by Sarah E. Toms, Amit M. Joshi Published 15 February 2024 in Technology • 7 min read • 
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In the past year, generative artificial intelligence (GenAI) has emerged as a transformative force in business, weaving through industries and reshaping the fabric of innovation. The impact of AI on drug discovery is just one example of its potential to reshape business practices. Pioneering biotech companies such as Insilico Medicine, based in Hong Kong and New York, are employing AI to revolutionize the identification and development of new drugs. Through the generation of predictive analyses, these platforms streamline the identification of compounds with the highest probability of success, potentially ushering in a new era for drug development pipelines – traditionally long and costly processes.
Beyond healthcare, AI permeates the global economy, leaving no sector untouched. In finance, for example, AI is already making significant strides, with banks integrating the technology for fraud detection, personalized customer service through intelligent chatbots, and informed lending decisions through credit scoring models. The application of AI extends to cybersecurity, personalized marketing through customer data analysis, and the fine-tuning of loan and investment portfolios, marking a paradigm shift in traditional financial practices.
It is not merely an option for organizations (if it ever was); it is an imperative for those seeking to harness the substantial business benefits from efficiency gains and skill augmentation to enhanced knowledge management. AI is not merely automating routine tasks, it is liberating professionals from mundane, repetitive work, unlocking the full spectrum of their creativity and problem-solving skills.
According to Goldman Sachs, the advent of GenAI has the potential to automate 25% of tasks in the US and eurozone, triggering a surge in productivity that could contribute to a 7% increase in annual GDP over 10 years. However, this technological shift may also disrupt the job market significantly, putting around 300 million full-time workers in major economies at risk of automation, according to the investment bank.
And yet, AI can empower employees too, with capabilities such as multilingual translation and the generation of complex analytics, code, and writing. It may not be Shakespearean, but it closely resembles human output. This shift is not only about efficiency gains but also equipping individuals with the skills needed to navigate the profound changes that AI brings to the workplace.
To secure a more sustainable and inclusive future, we need creative ideas and partnerships that reimagine the global economy. In Issue XII of I by IMD, we explore how the fast-emerging “impact economy” is changing the way we do business – and the world.
Companies that fail to adeptly incorporate AI risk falling behind, underscoring the need for organizations to build the necessary expertise to stay competitive. Already examples exist of organizations that are starting to feel the negative impact of GenAI. For instance, companies in the education sector such as Chegg, a California-based edtech business, have acknowledged being hit. Earlier this year, Chegg admitted that the increased popularity of ChatGPT among students was negatively impacting sales. This admission resulted in an immediate 50% decline in the company’s shares and its stock market performance has continued to languish.
However, the widespread and necessary embrace of AI does not unfold without its own set of problems (beyond any impact on the job market and vulnerable business models). Organizations must navigate these risks with care to ensure the responsible and secure adoption of AI.
AI systems can exhibit “hallucinations” and produce misleading outcomes and biases as well as posing potential copyright concerns. For example, deployed to research a brief for a court hearing in the US, AI invented previous cases. This kind of glitch can pose considerable risks, especially in critical domains such as healthcare and finance, where precision is paramount. The trick lies in refining AI models to minimize inaccuracies and enhance reliability. To do this, organizations should continuously monitor and retrain models with new data, prioritize “explainability”, and collect user feedback to make improvements, considering the ethical implications.
Bias in AI systems is another pervasive concern, reflecting the biases present in the data used for training. From gender and racial biases to socio-economic prejudices, AI systems may inadvertently perpetuate and even exacerbate societal inequalities. AI image tools have, for instance, depicted Africans as primitive, leaders as men, and prisoners as black. Addressing bias requires a concerted effort to develop fairness-aware algorithms and implement transparency in training data.
Organizations should begin by identifying potential biases in both the data and model outcomes, with a focus on attributes like race and gender, use diverse and representative training data, and employ metrics to quantitatively evaluate model impact on different groups. It’s also key to fostering a culture of awareness and commitment to fairness within the development team.
Additionally, organizations are exploring innovative solutions to some of these teething problems, such as Retrieval Augmentation Generation (RAG). This approach limits AI responses to a predefined set of data, providing a degree of control and reference. While some companies like Microsoft have pledged legal protection for users of its AI productivity tools, to cover potential copyright losses, this solution may prove problematic. It does not tackle reputational damage, for instance, which can be substantial. Striking a balance between innovation and risk mitigation is, therefore, essential.
