By: Antony Panteli, Head of CIO Advisory, LTIMindtree Consulting (EU & UK)

The 2023 LTIMindtree survey report on Generative Artificial Intelligence (Gen AI) adoption provides a comprehensive overview of the current landscape, revealing critical insights into the economic and ethical dimensions of Gen AI in the business context. This analysis distills the report’s findings to equip CXOs with a clear understanding of the implications of Gen AI and to guide strategic decisions for AI Adoption.

Economic implications of Gen AI

The economic influence of Gen AI is significant as it can enhance productivity, reduce costs, and create new value propositions. Agrawal et al. (2022) posit that AI, as a ‘prediction technology,’ decreases the cost of prediction, thereby altering business strategies and potentially leading to new forms of wealth creation. The LTIMindtree report supports this, revealing that 75% of US businesses have observed a cost reduction of at least 5% due to Gen AI adoption. However, the report also highlights the operational costs as a barrier for 67% of late adopters, underscoring the need for a balanced view of the economic trade-offs involved in Gen AI investments. Brynjolfsson and McAfee (2014) discuss the ‘second machine age,’ where digital technologies, including AI, bring about profound economic changes, emphasizing the need for businesses to adapt to these shifts strategically.

Ethical considerations of Gen AI

Generative AI within business ecosystems necessitates a rigorous ethical framework to address inherent challenges. The potential for AI systems to inherit and amplify biases from their training data is a significant concern, as highlighted by Schwartz et al. (2022). The practices of industry leaders reflect this issue, not just theoretically, as the LTIMindtree report reveals that a substantial 79% of executives regularly audit their Gen AI applications to ensure ethical compliance. Such audits are crucial in identifying and mitigating biases, thereby safeguarding against the erosion of trust and the perpetuation of inequality.

Bret and Dainow (2023) advocate for an ethics-by-design approach for integrating ethical considerations into the AI development process. This approach aligns AI operations with societal values and norms, ensuring AI acts as a force for good. It calls for a collaborative effort where technologists, ethicists, and policymakers work together to guide the ethical evolution of AI technologies. By embedding ethics into the Gen AI lifecycle, organizations can navigate the complex moral landscape, ensuring that their AI systems are not only technologically advanced but also socially responsible and trustworthy.

Actionable insights for CXOs

1. Strategic AI roadmap: Develop a Gen AI strategy that leverages the economic benefits while mitigating risks, informed by the predictive power of AI (Agrawal et al., 2022).
2. Data governance and quality: Prioritize high-quality data governance to ensure the integrity of Gen AI outputs, addressing the economic cost of poor data (Brynjolfsson & McAfee, 2014).
3. Ethics by design: Embed ethical considerations into every facet of Gen AI, from design through deployment and into production. Adopting principles of ethical compliance (Brey & Dainow, 2023).
4. Bias and discrimination audits: Regularly audit Gen AI systems for bias and discrimination, ensuring that ethical challenges are addressed proactively (Schwartz et al., 2022).
5. Transparent communication: Maintain transparency with stakeholders about Gen AI use, informed by the ethical imperative of trust (Floridi & Cowls, 2020).
6. Digital trust department: Establish a Department of Digital Trust to oversee the ethical deployment of Gen AI, recognizing the importance of ethics in AI (Floridi & Cowls, 2020).

Call to action for CXOs

CXOs play an important role in steering Gen AI adoption. They must balance the potential economic benefits with ethical considerations and operational excellence. It is essential that Gen AI systems are transparent, accountable, and aligned with both economic imperatives and societal values. While ethical guidelines are fundamental, they should be part of a broader strategy that focuses on economic efficiency, innovation, and competitive advantage.

Regular audits are crucial for ensuring ethical compliance in AI systems. However, they should also evaluate the economic impact and operational effectiveness of such systems. Communication channels should not only clarify the ethical dimensions of AI decisions but also their economic rationale and contributions to strategic objectives. Education plays a critical role in this process.

CXOs should ensure that their teams are trained in ethical AI practices and in recognizing AI’s economic potential and capitalizing on it. Public reporting should cover AI’s economic impact, which will help foster stakeholder confidence in the organization’s AI strategy.  CXOs are called upon to lead with a holistic vision, embedding these principles into every facet of their operations.

References

1. Prediction Machines, Updated and Expanded: The Simple Economics of Artificial Intelligence, Agrawal, A., Gans, J., & Goldfarb, A., Harvard Business Press, 2022
2. The second machine age: Work, progress, and prosperity in a time of brilliant technologies, Brynjolfsson, E., & McAfee, A., WW Norton & Company 2014
3. Ethics by design for artificial intelligence, Bret, P., & Dainow, B., Springer, 2023: https://doi.org/10.1007/s43681-023-00330-4
4. How to Design AI for Social Good: Seven Essential Factors. Science and Engineering Ethics, 26(3), 1771–1796, Floridi, L., Cowls, J., King, T. C., & Taddeo, M., Springer, 2020: https://doi.org/10.1007/s11948-020-00213-5
5. The State of Generative AI Adoption. LTIMindtree, 2023 https://www.ltimindtree.info/gen-ai
6. Towards a standard for identifying and managing bias in artificial intelligence, 1270(10.6028), Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P., NIST special publication, 2022: https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1270.pdf