Editorial
AI is both redefining and accelerating what’s possible in the enterprise, provided these capabilities are properly supported through digital transformation (DX). Any credible modernization effort today must recognize that AI and DX efforts are intimately connected in a highly symbiotic relationship: AI is the driver of many transformative capabilities and goals; and at the same time, the DX road map toward those capabilities and goals itself must include significant accommodations for the underlying system and architectural requirements for AI to function adequately.
Getting this alchemy right is especially critical in light of both the proliferation of AI tools and the expanding array of data sources and advanced technologies – such as IoT, edge computing, hyper-converged networks, and 5G connectivity — that feed them. Against this backdrop of increased democratization of AI tools and the increased power and complexity of the underlying technologies that drive AI, here are four key trends we can expect to see as the age of AI-driven transformation continues to play out in 2024.
Trend #1 – Integrity at the Data Layer Will Become Even More Mission-Critical
First and foremost, as organizations rush to adopt AI they’re discovering they must first have a solid data management and optimization strategy in place to avoid operational and security problems. In a modern version of the classic “garbage in, garbage out” scenario, bad search results, bad extraction, bad classification, and bad predictions result if the organization doesn’t provide seamless access and clear business context for all its data.
To achieve sustainable ROI, organizations need a standardized and well-integrated data layer that allows an AI platform to securely and properly access, analyze, enrich, and augment the data to reap patterns, insights and predictions. Standing up the right underlying data architectures — such as data fabric and other agile data frameworks — is critical, as these architectures allow AI applications to seamlessly access and contextualize data wherever it resides.
Trend #2 – Private AI Will Become Standard
The use of Private AI, as opposed to public AI models, will become a minimum requirement as organizations harness value from AI without allowing their own data to become part of a larger public or vendor-run data ecosystem. Private AI algorithms train exclusively on data that is specific to one user or company, and Private AI models are not shared beyond the organization. These attributes are key to retaining competitive advantage where an organization’s algorithms and models might otherwise be easily scrutinized by other companies, possibly even by direct competitors.
Trend #3 – Powerful AI Tools Will Become Even More User-Friendly
Whereas AI tools and capabilities were once the unique province of highly trained data scientists and engineers, there’s a growing democratization of AI that will continue with the help of more accessible, user-friendly tools for automation and process optimization. Furthermore, the advent of intuitive low-code options adds power, nuance and flexibility to AI applications without sacrificing usability. These factors are driving a trend toward more varied and powerful applications that make it easy for those closest to the business to map out the processes and delegate aspects to developers to build automation.
Organizations will need tooling that standardizes and simplifies modeling and algorithm training. This will allow for repeatability and faster development times and minimize the need to code from the ground up.
Trend #4 – AI Policy Will Become More Inclusive
The final trend is closely tied as a logical next step to Trend #3. The availability of AI to more kinds of users in a wider range of business settings can and should translate into a wider range of stakeholders weighing in on how AI capabilities are promoted and regulated in business and society. Such advocacy is already underway. For example, during recent Senate forums on AI regulation, tech leaders argued for more AI transparency and best practices to guide and lower the barrier to entry for smaller companies that can’t afford to design and build their AI from scratch.
These four trends are highly interrelated. AI requires a combination of data and processes, matched with the right tools, to be effective. Without these things working in tandem, AI will simply be a neat science experiment. By combining these pieces and closely aligning their AI applications with underlying workflows, businesses can successfully operationalize their AI deployments and create a scenario where automation, insights, and scalability drive real business value.
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