AI-assisted analysis can potentially enhance results for companies looking to cut their carbon footprint
The UK is seeing growing signs of climate disruption, with 2020 ranking in the top ten warmest, wettest and sunniest years on record and temperatures rising 0.9C over the past few decades.
For many businesses, this latest data only underscores existing concerns and the vital need to push ahead.
Not only has 2021 seen almost a third of FTSE 100 companies commit to the United Nations pledge of becoming net-zero by 2050, but more than half of UK CEOs have made plans to increase sustainability spending amid rising worries about climate change.
Accelerating carbon removal and mitigation goals is certainly a positive step forward, but ensuring organizations make effective and consistent headway isn't necessarily easy.
The obvious requirement is granular measurement; in fact, over 50 percent of CEOs already feel they should be doing more to measure their impact on the environment. Realizing this ambition, however, means navigating several complications.
For starters, there is the current lack of shared emissions reporting guidelines, which leaves significant room for confusion.
Businesses also face the tough challenge of precisely tracking all operations; including monitoring internal activities and ensuring suppliers aren’t just giving the appearance of alignment on sustainability principles.
So, could AI be the missing ingredient to keep them moving in the right direction?
Getting the complete green picture
At a basic level, the success of green projects depends on clear oversight.
Companies need the full business-wide picture to truly get the most from their net-zero products and services, which makes holistic oversight of corporate data crucial.
But simple as this might seem, it can be hard to achieve when silos are present.
Across many organizations, there is still a common tendency for teams to assemble and store data in separate systems, producing numerous fragments of insight that require significant time and resources to join up.
AI technologies, however, have the potential to rapidly translate data chaos into manageable order.
Using flexible API integrations, AI technologies can immediately gather data from multiple sources, before cleansing, merging, and syncing it in one unified platform.
Harnessing these capabilities will enable organizations to create a single view of energy consumption and net-zero purchasing workflows such as power purchase agreements (PPAs).
Blended with key external data sets, including real-time weather, satellite, local generator, risk assessment, renewable energy certificate, and market trends data, this information will allow decision-makers to review their green portfolio at a glance.
From there, it will then be easier to evaluate, forecast, and measure the company's carbon footprint, but with a higher level of accuracy.
Accepting supply chain accountability
Consolidating corporate data will give organizations a better window into their own activities, but it’s important to remember that they also hold responsibility for their entire network of supply chain partners.
Moving in step with third-party suppliers from the start is essential when building and rolling out green programs.
While adding suppliers into the mix does bring an additional layer of data, doing so will be critical to ensure coordinated performance against core goals and minimize greenwashing.
Fortunately, this is another area where AI can help.
The capacity of AI to seamlessly onboard varied data allows companies and suppliers to stay persistently in tune.
By pulling in information from live satellite feeds, AI technologies can generate a steady stream of insight, as well as automatically highlighting and anticipating possible supplier discrepancies.
For example, this could include instant notifications altering companies to anomalies on the supply side that could send sustainability initiatives off course; equipping them to quickly assess risk and respond.
Driving tangible change
It goes without saying that tackling climate change is an urgent and necessary objective for every organization.
With more UK companies aiming to accelerate progress and expand investment, AI-assisted analysis can play a valuable role in keeping them on track and enhancing results.
By harnessing intelligent modeling, key stakeholders can not only measure ongoing performance, but also use the data they gather to support efficient decision-making, spending, and optimization towards green targets.
Typically, consistent metrics are likely to include CO₂ saved versus goal reduction, alongside total cost savings, contracted renewable energy, and all uptime and downtime generators.
But there are plenty of ways for different stakeholders to customize the outputs AI models create. For instance, they may prefer bespoke dashboards displaying top-line qualitative data and tick-list scores, or more granular qualitative reports with deep-dive summaries.
The final outcome, however, will be information they can leverage for multiple purposes, such as checking performance against short-term goals and the likelihood of hitting longer-range targets and providing tailored updates for key stakeholders.
That’s not to mention the scope to use this data as a basis for determining where adjustments are needed to meet objectives.
As evidence of immediate climate change mounts, it's positive to see that more businesses are recognizing the requirement for urgent action.
But as they join the movement to dial back negative impact and set ambitious targets, it will also be paramount to consider how they will reach them.
Only when internal and external data sets are consolidated into a single platform will key stakeholders truly be able to measure the impact of their green initiatives, supply chain, and emissions reduction and mitigation as a whole.
Muhammad Malik is the CEO and founder of NeuerEnergy, a sustainability-focused technology, and infrastructure investment firm. Muhammad previously spent six years at Sprint, serving as its network engineering head for Europe, the Middle East, and Africa.