The Generative AI Future Is Now

Generative AI seemed to come out of nowhere, dominating headlines focused on the technology’s potential threats. Indeed, there are issues that must be addressed for generative AI to be used in a safe and trustworthy manner. But it’s important that enterprises focus on the benefits of AI and the most effective ways to harness the technology — because, if you don’t, your competition will be.

Sound familiar? You may recall the same kind of push and pull with the advent of the Internet and, later, cloud computing. Both technology models raised concerns, but ignoring them meant losing competitive advantage, losing customers, or, in many cases, losing business altogether. Just as we no longer can imagine work without the Internet or the cloud, we’ll likely feel the same about generative AI in the not-too-distant future. Partners will play a critical role in helping customers get to that place.

Generative AI is poised to deliver Internet and cloud-level advantages, especially for the enterprise. Generative AI will change the way jobs are performed in every industry, from healthcare to manufacturing to education to finance and more. McKinsey estimates that generative AI could add up to $4.4 trillion annually to the global economy.

VMware CEO Raghu Raghuram recently wrote about three challenges that must be addressed to capture the full value and potential of generative AI in the enterprise.

One of the biggest, notes Raghuram, is cost. Training and managing today’s large language models requires specialized computing power and high-speed networking with significant amounts of memory. It’s all hugely expensive, and training will remain an ongoing expense as models are updated.

Another challenge that organizations face is the lack of available skills to effectively harness the power of generative AI. Business and technology leaders have told Raghuram that they cannot move as fast as they would like with generative AI because they simply don’t have staff with the skills required to keep up with the rapid pace of innovation in the AI space.

The final challenge Raghuram presents is the one that more often than not makes headlines: trust. Raghuram notes that current AI models create significant legal, privacy, and regulatory threats, with the potential to harm customers and employees and, ultimately, the bottom line. Part of the issue is the hallucination factor — or, as Raghuram explains, generative AI systems’ tendency to create new content that is “nonsensical, irrelevant, and/or inaccurate.”

Enterprises have their work cut out for them, for sure, but the payoff will be worth it.

Raghuram sees a future in which companies mitigate the expense of generative AI by running their own customized AI models using open-source software and tools. Running more compact models on dedicated infrastructure will drive down costs associated with generative AI.

As far as the skills gap goes, he notes, the process and tools used to build generative AI applications can be simplified using reference architectures, which will enable organizations that lack in-house expertise to build generative AI-enabled applications.

The issue of trust may be the most challenging to overcome because it requires a collective effort to develop a set of ethical principles to ensure fairness, privacy, accountability, the intellectual property of others, and the transparency of training data. Raghuram notes that there is a large and growing ecosystem of organizations working to move this effort forward, with the open-source community innovating at the center of it.

It’s clear that we are at the precipice of something not just disruptive but transformative. Just like the Internet and cloud computing, our society will likely look back years from now and say, “How did we live without it?” But we’re not there yet, and that provides an opportunity for partners to provide their insight and expertise. Partners can guide customers on their journey toward embracing this gen AI revolution, helping to navigate through the gen AI hype and fear and implement practical and strategic gen AI solutions that align with their business goals.

VMware is at the forefront of this movement, working to help organizations address the challenges laid out by Raghuram in a way that balances innovation and growth with measured caution.

VMware’s multicloud and cloud-smart focus lays a foundation for effectively managing the data that drives generative AI. In addition, solutions such as the recently announced VMware Private AI will enable enterprises to customize models and securely and effectively run generative AI applications. VMware Private AI enables privacy and control of corporate data, a choice of open source and commercial AI solutions, quick time to value, and integrated security and management.

A thoughtful and purposeful combination of platforms, people, and processes will help enterprises wrap their heads around generative AI. Because generative AI is not the future; it is now.

This guest blog is part of a Channel Futures sponsorship.