Generative AI use cases for the enterprise

Remember how cool it felt if you first held a smartphone in your hand? The compact design and touch-based interactivity gave the look of a leap into the long run. Before long, smartphones became a lifestyle for organizations worldwide due to all they provide for business productivity and communication. Generative AI (artificial intelligence) guarantees an identical leap in productivity and the emergence of recent modes of working and creating.

Tools reminiscent of Midjourney and ChatGPT are gaining attention for his or her capabilities in generating realistic images, video and complicated, human-like text, extending the boundaries of AI’s creative potential. Generative AI represents a major advancement in deep learning and AI development, with some suggesting it’s a move towards developing “strong AI.” This evolution demonstrates that computers have moved beyond mere number-crunching devices. They are actually able to natural language processing (NLP), grasping context and exhibiting elements of creativity.

For example, organizations can use generative AI to: 

• Quickly turn mountains of unstructured text into specific and usable document summaries, paving the way in which for more informed decision-making.
• Automate tedious, repetitive tasks.
• Streamline workflows with personalized content creation, tailored product descriptions and market-ready copy.
• Design content, ad campaigns and progressive products that construct higher customer experiences.

Demystifying generative AI

At the guts of Generative AI lie massive databases of texts, images, code and other data types. This data is fed into generational models, and there are a number of to pick from, each developed to excel at a selected task. Generative adversarial networks (GANs) or variational autoencoders (VAEs) are used for images, videos, 3D models and music. Autoregressive models or large language models (LLMs) are used for text and language.

Like diligent students, these generative models take in information and discover patterns, structures and relationships between data points, which is how they learn the grammar of poetry, artistic brushstrokes and musical melodies.

Generative AI uses advanced machine learning algorithms and techniques to investigate patterns and construct statistical models. Imagine each data point as a glowing orb placed on an enormous, multi-dimensional landscape. The model meticulously maps these orbs, calculating the relative heights, valleys, smooth slopes and jagged cliffs to create a probability map, a guidebook for predicting where the following orb (i.e., the generated content) should most probably land.

Now, when the user provides a prompt—a word, a sketch, a musical snippet or a line of code—the prompt acts like a beacon, drawing the model towards a selected region on that probability map; the model then navigates this landscape, probabilistically selecting the following element, the following and the following, guided by the patterns it learned and the nudge of the users’ prompt.

Each output is exclusive yet statistically tethered to the info the model learned from. It’s not only copying and pasting; it’s creatively constructing upon a foundation of information fueled by probability and the guiding prompt. While advanced models can handle diverse data types, some excel at specific tasks, like text generation, information summary or image creation.

The quality of outputs depends heavily on training data, adjusting the model’s parameters and prompt engineering, so responsible data sourcing and bias mitigation are crucial. Imagine training a generative AI model on a dataset of only romance novels. The result might be unusable if a user prompts the model to write down a factual news article.

Harnessing the worth of generative AI

Generative AI is a potent tool, but how do organizations harness this power? There are two paths most businesses are traveling to appreciate the worth of generative AI:

Ready-to-launch tools:

The “AI for everybody” option: Platforms like ChatGPT and Synthesia.io come pre-trained on vast datasets, allowing users to tap into their generative capabilities without constructing and training models from scratch. Organizations can fine-tune these models with specific data, nudging them towards outputs tailored to particular business needs. User-friendly interfaces and integration tools make them accessible even for non-technical folks.

These public options offer limited control, less customization of model behavior and outputs and the potential for bias inherited from the pre-trained models.

Custom-trained models:

Most organizations can’t produce or support AI and not using a strong partnership. Innovators who need a custom AI can pick a “foundation model” like OpenAI’s GPT-3 or BERT and feed it their data. This personalized training sculpts the model into bespoke generative AI perfectly aligned with business goals. The process demands high-level skills and resources, but the outcomes usually tend to be compliant, custom-tailored and business-specific.

The most suitable choice for an enterprise organization is determined by its specific needs, resources and technical capabilities. If speed, affordability and ease of use are priorities, ready-to-launch tools is likely to be the perfect alternative. Custom-trained models might improve if customization, control and bias mitigation are critical.

Adopt a use-case-driven approach to generative AI

The key to success lies in adopting a use-case-driven approach, specializing in your organization’s problems and the way generative AI can solve them.

