What strategies can business and technology leaders adopt to effectively balance immediate AI investments with long-term sustainability objectives?

What strategies can business and technology leaders adopt to effectively balance immediate AI investments with long-term sustainability objectives?

Research shows C-levels’ interest in AI continues to drive strategy and investment, but to what cost is this affecting ESG targets and greener operations? We get insight on the tightrope balance from Belinda Finch, CIO at IFS; Dr Anino Emuwa, Managing Director of Avandis Consulting; and Levent Ergin, Global Chief ESG Sustainability Strategist at Informatica.

UK CEOs are feeling more optimistic about their immediate prospects and are prioritising Artificial Intelligence (AI) investments to gain a competitive advantage, according to the latest EY CEO Pulse Survey

The survey of 100 UK CEOs, which provides insights on AI, capital allocation, investment, sustainability and transformation strategies, found that 61% of UK CEOs said they felt more optimistic about their company’s profitability compared with 12 months ago.

When considering their strategic priorities for the next 12 months, half (50%) of all respondents are prioritising investment in technology, including AI, to improve growth and productivity, closely followed by enhancing data management and cybersecurity (49%). 

CEOs and investors diverge on sustainability focus over next 12 months 

Whilst UK CEOs are focused on technology investment in the immediate future, achieving net zero remains a long-term priority. 

Over half of respondents (56%) said sustainability was a higher priority compared with 12 months ago. However, CEOs are facing challenges in implementing robust ESG strategies, with 47% agreeing that their management teams struggle to present a strong business case for sustainability investments that clearly outline their financial benefits. Almost three-quarters (71%) agreed that activist shareholders are more concerned with their company meeting quarterly earnings targets than its performance against long-term sustainability metrics. 

Respondents also believe there is an opportunity for AI to support sustainability performance, with 70% agreeing that technology and AI hold the answers to many of the key sustainability challenges society faces. 

“For UK CEOs, by far the most compelling immediate priorities involve enhancing existing technology to improve growth and productivity, as well as boosting data management and cybersecurity,” said Silvia Rindone, UK&I Managing Partner for Strategy and Transactions, EY. “However, this has come at the expense of achieving sustainability targets which are now considered more long-term priorities.

“This divergence between short-term financial returns and decarbonisation is shortsighted, so business leaders must ensure they remain committed to delivering these targets to move towards a more sustainable future,” Rindone added.

Strategic transactions remain a key focus for UK CEOs

Strategic transactions remain high on the agenda for UK CEOs, with 98% saying they expect to actively pursue transaction initiatives over the next 12 months. Of these, 79% are considering divestments, IPOs, or spin-offs; 42% are looking at joint ventures or strategic alliances with third parties and 33% are considering M&A activity. 

Around two-fifths (42%) of those considering a strategic transaction said they were doing so to access new geographies, 39% to secure supply chains and 36% to acquire technology, new production capabilities or innovative start-ups. 

“UK CEOs are using strategic transactions to achieve their near-term priorities, with most looking to divest assets over the next 12 months to future-proof their business,” said Rindone. “However, it’s imperative that boards look beyond the short-term efficiency and productivity gains these transactions can bring and ensure they are continuing to focus on core operations, while actively looking for opportunities to raise capital to invest in their remaining portfolio.”

Belinda Finch, CIO, IFS

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. On-premise 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 on-premise 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 real-time, dramatically reducing CO2 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.

Dr Anino Emuwa, Managing Director, Avandis Consulting

Dr Anino Emuwa, Managing Director, Avandis Consulting

The first strategic consideration for business and technology leaders is to clearly define the role of AI within their organisation. Is AI to be leveraged for enhancing internal processes, or is it to be embedded within products and services? In other words, whether the organisation is an AI user or an AI creator? 

For AI users, the investment should focus on augmenting job functions and improving operational efficiencies. Here, the metric of success is the optimisation of internal processes. Time savings, cost reductions and employee feedback will be some metrics that can be used. On the other hand, for AI creators, the emphasis should be on how AI can provide solutions for client needs through innovative products. The investment metrics in this scenario will then revolve around client acquisition, sales, customer satisfaction and market differentiation. 

The AI landscape is dynamic and evolving. Hence, businesses might consider leaving 

development of AI technologies to specialised tech companies or acquisitions focusing instead on how best to deploy these technologies within their unique contexts. This approach allows non-tech firms to benefit from cutting-edge advancements without the burden of in-house development. 

The emergence of the Chief AI Officer role signifies the growing importance of AI governance within organisations. Just as Chief Technology Officers or Chief Information Officers were rare a couple of decades ago, the CAIO is becoming a crucial executive role today. This trend underscores the need for dedicated leadership to steer AI strategy and implementation. 

While the trajectory of AI’s evolution is uncertain, AI is here to stay. The most successful organisations will be those that empower their teams through comprehensive AI training and learning programmes. Investing in human capital ensures that the workforce is adept at utilising AI tools, driving both immediate efficiency and long-term innovation. 

AI should not be viewed as a separate entity but as an integral component of modern business strategy and companies will need to build a culture of continuous learning.

Levent Ergin, Global Chief ESG Sustainability Strategist, Informatica

Levent Ergin, Global Chief ESG Sustainability Strategist, Informatica

AI holds huge potential to help organisations achieve their sustainability goals. And advancements in technology are enabling AI itself to become more efficient and sustainable as well.

Let’s not forget, the ICT industry has made a significant shift to reduce its emissions – its carbon emissions now stand at around 1.7% of global emissions – significantly lower than previously forecast. With advancements in technology, we can expect to see a similar trend in how much AI contributes to global emissions.

Ensuring AI models use clean, trusted and reliable data is essential for reducing processing strain and energy consumption. The effectiveness of AI relies on the quality of the data it processes. Clean data – free from bias – enables AI algorithms to operate faster, more efficiently and reduces the time and energy spent training and retraining models. By providing AI models with unbiased data enriched with comprehensive metadata, organisations can ensure their AI models function both optimally, and sustainably.

Also, any AI programming and architecture should use leading-edge algorithms, data storage techniques and right-sized computing infrastructure. This ultimately reduces the amount of energy that would be required for similar data processing and analysis using less efficient processes and higher-powered end-user hardware.

On the other side, we’re also seeing more organisations leverage AI and Generative AI to unlock sustainability use cases. For instance, Large Language Models can process geospatial remote sensing satellite data to predict acute climate-related physical risks like flooding and hurricanes over short periods (two to four weeks). They can also forecast chronic risks, such as heat waves and sea-level rise, for longer timelines (2050-2100). These predictive capabilities enable better climate resilience planning and management of transition risks.

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