Generative AI for Business

The AI revolution is in full force, with businesses everywhere looking to Generative AI to transform their organizations. This next step in how humans interact with computers promises to upend virtually every industry on the planet.

What is Generative AI?

The history of technology is marked by before-and-after moments – advances that spurred new industries, enabled the transformation of others, changed how people interact with technology, and reshaped society. The public rollout of the Netscape web browser in 1994 was one such moment, the introduction of the Apple iPhone just 16 years ago another. We’re now living through an equally – if not more – significant moment with the emergence of generative AI. While the consumer applications of generative AI have dazzled us all, with ChatGPT and other services churning out remarkably polished essays and high-quality images in a flash, the implications for the enterprise are even greater. This is why business leaders across industries are examining where and how to incorporate generative AI throughout their organizations. While many large enterprises, such as Koch Industries and the U.S. Air Force, have long been digitally transforming with enterprise AI application software, the emergence of generative AI promises to accelerate such efforts at a rapid pace. The opportunities are enormous; the risks of watching from the sidelines just too great.

What Is Unique About Generative AI?

As the name suggests, generative AI enables computers to generate content based on a set of AI and Machine Learning (ML) algorithms applied to vast data sources. The user experience, akin to having a conversation, marks a fundamental shift in the human-computer interaction model. Generative AI enables software to “learn” the fundamental patterns of a corpus of images, text, and audio files, and then rapidly produce comprehensive, thorough results (although at this point inaccuracies aren’t uncommon.) These AI models use a variety of techniques, such as transformer models, generative adversarial networks (GANs), and variational auto-encoders. (See definitions).

A user types in a prompt, the software spits out what you asked for. Beyond ChatGPT, there’s now, Google’s Bard, and Microsoft’s generative-AI equipped Bing, with the tagline, “Ask real questions. Get complete answers.” To generate content—churning out an essay about Plato, for instance, or about the history of AI technology—generative AI services rely entirely on publicly available data. And that’s what stands between current generative AI offerings, and what’s needed to serve the enterprise.

Access to the appropriate enterprise data is the essential ingredient for generative AI for business. A financial services company, for example, doesn’t need a generative AI system that learns only from public data. It needs a system built for its domain (read The Significance of Domain Models) that generates comprehensive insights from its proprietary data. That might include deposit trends, information about its loans, and so on. Ditto for a manufacturing company, or healthcare institution. Generative AI for the enterprises also incorporates public data via Large Language Models (LLMs). A financial services firm would want real-time interest rate data in the mix, for instance. But it’s the domain-specific, enterprise data that promises to make generative AI so transformative for businesses.

The difference doesn’t stop there. Generative AI makes it possible for far more people across an organization to take advantage of the predictive insights generated by the underlying enterprise AI applications. Put simply: Generative AI available to the public creates what you ask for based on current (meaning past) data available across the web. Generative AI in the enterprise, by contrast, can produce vital insights based on what’s going to happen for a specific business, such as telling you which parts of your manufacturing facility will need maintenance when or which customers are likely to close their accounts.

C3 Generative AI Available on AWS Marketplace

Generative AI Terms and Their Definitions

Before we dive into the benefits of generative AI for the enterprise, it's important to understand the key terms in this field:

  • Natural Language Processing (NLP): A branch of AI that enables a computer to understand, analyze, and generate human language.
  • Transformer: A neural network architecture (introduced by Google in 2017) that can train significantly larger models on ever larger datasets than before. It allowed the emergence of recent LLMs to process and learn from sequences of words, recognizing patterns and relationships within the text.
  • Large Language Model (LLM): A deep learning model specialized in text generation. Today almost all LLMs are based on the transformer architecture (with variations from the original). As the name indicates, the real revolution is in the scale of these models, the very large number of parameters (typically in the 10s to 100s of billions), and the very large corpus of text used to train them.
    • Examples of commercial / closed-source models: OpenAI’s GPT3/3.5/4, GCP’s PaLM, AWS’s Bedrock, Anthropic’s Claude, etc.
    • Examples of open-source models: Google FLAN-T5 & FLAN-UL2, MPT-7B, Falcon, etc.
  • Pre-trained Model / Foundational Model: An LLM trained from scratch in a self-supervised way (without labels) on a very large dataset and made available for others to use. Foundational models are not intended to be used directly and need to be fine-tuned for specific tasks.
  • Fine-tuning: The process of taking a pre-trained model and adapting it to a specific task (summarization, text generation, question-answering, etc.) by training it in a supervised way (with labels). Fine-tuning requires much less data and compute power than the original pre-training. Well fine-tuned models often outperform much larger models.
  • Hallucination: A situation where the LLM generates a wrong output; the root cause is that the LLM uses its internal “knowledge” (what it has been trained on) and that knowledge is not applicable to the user query.
  • Multi-modal AI: a type of AI with the ability to process and understand inputs from different types of inputs such as text, speech, images, and videos.
  • Retrieval Model: a system (typically a Transformer) used to retrieve data from a source of information. Combining retrieval models with large language models partially addresses the hallucination problem by anchoring the LLM in a known corpus of data.
  • Vector Store: a type of data store specialized in managing vectorial representations of documents (known as embeddings), those stores are optimized to efficiently find nearest neighbors (according to various distance metrics), they are central architectural pieces of a Generative AI platform.
  • Enterprise AI: Providing predictive insights to expert users for improving performance of enterprise processes and systems. (See What Is Enterprise AI?)
  • Generative AI for Enterprise Search – Leveraging generative AI’s intuitive search bar to access the predictive insights and underlying data of enterprise AI applications, improving decision-making for all enterprise users.

