Companies that combine innovation and trust have a competitive edge. Discover the best practices that ensure ethical, sustainable deployment.
Nearly 80 years ago, in July 1945, MH Hasham Premji founded Western India Vegetable Products Limited in Amalner, a town in the Jalgaon district of Maharashtra, India, located on the banks of the Bori River. The company began as a manufacturer of cooking oils.
In the 1970s, the company pivoted to IT and changed its name to Wipro. Over the years, it has grown to become one of India’s biggest tech companies, with operations in 167 countries, nearly a quarter of a million employees, and revenue north of $10 billion. The company is led by executive chairman Rishad Premji, grandson of the original founder.
Kiran Minnasandram, VP and CTO of Wipro FullStride Cloud
He spearheads strategic technological initiatives and leads the development of future-looking solutions. His primary role is to drive innovation and empower organizations by providing them with state-of-the-art solutions.
With a focus on cloud computing, he architects and implements advanced cloud-based architectures that transform how businesses operate, while optimizing operations, enhancing scalability, and fostering flexibility to propel clients forward on their digital journeys.
As you might imagine, AI has become a big focus for the company. In this interview, we had the opportunity to discuss the importance of AI ethics and sustainability as it pertains to the future of IT.
Let’s dig in. Company values
Kiran Minnasandram: Ethical AI not only complies with the law but is also aligned with the value we hold dear at Wipro. Everything we do is rooted in four pillars.
AI must be aligned with our values around the individual (privacy and dignity), society (fairness, transparency, and human agency), and the environment. The fourth pillar is technical robustness that encompasses legal compliance, safety, and robustness.
KM: The struggle often comes from the lack of a common vocabulary around AI. This is why the first step is to set up a cross-organizational strategy that brings together technical teams as well as legal and HR teams. AI is transformational and requires a corporate approach.
Second, organizations need to understand what the key tenets of their AI approach are. This goes beyond the law and encompasses the values they want to uphold.
Third, they can develop a risk taxonomy based on the risks they foresee. Risks are based on legal alignment, security, and the impact on the workforce.
KM: AI adoption has and will have a significant impact on corporate sustainability goals. On the positive side, AI can enhance operational efficiency by optimizing supply chains and improving resource management through more precise monitoring of energy and carbon consumption, as well as improving data collection processes for regulatory reporting.
For example, AI can be used by manufacturing or logistics companies to optimize transportation routes, leading to reduced carbon emissions.
Conversely, rapid development and deployment of AI is resulting in increased energy consumption and carbon emissions, as well as substantial water usage for cooling data centers. Training large AI models demands significant computational power, resulting in a larger carbon footprint. Environmental impact
KM: As a starting point, enterprises will need to establish clear policies, principles, and guidelines on the sustainable use of AI. This creates a baseline for decisions around AI innovation and enables teams to make the right choices around the type of AI infrastructure, models, and algorithms they will adopt.
Additionally, enterprises need to establish systems to effectively track, measure, and monitor environmental impact from AI usage and demand this from their service providers.
We have worked with clients to evaluate current AI policies, engage internal and external stakeholders, and develop new principles around AI and the environment before training and educating employees across several functions to embed thinking in everyday processes.
By creating more transparency and accountability, companies can drive meaningful AI innovation while being cognizant of their environmental commitments.
