Q&A: Jason Beckett, Head of Technical Sales, Hitachi Vantara

Sustainability has become a worldwide priority – for individuals, businesses and the public sector. The definitions of sustainability vary, but true sustainability means carefully considering consequences in the future based on what we do in the present. 

This is not an easy calculation and requires multiple data sets and predictive powers – challenges that are a great match for Gen AI.

But how exactly can business leaders leverage GenAI technology to optimise sustainability goals, particularly in areas related to resource management, waste reduction, and energy efficiency?

Jason Beckett, Head of Technical Sales at Hitachi Vantara, shares his insights and expertise on how AI can propel sustainability with ClimateTech Digital.

Q. Should business leaders consider AI as a priority?

A recent survey of IT and business leaders found that 97% of organisations viewed Gen AI initiatives as one of their top five priorities. In addition, 90% of organisations believed investing in Gen AI would enhance business operations through the improvement of operational efficiency and employee productivity.

We can expect those businesses that are investing this early in GenAI to gain a competitive advantage, thanks to the enhanced business operations it can enable, over businesses who aren’t.

As we know from GenAI solutions such as ChatGPT, some GenAI functions are now easily accessible to consumers — but there’s a new challenge for business leaders. Many might not be empowered to use their own budgets to invest where they choose, since IT departments tend to make many purchase decisions across emerging technologies.

In fact, the same survey found that on average across an organisation, 39% of primary budget holders were IT leadership, followed by 23% of technical executive leadership and 18% of data team leadership.

Business leaders therefore need to make a compelling argument around the need to invest in Gen AI — and the ever-growing challenge of sustainability is a real consideration here. 

Q. How can the application of Gen AI and predictive analytics help reach sustainability benchmarks?

Sustainability is a compelling problem for businesses to consider, simply because it is about making sensible decisions in the present while calculating how these can be built into company success, as well as far-reaching and long-term impacts on people and the planet. These long-term decisions involve processing enormous data sets and traditional, standard analytics are not the ideal tool for organisations looking for comprehensive insights.

Gen AI is preferred since it can accelerate the process of extracting data insights from large amounts of data, which can otherwise be an arduous task. It can also work well with predictive analytics for this purpose, which can be enhanced in its prediction capabilities with the use of Gen AI.

Together, Gen AI and predictive analytics can play a crucial role in both establishing and meeting essential company sustainability benchmarks. For instance, they can help set benchmarks by analysing historical data and industry standards to determine achievable sustainability goals.

Once these benchmarks are set, these tools can assist in meeting them by continuously monitoring company practices, predicting future outcomes based on current actions and suggesting necessary adjustments to stay on track.

These benchmarks can encompass various aspects of sustainability, such as minimising carbon footprints and identifying eco-friendly practices. By providing data-driven insights and predictive models, Gen AI and predictive analytics can guide a company towards more sustainable operations. 

In fact, Gen AI can be particularly useful for scenario planning, especially for SMEs. Through different variables and data inputs, it can create a broad range of potential and realistic simulations, helping businesses plan ahead by forecasting environmental scenarios and developing strategies to avoid or lessen future risks.

More global problems that businesses can tackle with the use of AI include clean water and sanitation, affordable and clean energy, and energy services. This use is particularly beneficial for businesses who offer essential services for consumers — for example energy suppliers — but who face increasing pressure from consumers to maximise affordability, keep costs low, and show they have a sustainability plan.

Q. How can AI be used to minimise waste reduction?

AI can also be applied by businesses for waste management purposes, refining processes such as waste collection, processing and classification. For instance, AI-powered intelligent rubbish bins can automatically sort waste, reducing the need for manual sorting and thus improving efficiency.

Classification robots can identify and separate different types of waste, ensuring that each type is processed in the most appropriate and eco-friendly manner. 

Predictive models can also forecast waste generation trends, enabling businesses to plan their waste management strategies effectively. Furthermore, wireless detection systems can monitor waste levels in real-time, allowing for timely collection and reducing the risk of overflow and associated environmental hazards.

The use of AI models in this way can ultimately have wider positive implications for businesses, enabling them to reduce costs, improve safety and reduce environmental impacts linked to waste management.

Gen AI can also analyse data across multiple departments to provide a comprehensive overview of a business’s current waste reduction strategy and enable them to take actionable insights from there. This includes the ability to conduct waste stream analysis, optimise supply chain management and design eco-friendly products across the business. 

Once areas for improvement have been identified, Gen AI can automate many of these waste management processes, streamlining operations and ensuring manpower is better spent elsewhere. This, in turn, reduces human error and improves efficiency for organisations — something all businesses, at any stage of their growth, continue to seek.

Q. What considerations need to be made about Gen AI’s own footprint?

There are, however, environmental considerations in themselves that come with the use of Gen AI. Earlier this year, OpenAI chief executive Sam Altman warned during the World Economic Forum’s annual meeting that the next wave of Gen AI systems will consume vastly more power than expected, and that, “there’s no way to get there without a breakthrough.”

Not only do Gen AI systems consume energy, but they also need enormous amounts of fresh water to cool processors as well as generate electricity. So, while businesses must consider Gen AI as a tool for meeting their sustainability goals, minimising costs and optimising efficiency, they must also keep in mind its impact and the wider sustainability goals of their partners and the planet overall.

Gen AI can do wonders for business sustainability practices, but environmental considerations must be incorporated into AI strategies themselves to not only help businesses reduce their environmental footprint, but also align with broader corporate social responsibility goals. If businesses do this and assign Gen AI decision-making and budgeting to the right stakeholders, the positive impact for their employees, customers, business pipelines and sustainability impact for years to come could be even larger than we expect.

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