In this article

GenAI is a revolutionary force reshaping the very fabric of business and industry as we know it. With its transformative power, GenAI promises not just incremental change, but a seismic shift that can propel organizations to unprecedented heights of productivity and innovation.

Within this wave of change lies unparalleled opportunity. By grasping the intricacies of GenAI and charting a swift course toward integration, organizations can unlock a realm of possibilities, where every facet of operation—from the frontlines to the boardroom—is redefined and elevated.

From delivering unprecedented productivity gains to revolutionizing customer experiences, GenAI holds the keys to unlocking a future limited only by imagination.

GenAI and Industry 4.0: Driving a workplace paradigm shift

Generative AI will change business—and the world—in major ways.  GenAI has the potential to elevate worker productivity by 10% and contribute a staggering $1 trillion to the US GDP in just a few years.

Recent advancements in AI have driven a dramatic paradigm shift. Up until 2023, versions of AI (especially in terms of language, text, and images) were used for classification, such as sorting data and processing emails. It could do simple tasks, like taking meeting notes or processing your email. In 2023, AI began to transition towards generative capabilities. GenAI is capable of executing more complex tasks, including writing executive summaries, analyzing data, and even formulating business strategies. This shift marks a pivotal moment where AI transitions from merely categorizing data to actively generating new content based on learned patterns.

The next wave of AI, The Synthesis Age, started in 2024. It is a time when machine learning, classification, and generative capabilities merge to provide deeper insights akin to human cognition. Put another way, it is how the human mind works.

The trajectory is expected to extend throughout the decade, culminating in the Interactive AI Age around 2030. In this era, AI agents will seamlessly interact with each other, propelled by a common thread of high-quality data accessibility.

This evolution of AI aligns with the broader narrative of the Fourth Industrial Revolution. Just as previous industrial revolutions transformed societies—from mechanization in Industry 1.0 to the internet age in Industry 3.0—Industry 4.0 represents a new epoch characterized by AI with data as a fundamental asset and cyber technologies.

GenAI represents a profound shift in how we work, interact, and innovate. As organizations navigate the landscape, understanding the trajectory and evolution of AI evolution is key to staying ahead.

Key ingredients for GenAI success

To succeed with GenAI, you need high-performance compute infrastructure and high-performance data systems and tools. You also need AI and data engineering skills, as well as AI and Large Language Model (LLM) innovation.

These advancements are possible in large part thanks to NVIDIA for driving graphic processing units (GPUs); to NetApp for creating a place to bring all the data together; and to the open-source community and scientists for creating better algorithms and neural networks.

Gen AI: Already enabling outstanding productivity gains

Much has been said about the unforeseen effects of GenAI, like how mobile phones evolved beyond their original purposes. The implications of this transformative technology demand careful consideration. On the positive side, organizations are already reaping significant benefits from GenAI. Research shows remarkable productivity boosts across various sectors: 25% in customer support, 40% in strategy consulting, 55% in software engineering, and a whopping 65% in marketing and business writing.

According to McKinsey, GenAI will drive the next wave of productivity, with expected productivity gains by sector reaching as high as 240-460% for high tech; 240-390% for retail; 200-340% for banking; 170-290% for advanced manufacturing, and 150-260% for healthcare. 

With these kinds of gains, it's no surprise organizations in every major industry are discussing the integration of AI into their business strategies for outcomes such as increased efficiency, improved customer experiences, enhanced decision-making capabilities, and beyond.

GenAI delivers value to financial services organizations

To understand the power of GenAI, let's look at the impact it has already had on one of the most profitable industry segments in the world: US financial services – which, coincidently, is also recognized as an early adopter adept at turning technological advances into profitable outcomes.

Financial services have already seen significant productivity gains for knowledge workers from GenAI that led to improved customer satisfaction, according to the recent online FinTech Magazine article GenAI: Bringing endless possibilities to the fintech sector

Cheaper customer support and faster responses make clients happier, according to another recent study of a dozen early-growth fintech companies. To extrapolate, all this indicates at this early stage in GenAI that GenAI augments human performance to enable clients to experience higher levels of satisfaction rather than automating jobs out of existence.