The issues don’t stop there. Copyright concerns can emerge as AI systems generate content that may unknowingly infringe on intellectual property rights. Organizations must grapple with the question of whether AI-generated content can be used in customer-facing tasks without violating copyright laws, raising questions about the role of the regulatory environment and future business models.
Data security and privacy issues must also be addressed. Encryption, access controls, and regular audits will help reduce the risk of data breaches, while the lack of explainability – with AI operating as a “black box” – can be tackled through interpretable models, clear communication about limitations, and the importance of informed user consent through transparent policies.
Despite these considerable challenges, we believe the benefits of AI far outweigh the risks, and organizations that fail to adopt AI risk obsolescence. The inevitability of AI adoption necessitates the need to take a strategic approach, emphasizing the importance of defining problem statements, establishing feedback loops, auditing data and outcomes, and recognizing AI as a transformative force that requires a shift in business models and organizational thinking.
First and foremost, organizations must clearly define the problem statement: Why are we implementing AI, specifically? This statement will align initiatives with company goals, ensuring efficient resource allocation. Stakeholder engagement is vital in this process, so be sure to gather diverse perspectives.
In addition, measurable success criteria – set as quantifiable KPIs – will enable objective impact assessment, while early risk identification and ethical considerations, like bias and privacy, can be addressed through assessments and expert involvement. Regularly refining the problem statement, too, ensures adaptability to emerging and evolving AI insights.
Companies that fail to adeptly incorporate AI risk falling behind, underscoring the need for organizations to build the necessary expertise to stay competitive.
The establishment of feedback loops is crucial, allowing AI and the organization to learn from successes and failures along the way. Practical steps include updating the AI model to keep it relevant and prioritizing user feedback for ongoing enhancements to usability and performance.
But organizations should go further still. Regular auditing of data and outcomes is paramount to managing bias and ethical concerns. This practice ensures fairness and accountability in decision-making processes, by systematically reviewing and rectifying biases in training data. It also enhances transparency and explainability in AI systems, fostering trust among users and stakeholders. Establishing clear policies and procedures for handling sensitive information, meanwhile, ensures compliance with regulations. Continuous improvement is also critical here, so ensure that regular reviews are integrated into the AI’s development life cycle, creating a mechanism for stakeholders to report concerns while adapting ethical guidelines based on audit findings and emerging standards.
Lastly, organizations must recognize that AI is not just a technology but a transformative force that can shift an organization’s strategy. The benefits are vast but they come with significant challenges that require careful consideration and strategic integration. Ultimately, AI’s transformative potential is reshaping the way we approach problems and envision solutions. Organizations that embrace this transformative force with strategic foresight will not merely survive but thrive.
AI and its implications for business will feature as the spotlight theme in the March edition of I by IMD magazine.
Chief Learning Innovation Officer
Sarah Toms is Chief Learning Innovation Officer at IMD where she leads the Learning Innovation and AI strategy. Sarah previously co-founded Wharton Interactive, an initiative at the Wharton School that has scaled globally. A demonstrated thought leader in the educational technology field, she is fueled by a passion to find and develop innovative ways to make every learning environment active, engaging, more meaningful, and learner-centric. Sarah is an AWS Education Champion, and has been on the Executive Committee of Reimagine Education for 8 years. She has spent more than 25 years working at the bleeding edge of technology, and was an entrepreneur for over a decade, founding companies that built global CRM, product development, productivity management, and financial systems. In addition, Sarah is coauthor of The Customer Centricity Playbook, the Digital Book Awards 2019 Best Business Book.
Professor of AI, Analytics and Marketing Strategy at IMD
Amit Joshi is Professor of AI, Analytics, and Marketing Strategy at IMD and Program Director of the Digital Strategy, Analytics & AI program, Generative AI for Business Sprint, and the Business Analytics for Leaders course. Â He specializes in helping organizations use artificial intelligence and develop their big data, analytics, and AI capabilities. An award-winning professor and researcher, he has extensive experience of AI and analytics-driven transformations in industries such as banking, fintech, retail, automotive, telecoms, and pharma.
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