Accountability cannot be an afterthought, activated only in the wake of a crisis. It must be embedded from the outset and upheld at every stage. (Walther, 2025)

Artificial Intelligence (AI) has been used for everything from spellchecking to building construction to elderly care. Across all its use cases, AI remains misunderstood, misapplied, and mistrusted. The role of AI is being tested and expanded daily, creating greater reliance on its services. According to the 2025 International Energy Agency (IEA) report on AI and Energy, among large companies, AI adoption rates in corporates rose from slightly over 15% in 2020 to nearly 40% in 2024. Smaller firms use it much less, with a lack of expertise appearing to be the key constraint.

For AI to work effectively, it requires the use of massive data warehouses, which has then raised various concerns. A data centre is a physical facility that houses and runs large computer systems, typically containing multiple computer servers, data storage devices, and network equipment. These provide IT infrastructure services that enable organisations to store, manage, process, and transmit large volumes of data. These warehouses, therefore, provide the computing power and data storage required for large-scale AI systems. They are typically spread across disparate geographic zones for security purposes and to provide system redundancy and high service reliability (uptime). These centres, however, have proven to be quite a road bump for many organisations’ sustainability efforts. More on this later.

Kenya is at the forefront in East Africa and across the continent in building these AI superstructures. The Kenya Cloud Policy, approved by President William Ruto in January 2025, is touted as a forward-looking framework designed to enhance service delivery, strengthen cybersecurity, and foster innovation through the adoption of cloud-based technologies, with data warehouses as a key component. Several organisations, both public and private, have now taken the initiative to leverage such infrastructure within our borders as they seek to remain relevant in the data-led future. For example, the proposed Judicial Data Warehousing for Kenya’s Judiciary aims to generate judicial insights to enable faster decision-making and greater transparency. The Central Bank of Kenya’s Enterprise Data Warehouse for Real-Time Banking Supervision enables real-time monitoring of customer transactions across the banking sector. Additionally, Microsoft’s G42 $1 billion Data Centre Initiative, a green data centre in Olkaria, hosts cloud and AI workloads.

Kenya is also a trailblazer in corporate sustainability reporting, having established a clear regulatory roadmap for the adoption of the International Financial Reporting Standard (IFRS) S1 and S2. The roadmap issued in 2024 by the Institute of Certified Public Accountants of Kenya (ICPAK) sets out a binding reporting timeline for Public Interest Entities (PIEs), Large Entities, and SMEs, spanning 2027 to 2029. This transition signifies a fundamental shift from voluntary, fragmented ESG disclosures to standardised, financially material disclosures that meet the rigour of traditional financial reporting mapped to IFRS.

The key challenge in the new ESG reporting framework for most Kenyan corporations is the acute, systemic gap between the ambitious regulatory timeline and the current state of institutional capacity, particularly regarding specialised data systems, robust scenario modelling capabilities, and technical human resources.

The key challenge in the new ESG reporting framework for most Kenyan corporations is the acute, systemic gap between the ambitious regulatory timeline and the current state of institutional capacity. This gap is especially evident in specialised data systems, robust scenario modelling capabilities, and technical human resources.

In a previous article, “Leveraging Your Business’s Hidden Treasure: How to Begin Your AI Journey”, published by this publication, AI can reduce the effort and time required for corporate ESG reporting for most organisations. A 2024 McKinsey report on AI and the Blue Economy indicates that generative AI was used to analyse the sustainability reports of about 2,500 companies across 17 sectors, representing over 70 per cent of the world’s market capitalisation.

On the flip side, according to a 2025 World Economic Forum (WEF) report on the AI energy transition, data centres could account for around 3% of global electricity demand by 2030. While these figures may seem extensive or impressive, a new question might now arise: can AI address the systemic gaps identified in the long term? Will integrating AI systems into your organisation lead to a net positive or a net negative impact on its sustainability initiatives in the long run? This is neither a simple nor trivial question, and there is no single correct answer; it is not an obvious or straightforward choice for all types of organisations.

Undoubtedly, AI has already demonstrated its ability to tackle sustainability challenges for numerous global organisations. One such area where it has succeeded is in Energy System Optimisation, where AI enhances smart grid stability and efficiently integrates renewable energy sources like solar and wind by forecasting their supply variations and balancing loads to avoid blackouts during peak demand.

Second, AI has already accelerated decarbonisation efforts by enabling faster, more precise modelling and simulation of low-carbon strategies, as it can run thousands of scenarios more quickly and accurately than humans. It has also identified cost-effective pathways for emissions reduction and decreased planning time for numerous green projects worldwide.

Furthermore, the tool can optimise industrial resource efficiency, for example through virtual product design, to achieve lower emissions. It simulates manufacturing processes before production to reduce material waste and cut Scope 3 emissions in supply chains. Additionally, it can incorporate predictive maintenance into production routines to reduce downtime and energy wastage by detecting equipment failures early and minimising energy-intensive breakdowns, thereby extending asset life.

Finally, AI data warehouses could transform and drive significant growth in energy development and supply for communities and even nations. Given the warehouses’ high energy demands, investors would be interested in Renewable Contract Lock-Ins, in which long-term agreements provide price stability for renewable energy developers and buyers. Such agreements would lower investment risks, making projects more attractive to financiers. Ensuring that these energy investments adopt a community-based ownership model can also promote fair distribution and benefit underserved communities. Additionally, including a renewable source clause in such contracts improves overall grid resilience and energy security for affected communities and nations.