Rethinking Business Operations in the Age of AI

AI transformation is about fundamentally rethinking business operations rather than merely updating old practices with new technology—akin to the early internet days when businesses digitized existing inefficiencies. Such efforts only digitized the inefficiencies of the past rather than embracing the full potential of digital innovation.

Today, however, the stakes are much higher. The U.S., recognized as a global leader in AI innovation, is setting the pace for how industries worldwide will evolve. A recent report by the Brookings Institution highlights that the U.S. accounts for over 40 percent of global AI investments, a testament to its pivotal role in shaping the AI landscape globally.

In sectors ranging from healthcare to finance, AI is driving unprecedented shifts. The U.S. government’s proactive stance on AI regulation like the National AI Initiative Act of 2020 further solidifies the nation’s leadership.

As the U.S. makes significant strides in AI, it is essential to recognize that the global landscape is rapidly evolving too. Countries worldwide are embracing AI, drawing inspiration from the U.S. and charting their unique paths. This global dynamic is reshaping how businesses and organizations innovate and compete on an international scale. As AI reshapes industries, those who can navigate this transformation with agility and foresight will survive and thrive in the new era of intelligent organizations.

Strategies for a True Business Transformation

Breaking Silos

For organizations to fully realize the potential of AI, breaking down silos between departments is paramount. Integrating AI by fostering cross-departmental collaboration and enabling seamless data flow between divisions can prove to be beneficial.

This approach ensures that AI-driven insights are not confined to a single department and are leveraged across the organization, leading to a more cohesive and intelligent operation. The National Institute of Standards and Technology (NIST) emphasizes the importance of interoperability standards to promote collaboration.

Choosing the Right Model and Partner

Selecting the right AI model and implementation partner is crucial for success in a market where the AI landscape is rapidly evolving. A one-size-fits-all approach often leads to suboptimal results, as AI models need to be tailored to the specific requirements of each industry. For example, in the healthcare industry, AI models that focus on deep learning are ideal for image recognition in diagnostics, such as MRI scans or X-rays. An implementation partner in this case should specialize in healthcare data security, patient privacy regulations like HIPAA, and the ethical implications of AI in medical decision making. On the other hand, the retail industry might benefit from reinforcement learning models, which excel in dynamic environments, such as real-time pricing strategies or personalized marketing. An AI implementation partner for retail needs to understand real-time data processing, integration with existing e-commerce platforms, and customer data privacy laws like GDPR.

If a healthcare organization were to adopt a reinforcement learning model designed for retail, it would struggle with static medical imaging data, leading to inaccurate diagnoses. Conversely, if a retail company used a deep learning model optimized for medical imaging, it would waste resources on computational power without benefiting from the real-time adaptability needed for dynamic market changes.

AI Implementation with Purpose and Ethics

In an era where AI’s role in decision making continuously expands, prioritizing ethics and minimizing data bias has become paramount. As adoption surges across various sectors, the Federal Trade Commission (FTC) in the United States has set forth guidelines to ensure AI algorithms are fair and transparent, addressing the potential for biased outcomes. Organizations need to engage in practices that foster ethics in AI use, such as forming AI ethics boards and curating diverse datasets to counteract bias and uphold public trust while staving off regulatory consequences.

Strategic deployment of AI requires a deliberate choice of models and collaboration, ensuring they reflect the organization’s goals and ethical standards. Decisions between using open-source and proprietary models or determining the appropriate size of language models should consider not just technical merits but also their broader impacts.

AI systems must be designed to avoid amplifying societal inequalities, for instance, by perpetuating biased lending practices through models trained on historical data. Recognizing these challenges, the FTC’s initiative to foster fairness and transparency in AI underscores the importance of data integrity. For AI to drive innovation and inclusivity effectively, businesses must champion a solid data strategy that supports ethical AI outcomes, ensuring their developments contribute positively to society.

Training the Workforce

The success of AI implementation heavily depends on an organization’s ability to upskill its workforce. In the U.S., there is a significant push towards reskilling employees to work alongside AI and other emerging technologies. According to a 2023 study by the World Economic Forum, nearly 50 percent of U.S. companies have reported increased investments in AI training for their employees, highlighting the critical need for upskilling. The U.S. government also supports this transition with programs like the AI Initiative Act encouraging workforce development. By fostering a culture of learning and adaptability, businesses can transform into intelligent organizations that thrive in an AI-driven future.

New entrants to the workforce must be equipped with AI proficiency from the outset, and seasoned professionals must adapt to novel tools and methodologies to stay competitive.

Preparing for an AI-Driven Future

In an AI-driven future, businesses are on the brink of a revolution where decision making and efficiency reach uncharted heights. The promise of AI to drive growth, streamline operations, and personalize customer experiences posits an unprecedented competitive advantage. Yet, this transformation extends beyond mere profit margins and productivity metrics. It challenges businesses to rethink their ethical frameworks, operational transparency, and societal impacts. Embracing AI requires a delicate balance between leveraging technological advancements and maintaining a human-centric approach. Businesses that navigate this landscape thoughtfully will not only thrive but also redefine industry standards and foster a future where technology amplifies the best of human values.

Atul Soneja is chief operating officer at Tech Mahindra. A core part of Tech Mahindra’s leadership team, Soneja previously served as the COO at CitiusTech, where he successfully oversaw the overall delivery and operations for the organization. Prior to that, he spent over two decades at Infosys, managing multibillion-dollar services lines across financial services, retail, and manufacturing industry segments. He also headed the Infosys subsidiary, EdgeVerve, where he led the platforms and products business with a focus on AI and automation.