Creating a Winning AI Strategy: Five Key Priorities for CIOs

As enterprises explore the adoption of Generative AI and other AI-driven systems, they encounter both substantial opportunities and formidable challenges. AI promises to automate decision-making, enhance operational efficiency, elevate customer satisfaction, and deliver cost savings. However, realising these benefits demands meticulous data management and strategic planning.  

Moreover, a crucial factor emerging is the creation of AI value and return on investment. Organisations are increasingly focused on looking past the hype to grasp the tangible benefits and true productivity that AI delivers.

With global AI spending projected to reach $3 trillion between 2023 and 2027 (Gartner), AI is a top priority for CIOs. They are actively analysing, forecasting, and debating how AI can transform business operations, drive innovation, and create value. A major concern is determining which specific technologies to invest in. So, what should CIOs focus on to craft a successful AI strategy?

Let’s delve into the key factors influencing their decisions.

Connecting the Right Data to the Right People at the Right Time

AI systems thrive on data, yet the availability and quality of this data can be a limiting factor. Inaccurate or inaccessible data can undermine even the most advanced AI models. Legacy disk-based data platforms and architectures don’t serve modern business processes and CIOs are trapped in not being able to get the right data to the right people.

Regulatory compliance is another impediment to data access, with stringent data privacy laws and company policies governing how data can be stored, moved, or used. This can easily lead to some valuable data sets becoming inaccessible.

Data centralization often proves to be another hurdle for CIOs. It is time-consuming and labour-intensive, and it increases the risk of cyberattacks as valuable data is duplicated and moved to the location where AI work will happen. Meanwhile, as business operations generate increasing amounts of data, duplicate data accumulation becomes inevitable.

This, in turn, adversely impacts the quality, performance, and usability of the data, leading to incorrect analysis and decisions. Additionally, it is quite common for data to become trapped behind the applications businesses use.

CIOs, or CDOs in larger organisations, can tackle these challenges by setting up strong data governance, maintaining data integrity, and investing in secure, scalable data management solutions. They play an important role in helping CEOs and board members understand how tech inhibits or drives business value.

Leading with Flexible, Scalable Platforms

AI is most valuable when operationalized at scale, meaning it is deeply integrated into an organisation's core products, services, and business processes. Unfortunately, scaling AI in this sense isn’t easy. Deploying one or two AI models is very different from running an entire enterprise on AI.

According to a 2024 Gartner report, IT leaders are increasingly adopting unified, platform-based storage solutions to address these challenges. Such platforms should offer agility and risk reduction with a consistent, as-a-service experience that supports both traditional and modern workloads, including AI.

Designed for the cloud era, these platforms should provide flexible, on-demand storage with intuitive interfaces, non-disruptive upgrades, and robust SLAs covering performance, data resiliency and energy usage/sustainability goals.

For CIOs, the benefits are clear: these platforms simplify management with ease of use, scalability, and predictable pricing. They eliminate disruptive upgrades and complex refresh cycles, allowing CIOs to efficiently manage budgets while adapting to the evolving demands of AI initiatives.

Minimising Tech Debt and Maximising Value

If organisations prematurely implement generative AI, existing technical debt may continue to grow or, in some cases, become chronic. Therefore, setting a plan to address existing technical debt is crucial so new AI-driven initiatives don’t crumble. If used appropriately, gen AI could help eliminate old technical debt by rewriting legacy applications and automating a backlog of tasks.

From a financial perspective too, CIOs prefer as little tech debt as possible, and seek partners who can optimise costs across the entire infrastructure, including storage, compute, and networking. Apart from zero downtime and minimal or no technical debt, various factors influence their choice of technology vendors, including reliability, cost-effectiveness, future-proofing, robust data flow capabilities throughout the stack, reduced complexity and risks, and simplicity.

Building Transparency and Understanding

To move to strategic partnerships, CIOs are looking at building successful long-term relationships based on mutual trust and a vendor’s ability to quickly and succinctly understand enterprise priorities and propose effective solutions.

A CIO’s world is admittedly complicated and unless you're an insider, having an inherent understanding of all the key drivers wouldn’t be possible. Vendors should therefore be honest about their capabilities, making transparency during each stage of the process a non-negotiable.

Not every vendor can (or should) claim to be an expert in everything. Both because it’s impractical, and CIOs trust them more when they focus on their area of expertise.

Using Industry Benchmarking to Shape IT Strategy

And last, but not least, among the things that CIOs look closely at in this age of AI is industry benchmarking. CIOs are keen to understand what their peers – especially those in well-established and reputable companies – are doing and what vendors they are partnering with.

Benchmarking and evaluating the effectiveness of IT investments, both within and outside one’s own industry, are crucial for assessing return on investment and refining strategies for continuous improvement. For instance, some mining companies are exploring how Chinese manufacturers fine-tune efficiency gains for potential insights.

When it comes to - IT benchmarks - priorities often include operational efficiency, cost effectiveness, service delivery, innovation impact, and cybersecurity strength. Among these, the most critical - is linking IT benchmarks with business benchmarks to provide a holistic view of the organisation's performance.

Data Mastery: Key to AI Excellence

In navigating the complexities of adopting AI, organisations must balance significant opportunities with formidable challenges. A successful AI strategy is dependent on implementing and leveraging a robust data strategy. Despite hurdles like data fragmentation and regulatory compliance, the projected $3 trillion global AI spend by 2027 underscores its pivotal role in CIO agendas.

As CIOs analyse and forecast AI's transformative impact, their focus on selecting and integrating technologies aligns closely with business objectives. By prioritising data integrity, leveraging flexible platforms, managing technical debt, fostering transparency, and benchmarking industry standards, CIOs can chart a course towards sustainable AI success.

Matthew Oostveen, Field CTO - APJ, Pure Storage