Generative AI in Business: Essential Use Cases and Best Practices

Generative AI (GenAI) has been advancing at an extraordinary pace in recent years, revolutionizing how businesses operate and innovate.

Since the release of ChatGPT in November 2022, followed swiftly by OpenAI’s GPT-4 just four months later, the capabilities of large language models (LLMs) have soared. Anthropic’s Claude, introduced in March 2023, now processes 100,000 tokens of text per minute, a substantial leap from its initial capacity. Similarly, Google has integrated generative AI into its ecosystem with innovations like PaLM 2 and Gemini chatbot.

In business settings, generative AI adoption has surged, with 35% of companies integrating these technologies, according to IBM’s 2022 AI adoption index. This trend is underscored by IDC’s forecasts, projecting enterprise expenditures on generative AI to skyrocket from $16 billion in 2023 to $143 billion by 2027, with a robust compound annual growth rate of 73.3%.

So, which business applications do companies harness from generative AI benefits exactly?

Read on to discover the top 5 primary use cases of generative AI in business and best practices to guide your company in successfully adopting this emerging technology!

Top 5 Generative AI Use Cases in Business

Generative AI finds application across various industries such as healthcare, finance, advertising, and marketing. Beyond these industry-specific uses, it proves valuable for handling general business functions across all fields.

Below are the 5 major business processes and tasks that generative AI can significantly enhance or automate for organizations, irrespective of industry.

1. Automated and Personalized Customer Support

Generative AI tools stand poised to transform customer operations, elevating both customer satisfaction and agent efficiency through digital self-service and skill augmentation.

Research by the National Bureau of Economic Research in April 2023 revealed a 14% increase in issue resolution per hour and a 9% reduction in issue handling time at a company employing 5,000 customer service agents.

Furthermore, Accenture’s report highlights sectors like banking, insurance, capital markets, energy, and utilities as particularly ripe for realizing the generative AI benefits in customer service, potentially reducing expenses by up to 30%.

A prime example of generative artificial intelligence in action is seen with Expedia Group, where ChatGPT powers an AI assistant in their app.

As a result, users are able to consult an AI-powered personal assistant for travel advice instead of navigating Expedia’s website manually. To be specific, the ChatGPT integration enables the assistant to suggest destinations, hotels, and transportation options based on customer preferences and customer behavior. Users can then save these recommendations within the app and check availability for their chosen dates.

To optimize generative AI for this business use, Expedia has extensively trained OpenAI’s technology to handle an immense range of variables—1.26 quadrillion, to be exact. These include parameters like travel dates, hotel locations, room types, and pricing criteria. The intelligent assistant also utilizes Expedia’s flight data to analyze current prices against historical trends, enabling travelers to make informed decisions about when to book and potentially earn rewards.

2. Effective Content Marketing with Tangible Results

The next significant generative AI business use case lies in content creation for personalized marketing campaigns.

Generative AI tools excel in swiftly generating contextually relevant and coherent content on any subject, a task that typically takes experienced writers 2–6 hours for a 1,000-word blog post. They also help write content for search engine optimization, making it easier to rank on the first page of Google search results.

Already, generative AI produces 25% of all digital content, making it easy for forward-thinking brands to manage social media posts, website blogs, product descriptions, sales emails, presentation materials, etc. In some cases, companies have even opted to reduce costs by replacing in-house writers with AI tools.

Plenty of companies successfully adopt generative AI in content marketing. One such case in point is Tomorrow Sleep, a startup in the mattress industry seeking to boost organic traffic on its website. By employing MarketMuse, a GenAI-powered tool for researching and generating content, they achieved a dramatic increase in organic traffic from 4,000 to 400,000 visitors per month within a year. Apart from increased traffic, this AI integration also elevated the website’s authority, earning it featured snippets in search engine results.

Similarly, JP Morgan Chase in the banking industry partnered with Persado to enhance copywriting efforts. The AI-generated content achieved higher click-through rates compared to human-written content, leading to a 450% increase in ad CTRs for Chase.

