At the start of the 20th century, the Industrial Revolution transformed how we live and work, accelerating the speed and volume of goods we produce and scientific innovation.
In the 21st century, digital technologies have created breakthrough economic opportunities (and entire new industries), powered by cloud computing, mobile connectivity, advanced analytics, and more lately artificial intelligence (AI).
Now it’s time to think about the future we’re building. Leading organizations no longer apply digital technologies to improve operating costs or organizational speeds. Instead, they’re sizing up their potential to drive better ESG outcomes to be ahead in the emerging net-zero economies.
Tech-Led ESG Trends for this Decade
ESG strategies should be in the CIOs’ purview. After all, at its core ESG is a data problem, requiring businesses to first establish the current benchmarks and then chart the path forward to more sustainable operations.
The technology role will only expand from an axillary tool for ESG data collection and reporting to a central pillar for new operating models and sustainable products.
Our team has identified eight major trends, showing the massive potential of applying digital technologies to ESG opportunities.
1. CleanTech Will Become a Major Economic Sector
CleanTech (or ClimateTech) is an emerging sector of technologies, aimed at reducing the negative environmental impacts through energy efficiency improvements, sustainable use of resources, or or environmental protection activities.
Cumulatively, the CleanTech sector can expand to $650 billion, surprising the oil market’s value by 2030. Rapidly developing CleanTech segments include:
- Clean and renewable energy
- Circular economy solutions
- Food supply chain optimization
- Bioshpehre preservation
- Cabron measurement and reduction
- Green mobility
- ESG data analytics
- Sustainable fuels
Decarbonization of the transportation sector, for example, obviously accelerates the demand for electric vehicles (EVs), but also ancillary growth pockets:
- EV fleet management applications
- Multi-modal transportation management systems
- EV charging infrastructure and management software
- Better traffic management solutions
Effectively, leaders in the transportation space are switching to cloud-centric, platform-based operations. To support the emerging smart mobility sector, transportation companies will need to adapt their systems to:
- Collect big data, incoming from connected vehicles and smart road infrastructure (edge IoT devices, road sensors, CCTV cameras).
- Securely operationalize it in the cloud and transmit it back to road players with low latency to enable new applications like smart traffic light signals, remote traffic management, or dynamic road pricing among others.
This transformation process will require a mature data management strategy, as well as expertise with cloud, IoT, and big data analytics technologies. — areas, where 8allocate could help.
A green transportation sector will also accelerate transformations in the energy sector. Europe, for example, will need an extra 324 TWh of electricity to power a fleet of long-haul EVs. That’s without factoring in the consumption of passenger EVs and public transportation.
New types of energy generation systems will be required, plus better solutions for energy greed management — and many market players are already working in this domain.
Ogre, for instance, created an AI-driven platform for energy management, which can generate accurate demand forecasts, for connected renewable energy sources (wind turbines, solar panels, hydro stations). Thanks to real-time big data analytics and AI-based predictions, plant operators can forecast consumption to reduce financial losses from procurement errors, plus detect anomalies and future technical losses to reduce risks of service interruptions.
Overall, AI can help the energy sector process massive volumes of data to identify new patterns in energy demand and areas of operational inefficiencies.
2. Wider Adoption of Circularity Practices
The traditional linear economy has proven to be unattainable. Growing demand for new housing, more energy, products, and services inflate the demand for raw materials and resources, which are available in finite quantities.
That’s why companies and global economies at large (like the EU) are bullish on introducing better circularity practices — and digital technologies will play a key role in this process.
Circular economy helps extend the materials’ life cycle through smart re-usage. Data analytics, combined with new connectivity technologies, help organizations track material use across the supply chain and flag opportunities for reuse, recycling, and refurbishing.
IKEA, for instance, plans to become a circular business by 2030. To do so, the manufacturer released a data-driven Democratic Design tool, which evaluates all new products through five dimensions: function, form, quality, sustainability, and low price. Design that balances all five of these elements is considered democratic and sustainable, and thus ready to hit the shelves.