Why Is Generative AI Transformational for the Enterprise?

The ability for generative AI to create high-quality, contextually relevant content—text, images, videos—in a fraction of the time it takes today, is transformational for a wide swath of businesses and specific functions. Generative AI applications and use cases span practically all industries and organizations across manufacturing, healthcare, energy, retail, transportation, government, financial services, and so on.

With generative AI, marketers can rapidly create a broader set of personalized campaign content without adding more writers; financial analysts can produce granular custom reports for executives in minutes. Such advances will lead to dramatic cost savings, better customer experiences, and increase sales velocity. Those are just a few of the potential advantages.

Core to transforming a business is the positive impact generative AI can have on the enterprise search experience. Imagine using a search engine to access exactly what you need within your business, making it easy for users to access the most pertinent information, portions of reports, and predictive analytics from your enterprise data and external systems. By making data, analytics, and predictions broadly available across an organization through an intuitive search bar—and not just to the data analysts in the company— generative AI can vastly improve decision-making at every level in the organization. Suddenly, people throughout the ranks of an enterprise can take advantage of this powerful AI technology, boosting efficiency, productivity and, importantly, the ability to plan.

Take a machine operator as an example. Machinery operators typically monitor equipment performance and manufacturing conditions at a control board. They are responsible for triaging alarms, responding to urgent issues, and ensuring that operations safely and reliably meet production targets and quality specifications. It’s a demanding role. As a result, operators do not have time to read detailed manuals or aggregate information across systems to identify trends in performance. Furthermore, many manufacturers face an aging workforce, where deep expertise is leaving the organization as operators retire.

Generative AI poses a unique opportunity to overcome these challenges. A large language model (LLM) can be trained on a corpus of enterprise data – such as historical machine failures, work order logs, inspections, production performance, and OEM operating manuals – to synthesize information and make recommendations for less experienced operators.

While working directly from the control board, a machinery operator may ask a generative AI application: “The conveyor belt on production line A is broken. How do I fix it?” The Generative AI application will quickly return the exact troubleshooting steps from the equipment’s Standard Operating Procedure (SOP) document, along with additional commentary from recent work orders on the production line A conveyor belt.

With this generative AI application, lesser experienced operators instantly gain access to the knowledge and experience of the operators who came and learned before them – without requiring decades on the job. The AI application synthesizes all relevant information and makes it available in a useful and easy to grasp format, all from entering a simple prompt into the enterprise search bar. The result is an operator new to the job becomes more efficient, more effective, and can deliver better outcomes for the business.

Many business functions can benefit from applications of generative AI.

Generative AI can improve sales productivity by identifying the right opportunities to focus on; the technology can help boost conversion rates by generating personalized prospecting templates and sales scripts.

Detailed Use Cases: Generative AI can help enterprises:

  • Create sales emails that are personalized and context specific. This will increase open rates and click-through rates, improving overall sales pipeline performance.
  • Forecast sales and revenue by analyzing data from a variety of sources, including historical sales data, market trends, and customer behavior. This can improve accuracy of sales projections and enable better planning of marketing and sales efforts.
  • Improve sales reporting by automatically summarizing notes and action items from transcribed customer interactions.

Generative AI can create personalized content for email marketing campaigns and social media posts, summarize the current state of the market, and keep competitive positioning updated with changes in the market.