Kristin Lemkau, Chief Marketing Officer at JP Morgan Chase, noted:

Persado’s technology is incredibly promising. It rewrote copy and headlines that a marketer, using subjective judgment and their experience, likely wouldn’t have. And they worked.

So now, what’s next for your business?

It’s high time you considered investing in content-related generative AI business applications like Frase.io, Writesonic, Narrato, and Surfer SEO for increased organic traffic and Copy AI, Jasper AI, ClosersCopy, or Shortly AI for high-quality ad copy.

3. Business Process Automation that Brings Value

Business process automation (BPA) has traditionally been dominated by robotic process automation (RPA) and intelligent process automation (IPA) solutions. However, by leveraging its natural language processing capabilities, generative AI brings a broader and more sophisticated approach to BPA.

Given that language-based tasks make up 25% of all work activities, generative AI can streamline a host of business processes and workflows, including:

• Performing managerial tasks, such as prioritizing project management activities, scheduling meetings, and organizing emails.
• Searching for accurate information within IT infrastructures and summarizing content via conversational interfaces.
• Automatically creating standard or custom documents and reports.
• Efficiently entering information into technology systems.

McKinsey estimates that strategic use of generative AI in business can automate up to 70% of repetitive tasks, significantly increasing productivity with an annual improvement rate of 3.3%.

A notable example of successful generative AI-fueled BPA adopters is Leroy Merlin, the third-largest home improvement retailer globally.

Long story short, Leroy Merlin faced significant challenges in speeding up return, refund, and exchange procedures. Manual approvals, fragmented oversight, and data silos led to prolonged approval processes and frequent order cancellations, ultimately decreasing customer satisfaction and causing financial losses.

To address these issues, the company implemented intelligent automation and RPA from Appian to streamline refund payment transactions. However, integrating generative AI is what took their operational efficiency to the next level. The AI-powered solution incorporated intelligent document processing, extracting necessary information for initiating refunds and automatically filling in relevant customer details, thereby accelerating the entire refund process.

The impact was substantial. Previously, the refund and returns process took 15 days, but with the adoption of Appian’s automation capabilities, this timeline was drastically reduced to 1.5–2 days, greatly enhancing customer satisfaction.

4. Code Generation, Documentation, and Quality Assurance

Generative AI is revolutionizing software development by automating demanding tasks such as writing, completing, and verifying sets of code. However, one of its most pivotal applications in this realm is quality assurance, where GenAI models handle bug fixes, generate tests, and produce various types of documentation.

Generative AI tools for coding offer great assistance to non-developers by quickly creating code based on natural language queries. They also supplement the work of experienced developers by accelerating project timelines and reducing errors through features like code autocompletion, error detection and correction, and code refactoring.

Leading AI coding solutions such as GitHub Copilot, Amazon CodeWhisperer, ChatGPT, and Tabnine are gaining traction among developers. And numerous technology companies, including startups like Augmend, have been experiencing remarkable productivity gains through GenAI adoption in coding tasks.

Diamond Bishop, CEO of Augmend, shared:

AI is making it so small startups like ours can accelerate all aspects of the software development lifecycle. We’re a team of five devs, and we estimate productivity impact of almost 2X. Each of us leverage both Copilot and ChatGPT in day-to-day development, debugging, and learning. Knowing the right way to leverage these tools and how best to be an AI-augmented cybernetic developer is a skill in itself, as there are pitfalls (hallucinated APIs/functions) and prompt incantations to learn. But it’s quite valuable in areas you aren’t already an expert, which is many in a jack-of-all-trades situation at a new startup.

Bridget Frey, CTO at Redfin, a real estate company, also highlighted how generative AI systems, including large language models like ChatGPT, have significantly boosted efficiency for their engineers. They aid in tasks like migrating between programming languages, understanding legacy code, and writing functions for converting data formats. “Tasks that previously took engineers 30 minutes can now be completed in just one minute with AI assistance.”

