Generative AI in Enterprise Applications
As generative AI use expands across business functions, more enterprise application vendors are embedding, or planning to embed a variety of use cases inside their products. I have taken a look at several of these applications over the past few months and some common best practices seem to be emerging. Before we jump into those though, it is probably more useful to start with the use cases themselves. In the Arion Research eBook “Generative AI Business Use Cases” we took a look at the ways generative AI could be used for business. The following diagram is from that eBook:
Business Use Cases for Generative AI
There is much more detail in the eBook, but for this post, the top level model is sufficient. In addition to the horiozntal use case model though, the eBook also takes a look at some of the industry vertical use cases. The following diagram shows the use cases covered:
Industry Vertical Use Cases of Generative AI
Many of the use cases in the diagrams can exist in a stand-alone application or embedded form, but some of the use cases, by their function need to exist embedded in another enterprise application or suite of applications. In general AI, or more accurately traditional AI, is already embedded in many enterprise applications and functions. In some cases, traditional AI is sufficient for the use case and performs well. On the other hand, for some use cases, generatie AI is superior and is replacing traditional AI. Let’s look at both at a high level first, before we look at embedding generative AI.
Traditional AI versus Generative AI
It’s useful to understand the differences between traditional AI and generative AI. Traditional AI is good at understanding the world, while generative AI is good at creating new things.
Traditional AI is trained on a dataset of examples and learns to identify patterns in that data. Once trained, it can be used to make predictions or decisions about new data that is similar to the training data. For example, a traditional AI model could be trained on a dataset of images of cats and dogs. Once trained, the model could be used to identify whether a new image is of a cat or a dog.
Generative AI is also trained on a dataset of examples, but it learns to generate new data that is similar to the training data. For example, a generative AI model could be trained on a dataset of poems. Once trained, the model could be used to generate new poems that are similar in style to the poems in the training data.
Traditional AI versus Generative AI
Here's a list of some traditional AI use case examples that have seen improvements or enhancements when generative techniques were introduced:
Image Generation:
Traditional: Image filtering, editing, and basic transformations.
Generative: Creating entirely new images, photo-realistic image synthesis, generating art, or converting sketches to detailed images.
Natural Language Processing:
Traditional: Text classification, sentiment analysis, and machine translation.
Generative: Writing essays, stories, or poetry; generating conversational agents; or completing or expanding on textual prompts.
Music and Sound Production:
Traditional: Sound recognition or classification.
Generative: Composing new pieces of music, generating background sounds, or creating entirely new sound effects.
Video Production:
Traditional: Video classification or tagging.
Generative: Creating new video clips, animating still images, or generating scenes based on textual descriptions.
Fashion and Design:
Traditional: Pattern recognition or design categorization.
Generative: Crafting entirely new clothing designs, generating graphical patterns, or proposing interior design layouts.
Game Development:
Traditional: Pre-defined game levels or static game assets.
Generative: Dynamic game level creation, generating new game assets on-the-fly, or tailoring game experiences based on player behavior.
Drug Discovery:
Traditional: Predicting the biological activity of molecules.
Generative: Designing entirely new drug molecules with desired properties or optimizing existing molecules.
Content Personalization:
Traditional: Recommending existing content based on user preferences.
Generative: Creating personalized content based on user tastes, like custom news articles or stories.
Data Augmentation:
Traditional: Basic techniques like rotation, zooming, or flipping for augmenting datasets.
Generative: Producing entirely new synthetic data samples that retain the essential features of real data.
3D Modelling and Graphics:
Traditional: 3D shape recognition or categorization.
Generative: Generating new 3D models from sketches or textual descriptions, or designing new virtual environments.
Finance:
Traditional: Time-series forecasting, fraud detection.
Generative: Simulating financial scenarios, generating synthetic financial data for testing, or creating potential investment strategies.
Education and Training:
Traditional: Multiple-choice question generation, content summarization.
Generative: Creating comprehensive quizzes, generating descriptive or illustrative examples based on a topic, or producing educational scenarios or simulations.
