The pandemic-related events of the past two years have caused individuals to reconsider their sustainability goals. Regulatory requirements are being issued by governments. Investors and financial managers are adding sustainability criteria into their investment decisions. And customers and employees have become considerably more environmentally conscious, seeking manufacturers and businesses who share their ideals. These factors are collectively forging a new corporate agenda. Sustainability has firmly established itself at the center of boardroom and operational management discussions.
Despite this rising tide of influence, IBM’s Institute for Enterprise Value’s latest report, “Sustainability as a transformation catalyst: Trailblazers turn desire into action,” finds that only 35% of firms have implemented their sustainability strategy. As few as four out of ten businesses have identified measures to close their sustainability gaps and sustainable drivers for change. And only one-third have integrated sustainability objectives and measurements into business processes.
Companies want actionable environmental knowledge to meet sustainability objectives. However, current methods are sometimes onerous and complex, necessitating much manual effort, local weather and information science expertise, and computational power to fully exploit their data.
The good news is that digital transformation may support firms in this new era in being robust, adaptable, and profitable. Here are four ways a comprehensive data and AI strategy may play a vital role in changing business operations around a sustainability agenda.
Creating a more robust infrastructure
The results of climate change and depleting natural resources require businesses to extend the lifespan of their buildings, bridges, and water lines. By engaging in digital transformation to meet sustainability objectives, businesses can discover new opportunities to streamline existing operations, reduce costs, reduce waste, attract new customers, increase brand loyalty, and adopt new business models.
AI-powered remote monitoring and computer vision enable businesses to detect, predict, and prevent issues. They will also do condition-based maintenance based on operational data and analytics to reduce downtime and maintenance costs. Improved asset management can help businesses reduce their inventory of spare parts. And a company can save on energy costs by identifying a little problem before it becomes a much larger, more energy-intensive issue.
Developing a transparent, trusted supply chain
Provide chain leaders with transparency. If they cannot determine the exact number and location of their inventory, they will over-order, tying up an excessive amount of working capital. And if supply chain executives lack openness and information sharing with their deep-tier suppliers, it is incredibly difficult to trace products from the point of origin to the point of delivery in a reliable and managed manner. This makes establishing provider threat and defending the paradigm more difficult.
Reaching supply chain sustainability objectives necessitates a global, accurate, real-time perspective of inventory, as well as the capacity to exchange information across the supply chain ecosystem in a reliable manner. AI aids businesses in avoiding obsolete and unsalable inventory, reducing carbon emissions from logistical moves, optimizing success decision-making, and decreasing waste throughout raw materials, finished goods, and spare component inventories.
Deriving enterprise insights from environmental intelligence
Companies exposed to a multitude of external factors require particularly sophisticated prediction tools. Consumer goods companies like Unilever want information to estimate environmental impact and make sustainable decisions. Insurance coverage corporations resembling Canada’s Desjardins Insurance coverage need to higher predict disruptions to policyholders – for instance, earlier warning of impending hailstorms would allow policyholders to take precautions to avoid harm. Environmental intelligence capabilities aid businesses in preparing for and responding to climate events with AI-driven forecasts drawn from a combination of proprietary and third-party geospatial, climate, and IoT data. This streamlines and automates the management of environmental risks and operationalizes underlying processes, such as carbon accounting and reduction, in order to meet environmental objectives.
Decarbonizing the international economy
In the next years, utilities will continue to play a vital role in the energy transition by speeding global decarbonization through clean electrification – the process of replacing fossil fuels with electricity generated from renewable sources such as wind, solar, and hydro. And they will need a comprehensive asset management strategy for operations, maintenance, and the lifecycle of these renewable energy plants. Digital transformation may be essential to decarbonization, and it will help electrical energy ecosystems deliver clean energy to connected customers in a secure and dependable manner.
Companies worldwide have entered a new era of digital reinvention, propelled by hybrid cloud and AI advancements. IBM is perfectly positioned to help our clients advance Sustainable Development Goals (SDGs).
