value creation and innovation tailored to leadership needs and organization culture.

Future outlook

While AI currently lacks the autonomy to fully run a company, its transformative potential in specific areas is undeniable. So the question is not necessarily if AI can run a company but how leadership can improve AI as a powerful tool in strategic decision-making. By acknowledging AI’s challenges and limitations while strategically harnessing its power, leaders can use it as a companion to augment their capabilities. The call to action is clear: accept AI implementation as a transformative force for the future.

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