Virtual Assistants and Chatbots
Embedding generative AI in enterprise applications transforms the way businesses can use virtual assistants and chatbots. These modern and improved virtual assistants and chatbots can generate human-like text, code, and creative content, to unlock new possibilities for automation and efficiency across a wide range of enterprise applications.
Embedding generative AI-enabled virtual assistants and chatbots into enterprise applications can provide a number of benefits, including:
Enhanced customer experience: Generative AI can be used to create chatbots that are more engaging and informative, providing customers with a more natural and satisfying experience. For example, a chatbot powered by generative AI could be used to provide personalized product recommendations or to answer complex questions in a clear and concise manner.
Increased employee productivity: Generative AI can be used to automate tasks that are currently performed by human employees, freeing up their time to focus on more strategic initiatives. For example, a generative AI-powered chatbot could be used to generate reports, create marketing materials, or even write code.
Improved decision-making: Generative AI can be used to generate insights and recommendations that can help businesses make better decisions. For example, a generative AI-powered chatbot could be used to analyze customer data and identify trends, or to generate financial projections.
As generative AI technology continues to mature, we can expect to see even more innovative ways to embed it into enterprise applications. Some potential examples include:
Generative AI-powered search engines: Generative AI could be used to create search engines that are more adept at understanding natural language queries and providing relevant results.
Generative AI-powered content creation tools: Generative AI could be used to create tools that help businesses generate high-quality content, such as blog posts, articles, and marketing materials.
Generative AI-powered training and development tools: Generative AI could be used to create training and development tools that are more engaging and effective.
By embedding generative AI-enabled virtual assistants and chatbots into enterprise applications, businesses can improve customer experience, increase employee productivity, and make better decisions.
Automation
Automation is closely related to virtual assistants and chatbots, but also includes various other business processes. By embedding generative AI into enterprise applications, organizations can automate a broad spectrum of tasks across multiple business functions.
Benefits of Embedding Generative AI in Enterprise Applications
Process Efficiency: Generative AI can speed up processes by generating reports, crafting responses, or formulating plans based on the data it is trained on. This reduces manual effort and accelerates decision-making.
Data-driven Insights: Generative models can analyze massive datasets, identify patterns, and generate forecasts or recommendations. Businesses can leverage these insights for proactive decision-making.
Personalization: By understanding customer data and patterns, generative AI can create personalized marketing campaigns, product recommendations, or user experiences, enhancing customer engagement and loyalty.
Cost Savings: Automation invariably leads to cost savings. By reducing the need for manual intervention in various processes, enterprises can achieve significant reductions in operational costs.
Some Example Functions That Can Be Automated with Generative AI:
Customer Service: Automate responses to frequently asked questions, generate solution recommendations based on problem descriptions, and create tailored responses to customer queries.
Marketing and Advertising: Create personalized ad campaigns, generate content for promotional materials, or even design product mockups.
Finance and Accounting: Automate financial forecasting, generate budgetary recommendations, and craft financial statements.
Human Resources: Assist in drafting job descriptions, automating resume screening, and even crafting personalized feedback for employees.
Supply Chain and Logistics: Forecast demand, optimize inventory levels, and generate route optimizations.
Research and Development: Analyze research data, suggest hypotheses, or even generate content for research papers.
Sales: Generate sales pitches tailored to specific clients, automate follow-up communications, or even draft contract terms based on negotiations. For eCommerce personalization can be automated including dynamic product description and even dynamic product images that align with the buyer’s expectations.
Embedding generative AI into enterprise applications is not just about automation for the sake of efficiency; it's about transforming how businesses operate, think, and grow.
Prompt Engineering
Prompt engineering is the art of crafting effective prompts for large language models (LLMs). It is a crucial aspect of unlocking the full potential of LLMs, as well-designed prompts can elicit more relevant, informative, and creative responses. It is however, a skill in high demand and low supply.
The process of prompt engineering involves carefully considering the following elements:
The task at hand
The capabilities and limitations of the LLM
The desired format and style of the output
By thoughtfully crafting prompts that align with these factors, prompt engineers can guide LLMs towards generating high-quality outputs that meet their specific needs. In essence, prompt engineering is a form of dialogue with LLMs. By carefully choosing the words and phrases used in prompts, prompt engineers can convey their intentions and expectations to the model. This enables them to achieve a high degree of control over the generated output.