5. Optimized Enterprise Search and Knowledge Base

Last but not least, generative AI solutions are transforming both internal and external search functionalities within businesses.

For employees and internal users, these AI models search through typical sources like company files, applications, messaging tools, and web properties, swiftly locating, identifying, and summarizing enterprise resources related to their tasks or projects.

Similarly, generative AI enhances company websites and other customer-facing platforms, providing visitors with a self-service solution to find answers to their questions. While many companies have invested in chatbot support tools, generative AI-powered search significantly expands the resources available to these chatbots, improving their ability to answer user queries.

A notable case study of this use case is Duolingo’s use of Glean Chat to streamline knowledge access and improve efficiency. Essentially, Glean is a workplace search and generative AI assistant that understands all company content, employees, and activities, helping workers find exactly what they need across all applications.

Before using Glean, Duolingo struggled with sharing information and collaborating across teams. Knowledge was scattered across multiple systems like Google Drive, Slack, Confluence, and Zendesk, so it was hard for employees to find crucial information.

Glean significantly improved Duolingo’s knowledge discovery, but Glean Chat, a conversational assistant grounded in Glean’s trusted knowledge model, is what further enhanced user productivity by providing quick, personalized answers.

Employees used Glean Chat to summarize key documents and roadmaps, finding answers that would otherwise require multiple queries. This shift from asking peers via Slack to consulting Glean streamlined their knowledge-sharing process, reduced redundant efforts, and boosted overall productivity.

These figures speak for themselves:

• 5x Return on investment from the time saved using Glean for knowledge discovery
• 500+ hours collectively saved monthly by employees using Glean’s search
• > $1.1 million in yearly time saved by employees using Glean to find information

What are the key limitations of generative AI in business applications?

Generative AI, despite its remarkable capabilities, is also packed with major limitations. Here are three significant aspects that businesses should be mindful of:

1. Creativity and Innovation Limitations

While generative AI excels at generating innovative content, it’s important to recognize its limitations in terms of creativity. In fact, generative AI can’t fully replace human creativity, as it may lack the emotional depth, intuition, and cultural understanding that human creators possess.

Therefore, businesses should view generative AI as a supplementary tool for idea generation and inspiration rather than a sole content creator. It’d be best if human creators infuse content with emotional and cultural nuances on their own to make it truly exceptional.

2. Challenges in Understanding Complex Contexts

Generative AI struggles to grasp nuanced content, leading to potential misinterpretation and misapplication. Its difficulty with sarcasm, metaphors, and cultural nuances can result in contextually incorrect or inappropriate content generation.

You may not notice, but Microsoft’s AI chatbot, Tay, sparked controversy in 2017 by posting defamatory and offensive tweets. This prompted Microsoft to shut down the service within 16 hours of its launch. Similarly, modern generative AI tools may lack the contextual understanding needed to differentiate appropriate communication from inappropriate content.

To mitigate these limitations, organizations must incorporate human oversight and content review mechanisms, especially in social media moderation and customer support, which requires context understanding.

3. Limited Adaptability and Customization

Generative AI models often have difficulty adapting to specific business needs, especially when it comes to understanding industry-specific jargon.

Since such off-the-shelf models may not align with a company’s unique requirements, they necessitate substantial customization, which can be time-consuming and costly.

To address these challenges, companies should invest in data curation and extensive model fine-tuning. Collaboration with AI experts and focusing on domain-specific training data will greatly enhance the adaptability of generative AI models to different business contexts.

Key Considerations for Adopting Generative AI in Businesses

Implementing generative AI in your business is a significant move that demands thoughtful planning. Here are three crucial aspects to ensure a smooth adoption process.