Key considerations:

• Tech stack: Ensure your existing technology infrastructure can handle the demands of AI models and data processing.
• Model matchmaking: Choose an acceptable generative AI model in your specific needs.
• Teamwork: Assemble a team with expertise in AI, data science and your industry. This interdisciplinary team will help to make sure your generative AI is a hit.
• Data: High-quality, relevant data is the fuel that powers generative AI success. Invest in data hygiene and collection strategies to maintain your engine running easily. Garbage in, garbage out.

Generative AI use cases

Excitement about this recent technology has spread quickly throughout various industries and departments. Many marketing and sales leaders acted rapidly and are already infusing generative AI into their workflows. The speed and scale of generative AI’s ability to create recent content and useful assets is difficult to pass up for any discipline that relies on producing high volumes of written or designed content. Healthcare, insurance and education are more hesitant attributable to the legal and compliance efforts to which they need to adhere—and the shortage of insight, transparency and regulation in generative AI.

• Code generation: Software developers and programmers use generative AI to write down code. Experienced developers are leaning on generative AI to advance complex coding tasks more efficiently. Generative AI is getting used to mechanically update and maintain code across different platforms. It also plays a major role in identifying and fixing bugs within the code and to automate the testing of code; helping make sure the code works as intended and meets quality standards without requiring extensive manual testing. Generative AI proves highly useful in rapidly creating various varieties of documentation required by coders. This includes technical documentation, user manuals and other relevant materials that accompany software development.

• Product development: Generative AI is increasingly utilized by product designers for optimizing design concepts on a big scale. This technology enables rapid evaluation and automatic adjustments, streamlining the design process significantly. It assists in structural optimization which ensures that products are strong, durable and use minimal material, resulting in considerable cost reductions. To have the best impact, generative design should be integrated throughout the product development cycle, from the initial concept to manufacturing and procurement. Additionally, product managers are employing generative AI to synthesize user feedback, allowing for product improvements which can be directly influenced by user needs and preferences.

• Sales and marketing: Generative AI is assisting marketing campaigns by enabling hyper-personalized communication with each potential and existing customers across a wide range of channels, including email, social media and SMS. This technology not only streamlines campaign execution but in addition enhances the power to scale up content creation without sacrificing quality. In the realm of sales, generative AI boosts team performance by providing deep analytics and insights into customer behavior. Marketing departments are harnessing this technology to sift through data, understand consumer behavior patterns and craft content that really connects with their audience, which frequently involves suggesting news stories or best practices that align with audience interests. Generative AI plays an important role in dynamically targeting and segmenting audiences and identifying high-quality leads, significantly improving the effectiveness of selling strategies and outreach efforts. In addition, Well-developed prompts and inputs direct generative models to output creative content for emails, blogs, social media posts and web sites. Existing content will be reimagined and edited using AI tools. Organizations can even create custom generative AI language generators trained on their brand’s tone and voice to match previous brand content more accurately. 

• Project management and operations: Generative AI tools can support project managers with automation inside their platforms. Benefits include automatic task and subtask generation, leveraging historical project data to forecast timelines and requirements, note taking and risk prediction. Generative AI allows project managers to go looking through and create easy summaries of essential business documents. This use case saves time and enables users to concentrate on higher-level strategy slightly than each day business management.

• Graphic design and video: With its ability to create realistic images and streamline animation, generative AI might be the go-to tool for creating videos while not having actors, video equipment or editing expertise. AI video generators can immediately create videos in whatever languages they should serve each region. It might be some time before generative AI-created videos can effectively replace human actors and directors, but organizations are already experimenting with the technology. Users also use image generators to edit personal photos to create professional-looking business headshots for business use on Slack or LinkedIn.

• Business and worker management: In customer support, generative AI will be used throughout the decision center. It could make essential documentation easy to access and search, putting case-resolving information on the fingertips of support agents. Generative AI-powered tools can significantly improve employee-manager interactions. They can structure performance reviews, offering managers and employees a more transparent framework for feedback and growth. Additionally, generative conversational AI portals can provide employees with feedback and discover areas for improvement without involving management.

