The 3 Pillars of Practical AI

In a prior article, we outlined the basics of Practical AI — WWT's proven approach to AI solution development that couples an emphasis on achievable outcomes with the development of scalable, mature and aligned AI and data strategies.

In this article, we dig into three pillars that make Practical AI the ideal approach:

1. AI & data delivery: To achieve the art of the possible in AI solution design, organizations should focus on identifying and aligning business and technology objectives to achieve specific outcomes.
2. Lab experimentation: Next, organizations need a way to compare, contrast, validate and integrate AI solutions before they commit to purchasing and installing these products.
3. High-performance architecture: Finally, organizations need to assess the maturity of their IT infrastructure, reference architectures and application development lifecycles to determine if they are capable of enabling data-intensive, AI-powered solutions.

Importantly, organizations should understand that no AI solution will be truly scalable without attention to Responsible AI — the practice of designing, developing and deploying AI systems in a way that is safe, ethical and fair. We recommend incorporating the key concepts of Responsible AI into your strategy and development lifecycle.

Let's explore each pillar of Practical AI in more depth.

Pillar 1: AI & data delivery

Before organizations attempt to roll out custom AI chatbots, develop digital twin simulations or secure GPT-like models, they need a comprehensive data strategy and architecture in place. One that optimizes their ability to deliver reliable data at scale, wherever and whenever it is needed across the business.

While generative AI (GenAI) solutions continue to garner much of the attention, organizations should be aware that there is a wider range of AI/ML solutions in the market. Given the complexity of the space, organizations should strive to ensure the solution they're interested in is, in fact, the right AI solution for their business. 

To do so, they should start by identifying the specific use cases and business results they want AI to help them achieve. Letting outcomes lead the way allows organizations to properly align the underlying business and technology objectives, resources and data-delivery mechanisms required to attain their goals.

WWT has been navigating the growing field of AI/ML solution development for close to a decade. Whether it was a computer vision solution that increased client productivity by 35 percent, or a predictive model that saved $5 million per year and helped a client achieve 99 percent regulatory compliance — our outcomes-focused approach is the quickest way to transformational AI success.

Aligning business and technology objectives to achieve targeted results is a key pillar of Practical AI — one that pays off when paired with the next two pillars.

Pillar 2: Lab experimentation

Adding a layer of AI to your organization's IT capabilities can seem like a relatively simple task, especially given the surplus of solutions flooding the market. Considering the speed at which offerings are released and the growing complexity of the space, how can business and IT leaders be sure a marketed AI solution will deliver?

The deceptively simple answer is "by testing potential AI solutions against each other." But how?

As our CTO Mike Taylor recently announced, WWT is building a first-of-its-kind composable lab environment — the AI Proving Ground — where our clients can compare, contrast and train various AI models against each other using the latest high-performance architectures, hardware and software from industry-leading innovators like NVIDIA.
 

The AI Proving Ground's modular lab environment is designed to play a critical role in helping organizations across industries simplify and accelerate the AI solution decision-making process. 

It also represents a logical extension of our decade-long investment in AI/ML research and solution development, our extensive partner ecosystem, and more than three decades of designing, implementing, securing and optimizing the complex IT environments needed to deliver transformational business results.

Lab experimentation in an environment like the AI Proving Ground is the second pillar of Practical AI. Yet an organization's ability to operationalize a validated AI solution ultimately depends on something else — the maturity of the organization's IT infrastructure and architecture.

Pillar 3: High-performance architecture (HPA)

Organizations will struggle to adopt AI in a streamlined and fiscally responsible fashion unless their technology stack can support the data demands. After all, what will truly separate you from the competition is the ability to leverage AI in ways that positively impact application functionality and that drive data differentiation at speed in the eyes of customers and end-users.

This is why prudent executives and IT leaders should be asking: What is the most efficient way to harness the power of AI solutions? What data performance levels must be attained to truly differentiate my products and services?

The short answer to these questions involves high-performance architecture (HPA). 

Our concept of HPA unites the historically separate development workflows of AI/ML and high-performance computing (HPC) with the advanced IT infrastructure components needed to power advanced AI solutions. 

The common components of HPA typically include:

• GPU-accelerated systems: The right amount of combined GPU/CPU processing power and memory is needed to train and run modern AI engines. Related technologies and terms include HPC, supercomputing, accelerated computing, emergent computing, heterogeneous computing, quantum computing, and Compute Express Link (CXL).
• Storage and memory: The ability to reliability store, clean, scan and recall massive amounts of data (think petabytes and exabytes) is needed to train today's big data AI/ML models. Related technologies and terms include file storage, object storage, parallel file system storage, data fabric, streaming storage, synthetic data, computational storage, and emergent storage.
• Networking hardware and software: These segmented, high-bandwidth and low-latency networks are dedicated to reliably generating outputs from AI/ML applications; seamlessly transferring data internally between IT systems and departments; connecting applications, compute and storage layers; and ensuring protection from advanced threats. Related technologies and terms include smart network interface cards (SmartNics) and data processing units; secure, smart and fast network fabrics; computational networking; and photonics (SOC, switches and backplanes).
• Automation platforms (including software and applications): The right mix of data science tools and infrastructure optimization is a prerequisite for effective HPA. Related technologies and terms include virtualization, composition and autonomic infrastructure.
• Orchestration and scheduling software: The key building blocks needed to develop and deploy domain-specific, end-to-end AI workflows — from data preparation to model training to inference and deployment,
• Enterprise AI security: A proven cybersecurity wrapper around everything.

High-performance architecture is vital to each phase of an effective AI/ML workflow, from model development through deployment. Without the right architectural foundations, organizations will struggle to realize the full value of their investments in AI systems. 

It should be noted that your strategy, governance schema, and AI/ML skillsets are likewise important to HPA success:

• Data and AI strategy and governance: Your data management and governance strategies are key to ensuring data is readily accessible to the systems, users and AI models that need it; they also help ensure your data is resilient, scalable and properly secured from internal and external threats.
• Talent and skills: Despite the potential time savings from automation, you will need the right mix of data scientists, data engineers, security experts, software developers and infrastructure experts to build and deploy AI models. These experts will be needed to maintain your HPA lifecycle as advances in AI come to fruition. Related terms include AIOps and MLOps.

Benefits of HPA

The benefits of HPA will vary depending on whether you're in the C-suite, an IT/ICT lead, or a consumer:

• For business leaders: HPA can help increase profitability and competitiveness, reduce risk exposure, retain talent, access new markets, maintain regulatory compliance, improve customer experiences, and deliver business outcomes.
• For IT/ICT leads: HPA can help harden security posture, modernize IT infrastructure, and maintain best practices while incorporating new technologies, improving value-to-investment ratios, improving BCDR capabilities, and optimizing operations.
• For consumers: HPA can help enhance connectivity and productivity, facilitate seamless global collaboration, streamline change management processes, improve technical literacy, and deliver better experiences.

Conclusion

The surest path to AI success can be achieved by a relentless focus on the outcomes related to AI and data delivery, lab experimentation, and high-performance architecture — the three pillars of Practical AI.

For more information, we recommend starting with a Data Strategy Briefing from our experts. They are adept at breaking down the data strategy development process into a series of digestible steps that examine the flow of data from source to actionable insight.