Industries with organizations that have roles most exposed to LLM automation over the next several years can anticipate the most significant impact from GenAI. Implications of automation or augmentation include direct productivity gains, improved customer satisfaction, higher employee performance with augmentation, faster decision cycles, rapid compliance with Market Risk Amendment (MRA) regulatory requirements, as well as access to collective organizational intelligence or AI Operations as a Service (AIOaaS). Indirect impacts and implications encompass retaining organizational data-driven practices to replace tribal knowledge, the reskilling and upskilling of employees, the transition from IT project management to AI product management, and the adoption of AI Risk Management, also known as responsible AI–emphasizing the ethical and accountable development, deployment, and use of AI technologies.

Managing generative AI: From concept to continued success

To initiate GenAI solutions effectively, it's helpful to think of GenAI as the smartest, best new employee you've ever hired who knows a lot but doesn't know your company. It needs to be managed, guided, pointed in the right direction, and rewarded. It needs to learn your culture and understand its role in your organization. Like a proof of concept (POC), recognizing that without ongoing management, GenAI risks failure and must evolve into a continually managed product.

Navigating a path to Practical AI: Understanding your goals and utilizing a maturity framework

Practical AI is WWT's proven approach to delivering AI solutions. It's a methodology we've stress-tested and refined over the course of a decade in which we heavily invested in research and development (R&D) while delivering end-to-end AI and machine learning (ML) solutions to clients across industries.

To successfully implement Practical AI solutions, it's important to understand the specific AI experiences you're aiming to create and the objectives you're looking to achieve.

Practical AI Workflow

Level 1: Incubate, defining your AI roadmap

At this stage, you embark on defining your organization's appetite for AI, establishing a roadmap for AI maturity, and conducting a comprehensive assessment of your current state. This involves aligning on an AI growth strategy and initiating a pilot project in one line of business to demonstrate immediate value through AI implementation.

Level 2: Optimize: making internal efforts more efficient

Here is where you target the low-hanging fruit—your opportunities that are readily accessible for improvement. For example, this could be efficiency enhancement across areas such as contact centers, coding, marketing and media, HR functions, and tactical decision-making.

Typically undertaken by a team of 2-8 people with a low risk tolerance, this low-risk initiative spans roughly two to six months with a cost ranging from $300K to $2M. Successful execution can yield significant outcomes, including a 25% reduction in workflows and a 3-5x return on investment.

Level 3: Accelerate: Making existing products or services better

Your focus shifts to improving your products and your customers' experiences. It could involve the use of high-performance architecture (HPA) to design, test, and iterate products faster. It could address franchise performance, building core R&D, using a GenAI interface for proprietary applications, and much more.

These initiatives typically require a team of 6-12 people and a medium risk tolerance, take six months to a year, and cost between $900K to $15M. Typical outcomes could include top-line revenue growth, improved margins, and reduced customer churn. Ultimately, it could drive a 10x ROI. 

Level 4: Transform: Creating fully integrated AI-driven product lines

The highest maturity level is about major transformation. For example, using GenAI to build your B2C and B2B platforms, personalizing offers for pricing or treatment, or developing AI-created content for consumers are just some examples.

This initiative typically requires a team of at least 10 people and a high-risk tolerance, takes one year to 18 months, and costs between $5-20M. Typical outcomes could include the creation of new high-value business lines or intellectual property. The good news is the payoff at this level of maturity, you could drive a 100x ROI.

Determining your AI readiness

Here are eight questions to help you assess your organization's AI maturity level.

  1. Is your organization committed to AI initiatives?
  2. How would you grade your data quality and governance?
  3. Do you have access to training data and an AI model to train on?
  4. Do you have a budget for AI technology?
  5. What is the current level of AI usage in your organization?
  6. What is your leadership's influence over the initiative?
  7. Are your clients ready to use AI?
  8. What is the threat of AI from competitors within your category?

Your total score will show you your current level:

0-2: Incubate

3-7: Optimize

8-18: Accelerate

17-24: Transform

Steps to building an AI ecosystem

Another major effort in harnessing AI will be building out a diverse AI ecosystem. This will require concentrated strategy and action, plus can take an average of 6 months to 2 years, depending on its complexity and your commitment. 

The following high-level process is a useful framework as you begin.