• Customer support and customer support: While chatbots are still widely used, organizations have began merging technologies to alter how chatbots work. Generative AI advancements aid the creation of more progressive chatbots that may engage in naturally flowing conversations, enabling them to grasp context and nuance much like how a human representative would. Generative AI-powered chatbots can access and process vast amounts of knowledge to reply customer and agent queries accurately; unlike human agents, AI chatbots can handle customer inquiries across the clock to offer a seamless user experience, night or day. The shift from traditional chatbots to generative AI-powered companions remains to be in its early stages, however the potential is undeniable. As technology evolves, we will expect much more sophisticated and interesting AI interactions, blurring the lines between virtual and human assistance.

• Fraud detection and risk management: Generative AI can quickly scan and summarize large amounts of information to discover patterns or anomalies. Underwriters and claims adjusters can use generative AI tools to scour policies and claims to optimize client outcomes. Generative AI can generate custom reports and summaries tailored to specific needs and supply relevant information on to underwriters, adjusters and risk managers, saving time and simplifying decision-making. However, human judgment and oversight are still essential for making final decisions and ensuring fair outcomes.

• Generating synthetic data for training and testing: Enterprises can leverage AI to generate synthetic data for training AI models, testing recent products and simulating real-world scenarios. This can reduce reliance on actual data, which could also be sensitive and must remain private or come from an expensive external data source. No longer certain by the constraints of gathering and preparing real-world data, development cycles will be accelerated. With available synthetic data sets, corporations can rapidly iterate on AI models, test recent features and convey solutions to market faster.

Here are key takeaways for the moral implementation of your organization’s generative AI use cases:

• Protect sensitive data: Use only depersonalized and nonsensitive data to avoid exposing vulnerable information and comply with regulations.
• Stay informed: Follow industry news to discover reliable tools and avoid unethical AI practices.
• Develop an AI policy: Create guidelines for internal AI use and investments in third-party tools, drawing from available templates.
• Invest in upskilling: Investment in reskilling and upskilling programs is crucial, empowering staff to develop skills immune to automation.

Best practices are evolving rapidly. While the potential of generative AI is exciting for a lot of organizations, navigating this landscape requires a balancing act between progress and prudence.

Future of generative AI

According to McKinsey,¹ generative AI is not going to likely outperform humans anytime this decade. However, we may even see a major leap in generative AI capabilities by 2040. McKinsey expects AI to succeed in a level where it will possibly compete with the highest 25% of human performers across a wide selection of tasks. Meaning, AI will write high-quality creative content, solve complex scientific problems or make insightful business decisions on par with expert professionals. Jobs which have historically been automation-proof might be further affected by generative AI. Professionals in education, law, technology and the humanities will likely see generative AI touch their career sooner. 

Panelists at an MIT symposium² on AI tools explored various future research avenues in generative AI. One significant area of interest is the combination of perceptual systems into AI. This approach would enable AI to mimic human senses like touch and smell, moving beyond the traditional concentrate on language and imagery. The potential for generative AI models to surpass human capabilities was also discussed, particularly within the context of emotional recognition. These advanced models might use electromagnetic signals to interpret changes in an individual’s respiration and heart rate, offering a deeper understanding of their emotional state.

Experts anticipate that bias will remain a persistent aspect of most generative AI models. This challenge is anticipated to offer rise to recent marketplaces centered around ethical data sets. Moreover, a dynamic scenario will likely unfold, characterised by ongoing competition between corporations and content creators using generative tools.

As these tools grow to be more widespread within the workplace, they may inevitably bring changes to job roles and necessitate recent skills. Alongside these developments invariably comes increased misuse of generative capabilities. As users gain the facility to create diverse types of content, including images, audio, text and video, the likelihood of malicious misuse is anticipated to rise. This scenario underscores the importance of developing robust mechanisms to mitigate such risks and ensuring the responsible use of generative AI technologies.

Generative AI will proceed transforming enterprise operations across various industries, very like the smartphone transformed business communication and productivity. From automating mundane tasks to fostering creativity in content creation and beyond, the potential of generative AI is vast and varied.

However, navigating ethical considerations, maximizing data security and adapting to evolving best practices are paramount. For enterprises able to explore the total spectrum of possibilities that generative AI offers, guidance and insights are only a click away. Learn more about harnessing the facility of generative AI for your online business by exploring IBM watsonx, the AI and data platform built for business.

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Footnotes:

1https://www.mckinsey.com/featured-insights/mckinsey-explainers/whats-the-future-of-generative-ai-an-early-view-in-15-charts

2https://news.mit.edu/2023/what-does-future-hold-generative-ai-1129