Posted by Marbenz Antonio on June 23, 2022
People are reconsidering their sustainability objectives as a result of the pandemic-related events of the last two years. Regulatory standards are being issued by governments. Sustainability criteria are being incorporated into investment decisions by investors and financial managers. Customers and employees are also becoming more environmentally conscious, looking for companies and workplaces that share their values. These forces are forming a new corporate agenda. Sustainability has rightfully taken center stage in the boardroom and operational management discussions.
Despite this growing influence, a new analysis from IBM’s Institute for Business Value, “Sustainability as a transformation catalyst: Trailblazers turn desire into action,” finds that only 35% of organizations have implemented a sustainability plan. Fewer than four out of ten businesses have identified either actions to close their sustainability gaps or sustainability motivations for change. Only one-third of businesses have integrated sustainability targets and KPIs into their business processes.
To achieve their sustainability goals, businesses require actionable environmental insights. However, present methods are usually time-consuming and complex, necessitating much human work, climate and data science expertise, and computational power to fully exploit their data.
The good news is that digital transformation may assist businesses in being resilient, adaptive, and profitable in this new era. Here are four ways a comprehensive data and AI strategy might help reshape business operations to focus on sustainability.
Climate change and decreasing natural resources necessitate businesses to extend the life of their buildings, bridges, and water connections. Companies can discover new opportunities to streamline their operations, save costs, eliminate waste, attract new customers, increase brand loyalty, and embrace new business models by starting on digital transformation to achieve sustainability obligations.
AI-powered remote monitoring and computer vision assist enterprises in detecting, forecasting, and preventing problems. In addition, they can undertake condition-based maintenance based on operational data and analytics to reduce downtime and maintenance costs. Improved asset management can assist businesses in reducing their spare parts inventories. A corporation can also save energy by identifying a tiny problem before it grows into a larger, more energy-consuming problem.
Supply chain executives require visibility. When they are unable to track the exact amount and location of their goods, they may over-order, tying up too much working capital. And if supply chain leaders lack openness and data exchange with their deep-tier suppliers, tracking products from point of origination through delivery in a trusted and controlled manner becomes extremely challenging. This makes identifying supplier risk and protecting the brand more difficult.
Reaching supply chain sustainability goals necessitates a worldwide, accurate, real-time inventory picture, as well as the capacity to communicate data across the supply chain ecosystem in a trustworthy manner. AI assists businesses in avoiding obsolete and unsold inventory, lowering carbon emissions from logistical activities, optimizing fulfillment decision-making, and minimizing waste across raw materials, completed items, and spare parts inventories.
Companies that are vulnerable to a wide range of external influences require particularly advanced prediction systems. Unilever and other consumer products corporations want data to help them estimate the environmental effect and make sustainable decisions. Insurance businesses like Desjardins Insurance in Canada want to better foresee disruption to policyholders — for example, earlier warning of impending hailstorms might let its clients take steps to avoid harm. With AI-driven forecasts derived from a combination of proprietary and third-party geospatial, weather, and IoT data, environmental intelligence capabilities assist businesses in planning for and responding to weather occurrences. This streamlines and automates environmental risk management and operationalizes underlying processes, such as carbon accounting and reduction, to achieve environmental goals.
Utilities will continue to play a critical role in the energy transition in the coming years, speeding global decarbonization through clean electrification – the process of replacing fossil fuels with electricity generated from renewable sources such as wind, solar, and hydro. They will also require a thorough asset management strategy for these renewable energy plants’ operations, maintenance, and lifetime. Digital transformation will be important to decarbonization, allowing power ecosystems to deliver clean energy to connected consumers safely and dependably.