Dave Link, CEO, ScienceLogicMarch 28, 20254 Min ReadMopic via Adobe StockA hallmark of successful innovation is when organizations get good enough at solving todays challenges that theyre able to focus on future technology investments and use cases. When the forecasting becomes long-term, we get into the realm of futureproofing, where CIOs and their teams weigh specific near-term IT choices and investments to support far-off leapfrog innovation objectives.Futureproofing in the age of AI adds a layer of uncertainty when it comes to planning for fast-evolving capabilities and use cases that may not exist yet. However, enterprises can gain confidence in future-proofing AI by rethinking how they gather and organize the underlying data that feeds AI.Futureproofing for the UnknownIT innovation is about meeting current enterprise needs while also expanding whats possible to achieve through technology in the future. Previously, futureproofing entailed keeping licenses up to date, anticipating software upgrades or end of life issues, and adding infrastructure to meet planned new capacity demands.Now, AIs autonomous and scalable nature is transforming these future innovation strategies. The same self-learning algorithms and auto-resolution schema that allow humans to step back and let AI make more decisions and autonomous insights are also complicating predictions for where AI should be taken, or be allowed to take itself, into the future.Related:Think of future-proofing AI as a spin on the DevOps principle of designing for the unknown, in which developers design applications with the hopes that they will remain interoperable with future technologies. Today, it has become a issue of futureproofing for the unknown, given the highly autonomous nature of AI and the endless possibilities for new use cases.The more we understand this constantly evolving nature of AI -- a market poised to realize a compound annual growth rate of 37.3% through 2030 -- the more we grasp how future-proofing has less to do with the programming and licensing level, and more to do with the data and infrastructure level. Software has evolved from an application-centric to a data-centric design, with data becoming a foundational input for application development.Supporting AI Evolution Through Extensibility at the Data LayerThe irony of future-proofing AI is that supporting its long-term growth requires precise, immediate IT adjustments. CIOs, CTOs and other technology leaders must ensure their teams are covering essential bases at the data layer to ensure flexibility. It's what we call extensibility to accommodate new and potentially unforeseen use cases for AI.Related:Achieving extensibility begins with ensuring consistent data standards and availability at all times. To innovate and grow, AI systems need unfettered access to databases and sources, requiring consistent standards and metadata across different systems for reliability. Furthermore, data should be secured with dynamic authentication protocols that facilitate smooth and safe access.Particularly for AI, its essential to add proper business context to data without over-formatting it. This is ultimately the most critical balance to strike in future-proofing AI: The just right amount of data cleansing and formatting can position data for broad reuse as AI systems innovate. Too little formatting leaves the datas relevance indecipherable; too much formatting and the data becomes too rigid for AI to leverage for novel applications.Unsurprisingly, human analysts have historically managed this nuanced balance. However, recent developments in unsupervised AI have unlocked algorithms that can now analyze unlabeled data to derive emerging structure and patterns.Leveraging New Capabilities and Use CasesGiven that 90% of data generated by organizations today falls firmly within the unstructured category, proper extensibility at the data layer that incorporates both structured and unstructured data for AI processes can drive powerful new applications in the enterprise.Related:For instance, generative AI can now automate many IT operations functions, creating an educated and context-aware support tool that redefines the status quo of what's typically expected from an AI advisor. This is possible thanks to data pipelines that rapidly pull in structured and unstructured data sources and render them into a highly usable framework for GenAI to independently manage configuration analytics, bug reports, knowledge base resolves, standard operating procedures, and service-level agreements.Another example, AI-powered digital twins can harmonize structured and unstructured data together to model the behavior of new infrastructure and systems before theyre built. This allows teams to proactively manage operational issues such as limiting process interruptions and minimizing downtime in a power utility.Future-proofing AI starts with future-proofing AI data. While the finer details can be left to team specialists, C-suite tech leaders must grasp the importance of data extensibility efforts, as successful implementation ensures AI's future.When organizations modernize their data architectures with AI innovation in mind, they lay the foundation for new capabilities and use cases to flourish. And given that most enterprises keep their data archived for at least seven years to align with federal compliance standards, this foundation is constantly expanding. The sooner organizations streamline data management for AI, the faster they can future-proof investments and unlock new value.As AI levels the playing field, the software and technology ecosystem evolve rapidly, only scratching the surface of its transformative potential. These shifts are disrupting traditional boundaries, and the race for unique innovations is unfolding in real time.About the AuthorDave LinkCEO, ScienceLogicA technology advocate committed to innovative IT management solutions that leapfrog paradigms and optimize business outcomes, David Link is founder and CEO at ScienceLogic, a global leader in automated IT operations and observability. Throughout his career, Dave has worked to solve the pressing problems facing IT organizations delivering smarter and more targeted IT management tools to the market. Leveraging market transitions in cloud, AI, and analytics, he continues to scale ScienceLogic with a laser focus on customer success. His proven leadership integrating technology, product, and business model shifts has been instrumental to the company's consistent growth and leadership presence.See more from Dave LinkReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like