EDITOR ’ S QUESTION
BELINDA FINCH , CIO , IFS
There have been many dramatic shifts in the market resulting in investors , stakeholders and entire markets scrambling towards AI adoption . For AI to be a true business enabler , all stakeholders within your organisation must sit down and align on specific goals that drive key areas of your business including sustainability . Balancing immediate AI investments and long-term sustainability objectives is a tightrope that tech and enterprise leaders must cross carefully .
Avoiding the ‘ AI landfill ’
AI runs the risk of becoming like fast fashion . Across businesses , employees are being encouraged to experiment and innovate without any thought to licence and compute costs , or sustainability . Many of these AI projects will be used once and discarded , leaving a trail of data and cost behind . This ‘ AI landfill ’ will create tech debt and impact their sustainability goals .
Cloud vs on-premise
AI operations require substantial computing power , making cloud solutions more expensive as businesses are tied into long-term consumption-based contracts . To mitigate costs , some enterprises are returning to on-premise and adopting a sandbox model for AI . While this might reduce short-term cost pressure , it brings us back to the sustainability problem . Onpremise data centres can lead to increased energy consumption and a higher carbon footprint if not managed properly . So , a smart data strategy should be developed if bringing sandbox AI projects into onpremise sites .
An industrial AI strategy
The data strategy should also look longer-term . Once the hype of AI recedes and businesses begin
to harness the technology , their compute strategy will become clearer . This is why in striking the balance between AI and sustainability we advocate a composable cloud architecture that enables organisations to remain agile . Adopting an industrial AI strategy will enable the organisation to take a strategic view of where focus and resources on AI should go , as well as look to embed AI into key operations so it becomes part of the fabric of the enterprise .
Early adopters of industrial AI are repairing the sustainability benefits
Early adopters of industrial AI are already seeing benefits from a sustainability perspective in servitisation and predictive maintenance of industrial vehicles and equipment . AI is extending equipment life spans through predictive maintenance , while optimising field service engineer schedules in realtime , dramatically reducing CO 2 emissions . Across operations , industrial AI is automating data collection , offering real-time visibility into ESG performance and having a significant impact on organisational sustainability goals .
Balancing AI investments with long-term sustainability objectives requires a multifaceted approach . By reevaluating data centre strategies , prioritising strategic AI deployments and aligning objectives with stakeholders , business and technology leaders can achieve a harmonious balance . Adopting an industrial AI approach will enable organisations to harness the full potential of the technology while ensuring that their operations are sustainable and resilient in the long run .
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