How to improve AI efficiency beyond cost optimisation

Artificial intelligence (AI) is transforming how organisations operate, innovate and compete. Yet, as AI adoption accelerates, so do the demands on energy, water and hardware required to power infrastructure, process data and deploy models and applications at scale. These growing resource needs are creating new challenges for cost optimisation, as well as environmental sustainability and operational resilience, especially as global concerns about resource scarcity intensify. In today’s environment where technology-driven expenses are putting pressure on margins, organisations can no longer afford to treat AI efficiency as an afterthought. AI efficiency is the strategic optimisation of cost, performance and resource use across the AI technology stack to maximise business value. It drives financial results, resilience and sustainability, while reducing waste and risk. Business outcomes, not technical metrics, are the true measure of AI efficiency. Gartner research shows that at least 50% of generative AI projects will overrun their budgeted costs by 2028 due to poor architectural choices and lack of operational know-how. This trend signals a fundamental shift forcing organisations to move beyond adopting the latest technologies for their own sake. The days of chasing marginal performance gains at any cost are over. The future of AI belongs to those that deploy smarter solutions, not just bigger large language models (LLMs). Organisations need to pivot from scale-at-any-price to efficiency-first strategies if they want sustainable growth, protected margins and long-term leadership as AI adoption increases. Hidden costs of inefficient AI practices The rush to adopt AI regardless of strategic fit can often lead to costly missteps. Inefficient AI operations drive up expenses and reduce operational resilience, ultimately undermining investor confidence. As a result, stakeholders are increasingly focused on managing both the cost and value of AI use cases. Over-engineering AI by building systems that are more complex than necessary or misaligned with...