What is generative AI?

Artificial intelligence (AI) as an idea has existed for thousands of years. Mechanical beings, living statues, and other artificial lifeforms can be found in mythology dating back to antiquity, and for nearly as long as there have been philosophers there have been philosophical musings about whether it would ever be possible to mimic human cognitive processes through mechanical (or digital) means.

Today, those musings have taken on greater significance. Modern AI is no longer confined to the realm of speculation; widely available and increasingly powerful, automation and AI are changing the way that people across all walks of life approach tasks, get information, and share ideas. At the forefront of this revolution is generative AI.

Generative AI is a form of artificially-intelligent computer system or algorithm capable of creating original content—quickly, effectively, realistically, and with some human oversight (to identify and eliminate inaccuracies). This can include anything from in-depth and highly accurate written articles to unique music and original images and is even capable of extensively altering the content of digital video clips.

As groundbreaking as generative AI is it is not an entirely new concept. It has a significant history, built on many of the foundational advances in automation technology that were pioneered during the 20th and early 21st centuries.

Given how closely tied the rise of generative AI is to the slow march of digital progress across the last century and a half, it is difficult to identify a single ‘creator’ responsible for introducing real generative AI to the world. That said, a few names stand out as those to whom generative AI owes its existence.

In addition to Russian Mathematician Andrey Andreyevich Markov (the brain behind the aforementioned Markov Chain), one of the earliest major contributors to today’s generative AI is Joseph Weizenbaum, the computer scientist who developed ELIZA. One of the first natural language processing computer systems ever created, ELIZA was developed during the 1960s. She and the generations of chatbots that would follow her had distinct limitations, but the promise of generative AI was there; it was simply waiting for technology to catch up.

The true birth of generative AI can in many ways be attributed to Ian Goodfellow and others who introduced the concept of Generative Adversarial Networks (GANs) in 2014. GANs revolutionized the field by introducing a framework where a generator network and a discriminator network interact and compete against each other—the generator network tries to produce content that the discriminator network classifies as real, while the discriminator network aims to correctly identify the generated content as fake. This adversarial relationship between the two networks drives them to learn, adapt, and improve.

With organizations such as OpenAI now playing a pivotal role in advancing generative AI, we are seeing even more significant breakthroughs in generating responses that are both coherent and contextually relevant. This advancement is only accelerating as more and more users embrace the possibilities of AI-generated content—both personally and professionally.

To reiterate, generative AI operates on the principle of machine learning through the application of neural networks, allowing the AI to generate new content from provided prompts. But before a prompt is ever introduced, the AI needs to be properly trained through detailed, extensive data sets.

Enormous amounts of relevant data are fed into the AI’s algorithms, and various AI engineers and machine learning specialists help the AI to correctly understand and classify the data. This data can consist of written text, graphics, images, code, or any other relevant content, and will become the foundation for the AI’s capacity to locate patterns and generate original work. The generative AI analyzes this information, extrapolates the underlying rules that govern the content, and continues to fine tune its parameters as new data is added.

The earliest versions of generative AI used complex data submission processes involving specialized tools, APIs (Application Programming Interfaces), coding languages, and extensive computer science training. But this is all changing; recent advances are focusing on improving the user experience. Rather than working within the constraints of specific systems, those who interact with today’s generative AI experiences can simply describe their requests using plain language. This approach creates a more conversational series of interactions with the AI, and even allows users to provide feedback to the AI on issues such as style, tone, and other elements which can then be applied in subsequent content iterations.

As with all forms of AI, generative AI is closely tied to machine learning. Machine learning algorithms serve as the foundation for training generative models in generative AI. 

These models learn patterns and features from large datasets, enabling them to generate new content that resembles (but is distinct from) the data they were trained on. Machine learning algorithms enable generative systems to learn the underlying patterns, styles, and distributions within the training data. This learned knowledge is then leveraged to generate new content that exhibits similar characteristics to the parameters it was originally provided. In other words, it allows it to create something that is completely artificial, but that looks, sounds, and feels as though it were made by a human.

Generative AI models are sophisticated algorithms, trained on vast amounts of data and designed to learn patterns within the data to generate new content. Three notable generative AI models that have recently gained public attention for their unique capabilities are:

At the risk of oversimplifying, it is not entirely incorrect to say that generative AI could seriously disrupt or even make obsolete the number of roles and responsibilities in business, or even majorly impact entire industries—driving innovation, streamlining processes, and transforming business operations, but also possibly forcing certain organizations to adapt to changing expectations.

But most experts believe that the positive outcomes of generative AI outweigh the dangers. And while we do not yet have enough data to accurately predict which businesses are likely to experience the greatest changes, the following industries appear most likely to be enhanced by generative AI:

• Aerospace
  Generative AI is capable of enhancing the effectiveness of flight simulations, optimizing aerodynamics and fuel efficiency, and allowing for improved aircraft designs.

• Architecture
  Giving architects the power to create detailed structural analysis while also generating innovative design options to meet specific criteria, generative AI has the potential to change the way architecture works with technology.