Detailed Use Cases: Generative AI can help enterprises:

  • Create SEO-optimized blog posts on topics relevant to a company’s target audience, improving a company’s audience engagement without significant investment.
  • Create engaging and brand-compliant social media posts to improve quality and frequency, driving audience engagement.
  • Generate informative web content at higher frequencies, ensuring your site stays up to date while improving a company’s website ranking in search engines.
  • Perform comprehensive, accurate, and rapid market and competitive research to help inform a company’s overall positioning and go-to-market strategy.

Generative AI’s powerful capacity to leverage the latest enterprise data and predictive models will help improve manufacturing performance, increasing efficiency and throughput.

Detailed Use Cases: Generative AI can help:

  • Create optimal production plans based on the latest demand picture, supply constraints, and resource availability. This will free up time for manufacturing leaders to focus on strategic aims, while also increasing manufacturing utilization, reducing production costs, and improving visibility across the manufacturing plant.
  • Optimize manufacturing processes by analyzing data from sensors and other sources to create alerts about impending bottlenecks or quality issues. This can improve productivity, reduce costs, and increase the overall manufacturing yield.
  • Predict operational reliability issues and assist line workers in proactively performing maintenance to prevent failures. Generative AI domain-specific models can learn from years of codified manufacturing know-how that is buried in documents and provide the most effective and precise mitigation factor that will help reduce downtime and repair costs while improving manufacturing reliability and customer satisfaction.
  • Improve product quality by analyzing data from sensors, cameras, and other sources to detect defects and other issues early in the production process. This can reduce the risk of product recalls, improve customer satisfaction, and reduce the cost of warranty claims.

Generative AI has the potential to improve monitoring, analysis, and management of supply chains, revolutionizing global operations and delivering significant benefits in terms of cost savings, efficiency, and sustainability.

Detailed Use Cases: Generative AI can help enterprises:

  • Identify potential suppliers by analyzing large amounts of data to identify suppliers that meet specific procurement criteria such as product availability, quality, price, and service levels.
  • Improve sourcing productivity by creating RFPs, evaluating bids, creating shortlist recommendations, and drafting contracts. This will free up time for procurement professionals to focus on more strategic tasks.
  • Optimize logistics operations by analyzing data such as traffic patterns, weather, and fuel prices to recommend the most efficient routes and modes of transportation.
  • Assess the environmental impact of supply chain operations, allowing for assessment and reduction of carbon footprint and achievement of sustainability goals.

Generative AI can summarize the salient points of legal documents, search through large corpuses of legal documents to identify the most relevant ones, and quickly prototype new content such as patents, wills, and contracts.

Detailed Use Cases: Generative AI can help enterprises:

  • Assess risk by analyzing data from a variety of sources, including financial reports, customer behavior, and market trends to identify potential risks early and take proactive mitigation measures.
  • Detect fraud by analyzing transaction data and identifying patterns that are indicative of fraudulent activity. This can help companies reduce losses and prevent reputational damage.
  • Ensure regulatory compliance by analyzing legal and regulatory requirements and identifying gaps in existing policies and procedures. This can help companies avoid costly fines and penalties and maintain the trust of stakeholders.
  • Analyze contracts to identify potential risks and ensure compliance with relevant regulations, improving efficiency and reducing the risk of legal disputes.
  • Improve cybersecurity by analyzing network traffic and identifying potential threats in real-time, helping companies prevent data breaches and other cyberattacks while maintaining the confidentiality and integrity of sensitive information.
  • Monitor compliance with regulations, such as those related to environmental protection or labor standards, and avoiding costly fines and reputational damage.
  • Automate reporting across any compliance and regulatory frameworks (e.g., ESG). Executives and compliance teams can delegate mundane and manual reporting tasks to generative AI models and start focusing on strategic decision making to advance the company’s performance further.

Generative AI can quickly draft reports and update content to improve and manage investor relations, automate document creation, such as invoices, purchase orders, and receipts, and identify market trends from external data sources to inform financial planning and risk management.

Detailed Use Cases: Generative AI can help enterprises:

  • Automate reporting for internal controls, improve productivity of the financial analyst teams, and free up time for more strategic analysis and tasks.
  • Create financial forecasts and scenarios more frequently and keep them updated as new data emerges.
  • Manage contracts with suppliers, ensuring compliance with contractual obligations and reducing the risk of disputes or legal issues. This can save time and resources while improving transparency and accountability.

Generative AI can analyze employee data, such as performance and engagement, and identify opportunities to improve productivity and retention, create personalized training and development plans tailored to individual employees, and keep track of material issues within the organization.

