In the realm of enterprise technology, the rise of Generative AI (GenAI) marks a significant transformation, reflected in the escalating investments by businesses. According to a recent Gartner report, global spending on GenAI is projected to reach a staggering $644 billion this year, representing a nearly 77% increase from the previous year. This ambitious surge highlights the enthusiasm among organisations to harness GenAI at scale, even as the technology itself remains in a nascent stage. Despite executives ranking GenAI as a top strategic priority, the path from potential to profit is fraught with challenges; data reveals that approximately only 48% of AI projects make it into production, and around 30% of GenAI initiatives are ultimately abandoned due to unclear business value.
An important factor determining the success of GenAI deployment is data readiness. Businesses must ensure their data is well-managed and prepared for GenAI applications, as these systems can process a wide variety of unstructured data, from social media posts to audio recordings. Doug Woolley, general manager at Dell Technologies South Africa, emphasises that without proper data management, organisations often encounter unforeseen challenges after implementing GenAI. He insists, “You need to clean up your data before moving forward with any projects,” signifying that data hygiene is crucial in mitigating potential project failures and avoiding costly bottlenecks.
As companies navigate the complexities of GenAI, they need to consider the security implications associated with its implementation. While organisations desire the efficiencies offered by external GenAI services, they also face the dilemma of trust—whether to rely on established players like OpenAI or to embrace open-source alternatives. Ofir Israel, Check Point’s vice president of threat prevention, highlights the dual challenge of allowing GenAI applications to flourish while maintaining security. “You could block all GenAI applications,” he suggests, “but miss out on tremendous sales opportunities, or allow everything and hope nothing leaks.” Achieving a balance between accessibility and security is a pressing concern for enterprises looking to adopt this technology.
To effectively leverage GenAI, business leaders require comprehensive visibility into its usage. Israel points out that context is critical: understanding how employees use GenAI could be the difference between an effective tool and a potential security threat. New risks emerge with GenAI, such as prompt injection and the exploitation of AI agents capable of executing complex commands. With every new permission granted, organisations must remain vigilant; as Israel notes, “wherever there is freedom, an attacker sees a potential attack surface,” necessitating innovative protective solutions.
Beyond ensuring safety, organisations must also evaluate the cost-effectiveness of GenAI investments. While it may be straightforward to calculate ROI in restricted use cases, such as contact centre automation, a broader roll-out presents complexities in quantifying value across diverse departments. Hans Zachar, Nutun’s CIO, advises adopting a holistic approach to measure the benefits of GenAI, recognising that the interplay between human creativity and AI capabilities may yield value that traditional metrics cannot capture. He conveys, “You almost have to push the tools into the environment and let the creativity of the people show you the value.”
However, the financial implications of adopting GenAI should not be underestimated. As Zachar points out, the foundational infrastructure required for AI capabilities is merely the tip of the iceberg. The computational demands of training and running large language models (LLMs) can substantially inflate costs, particularly given the quadratic scaling of expenses associated with increased data input. As Johan Robinson from Red Hat emphasises, this reality can have significant consequences for budgets, a challenge further compounded by the need for high-quality training data, talent acquisition, and ongoing model maintenance. With these layers of cost in mind, many organisations are re-evaluating their strategies, potentially favouring smaller, more focused models to manage expenses while achieving effective results.
Understanding the full scope of GenAI investments is vital as companies strive to integrate this technology into their operations. The Gartner report indicates that not only will the GenAI spending rise, but it will also reshape the broader landscape of IT investment, with worldwide IT spending estimated to escalate by 7.5% next year, driven largely by GenAI’s computational demands. As organisations balance the allure of GenAI’s potential with the intricacies of its implementation, the overarching question remains: how can businesses harness this technology effectively and judiciously?
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Source: Noah Wire Services
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