• Automotive
  Vehicle components demand extensive design and testing to ensure improved performance while also reducing weight (to improve fuel efficiency). Generative AI aids in both processes, enhancing safety and reducing costs.

• Consumer marketing
  Generative AI can analyze customer behavior and generating personalized marketing efforts and recommendations based on past behavior, preferences, and other relevant data. This allows for target advertising and promotes increased customer engagement.

• Defense
  As with the aerospace and automotive industries, generative AI may play a key role in the designing and testing of components of new defense technologies.

• Education
  Every student is different; generative AI can help educators create personal learning experiences that play to those differences, improving student engagement and helping ensure that everyone has access to a teaching curriculum that meets their needs.

• Electronics 
  Generative AI can be applied to circuit design within the electronics industry. Incorporating learnings from previous designs, generative AI can develop new advanced systems while also enhancing efficiency.

• Energy
  With increasing energy demands world-wide, the energy industry is extremely interested in generative AI to help improve grid management and energy-use forecasting.

• Engineering
  Reducing material waste, enhancing energy efficiency, improving the quality of products both physical and digital—the ability to generate and test designs can greatly cut down on the engineering timelines and assist engineers in producing better designs.

• Entertainment
  From enhancing visual effects in film and television to creating more-interactive storytelling in video gaming, generative AI may completely change the entertainment industry.

• Finance
  Generative AI has the potential to enhance the finance industry on both sides of the counter, informing risk assessment, improving fraud detection, and generating personalized investment recommendations. This will optimize financial modeling while also helping realign focus on improving customer experiences.

• Healthcare
  The increasing need for dedicated healthcare services is outpacing the number of trained medical personnel. Generative AI allows doctors to do more and help a larger number of people, providing assistance in personalizing treatment plans and creating accurate diagnosis. Additionally, generative AI can improve other areas such as medical image analysis.

• Manufacturing
  The manufacturing industry is always looking for opportunities to reduce costs and energy demands without hurting product quality or output. Generative AI can help redesign and optimize processes for optimal cost per unit manufactured, while still emphasizing quality control. In addition to supporting the development of advanced manufacturing techniques, generative AI can be applied to creating more effective safety education programs.

• Pharmaceuticals
  Generative AI is effective in drug discovery and virtual compound screening. This makes it possible to accelerate the research, development, testing, and deployment of pharmaceutical solutions.

With its ability to create intelligent, accurate, original content without any ‘hand holding’ from human operators, generative AI offers numerous benefits for businesses across a range of sectors. By leveraging the creative power of AI, generative models have the potential to drive innovation, streamline processes, and unlock new opportunities. Key business benefits of generative AI include:

Generative AI can optimize processes, automate tasks, and reduce operational costs, enhancing efficiency in areas such as content creation, design optimization, or predictive maintenance. By automating repetitive or time-consuming tasks that once demanded creative input, businesses can save resources, increase productivity, and allocate valuable professionals to more strategic endeavors.

Traditionally, customer service and support relied on direct 1:1 interaction, with agents needing to devote their entire attention to resolving individual customer issues. Generative AI allows businesses to deploy AI-powered chatbots and virtual assistants, capable of providing human-level support at scale. These AI-powered agents can assist with queries, offer solutions, and even discuss purchasing options, enhancing customer satisfaction and loyalty. By automating customer service, businesses can reduce costs, improve overall customer experiences, and allow their trained human agents to accomplish more in less time.

Generating personalized content, creating targeted advertising campaigns, and sharing optimized product recommendations based on customer preferences and behavior—generative AI offers significant benefits in marketing. By delivering marketing strategies customized to individual buyers, organizations enjoy more customer engagement, improved conversion rates, and stronger brand loyalty. 

Each of the advantages listed above comes back to this one benefit: increasing company revenue. Generative AI opens new opportunities, allows companies to create advanced products and services at lower cost, expands customer support and service capabilities, and gives organizations better insight into the risks that threaten their business—all of which contribute to greater financial success. 

Obviously, generative AI carries with it immense potential for optimizing and streamlining business processes and personalizing customer interactions. However, it is worth recognizing that generative AI has its share of risk and limitations. Here are some potential issues associated with generative AI that organizations need to be aware of:

• Difficulty adapting to new circumstances
  Generative AI models may struggle to tune their outputs to new circumstances, requiring ongoing fine-tuning and adjustment to ensure relevance and accuracy.

• Harmful bias
  Because generative AI models are built on a foundation of data, they may carry certain biases from that training data across into their outputs. Organizations must have policies or controls in place to detect and address biased content so as not to inadvertently spread harmful preconceptions.

• Intellectual property rights
  As advanced as today’s generative AIs are, they still require training on available data to produce content. This creates murky waters when it comes to copyrighted materials—these technologies AIs may be using intellectual property that does not belong to their parent organization. At the same time, users who share confidential information with AIs may find that it is no longer confidential but has become part of the AI data set.

• Lack of transparency
  The recent focus on ease of use and conversation-like input/output has created an environment where it can be difficult to really understand how the generative AI works and where its original data comes from. Many organizations may not wish to take full advantage of generative AI without a clearer picture of its inner workings.