The tool also helps IKEA calculate each product’s lifespan, based on the data about product usage and handling during ownership. Moreover, the company plans to include built-in features for convenient reuse, refurbishment, remanufacturing, and recycling in its new range, to promote further sustainability.
By intelligently using data, IKEA increased its circular capabilities of the lowest-scoring products (to 36%) and maintained full circular capability (100%) for the highest-scoring products.
Apart from design optimization, emerging digital technologies like machine learning and parametric 3D modeling can help businesses in the manufacturing and construction sectors select and source more sustainable materials, better track material provenance, reduce energy consumption during production stages and suggest new opportunities for material reuse and recycling.
3. ESG Benchmarking, Based on Real-Time Data
ESG disclosures are becoming mandatory for publicly traded companies around the world, but meeting these requirements is challenging.
ESG reporting requires data from multiple sources, including human capital, greenhouse gas emissions, supply chain, and transportation among other areas of governance. These insights are produced and stored in multiple transactional systems, which lack interoperability.
New approaches to data management and analytics will become the key areas of focus for organizations. Modern ESG analytics solutions can help businesses determine their current posture and track progress toward new targets.
Downer Group, for instance, went with IBM Envizi ESG Suite to centralize its ESG-reporting efforts. The analytics platform tracks the company’s GHG emission levels and energy consumption across 300 operating sites via a single interface. It also helps measure the progress toward specific ESG goals such as a 50% reduction in GHG emissions by 2023 against a 2020 baseline, plus automatically report on progress to regulating authorities.
IDC predicts that 75% of large enterprises will adopt ESG analytics and reporting platforms by 2024 — and generate sustainably driven cost and competitive advantages. For software vendors, this translates to a higher demand for ESG-focused analytics features, as well as self-service BI tools for benchmarking. So new ESG software development can be a promising area for new product development.
4. AI Takes Charge of Operations Management
While data analytics helps companies set benchmarks and measure ongoing progress, AI and ML can help locate new opportunities for ESG improvements and suggest prescriptive actions for higher operational efficiencies.
For example, 8allocate recently helped a major US logistics company deploy a mobile AI-powered application for automatic container label recognition, which helps the company process dispatches faster.
KDDI, Japan’s second-largest communications service provider, in turn, uses AI to improve its energy management. Based on Nokia AVA for Energy Efficiency, the platform can assess energy demand and traffic patterns in real-time and automatically adjust the amount of power being consumed by Radio Access Network (RAN) resources. The system also self-coordinates across multiple neighboring cells to deliver the best power savings within a coverage area. Thanks to it, KDDI reduced power consumption by 50% in low-traffic environments and by up to 20% per cell.
Other emerging use cases of AI for ESG include Facility management with digital twins,
predictive risk intelligence, transportation route optimization, and optimized workforce management among others.
5. Urban Management Will Receive a Tech Makeover
Overcrowding is becoming a major problem. By 2030, there will be 43 megacities with a population of 10+ million. Such densely populated areas will require better mobility networks, improved traffic management, and smarter planning for new construction projects. Climate risks will also require faster, more consolidated responses from city managers
Digital technologies can help design and maintain smarter, greener, and more inclusive cities of tomorrow. UNDP’s Strategic Plan for 2022-2025, in particular, calls for exploring new applications of digital technologies to increase cities’ capabilities for withstanding heat waves, flooding, and other adverse climate events.
The city of Toronto developed an online Heat Vulnerability Index to alert residents about potential exposure and direct them to nearby cooling centers. Such maps can be created with real-time data through partnerships with meteorological agencies and installations of temperature sensors around urban areas. Madrid recently installed a network of 27 meteorological sensors to measure temperature, humidity, and other parameters across the city and better coordinate urban services in response to climate events.