Identify strategic use cases

Once you've defined your organization's AI appetite, you are ready to develop a slate of use cases as a starting point. First, define potential use cases by department and assess the value of each use case against its complexity to determine priority based on organizational need.

Then, analyze foundational data capabilities for each use case. This will enable you to determine what types of technologies and solutions you will need. You can use the WWT Advanced Technology Center (ATC) to try out solutions including digital twins, AI workload replication, federated machine learning, and AI middleware.

Weigh options and design prototypes

You can use the WWT ATC to test and compare options for GenAI and deep learning, to fine-tune large language modeling (LLM), computer vision and image modeling, and to select vector databases (DBs) and LLM Operations (LLMOps). You can also test options for edge-compute and AI inference, as well as LLM/GenAI embeddings in edge-compute products.

Build a minimal viable product (MVP) with a strategic roadmap

This step involves building a model hub for MVP orchestration and enabling your AI ecosystem with GPU capacity forecasting, AI stack comparisons, analyzing public cloud vs. specialist GPU cloud vs. on-premises analysis, estimating total cost of ownership (TCO), testing thermal modeling, and estimating ESG impact.

Scale AI production across your organization

The next step is to build a customized high-performance architecture for your organization to support capabilities including chatbots, GenAI, deep learning and the like, digital twins, and predictive AI. You will also need to address issues including regulatory compliance, AI adoption and OCM, off-the-shelf AI, custom software, product integration, dashboards, and machine learning operations (MLOps).

A framework for AI and data

Earlier in the progression of AI, organizations differentiated themselves with algorithms. But now, open source has leveled the playing field. Today, you must differentiate your organization with your data. Harnessing AI effectively is predicated on maximizing your data.

As you think about making the most of AI, you first need to create your vision for the AI experience based on the optimal use of your data. From there, you consider how to achieve that using AI solutions and cyber security. Then you need to address data strategy and architecture. And finally, you need to develop a high-performance architecture.

When you successfully address and manage all four of these areas, the outcomes can be such things as: extraordinary customer experience, improved employee productivity, accelerated sales processes, operational excellence, increased innovation velocity, and the implementation of intelligent automation. 

Bring in the expertise you need 

To ensure your success using AI to drive business outcomes, consider working with a partner with expertise in AI. WWT and NetApp offer a large and diverse array of services and solutions to help you every step of the way. 

How NetApp completes the data pipeline for GenAI

GenAI generates significantly large datasets, complex model training and inferencing. It also needs real-time data processing and enhanced security. As such, a highly targeted infrastructure approach, emphasizing scalability, data movement efficiency, and security is required, and NetApp is among the leaders in this specialized space as it offers a comprehensive solution that addresses the unique challenges of GenAI workloads.

NetApp ONTAP AI, with its integration of NVIDIA DGX systems and all-flash storage, is positioned to meet GenAI demands for a scalable, secure, and available solution—to empower organizations to leverage GenAI to its fullest potential.

With its unmatched performance, efficient data management, and robust security features, 

NetApp ONTAP AI is not just a platform for today's GenAI projects, but a solution built to grow with a company's GenAI needs.

Here are the features and functions that make NetApp ONTAP AI a premier infrastructure to meet GenAI demands for high-performance architecture:

Enhanced scalability and performance 

GenAI applications require infrastructures that can handle extensive parallel processing and manage vast amounts of data very efficiently. This includes the need for high-performance computing (HPC) capabilities, which are critical for training large and complex models.

Data management for large datasets

Given the large datasets needed for training GenAI models, efficient data management becomes vastly important. This involves not only storage efficiency but also the ability to quickly access and preprocess data for training.

Real-time data processing 

Many applications leveraging GenAI rely on real-time data processing capabilities. This requires an infrastructure capable of swiftly accessing and processing data with minimal latency.

Advanced security for generated content 

GenAI often deals with generating new content, which can include sensitive or proprietary information. ONTAP provides strong security measures to protect the integrity and confidentiality of both the input data and generated content.

Ecosystem integration for development flexibility

GenAI development may involve a broader range of tools and frameworks to experiment with novel model architectures or training techniques. Support for a diverse ecosystem is required to foster innovation and adapt to the rapidly evolving GenAI landscape.

Learn more about AI and NetApp Connect with a WWT Expert