1. Evaluating Business Fit

First, determine how generative AI suits your company. Consider these points:

• Operational needs: Pinpoint specific areas where generative AI can streamline your company’s operations, boost efficiency, or increase revenue.
• Data analysis readiness: Check the quality and amount of your data. Since generative AI relies on extensive data, having clean and vast datasets is a must.
• Scalability: Ensure that the generative AI solutions you choose can grow with your business needs.
• Skillset alignment: Assess your current team’s skills to decide if you need additional training or new hires to support AI adoption.

2. Developing an Implementation Plan

To successfully integrate generative AI, you need a well-thought-out strategy. Here’s how you can start:

• Pilot programs: Begin with small pilot projects to test how generative AI performs in specific areas of your business.
• Collaborative learning: Promote teamwork across departments to build a collective understanding of generative AI.
• Iterative development: Use an iterative approach to continually improve the AI systems based on feedback and changing business needs.
• Scalable infrastructure: Invest in scalable infrastructure to meet AI’s growing demands as it becomes more embedded in your operations.

3. Ensuring Ethical Use 

Finally, it’s crucial to navigate the ethical and legal landscape of AI use. Below are some strategies:

• Ethics boards: Set up ethics boards or committees to oversee and guide the ethical implications of GenAI applications.
• Transparency practices: Make sure your AI decision-making processes are transparent so that all stakeholders understand how AI is being used.
• Regulatory adherence: Stay updated on legal and regulatory requirements related to AI to ensure compliance with industry standards.
• Continuous monitoring: Establish systems to continuously monitor generative AI business applications, allowing you to address any ethical or compliance issues quickly.

Best Practices for Implementing Generative AI in Business

Integrating generative AI technology into business operations requires careful planning and strategic decision-making across several key practices.

Below are the top 4 best practices for your successful generative AI adoption.

1. Choosing the Right Models

One of the most important decisions you’ll face when applying generative AI in your business is choosing generative AI models. This decision plays a pivotal role since it dictates the type of outputs you’ll receive and how you can utilize them effectively.

For simplicity, here we categorize them into public and custom (or private) models.

• Public generative AI models like ChatGPT are readily available for companies to use out of the box. They offer low entry barriers—you only need an account to start interacting with them. However, these models typically generate generic outputs that may not meet specialized needs. In addition, there are concerns about data privacy and security when using public models.
• In contrast, custom generative AI models are trained using your organization’s data, allowing them to provide more tailored and nuanced responses that are specific to your industry and operational context. This customization enables AI to perform tasks at a more sophisticated level, akin to human expertise in specialized domains. These custom models can either be developed from scratch or built upon existing open-source models.

Each approach above offers distinct advantages, and the choice depends largely on your business requirements, available data, and integration capabilities.

To determine the most suitable generative AI model for your enterprise, you should assess your specific needs and objectives. Consider where AI can deliver the most value, the type of data you possess, and how these factors align with your overall business strategy.

For optimal decision-making, we suggest exploring GenAI consulting services. With dedicated assistance from generative AI experts, your company can develop a strategic plan and select the right technologies, ensuring a higher success rate for your implementations.

2. Getting Your Data Ready for Generative AI

Your data’s readiness determines whether your business stagnates or stays competitive in the evolving landscape of generative AI technology.

As this technology matures rapidly, laying a strong foundation—encompassing data strategy, quality, culture, and governance—becomes increasingly essential to harnessing its transformative potential and driving organizational success.

Thus, assessing the readiness of your organization’s data ecosystem is a critical first step in effectively integrating generative AI.

A few key data criteria to evaluate are:

• Data diversity: It’s important that your data covers a wide range of inputs and perspectives, ensuring Generative AI models are trained to produce balanced and unbiased results.
• Data accessibility: Data should be readily accessible to authorized users and systems, facilitating smooth data flows necessary for Generative AI applications.
• Data quality: The data must be of high quality and free from errors or inconsistencies so as not to compromise the accuracy of generative AI outputs.
• Data structure: Whether structured or unstructured, your data should be well-organized to ensure efficient processing and analysis by generative AI algorithms.