Because of both the complexity of the necessary skills and the extremely limited availability of qualified employees and prospective employees, some application vendors are taking a somewhat different approach. By moving the prompt engineering back a layer the users can simply interact with the generative AI application in a more natural way, with an approach that is similar to a simple web search. There are many new stand-alone generative AI applications that are taking this approach, as well as providing tools and features that can enhance a prompt for the user. Enterprise applications vendors are also rolling out enhanced versions of their applications that virtually remove the need for prompt engineering in the business functions. Limiting the need for prompt engineering to a smaller set of employees will help businesses feel more confident in moving ahead with generative AI use, and relieve some of the pressure on finding and/or building the skills internally.
Trust and Safety
As enterprise vendors embed more generative AI into their applications, the need to provide a way to remove or mitigate as much risk as possible are essential for driving sales and user adoption. “Trust and safety”, as it is referred to among many enterprise vendors embedding generative AI in enterprise applications and platforms, refer to the protocols, technologies, and practices used to ensure that these tools operate securely, responsibly, and in accordance with legal and regulatory requirements. These measures are crucial not only for protecting sensitive data and maintaining privacy but also for instilling confidence among users, stakeholders, and customers. Ensuring trust and safety in digital solutions is a multifaceted challenge, particularly as technologies and regulations evolve.
Here's an overview of trust and safety layers in enterprise applications and AI platforms:
Data Protection & Privacy:
Encryption: Protect data both at rest (stored data) and in transit (data being transferred).
Data Masking: Hide specific data within a database so that users can't see the actual data, only a masked version.
Access Controls: Implement strict user authentication and authorization protocols.
Data Retention Policies: Specify how long data is stored and provide mechanisms for data deletion.
Compliance Audits:
Regular Audits: Conduct periodic checks to ensure adherence to internal and external regulations.
Logging & Monitoring: Keep records of data access, modification, and deletion.
Report Generation: Tools to produce compliance reports for internal reviews or regulatory bodies.
AI Ethics & Fairness:
Bias Detection: Tools and methodologies to detect and correct biases in AI models.
Explainability: Ensure AI decisions can be understood and justified to stakeholders.
Transparency: Openness about how AI systems work, the kind of data they use, and their decision-making processes.
User Consent & Control:
Opt-in/Opt-out Mechanisms: Allow users to give or withdraw their consent for data collection or specific features.
Data Portability: Allow users to download and transfer their data.
Infrastructure Security:
Firewalls & Intrusion Detection: Protect against unauthorized access and potential breaches.
Patch Management: Regularly update software to fix vulnerabilities.
Secure Deployment: Use safe and recognized methods for deploying applications.
Threat Intelligence & Response:
Real-time Monitoring: Constantly monitor for potential threats or irregular activities.
Incident Response Plans: Establish protocols to respond swiftly to any security breaches or incidents.
Third-party Vendor Management:
Vendor Audits: Ensure third-party vendors follow required security and compliance protocols.
Secure Data Sharing: Use secure methods when sharing data with vendors or other external entities.
Training & Awareness:
Employee Training: Educate employees about security best practices and potential threats.
User Education: Provide resources or tutorials to users about how to use the platform securely and responsibly.
Feedback & Reporting Systems:
User Feedback: Allow users to report potential security issues or areas of concern.
Whistleblower Protections: Provide channels for internal stakeholders to raise concerns without fear of retribution.
Continuous Improvement & Evolution:
Stay Updated: Regularly review and update trust and safety protocols based on technological advancements and changing regulatory landscapes.
Research & Development: Invest in R&D to improve trust and safety features continuously.
Incorporating these layers into enterprise applications and AI platforms helps businesses:
Meet Compliance & Governance Goals: Adhere to industry standards and regulations.
Increase User and Customer Trust: Make users feel safe using the platform.
Mitigate Regulatory Risks: Reduce the likelihood of regulatory penalties or legal repercussions.
By proactively addressing these areas, businesses can ensure that their applications and AI platforms remain trustworthy, safe, and compliant, thereby strengthening their reputation and customer loyalty.