What difference has the rise of AI made to the ‘right now’ reality of a CIO’s workload?
Joel Rennich, VP of Product Strategy, JumpCloud
AI has transformed the operational and strategic landscape for CIOs, shifting their roles from traditional information technology management towards a more nuanced, predictive and data-centric leadership.
This shift is not merely about the integration of cutting-edge technologies but also about navigating the complexities of data governance, privacy and ethical AI use in a corporate environment. At the heart of this transformation is AI’s capability for analyzing disparate data sets, identifying patterns and predicting trends.
CIOs deploying predictive analytics to pre-empt security breaches and fraudulent activities acknowledge that the task requires a delicate balance between promise and innovation and the technical realities of AI applications.
AI technologies raise – rightly – critical concerns regarding data governance and the safeguarding of personally identifiable information (PII).
CIOs are now wrestling with establishing robust data management frameworks that comply with evolving regulatory standards while upholding the integrity and privacy of sensitive information.
For CIOs, this heightened focus on data governance underscores the need to possess or develop (quickly!) a deep understanding of both the technological and legal aspects of AI deployment. What most practitioners know is that despite the hype around autonomous AI and the potential for a real-world Skynet, AI requires a nuanced balance of automation with human oversight.
Considering the weight of the challenges AI introduces – technical and operational adjustments, data governance and privacy concerns, resource allocation, managing outsized expectations from various stakeholders, LLM bias etc.
CIOs must lead by designing and implementing workflows that combine AI-driven efficiencies with the irreplaceable value of human judgment and ethical consideration. Getting the balance right will require CIOs to emphasize that, at least in these early days, AI is best used to augment human decision-making. As they grow in sophistication, AI systems will increasingly require a workforce with specialized skills and competencies in machine learning, data science and AI ethics.
CIOs should be investing now in initiatives for upskilling existing personnel while also looking to hire specialized talent.
Working with AI vendors and technologies also introduces complexity and CIOs must navigate this landscape with discernment, evaluating potential AI solutions against their organization’s specific needs, strategic objectives, and compliance requirements. Internally, CIOs play a pivotal role in demystifying AI, and will need to set realistic goals for its deployment and articulate both its potential value and limitations to stakeholders. Some questions for CIOs to consider around AI initiatives:
- What specific organizational challenges can AI address, and what measurable outcomes should we expect?
- How will AI implementation affect our data governance and privacy policies?
- What infrastructure and resources are required to support AI initiatives, and how will we manage these investments?
- How can we foster a culture of AI literacy and innovation within the organization?
- What ethical considerations and societal impacts should we consider in our AI deployments?
CIOs are now at the forefront of a technological renaissance. Their success will hinge on how well they’re able to balance using AI both strategically and responsibly.
Anil Inamdar, the Global Head of Data Services at Instaclustr (part of NetApp)
CIOs leading AI adoption are now spending much of their time vetting AI/ML platforms to revamp legacy environments and using AI to fast-track broader business goals. But this shift is introducing new focal points that reshape CIO workloads – especially as they embrace AI-powered data analytics, application development and platform engineering strategies. Today (and increasingly going forward), CIOs are pursuing integrated AI/ML processing platforms that can serve as the foundational keystone for harnessing AI advantages now and into the future. Integrated AI/ML processing platforms encompass data-layer technologies, storage, AI/ML frameworks, and infrastructure orchestration, offering a centralized and comprehensive environment for AI/ML utilization across a CIO’s organizations.
Backed by this platform, a CIO’s developer, DevOps, and data scientist teams gain a much more collaborative playing field for creating, continually iterating, and optimizing AI/ML models and applications. But getting this right naturally is streamlining the CIO’s workload as well. Ultimately, integrated AI/ML processing platforms simplify and accelerate AI/ML development, training, deployment and management, making them essential to CIO strategies right now. Many CIOs accustomed to dividing their attention across multiple on-prem and cloud infrastructure deployments – or that devote a portion of their workloads to investigating what they might be missing out on – are now simplifying their workloads by harnessing hybrid multi-cloud data platforms.
Doing so enables enterprises to better utilize AI across their operations and analytics functions. A hybrid data platform strategy allows CIOs and their organizations to manage, store, replicate and process data in a unified manner across the on-prem, private cloud and public cloud infrastructures of their choice.
That advantageous flexibility allows CIOs to select the most opportune infrastructures based on their piecemeal benefits when it comes to availability, scalability, reliability and their ability to support AI/ML initiatives (while commanding singular systems). AI implementations are also transforming CIO workloads around application development, as AI/ML-powered assistance arrives to change coding as we know it.
AI coding assistants and platform engineering internal developer platforms (IDPs) are eliminating the busywork of development, clearing developer bandwidth to zero in on innovation and removing barriers of entry when it comes to harnessing powerful advanced technologies (of which AI itself is an example).
This serves to democratize, streamline and accelerate application development and iteration that optimizes operations and CX.
In the same way, it streamlines and accelerates CIO decision-making, allowing them to concentrate on more forward-thinking and needle-moving strategies as well. Speaking of decision-making, data analytics enhanced by AI/ML, generative AI and breakthrough data science techniques now offer CIOs greater insights and clarity – delivered near-instantly – than have ever been available. CIOs with the foresight to implement these advantages will make data-driven decisions that outmaneuver competitors, both in terms of internal operational capabilities and in the marketplace.
CIOs a step behind in enlisting AI/ML advantages will struggle with limitations and relatively inefficient workloads – and that’ll be an increasingly costly disadvantage.
Brian Sathianathan, Chief Technology Officer, Iterate.ai
The past year – really, the past few months – of AI advances should already be changing most CIOs’ workloads.
From private LLMs for code generation to improved decision-making, AI is fundamentally changing how CIOs operate and lead. Automation is one clear workload difference. The question right now for many CIOs is just how much they can confidently automate.
But with ever-more-capable algorithms, AI is handling repetitive tasks and complex technical processes that free up the office of the CIO for more strategic and business-growing initiatives.
Tasks such as provisioning and managing IT infrastructure, monitoring system performance, and resolving network issues are now expedited with AI, allowing CIOs to focus even more of their time on high-level planning and decision-making. Another big workload difference: CIOs are taking advantage of what AI is bringing to their organization’s software development lifecycle.
CIOs at larger enterprises, in particular, are working to develop private LLMs for code generation across their companies.
CIOs currently using public LLMs like OpenAI’s ChatGPT might be fine for some lower-priority use cases, but public LLMs pull source data from, well, the wild internet – meaning output can be very risky for critical or sensitive tasks.
CIOs now getting the private LLM strategy right are automating key parts of the software and application development lifecycle while ensuring far more security than public LLMs (and reducing the burden put onto a CIO’s engineering teams).
The result here is faster project delivery, which is helping CIOs demonstrate more value to their organizations. Critically, AI is also empowering CIOs with improved decision-making capabilities.
CIOs are using AI’s analytical power to predict future IT needs, optimize resource allocation, and identify potential risks.
The ability to make data-driven decisions has always been a game-changer, of course, but CIOs who are now smartly tapping advanced AI tools to help with this are particularly well positioned. Another change to a CIO’s workload is their own research and experimentation with bleeding-edge AI technologies.
There are so many new AI capabilities and startups emerging, seemingly daily, that many CIOs I work with are setting aside dedicated time for putting different AI technologies through the proverbial wringer to determine viability.
It’s no small task, and most CIOs understand the value of being able to adapt quicker than competitors to business-improving AI solutions.
Finding the signal in the noise isn’t easy right now, but CIOs devoting the time and getting it right are certainly being rewarded.