Learn more: Data for Generative AI

3. Understanding the Cost Implications

The financial aspect plays a significant role in developing generative AI applications. Depending on the approach taken, costs can vary widely. However, initial expenses typically cover data acquisition, computing resources, expert personnel, and integration into existing systems.

Regarding specific development rates, McKinsey categorizes GenAI production costs into three main types:

• Takers who use pre-built GenAI applications can expect to spend between $0.5 million and $2 million.
• Shapers who customize an existing large language model for specific uses may invest from $2 million to $10 million.
• Makers, who develop foundational models from scratch (e.g., OpenAI), face costs ranging from $5 million to $200 million.

While the initial investment in GenAI development can be substantial, strategic planning and diligent ROI tracking can help your business yield significant efficiencies, innovations, and competitive advantages.

Particularly, measuring the ROI of GenAI projects plays a big role in ensuring their financial viability and strategic success. Organizations can gauge ROI through various metrics, including:

• Enhanced operational efficiency
• Cost savings through automation
• Revenue growth from new products or services
• Increased sales productivity
• Improvements in customer interactions

4. Having the Right Skills and Resources in Place

Like any new technology, introducing generative AI into your business requires a specialized skill set and robust resources. This transformation demands a multidimensional approach, blending human expertise with technical infrastructure.

On the one hand, this involves forming a collaborative team comprising data scientists, AI specialists, domain experts, software developers, data engineers, and legal professionals tailored to your organization’s needs.

On the other hand, companies need to secure adequate technical resources. These include powerful hardware such as GPUs for training generative AI models and scalable software infrastructure provided by cloud platforms like Amazon Web Services or Google Cloud.

For those overwhelmed by these requirements, partnering with a specialized Generative AI consulting company is a great alternative due to their great expertise in deploying customized solutions and staying current with advancements in the field. This strategic collaboration also enhances project execution and effectively addresses unique business requirements.

Harness Generative AI Power for Your Business with Neurond

Generative AI offers transformative possibilities, from automating customer support to enhancing content marketing and optimizing business processes. As this technology continues to evolve, so do its potential applications. To stay competitive, your organizations must partner with a trusted GenAI development company.

At Neurond, we excel in responsibly developing GenAI solutions across diverse sectors such as healthcare, fintech, eCommerce, education, and more. Whether you’re customizing generative AI systems or crafting a robust implementation strategy, we’re here to assist! With deep expertise in employing generative AI models like generative adversarial networks, transformer-based models, and recurrent neural networks, our developers are committed to delivering cutting-edge solutions tailored to your unique needs.

Contact us now!

FAQ about Generative AI in Business

1. Can small businesses use generative AI effectively?

Absolutely! There are various ways small businesses can effectively take advantage of generative AI – from streamlined marketing material creation, optimized customer service, sales generative AI, and enhanced data analysis to improved new idea generation, risk management, and much more!

2. Which industries can benefit the most from generative AI?

Generative AI can be particularly beneficial in high-tech, banking, pharmaceuticals and medical products, education, telecommunications, healthcare, insurance, media and entertainment, advanced manufacturing, and consumer packaged goods industries.

3. What are the potential risks and challenges of using generative AI in business?

The six critical risks of generative AI for businesses are providing inaccurate output, perpetuating bias, endangering data privacy and security, ethical risks, intellectual property and legal challenges, and indecipherable black boxes.

4. How can businesses measure the success of their generative AI initiatives?

Segmenting KPIs into three core buckets offers a comprehensive view of the value generated by your generative AI investments. Here are the recommended metrics for each category:

• Model quality: Quality index, error rate, latency, accuracy range, safety score, etc.
• System quality: Data relevance, data and AI asset and reusability, throughput, system latency, integration and backward compatibility, etc.
• Business impact: Adoption rate, frequency of use, session length, queries per session, query length, abandonment rate, customer satisfaction, etc.