• New cybersecurity dangers
  Cybercriminals around the world are already using generative AI programs to target their victims and circumvent security layers more effectively. Attackers may even use company-based AIs as system entry points, taking advantage of vulnerabilities in the program to gain access to sensitive data. Keeping up to date on all cybersecurity advancements and vulnerability patches may be the only way to counter these growing threats.
  

• Potential problems with accuracy and appropriateness
  Generative AI systems may generate inaccurate or unproven answers. Generative AI ‘hallucinations,’ where the AI may simply make up answers, represent major issues, particularly in scenarios that demand complete accuracy and validity. It is crucial to carefully assess the outputs for accuracy, appropriateness, and actual usefulness before relying on or publicly distributing any information produced by a generative AI.

When incorporating generative AI into business operations, it is essential to adopt best practices that ensure responsible and effective utilization. By following these guidelines, organizations can maximize the benefits of generative AI while mitigating risks and challenges:

• Become familiar with common failure modes and workarounds
  Generative AI models can exhibit failure modes where the output may introduce errors or otherwise fail to meet expectations. Study the common failure modes associated with your chosen generative AI tool and develop strategies to work around these issues. This can involve refining input prompts, adjusting parameters, or employing post-processing techniques to improve the quality of the generated content.

• Be ethical and comply with legal requirements
  When in doubt, err on the side of ethics and legality. Comply with any regulations, privacy laws, or intellectual property rights relevant to your situation. Implement measures to protect sensitive data and user privacy, and regularly assess the ethical implications of the generated content.

• Be transparent about what content is AI-generated
  To maintain transparency and ensure users are aware that they are interacting with AI-generated content, clearly label all output created by generative AI systems. This helps manage expectations and fosters trust with users and consumers.

• Double check for accuracy
  When the accuracy of information is critical, verify and cross-reference the output of generative AI models with primary sources. This helps ensure the reliability and integrity of the generated content, particularly in industries where accuracy is essential (such as law or healthcare).

• Stay up to date
  Generative AI technology is constantly evolving. Keep up with advancements, research, and best practices in the field and continuously monitor the performance and outputs of the generative AI models. Iterate on these models and processes as needed to enhance their effectiveness and address any emerging challenges.

• Watch out for biases
  As has already been addressed, generative AI models can inadvertently perpetuate biases present in the training data. Be vigilant in identifying and addressing any preconceptions or prejudices that may emerge in the generated content; regularly evaluate the output for potential biases and implement measures to mitigate their impact.

Although generative AI has been around for decades, its recent rise in terms of capability and availability has been meteoric. As this technology continues to evolve, we can anticipate enhanced creativity and innovation across essentially every industry, allowing for more sophisticated and realistic outputs in content generation, design, music composition, visual arts, and more. Generative AI will also continue to drive personalization and customization, creating laser-focused recommendations, personalized experiences, and unique products designed to address individual customer needs.

Within the next ten years, we can expect multimodal capabilities to emerge. This integration of models from various domains will lead to more immersive and interactive outputs, transforming areas like virtual reality, augmented reality, and mixed reality experiences, to name only a few. But this expansion won’t be made without caution; ethical considerations will take center stage, focusing on addressing biases, improving transparency, and ensuring responsible use of generative AI.

Additionally, the processes that support generative AI itself will become more streamlined, eventually allowing these AIs to continuously learn, adapt in real time, and become more truly ‘intelligent.’ This adaptability will make generative AI more valuable in dynamic environments and foster human-machine collaboration. While challenges such as bias detection and ethical considerations must be addressed, the future of generative AI is promising.

Generative AI holds immense power and potential, enabling organizations to enhance productivity, streamline workflows, and create exceptional customer experiences. However, it is essential to approach the use of AI-generated content responsibly, considering the risks and challenges involved. To assist organizations in effectively harnessing the benefits of generative AI, ServiceNow has introduced the Generative AI Controller and Now Assist for Search. These tools seamlessly integrate generative AI capabilities into the Now Platform^®, empowering users to leverage the power of AI without the need for platform changes or complex integrations.

With the Generative AI Controller, organizations can connect the Now Platform to leading large language models (LLMs) such as OpenAI, Azure, and ServiceNow’s proprietary LLM. These integrations allow for the seamless utilization of generative AI capabilities within existing workflows, enabling enhanced search functionalities, personalized conversations, and improved user experiences. And, Now Assist for Search (powered by generative AI), enhances search capabilities further by providing more specific and direct answers to queries, ensuring users receive the information they need quickly and accurately, filling gaps in customer experience, and delivering smoother interactions.

Additionally, ServiceNow recently partnered with Nvidia to develop more extensive generative AI capabilities for enterprise business, expanding ServiceNow's AI functionality and offering new applications for generative AI for IT departments, customer service teams, employees, and developers, and more.

Generative AI represents major opportunities for enterprise business, but also carries some risk. Click here to learn more about how ServiceNow's generative AI capabilities can transform your organization, while helping ensure that you don’t fall prey to the potential dangers. Put generative AI to work for your business, on your terms, and take your organization further than ever before.