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Every AI Platform Needs These Five Qualities

Artificial intelligence is captivating technical development teams and the general public alike thanks to the rise in generative AI technologies. While the technology has been around for quite some time, the barrier to entry is the lowest it has ever been in the wake of the recent consumer-accessible advancements. Whether it is creating art, writing articles, or serving as a tutor to someone learning a new concept, we see AI as an augmenter, partner, or assistant available to anyone.

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For the enterprise, generative AI has the potential to become an invaluable tool to drive competitive differentiation, process improvements, and innovations. Generative AI will certainly take its place alongside more mature AI technologies such as natural language processing (NLP) to machine learning (ML) as an essential component of every company’s AI stack.

Selecting the right AI platform for your organization and teams lets you quickly develop and deploy models that drive real-time decisions and improve employee and customer experiences. While there is no one-size-fits-all approach to selecting the best AI platform for your enterprise, there are several capabilities that we believe that all such platforms should support. Here are five considerations and capabilities AI platforms should have to allow your teams to fully realize AI’s potential. 

Automation: Automation is a proven method for meeting the demands of today’s digital economy and increasing enterprise agility without sacrificing performance. Despite market volatility, automation is a proven method for meeting the needs of today’s digital economy and increasing enterprise agility without sacrificing performance. Great AI platforms will help teams accelerate the automation of proven models with a consistently solid performance. Consider integrating open-source orchestration tools into the AI platform as part of the automation journey. 

IT Enablement and Governance: Harnessing the power of AI requires teams to maintain control over the software supply chain. Though users expect to collaborate cross-functionally quickly and seamlessly, IT administrators must be able to provision and deactivate user accounts and have visibility to where and how business and customer data is used to identify, address, and track issues. Teams must consider the vast governance landscape before adopting technology to ensure no red tape is crossed and they comply with things like Europe’s GDPR (General Data Protection Regulation), CCPA (California Consumer Privacy Act), and other privacy regulations. Before embracing a new AI tool, ask whether your team has the tools to manage data catalogs, metadata, and other artifacts. 

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Scalability: Scaling model training has risen in importance thanks to the need to centralize workflows that support collaboration with others. Deploying massive computing power needed for AI adoption requires significant supporting infrastructure. The challenge is not finding the computing power but managing an environment that supports it. Your AI platform must be able to handle the organization’s requirements, use cases, and throughput as you scale your AI application, leaving AI evaluators to consider things like how the data scientists can create environments for experimentation and model training and how easy and efficient is it to set up an online prediction system with the platform. A clear understanding of the platform’s scalability—both in necessity and capacity—ensures teams won’t get stopped short of projected outcomes or initiatives in the works.   

Security: What does access control look like with the platform? How does the platform track logging, monitoring, and alerts? How many levels deep does your team monitor common vulnerabilities and exposures (CVEs) that affect users of your platform? These questions must be addressed when evaluating a new AI platform. Chief Security Information Officers (CISOs) and IT administrators must be prepared to secure the open-source software supply chain. Public sources often have risks that must be monitored and mitigated—a difficult feat for IT administrators. Identifying a platform with maintainers and authors familiar with the tools and techniques your team will use can be a great way to ensure fewer vulnerabilities that could expose the organization. 

Support: Last, a critical component of any AI platform is support. Look for an AI platform backed by experts who understand the challenges of developing AI and can provide enterprise support at scale. Teams need reliable bug reporting and tracking, engaging training resources, and ongoing support for successful AI adoption. Ask how they prioritize support when their users are affected.

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The AI landscape is rapidly evolving, with entrepreneurs and leading organizations constantly unveiling new developments. Sifting through the hype and understanding how AI can serve your organization is an essential requirement when evaluating new technology. It’s easy to get lost in the hype of emerging technologies and the latest and greatest innovations, so clearly understanding the top capabilities every platform should have is pivotal to long-term ROI. By checking the boxes to the five capabilities listed above, you can set your organization up for long-term success with AI.

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