CXOToday has engaged in an exclusive interview with Mohammed Rafee Tarafdar, Chief Technology Officer, Infosys
- How can organizations pinpoint areas needing improvement for overall sustainability? Are there any strategies to set achievable goals for positive change?
To achieve goals for positive change, it’s important to think strategically and develop a longer-term vision based on the industry and market an organization is operating in. The longer-term vision can then be broken down into tangible focus areas and aspirational goals. UN has defined about 17 sustainable development goals, and these are good starting point for any organization to use as a base to identify the ones applicable to them and then do an assessment against these. This should help identify the improvement areas. At Infosys as part of our ESG vision 2030, we have identified our goals across 3 key pillars of a) Environment – climate change, Water, & Waste, b) Social – enabling digital talent at scale, Tech For Good, diversity, equity & inclusion, energizing local communities, & employee wellness and experience, c) Governance – corporate governance, data privacy, and information management. We have well defined goals for each of these areas that are tracked and governed periodically.
- How does AI, particularly Infosys Topaz, enhance sustainability by reducing risks and boosting energy efficiency?
AI can play a significant role in enhancing sustainability and at Infosys we are using AI to make all our campuses sustainable and energy efficient. Few areas of focus for us are – using AI to make energy generation and consumption projections and making recommendations for improving energy consumption, water demand forecast and management, predicting chiller plant load and taking actions to improve efficiency, actionable insights for cleaning of solar panels, predictive maintenance of inverters and other equipment.
- What steps can be taken to ensure responsible sourcing while cutting environmental impact? How can technology aid this in the supply chain?
Today most organizations require their suppliers to comply with disclosure requirements to ensure compliance to their environmental, social requirements. Suppliers are also required to provide the contribution to emissions of the purchasing organization as purchased goods and services contribute to larger sources of Scope 3 emissions for any company. Organizations which are environmentally conscious and have declared their net zero goals are increasingly sourcing from suppliers which are committed to reducing their environmental impact. Technology plays a key role in collection and analysis of this data from suppliers. Advanced techniques like Product Carbon Footprint assessment which accurately calculates the carbon emissions associated with every step of product manufacturing and such large data crunching are best done through technology platforms. AI can be a key tool to analyse this vast data which lies in various formats, documents, and sources across the entire value chain of any enterprise, along with application of applicable legal requirements and standards to help organizations understand the emissions associated with goods and services they procure from their supply chain.
- What tools or methods streamline ESG reporting in terms of timesaving, transparency, and accountability?
There are numerous tools which are currently in use to help streamline ESG reporting. Most of the organizations have been managing ESG reporting using a manual process of data collection, computation of emissions and then complying with various reporting standards as applicable e.g. GRI, TCFD, DJSI to name a few. Many SaaS platforms have emerged which help in automating a large part of the data collection by integrating with enterprise data lakes and other operations systems along with manual collection of data through forms and direct integration with source systems (e.g. utility companies for getting energy bills). This results in significant reduction in effort on part of the team involved in reporting and submitting for assurance from auditors. These tools also allow for a consistent application of computational standards like emission factors and keep the data updated as and when changes are recorded. The data once captured can be presented in numerous formats to comply with various reporting standards, thereby eliminating need for multiple data collection processes and errors induced thus. The most popular platforms for ESG reporting to help automate the reporting process are Sphera, Envizi, Diligent, Figbytes etc.
- What upcoming trends and challenges in sustainability are set to impact businesses in 2024?
We are seeing these 5 key trends:
- a) Legal Changes – multiple new regions are adopting more and more stringent reporting and assurance requirements like CSRD in Europe and BRSR in India. We expect more countries to revise regulations and mandating organizations for more disclosures and stricter adherence to net zero goal timelines.
b). Supply chain Transparency – Organizations are increasingly looking to work with their supply chain to reduce their ESG impact. Regulations like CSRD are mandating stricter social and governance disclosures from suppliers.
c). Double Materiality – Organizations are now required to not only assess their impact on environment but also to assess and report on the climate risk their business is exposed to for reporting purposes. Stricter disclosures for sustainability are being mandated along the lines of financial disclosures.
d). Role of Technology – Sustainability reporting and analytics require data crunching in large volumes thereby making a perfect playing ground for more and more AI applications. Organizations will need real time analysis of ESG metrics as they make key decisions on a daily basis to improve their performance and that means more and more usage of automation (IoT, digital systems) and AI enabled technologies.
e). Cultural Change – Sustainability will have to become a focus topic for every level of organization instead of being a board room topic only. That will mean a drastic effort to train and educate employees on sustainable behaviour and decision-making process. Systems and processes along with KPIs need to be modified to reflect this change and enable organizations and employees.
- Best practices in generative AI: What are the top practices for using generative AI to drive sustainability goals?
Based on our experience of implementing generative AI at scale, I would suggest 4 key practices – a) Adopt a Poly AI strategy, as the models will keep evolving and in future, we will have more options. Having an architecture that enables organizations to pick best AI service provider and model is key to deliver business value and outcomes b) Invest upfront effort is streamlining and curating data & knowledge required for the AI projects. Having quality data is key to ensuring high quality AI products c) Be responsible by design. AI regulations are evolving in parallel to the technology, so ensuring that the responsible AI guardrails and controls are built in upfront is key to ensuring compliance and safety d) Generative AI capabilities have to be in flow to ensure they don’t add more tasks to humans but help amplify their potential.