Detailed Use Cases: Generative AI can help enterprises:

  • Automate employee service tasks, such as answering frequently asked questions and resolving simple issues. This will free up time for HR professionals to focus on more complex issues.
  • Create virtual assistants that help employees with a variety of tasks, such as adjusting payroll contributions, scheduling performance review sessions, or providing answers to queries for new employees. This can help businesses to provide a more convenient and personalized employee experience.
  • Track employee sentiment by analyzing employee feedback and social media posts to understand how employees feel about the company’s performance and culture, identifying areas for improvement and proactively addressing employee morale.

Generative AI can create code, tests, and documentation to boost developer productivity, search across logs to facilitate forensic analysis of security and software issues, and to automate IT knowledge retrieval through self-serve generative chatbot interfaces.

Detailed Use Cases: Generative AI can help enterprises:

  • Develop applications more quickly and efficiently by automating certain aspects of the development process. Generative AI models can be configured to automatically generate code, perform unit tests, identify bugs, and provide other developer co-pilot features to significantly reduce application development timelines and improve the overall quality of those applications.
  • Automate IT support, from answering frequently asked questions to resolving simple issues, freeing up IT professionals to focus on more complex issues.
  • Improve IT security by detecting and preventing cyberattacks. This will help businesses to protect their data and systems from unauthorized access.
  • Manage IT assets by tracking the location and usage of devices, helping businesses improve efficiency, reduce costs, and avoid costly cybersecurity risks.
  • Manage IT projects by scheduling tasks and tracking progress, helping businesses complete projects on time and within budget.
  • Optimize network performance by analyzing data from sensors, servers, and other sources to identify potential bottlenecks and proactively address them. This can improve network uptime and reduce the risk of downtime.
  • Improve cybersecurity by analyzing network traffic and identifying potential threats in real-time. This can help companies prevent data breaches and other cyber-attacks.

The Significance of Domain Models for Enterprises

A significant breakthrough in generative AI came with the development of transformer models—neural networks that can learn the context of sequential data—to rapidly create high quality, comprehensive responses to user queries. Transformer models were first described in an important Google research paper in 2017 called “Attention Is All You Need,” and they have been advancing ever since.

The capabilities of generative AI have both amazed and scared the public, some of which detracts from the opportunity for businesses with generative AI. Because the current versions of popular generative AI models, such as Chat GPT and Google Bard, have been trained on a wide body of publicly available data, they lack the nuances required for specific domains, which is where domain models come in.

For a manufacturer or health care service to take advantage of generative AI, they need a system that generates content—reports, analysis, etc. — based on data relevant to their domains, including such data sets as terms specific to a company. Most of an enterprise’s specific data is proprietary, sensitive, and sits behind a firewall. As such, applying generative AI to the enterprise requires domain models.

Domain models combine the fluency and comprehensibility of emerging transformers with the domain-specific data and knowledge of an enterprise. With access to the corpus of data, best practices, and policies specific to an enterprise, generative AI models can, for example, answer inventory-related search queries for supply chain analysts, create brand-compliant graphics for marketing managers, and identify potential compliance exceptions in new contracts.

Domain models will generate the greatest value for enterprises by finding contextually relevant and policy-compliant responses to user queries. Sophisticated domain models have the greatest value potential for enterprises by not just tapping into existing enterprise data, but by calculating metrics and analyzing that data to produce value-added content for enterprise users.

Supply chain executives trying to identify how well their company is fulfilling customer orders will be less interested in looking at each order’s fulfillment history. Rather, they’d want to see On-Time-In-Full (OTIF) history by product line, customer, or region. The ability of domain models not just to find and regurgitate data, but to combine data and create business-relevant metrics analysis that are predictive in nature is where generative AI models can become invaluable to enterprise users.

Domain models also build upon existing analytical frameworks and business logic to find correlations between data, identifying cause-and-effect and generating analytic-backed insights. In the above example of a supply chain executive investigating service levels, domain models will not just produce an OTIF report, but also an analysis of potential drivers of poor OTIF, caused perhaps by a specific transportation provider or linked to the timing of orders relative to the planning cycle of the company.

Domain models will be able to support enterprise users not just in analyzing the past but in taking proactive actions by using predictive insights. The supply chain executive in the above scenario will be supported by their domain generative model by identifying the future orders with the highest risk of delays along with explanations of the drivers of each order’s risks.

Enterprises leveraging domain generative models will capture outsized value not just by effortlessly generating insights for their users, but by also giving them the ability to act on those insights. By integrating generative AI capabilities with personal productivity tools and enterprise applications, companies will give their users the ability to:

  • Take personal actions such as generating reports or setting alerts
  • Initiate collaborative actions by kicking off a new project or create the agenda of a group meeting
  • Kickoff workflows in enterprise applications such as accelerating supply orders, creating support tickets, or generating a customer discount.

