Businesses can integrate generative AI tools into workflows safely and responsibly. By doing so, enterprises can reap the benefits of faster product development and enhanced customer experiences.
For instance, IT and software teams can use generative AI to produce instantly correct code. This will save them time and resources.
Increased Efficiency
One of the most valuable generative AI business use cases is its ability to speed up tasks and allow employees to focus on higher-impact work. For example, generative AI can automate and streamline repetitive manual data entry and information summarization tasks, helping organizations save time and money.
Generative AI can also speed up complex processes requiring high expertise. For instance, in the automotive and aerospace industries, generative AI can scrutinize copious amounts of data to produce optimized designs grounded on specific parameters, reducing product development duration and cost.
However, this requires significant capital investment and advanced technical expertise. It can also require large-scale computing infrastructure to train models. Additionally, generative AI models are vulnerable to hallucinations and can create biased or inappropriate content. Therefore, implementing appropriate controls and oversight is essential to ensure responsible use. For instance, if you are using generative AI to create customer-facing content, it is necessary to be transparent with your customers and employees about the fact that they are interacting with a machine.
Enhances Customer Experience
Enhanced customer experience is a core value in many businesses, and generative AI tools can help deliver. These capabilities can include automating repetitive tasks so employees can focus on higher-impact activities, reduce response times, and create personalized interactions based on previous customer data and interaction history.
For example, a foundation model can enable companies to automatically generate responses to typical customer service questions to provide a fast, high-quality answer that adheres to company policies and brand voice. This can dramatically reduce employees’ time to address a customer request and ultimately improve the overall customer experience.
Additionally, generative AI for businesses can help identify potential fraudulent activity in areas such as email phishing, which can help protect consumers and targeted brands. However, responsible use of these technologies is critical to ensure the technology is not misused in ways that can negatively impact consumers or companies. These safeguards should include transparency and governance and the proper oversight of a company’s generative AI tools to ensure they are correctly used.
Increased Productivity
The technical automation of work activities enabled by generative AI models could increase productivity growth meaningfully. However, the technology is unlikely to shift more physical or cognitive work activities like previous technologies did.
Generative AI tools such as ChatGPT can produce a variety of credible writing in seconds and quickly respond to criticism to make it more fit for purpose, streamlining the workflows of organizations that need to create marketing copy, IT documents, or hardware design. These tools are a powerful supplement to a human writer or content creator, and they can also help with other business needs, including creating higher-resolution images for medical purposes.
Generative AI applications can tune existing foundational models—machine learning systems that analyze and learn from data—to improve their capabilities. Tuning requires adding a layer of proprietary data to alter the model’s behavior significantly. Still, it is one of the most flexible and cost-effective ways to customize an AI platform for specific business use cases.
Enhances Business Agility
The flexibility offered by generative AI helps businesses accelerate application modernization and create opportunities to achieve business impact. Its ability to quickly ingest mountains of data, synthesize insights, and generate options for knowledge work enables it to empower employees with new capabilities, allowing them to focus on higher-impact projects.
For example, suppose your e-commerce brand finds that its customers demand a particular product from social media. In that case, it can use generative AI to create the appropriate content for an ad campaign or website landing page. This reduces customer service representatives’ time answering queries and frees them up to work on more high-impact projects.
While IT and analytics teams need to explore generative AI, it’s equally important for business executives to be intrigued, ambitious, and vocal about what the technology can accomplish. Those who don’t will be left behind by organizations that can identify what adjustments need to be made, define those accurately, and implement them quickly. This level of agility will allow a company to deliver the exceptional performance it needs to succeed.
Increased Innovation
As generative AI evolves, the technology can automate more work activities supporting business operations. This allows organizations to free up resources, reduce labor costs, and focus on more strategic goals.
For example, generative AI can create new outputs across various modalities to help with digital marketing campaigns and commerce strategies or personalize customer information. It also accelerates the design process by creating unique designs that meet certain specifications. Generative AI can produce design options in manufacturing that improve performance, efficiency, and cost.
Generative AI’s natural-language capabilities also increase its potential to automate work activities linked to knowledge and expertise. But this capability carries risks that can hurt people’s feelings, create misunderstandings, obscure truth, and incite violence and war. As such, generative AI requires careful and responsible use to avoid hallucinations that can harm your employees, customers, or citizens. Some recommend that your organization gradually enable generative AI and establish processes and guardrails to track biases, ambiguities, and other trustworthiness issues.
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