Generative AI: 5 Things Business Leaders Must Know

From writing software code to optimizing business operations, generative AI is driving innovation and efficiency across the business world. This transformative technology comes with a steep learning curve for some businesses, which face a number of decisions. These include deciding whether to build or buy, gauging how much to spend, and considering generative AI trust and guardrails issue.
Generative AI creates completely new data from existing datasets. This data can be in a variety of formats, including text, images, music and even code—and it can produce results that weren’t in the original input. This versatility allows for an impressive range of creative problem-solving and efficiency-boosting applications.
Let’s look at five things every business leader needs to understand about generative AI to maximize their return on investment:
1) Generative AI is Revolutionizing Business and Job Functions
Generative AI is driving business innovation and efficiency. The technology’s ability to generate new data from an input  allows for an impressive range of applications.
Generative AI in the workplace isn’t merely about harnessing data or driving efficiency — it’s about fundamentally reshaping the approach to work. Yes, generative AI can make work faster and easier, but it also opens up entirely new possibilities for innovation and creativity.
What’s key about generative AI technology is its potential to be tailored to the needs of each business. Every business has its own unique or priority datasets.
By identifying their specific needs, businesses can use generative AI to gain a competitive edge. For instance, applied in healthcare, generative AI can help analyze patient data, create a summary of the key points, and then use this summary to draft an action plan for delivering better care.
Also see: 100+ Top AI Companies
2) Build vs. Buy
One critical business decision that enterprises face when implementing artificial intelligence is whether to build or buy the software. There are several factors to consider, including:

Unique data and domain expertise: Custom generative AI becomes valuable when businesses own unique data or specific domain expertise and aim to use  that data effectively.
Availability of talent: The skills required to build AI tools are specialized and in high demand. Enterprises need to consider whether they have access to this talent and whether they’re more familiar with open-source software or commercial offerings.
Project timeline: Building AI tools takes time. If an enterprise needs AI functionality ready quickly, buying it is the best bet. If the time frame is more flexible, building it might be the more practical option.
Integration with existing software: It’s crucial to consider whether the vendors an enterprise works with can integrate the AI tools under consideration. Understanding the organization’s procurement process and allowing enough time for it can save the project from unexpected delays.
Cost-effectiveness: While it may initially appear more affordable to develop AI tools internally, it’s essential to factor in expenses related to team building, software acquisition and long-term maintenance. If the tool serves as a crucial competitive advantage for the business, it may justify ongoing costs. However, if similar functionality is already available through a tool the company is currently paying for, it doesn’t make financial sense to build and maintain redundant software.

3) Identify the Industry Use Case
Generative AI has a wide range of applications across a variety of industries. Common ones include text generation for crafting captivating marketing copy, summarization for condensing extensive news articles and emails, and image generation for developing unique brand visuals or gaming characters.
AI-powered chatbots are enhancing customer service with intelligent, real-time responses in almost every industry, while translation capabilities are making information accessible across languages. In the coding world, generative AI boosts efficiency by dynamically generating comments and functions.
Healthcare is using generative AI to reduce drug discovery timelines. Game developers are using it to create dynamic game characters. The financial sector is using it to bolster security against transaction fraud and for algorithmic trading. Retail is using generative AI for automatic price optimization.
Businesses find success most quickly when they have an initial project that aligns with their unique needs. For help getting started, ask technology providers for examples they can share from companies with similar project requirements.
Also see: Best Artificial Intelligence Software
4) Investing in Generative AI
Implementing generative AI requires substantial financial investment. It involves procuring high-end infrastructure, hiring skilled talent, assembling the necessary software components and gathering the needed data.
That said, not every enterprise needs to invest in generative AI from scratch. Many are using pre-trained models, or foundation models, which are more cost-effective to train because businesses only have to augment the model with their own data and apply guardrails associated with their brand.
While the cost of implementing generative AI can be high, the investment return of increased efficiency, innovation and competitive advantage can make it worthwhile.
5) Ensuring Trust and Safety in Generative AI Models
As generative AI grows more advanced and widespread, guaranteeing the safety and responsible use of AI models becomes increasingly paramount. Large language model guardrail tools — software applications that keep generative AI models from deviating from their intended purpose — can help developers set boundaries on AI models. These can be set in three areas:

Topical guardrails: These prevent AI models from veering into undesired areas of use. For example, a customer service assistant powered by generative AI could be prevented from answering questions about the weather if that function isn’t within its designated scope.
Safety guardrails: These ensure that AI models provide accurate and appropriate responses. They can filter out unwanted language and make sure that references are made only to credible sources.
Security guardrails: These restrict the AI model’s connections to only known, safe external third-party applications.

Also see: Generative AI Companies: Top 12 Leaders 
Harnessing the Power of Enterprise Generative AI
Generative AI is poised to transform the world, offering vast opportunities for companies to innovate, improve efficiency and solve complex problems. As business leaders, understanding and using the power of generative AI will be vital to staying competitive in the rapidly evolving business landscape.
Following the five principles above will help businesses stay ahead of the curve and reap the rewards that this AI technology has to offer.
About the Author: 
Amanda Saunders, Senior Manager, AI Software at NVIDIA
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