Boston-based startup Tomorrow is taking weather prediction a notch further with the help of AI. Its AI weather engine relies on data from the first commercial weather radar satellite to aggregate the latest information and provides users with hyperlocal forecasts and highly probabilities weather modeling capabilities. The solution provides companies in the transportation, aviation, mining, and defense industries among others with high-fidelity weather data and predictive risk modeling to make critical operational decisions.
AI can also help cities be more inclusive for people with disabilities. The AI Group, set up by the city of Amsterdam, uses image recognition software to detect barriers (e.g., broken sidewalks) on Google Street View images and then deploy urban services to fix the issue.
Finally, a combination of advanced analysis and 3D modeling provides urban planners with a richer spatial context. 3D digital models are more affordable to create for new urban projects, plus they allow rapid prototyping and can be effectively re-used for multiple projects. By using 3D models, planners, architects, and developers can iterate on new projects faster and evaluate the project’s impact on the local communities using real-world data. So this is an area also ripe for new software development.
6. Carbon Reduction as a New Driver for Public Cloud Migration
Cloud adoption has many operational advantages, with carbon reduction being one of them.
Top cloud service providers (CSPs) already power a significant portion of their operations with renewable sources. For four consecutive years, Google has been matching 100% of its global annual electricity consumption by data centers with purchases of renewable energy and is currently working on establishing on-prem renewable infrastructure.
Microsoft plans to shift to a 100% supply of renewable energy by 2025. AWS plans to power its operations with 100% renewable energy by 2025 and already has 90% of the electricity consumed, attributed to renewable sources.
Apart from equipment and maintenance costs, on-premises data centers also have a significant carbon footprint attached to them. Cloud adoption can help eliminate it. John Mennel, managing director at Deloitte, says that cloud computing adoption can save companies up to 80% on energy costs.
In other words: CIOs can contribute to sustainability goals by selecting CSPs with a low carbon footprint and adopting efficient migration approaches — an area of expertise at 8allocate.
7. Accelerated Human Capital Development
Pervasively, companies are facing critical skills shortages and can’t access enough specialized talent, especially with IT and AI competencies.
In Europe, three-quarters of companies have difficulties with sourcing workers with appropriate skill sets. That makes sense given that 44% of people in the EU and a third of Europe’s workforce lack basic digital skills. Gartner also found that even with the recent wave of tech layoffs in the US, tech skills remain in short supply.
There’s no universal solution to resolving skills shortages. Rather this process requires a combination of human capital development and management practices organizations can (and should!) take to nurture a future-proof workforce.
This can include outbound actions such as expanding talent sourcing strategies to new markers to overcome local shortages. CEE countries like Ukraine, Poland, and Romania are promising hotspots, with large talent pools and reliable software development partners.
Internally, organizations are also sizing up different technologies to help with talent acquisition and development. IBM designed an AI-powered decision support tool that helps HR teams better plan compensation. The platform analyzes dozens of internal and external data sources (e.g., stats from the Bureau of Labor Statistics) to provide recommendations. The tool is already used by thousands of first-line managers across IBM offices.
Wealth management firm BakerAvenue, in turn, uses a DEI analytics platform to identify better career paths for its female employees. With the tool’s data-driven recommendations, BakerAvenue increased the number of women in leadership positions by 2X and the total number of women in its workforce by 40%.
Emerging technologies also help deliver better workforce training, helping new employees safely use complex equipment and follow the required operating procedures. 8allocate, for example, recently helped implement an employee training solution for Hololens 2 glasses, powered by Microsoft stack for AI, and 3D modeling, which substantially improved employee training experiences.
8. Greater Supply Chain Efficiency and Transparency
The past several years have uncovered the fragility of global supply chains in the face of adverse economic, geopolitical, and health events. On average, supply chain disruptions now cause between 6% and 20% of revenue losses.
Moreover, global governments and consumers alike are also calling for more sustainable supply chains, penalizing companies with problematic practices in human rights, working conditions, and ethicality.
