Aaron White, Vice President and General Manager Sales, Nutanix APJ
Many CIOs are now grappling with how to quickly, efficiently, and securely take advantage of the power of generative AI and ML applications, while simultaneously evaluating the new use cases that emerge every day from improving customer service and developer productivity, to increasing operational efficiency and more.
In my conversations with CIOs, the vast majority of them see the opportunities AI presents but they are struggling with growing concerns regarding intellectual property leakage, compliance, and privacy.
Despite the near universal agreement that AI is a business priority, a significant amount of work still needs to be done. The technology’s emerging nature means there is a lack of strategic best practices, established guardrails or even reference architectures. As such, many organisations are still determining which IT environments are best to run different parts of their AI processes and workloads. While some might default to the public cloud, this may not be the best fit for applications which require access to sensitive and business critical data.
A related point for any CIO to consider is the emerging nature of the regulations governing the use of AI and the legislative differences in each jurisdiction the organisation operates. One of the biggest pitfalls enterprises must avoid when implementing AI models – or any emerging technology for that matter – is running afoul of data security and sovereignty regulations. We’re only just starting to figure out what questions to ask in the regulatory space so it will be some time until we have the answers. Until then, enterprises need to maintain control of their data and applications.
When it comes to the skills required to implement, maintain, train and manage an advanced AI model, CIOs need to be clear-eyed about the capabilities their organisations have. If the necessary skills don’t currently exist within the organisation, what training will be required? Alternatively, if new talent will need to be hired, where will that talent come from? This is particularly challenging in nations with pronounced technical skills gaps, such as Australia.
AI will be a core driver of innovation and business differentiation in the years to come, re-imagining how businesses, governments and society operates.
But its implementation must be approached with a careful consideration of the infrastructure, governance, and skills required to truly reap its rewards.
Sunil Chavan, Vice President Asia Pacific and Japan, VAST Data
Until recently, the role of the CIO, while entrenched in all things technology, has been limited by capability and capacity for which humans can develop. AI is changing that very dynamic and creating opportunities for CIOs to expand their organisations’ and their teams’ technological advancement. They are no longer bound by any finite amount of storage and processing power.
AI is breaking through previously known barriers and creating the potential for CIOs solve their most pressing challenges, from improving customer experience to bolstering cyber security posture, increasing operational efficiency and driving real value from the vast datasets their organisations hold.
But as the reach and application of AI extends, it is crucial we provide CIOs the resources they need to flourish. That means new, modern data infrastructure ready for the AI era, just as road networks were redesigned when we moved from horse and cart to the automobile. This is a defining cornerstone for whether and how CIOs will succeed with AI.
The Asia-Pacific region is responding to the demand – a recent report showed data centres across the region are growing in scale and new markets are being evaluated for expansion as operators anticipate increased demand from continued digitisation and wider adoption of AI.
But more of the same won’t be enough to position CIOs for success.
Deep learning can’t operate using a collection of tools and technologies primarily designed in an era that dramatically predates the modern AI standard of 2023.
CIOs need a platform that consolidates all components required built on a tierless and limitlessly scalable data platform architecture designed specifically with AI in mind.
Designing and configuring the right data platform is the mainspring of AI potential. Organisations and infrastructure builders must apply careful thought to ensure those platforms are able to accommodate and optimise CIOs’ increasingly hefty data and computational appetite driven by AI.
By consolidating and unifying infrastructure in a scalable system that is built from the ground up, specifically for AI, organisations can easily store, catalogue and compute on structured or unstructured data from anywhere in the world – at any scale.
With that ecosystem in place, CIOs can easily leverage AI and their data to solve challenges, create new opportunities and move even higher up the value chain of the C-suite.
Byron Fernandez, CIO and EVP, TDCX
The generative AI boom presents CIOs with an opportunity to turn the potential of this tech into lasting value for the business. Through conversations with dozens of tech leaders and an analysis of some of our clients’ generative AI initiatives, I have identified three key factors for CIOs to consider.
Where does AI fit in the business?
It is important to first understand the company’s readiness for the adoption of generative AI. Particularly, business leaders need to align on the risk appetite for using generative AI and how it fits into the wider business strategy. This will in turn enable the business to determine company-wide policies and guidelines.
For other C-suite executives, AI isn’t about algorithms but tangible outcomes. To demonstrate this, commission an internal assessment on generative AI’s projected return-on-investment (ROI) for your business. For example, quick wins could focus on productivity improvements but what is the potential for revenue generation that can offset the adoption costs? A Gartner research paper found that by 2028, half of enterprises that have built large AI models from scratch will abandon their efforts due to costs, complexity and technical debt in their deployments. CIOs therefore need to carefully balance the need for generative AI and their companies’ long-term business strategy.
To build or not to build?
“Should we buy or build?” is a common decision CIOs have to make when it comes to developing generative AI capabilities. The fundamental rule holds true: a company should allocate resources based on where it can establish a distinctive advantage or maximize internal resources. Hence, what is also important is to determine if existing employee skillsets are aligned with the requirements.
While there are many advantages to using off-the-shelf AI software, there are times when building software may make more sense due to domain expertise in niche industries or the team’s ability to create core AI tools that provide distinct differentiators from competitors. For example, at TDCX, we harnessed our current capabilities to create an AI-based solution and integrated it with our client’s system to monitor customer feedback. This enabled us to identify the causes of customer attrition and subscription cancellations, all while maintaining cost efficiency and creating further moats around our proprietary tech approach.
In order to make the best decision for their companies, CIOs must understand the capabilities and limitations of their team, and they should also consider the maintenance costs of supporting their AI tools. By carefully evaluating the different aspects and factors involved when choosing between buying or building AI tools, CIOs can arrive at an informed decision that aligns with the needs of their organization. Another useful point to consider for emerging technologies like generative AI, is that they are evolving toward maturity – and the people supporting these emerging technologies must also continue to evolve with the technology and keep up with its progress.
Ownership of the generative AI strategy?
The success of a generative AI strategy relies on having an empowered team that is accountable for delivering on the project. Having a dedicated generative AI team or a project manager within an agile team serves as a Driving force on product development and roadmap alignment to achieve business goals. To get started, the team can begin by focusing on a limited set of high priority use cases and gradually broaden their scope as they develop reusable capabilities and gain insights into best practices.
By leveraging generative AI, companies can enhance their efficiency and capabilities in many areas, such as conducting in-depth customer data analysis, creating personalized CX journeys and targeted marketing strategies. Such initiatives will ultimately help in boosting customer loyalty and revenue.
In doing so, CIOs can not only adapt to advances in AI but also differentiate themselves as a business leader. The fast-changing business landscape demands that CIOs go beyond the tech domain and into business use cases. With a clear business problem statement and tech strategy, CIOs are on a sure path to help companies Drive cost savings, operational efficiency, and deliver better experiences.
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