Let’s get back to our supply chain executive investigating customer delivery fulfillment. Using domain generative models, the executive can identify past track records and risks to future deliveries, as well as making changes to production schedules and transportation arrangements to expedite critical orders at risk of delay.

Key Considerations for Adopting Generative AI in the Enterprise

Organizations looking to adopt generative AI technologies for enterprise use cases stand to gain a major productivity boost. Yet as with many leading-edge technologies, several factors exist that business leaders should consider when choosing a generative AI technology partner.

Data Availability and Quality
Generative AI domain models require large amounts of data to be trained effectively. Difficulty in obtaining or collecting relevant data, particularly for specialized or rare situations can limit the scope and effectiveness of the model. Furthermore, limited availability of high-quality data can lead to poor performance and accuracy of the model. Consequently, enterprises should consider both the availability and quality of data to train and operate generative AI systems.

Security and Privacy Risks
In addition to availability and quality of data, unauthorized use of sensitive data for training or operating the model can also expose the enterprise to security and privacy risks. Vulnerabilities in the model architecture could be exploited by hackers to gain access to sensitive data and cause harm to individuals and enterprises. Moreover, lack of transparency and accountability in data handling and use could lead to potential regulatory noncompliance.

Intellectual Property Risks
With public-facing generative AI systems, enterprises may be exposed to intellectual property infringement risks. By design, public generative AI systems are trained on internet data. Consequently, generated content similar to existing works that are protected by intellectual property laws could lead to legal actions and potential financial damages. Ongoing debate around the ownership and authorship of generated content from public generative AI systems could lead to legal disputes and challenges.

Bias and Inaccuracy
As the generative AI model is trained on available data, bias and inaccuracy in the training data can be perpetuated or even amplified in the output of the model, leading to discriminatory, legally risky, or unsafe outcomes. Furthermore, inaccurate answers due to outdated or irrelevant data or model hallucination can lead to unintended consequences and exacerbate an existing issue.

Domain Models
With a customized generative AI model trained on the enterprise’s own data and deployed on top of enterprise data and applications, enterprises can reap the benefits of generative AI systems while minimizing risks.

First, many enterprises likely have a wealth of high-quality data that is subject to rigorous data management practices and quality checks. Secondly, enterprise data is often subject to access controls and security measures, which can help prevent unauthorized access to sensitive data. Moreover, as enterprise data is typically owned by the organization, generating content based on enterprise’s own data eliminates intellectual property rights risks. Diverse, representative, and high-quality enterprise data can also help minimize bias in model outputs. Finally, while limiting hallucinations is an ongoing research area, generative AI systems that have a built-in feedback loop with human evaluation, such as the C3 Generative AI Product Suite, will be able to eliminate hallucination and inaccuracy over time.

The Future of Generative AI in the Enterprise

Advances in technology are once again promising to transform business and society – and this time it’s happening at hyper speed. Generative AI presents a new epoch of human efficiency and effectiveness, affecting society and industry in ways we have yet to understand. When we look back, as discussed above, this moment will be as groundbreaking as rollout of the personal computer and introduction of the Apple iPhone.

With generative AI solutions, business leaders will drive their organizations to previously unattainable and unthinkable speed and scale of execution. Companies will automate and optimize business processes, generate novel insights, and deliver new customer experiences faster than ever before.

While the general public has become enraptured by charming and relatable examples – from writing puns to creating a photo of an astronaut riding a horse – business leaders need to focus on well-scoped problems that generate economic value.

Developing and implementing generative AI technology for business transformation requires a thoughtful and deliberate approach. First, business leaders must identify tractable use cases where the solution will be useful, drive value, and where stakeholders agree to measurable success metrics.

Tractable use cases include automating repetitive tasks, or synthesizing insights from unstructured data and documentation. This could be achieved through streamlining high-volume tasks to save time, or unlocking once all-but-impossible to access information deep in documentation and disparate data sources. Success metrics will measure the business impact, and could be tied to economic benefit, customer service, sustainability outcomes, or business efficiencies.

As you can see, all this is leading to major changes within a given enterprise. To scale these technologies and solutions, leaders will need to evaluate existing workflows of people who already perform job functions generative AI can transform. Successful enterprise implementations of generative AI will embed solutions into existing customer and employee workflows, rather than serve as standalone tools.

The simplicity of using generative AI—for a consumer or a business user—should make adoption a non-issue. The possibilities really are limitless, and enterprise adoption should occur in months rather than years, ultimately making generative AI a moment in technology history whose significance will soon be undeniable – for enterprises everywhere.