In the last year alone, over 117 sanctions have been imposed on companies found of using forced labor of Uyghur populations, resulting in 2,325 shipments denied and 988 pending for a total value of $826 million, according to the U.S. Customs and Border Protection.
On the other hand, consumers are ready to pay a 2% to 10% price premium to companies, exhibiting greater supply chain transparency. This is particularly important for the food, apparel, and manufacturing sectors, which bear the brunt of consumer dissatisfaction.
Better supply chain analytics can help bring more visibility into the global supply chains, promoting better governance of downstream suppliers, plus more sustainable sourcing, transportation, and processing of goods.
Patagonia, for example, has been successfully running a Supply Chain Environmental Responsibility Program for a couple of years now. The company uses industry-wide tools, such as the Higg Index, as well as third-party certification programs, such as the bluesign® system, to demonstrate supplier compliance. The company also boasts an advanced Material Traceability Tracer, which allows it to clearly communicate the provenance of all sourced materials.
Other adopted supply chain analytics such as Nike offer real-time, public insights into individual factories, including full data on the labor force, type of products made, and more.
The UK retail chain Marks & Spencer also has a similar interactive map of its food and apparel manufacturers.
To achieve the same level of transparency, businesses need to implement real-time tracking, monitoring, and verification of incoming supplies. Investing in streamlined data management then unlocks opportunities for more advanced analytics.
For example, using AI to reduce operational waste. Inventory management systems with predictive analytics capabilities can dynamically adjust inventory levels, based on the demand trends to avoid overstocks. By analyzing vehicle telematics data, companies can also optimize transportation routes, reducing fuel consumption and GHG emissions.
Lastly, supply chain traceability solutions allow streamlined reporting and compliance with ESG standards, leading to greater consumer and investor trust.
To Conclude
By 2050, digital technologies can reduce global emission rates in the most polluting sectors by 20%.
That said, carbon reduction and clean energy generation represent only a fraction of new opportunities in the ESG sector. More sustainable, digital-led product and building design, powered by digital twins, real-time transport optimization, immersive employee training, and greater supply chain transparency — there are plenty of green growth vectors tech-led companies can explore to make the world a better place.
8allocate would be delighted to further assist you with developing novel ESG and ClimaTech solutions through proactive support with product discovery, technology consulting, and project implementation with dedicated development teams.
Frequently Asked Questions
Quick Guide to Common Questions
How are emerging technologies shaping ESG strategies?
Emerging tech plays a critical role in sustainability efforts. AI and data analytics enhance real-time ESG reporting, energy optimization, and supply chain transparency. Companies are leveraging predictive analytics and automation to track environmental impact and improve compliance.
How does AI improve ESG benchmarking and reporting?
AI-powered ESG platforms automate data collection across multiple sources, reducing manual errors and improving auditability. Businesses can track carbon footprints, energy consumption, and regulatory compliance in real-time.
How do companies use AI to enhance supply chain sustainability?
AI helps optimize logistics, reduce emissions, prevent waste, and increase traceability in supply chains. Predictive models identify more sustainable sourcing strategies and detect compliance risks with ESG standards.
How does technology support carbon reduction initiatives?
AI-driven energy management systems, cloud migration, and operational efficiency improvements help businesses lower their carbon footprint. Predictive models adjust energy consumption in real time to minimize waste.
What role does AI play in workforce sustainability and talent development?
Companies use AI-driven workforce planning and training models to bridge skill gaps and improve productivity. AI supports DEI (Diversity, Equity & Inclusion) analytics, workforce retention strategies, and fair hiring practices to create sustainable human capital policies.
Why is cloud adoption becoming a priority for ESG?
Public cloud providers are increasingly powering data centers with renewable energy. Cloud migration helps companies cut infrastructure-related emissions, scale operations efficiently, and reduce IT energy consumption.
How do predictive analytics and automation improve ESG compliance?
Automated compliance tools track real-time ESG metrics, flag risks, and generate accurate reports based on international sustainability frameworks, ensuring transparency and accountability.
