Putting AI to Work: Generative AI Meets the Enterprise

Five days after its launch, ChatGPT exceeded 1 million users¹. Generative AI (GenAI), the basis for tools like OpenAI ChatGPT, Google Bard and Meta LLaMa, is a new AI technology that has quickly moved front and center into the global limelight. 

GenAI’s hallmark is the ability to answer almost any question on demand, converting text-based queries into masterful creations, such as songs, poems, art or college essays. GenAI then builds a network of related topics, generating an enormously expanded base of information, often visualized as a knowledge map. 

GenAI Meets the Enterprise

While we’ve seen initial consumer interest in GenAI tools and use skyrocket, GenAI capabilities are fast moving to the enterprise world. Today, an estimated 60% of IT leaders are looking to implement GenAI².

At the same time, concerns exist. Most notably, for about 71% of IT leaders, angst about security creates a barrier to adoption, mandating that approaches, infrastructure, data strategies and security be appropriately aligned³. Finding the right, fit-for-use GenAI model for enterprises is the key to mitigating its risks, dispelling concerns and ushering in GenAI’s mainstream adoption in the enterprise world.

Overcoming GenAI challenges holds epic potential for enterprises. From improving customer interactions to automating complex business processes, GenAI models have the power to revolutionize the ways businesses operate, opening new possibilities for every industry in every part of the world.

Pivoting to Purpose-built GenAI

To enable that revolution, the way enterprises use GenAI will likely differ from general-purpose Large Language Models (LLMs) like ChatGPT. Instead, enterprises are likely to use GenAI models that are trained and tuned to solve specific problems; for instance, to enable automated customer support, financial forecasting and fraud detection. Although LLMs such as ChatGPT can be used for enterprise applications, the accuracy of their results will not match that of a purpose-built model created to meet a specific need.

Other major benefits of purpose-built GenAI include:

• Data security and compliance: AI’s unique capability is handling and analyzing huge datasets with unprecedented speed and efficiency. Simply put, if AI is a rocket ship, data is the fuel. The more data that a company can use to tune the purpose-built LLM, the more powerful the GenAI model becomes. Enterprises can tune their LLMs with customer purchase history, support or telesales logs, health records, financial records and other data sources. Consequently, handling this data in a secure manner will become even more important than it is today. Experienced advisors can help guide organizations size up a security strategy with minimal disruption to existing systems and approaches.

  Additionally, some industries, such as healthcare and finance, must comply with stringent             regulations regarding data privacy and security. The privacy of data that is entered into third party      LLM services is the subject of intensifying debate. Thus, enterprises that need to retain control     over their data must tread carefully. 

• Agility and time to market: Most enterprises will need to update their GenAI models regularly, which is easier to do with purpose-built GenAI models. The time required to train general-purpose LLMs can take months. That’s because massive amounts of data are required for training, eclipsing deployment speed and time to market, the very benefits GenAI enables. Reducing time-to-model and facilitating faster data-to-decisions can improve business efficiency.

• Performance: Purpose-built models also provide performance benefits over general-purpose models. Specifically, enterprises using third party general-purpose LLMs may not be able to optimize performance and reduce the latency of their GenAI workloads. This is problematic for applications that require real-time processing or low-latency response times, a key attribute of GenAI adoption.

• Cost: The smaller amounts of training data needed for purpose-built GenAI models also translate into cost savings. As a result, purpose-built LLMs often cost far less to train and re-train compared to general-purpose LLMs like ChatGPT.

Moving Enterprise GenAI Forward 

For enterprises, GenAI holds the profound potential to automate complex processes, improve customer interactions and unlock new possibilities with better machine intelligence—and CIOs are the key to moving it forward. Together with organizations like yours, Dell and Intel are driving the next wave of innovation in the enterprise AI landscape.  

To help organizations move forward, Dell Technologies is powering the enterprise GenAI journey. With best-in-class IT infrastructure and solutions to run GenAI workloads and advisory and support services that roadmap GenAI initiatives, Dell is enabling organizations to boost their digital transformation and accelerate intelligent outcomes. 

Intel. The compute required for GenAI models has put a spotlight on performance, cost and energy efficiency as top concerns for enterprises today. Intel’s commitment to the democratization of AI and sustainability will enable broader access to the benefits of AI technology, including GenAI, via an open ecosystem. Intel’s AI hardware accelerators, including new built-in accelerators, provide performance and performance per watt gains to address the escalating performance, price and sustainability needs of generative AI.

To find out more visit our website. 

To learn more, please see:

How Generative AI Tools Like ChatGPT Could Revolutionize Business

Taking on the Compute and Sustainability Challenges of Generative AI

Unleashing the Power of Large Language Models Like ChatGPT for Your Business

^[1] https://twitter.com/gdb/status/1599683104142430208

^[2] https://www.techrepublic.com/article/salesforce-openai-chatgpt-powers-einstein-ai/

^[3] IT Leaders Call Generative AI a ‘Game Changer’ but Seek Progress on Ethics and Trust – Salesforce News