Exploring Generative AI’s Rise Across the Enterprise

Industries are buzzing about generative AI, a category of machine learning that applies existing learning algorithms to create text, code, images, or video. Machine learning models and enterprise AI tools have experienced rapid growth over the last three years, with companies investing significant resources into digital technologies that deliver huge efficiencies for business operations across the enterprise, explores Assaf Baciu, co-founder and COO of Persado.

While large language models (LLMs) powering today’s generative AI have been around for about 5 years, more recent developments in AI and machine learning (ML), such as the launch and swift rise in popularity of tools such as ChatGPT, have unlocked greater access to generative AI’s capabilities. 

At the enterprise level, generative AI has the potential to scale business communications, improve customer experiences, and increase revenue while differentiating your brand from the competition. With that, it’s imperative to note that having unencumbered access to these flashy and enticing new tools also comes with its own levels of risk. Understanding generative AI — and being intentional with its use — is almost, if not equally, as important as the results you’re trying to achieve. 

Where Is Generative AI Headed?

Over the next few years, generative AI will become a rocket ship for the enterprise — for both business leaders and individual employees. 

At the employee level, generative AI will supercharge employee efficiency: teams will be able to create and scale more accurate information and communication at every level. With the right knowledge base, this type of technology will also provide consistent brand messaging, resulting in fewer overall approvals and enabling your teams to truly trust their AI. 

Machines’ ability to generate, summarize, and perfect enterprise content and communications will digitally transform every function of organizations, from legal to marketing to customer services. Generative AI can, and likely will, be used to help legal teams draft contracts and motions by generating text that is similar to a given input in style and content. Artificial intelligence can also assist lawyers by delivering instant access to volumes of legal information and research. For example, a lawyer could enter a legal question or topic, and the AI system can generate relevant cases, laws, cases, or authorities. 

While legal teams’ uses of generative AI offer the promise of cost savings, another use case promises sizable revenue gains: Marketing. 

Gartner predicts that by 2025, 30% of marketing content will be generated by AI, up from less than 2% in 2022. generative AI will impact customers across communications channels, including SMS, email, web, and social media. One example is when consumers reach the shopping cart stage — arguably the most impactful stage of e-commerce. Using insights gleaned from generative AI specializing in marketing communications, brands can engage

each customer with the best message at the right moment, motivating them to complete their purchase rather than abandoning their cart altogether. 

See More: The Risks & Rewards of Generative AI

Individualize It: The Effectiveness of Personalized Language 

Retail is another market where generative AI will have a massive impact. Especially given that consumer mindsets have shifted post-pandemic — and continue to evolve almost daily — retailers must continually shift messages to meet customers where they are: online. Brands need to adapt by producing highly personalized content and finding new ways of engaging with their customers. To stand out among the competition, retailers have made significant investments in enterprise technology to expand their personalization efforts. 

However, it can be extremely difficult to get personalization at the enterprise level right. Recent Coresight research found that 71% of brands and retailers say they excel at personalized marketing, yet only 34% of consumers say they experience excellent personalization from the brands they interact with, revealing a sizable gap in the customer experience. Many enterprises currently using generative AI for language generation are likely not using the technology to its fullest potential; rather, they are relying on it to simply create a high volume of content that is just “good enough.” 

Generative AI alone will not produce personalized language that can speak to your individual customers. However, by pairing generative AI with a specific knowledge base of real customer interactions, your brand can create a personalized language that motivates — at scale — to acquire, convert, and retain customers. 

Most of today’s AI chatter is about transformer models trained on large, general data sets — such as the Internet. These public models create general output that is comprehensible and presumably relevant. However, they are not optimized for specific tasks, such as motivating 

behavior or evoking emotion. For that objective, you’ll need additional complementary transformer models that are trained to generate enterprise communications, working in conjunction with structured, real-world behavioral data that show how consumers will respond to your outputs. When executed correctly, this level of generative AI-powered personalization can increase conversion by up to 41%, making it imperative for brands to invest and scale personalization efforts sooner rather than later. 

Generative AI in 2023 and Beyond 

We’ll see generative AI further blur the lines between in-person and digital commerce throughout 2023 and beyond. For example, with the right generative AI platform, your brand will be able to generate personalized language, images, and videos and even combine them with multimedia experiences to recreate the bespoke brick-and-mortar experience. And, as AI algorithms continue to grow their datasets, marketing teams will be able to extract deeper insights and a greater understanding of their customers’ needs. Eventually, marketers and business leaders will have the insights necessary to adjust their strategies in real-time, righting the course and achieving their business goals faster. 

As we can already tell, 2023 holds the greatest potential for brands looking to expand their current AI capabilities. Technologies such as generative AI and ML have the potential to amplify brands’ personalization efforts beyond the traditional “Hi [Insert First Name],”

greeting. Companies looking to remain competitive — and desire to be at the forefront of innovation — need to consider implementing game-changing technology such as generative AI. In doing so, they will play an active role in reshaping the enterprise landscape for years to come.

What are your latest thoughts on the pace of evolution in the generative AI space? Share with us on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window . We’d love to get your take on this!

MORE ON GENERATIVE AI

• Putting Generative AI to Work Responsibly
• The Future of AI in Sales: Embracing Generative Technologies
• Consumers Want Brands To Be Transparent About AI Usage To Increase Adoption
• Five Reasons to Use Generative AI to Automate Building Designs
• 10 Noteworthy Organizations Leveraging the Power of Generative AI