65% of organizations reported that they already implemented GenAI across their business units, according to the latest McKinsey’s report. The reason for this popularity is simple: generative AI can easily imitate human cognitive abilities and create different types of content in less than a minute.
Generative AI capabilities introduce plenty of ways for enterprises to boost their team efficiency and optimize resources. Continue reading to discover how top industry leaders use generative AI tools in their business operations.
What you need to know before implementing GenAI
AI tools aim to streamline and accelerate business processes, and generative AI is no different. The ability of generative AI to process natural language allows businesses to automate up 70% of their daily workload.
However, regardless of all the hype around GenAI, it is not some magical, standalone solution. Instead, it is a powerful tool complementing a more extensive software system. According to McKinsey, generative AI combined with additional technologies can boost productivity by 0.5% to 3.4% annually from 2023 to 2040.
While generative AI for enterprises holds a huge number of benefits, business owners need to understand that there are several crucial steps they need to take before implementing generative AI:
#1. Choose the business area where GenAIs can have the most impact. While adding GenAI tools wherever possible may be tempting, such a disorganized approach can lead to wasted resources. It is better to identify the most troubling and ineffective areas within your business first and then enhance them with GenAI’s capabilities.
#2. Prepare your team members for the transition. Ensure your employees understand that generative AI is not their replacement but a tool that can relieve the burden of repetitive and time-consuming tasks. Once you have a clear GenAI implementation plan, train your team members to use the new tool effectively.
#3. Focus on the quality of your data. 72% of industry leaders state that data management is the biggest barrier to leveraging AI tools. So, if your data is not prepared correctly for generative AI, your business is not ready either. That’s why we recommend investing in a data science team that will take care of your data. They will clean and consolidate your data so that GenAI solutions can interpret it effectively. Focusing on the data quality will minimize the risks of inaccurate AI results, reduce bias, and ensure you achieve the best outcome.
6 real-life GenAI use cases optimizing enterprise costs and workflow
Generative AI business use cases present various applications, from simple text generation to more complex tasks like streamlining product development. Below, we outlined how industry leaders use generative AI to optimize their costs and workflows to achieve greater efficiency:
Automated report generation
Businesses generate a vast amount of data on a daily basis, which should be structured and processed properly to generate a report. Traditionally, this task falls on the shoulders of employees who manually collect data from different sources and combine it into one coherent file. Sometimes, this task can take from hours to several days.
Leveraging generative AI with its natural language processing (NLP), companies can save employees' valuable time and translate the collected metrics into a clear analysis with minimum errors and maximum valuable insights for companies.
For instance, Accenture, a global professional services company, revealed that it is leveraging Llama 3.1 capabilities to create custom LLM for ESG reporting. The company expects that this solution will enhance the quality of reporting by 20-30% and boost productivity by 70% compared to its current reporting process. With LLM's multilingual functionality, Accenture plans to expand its solution further for its clients, localizing it to different regions.
Smart knowledge base management
The knowledge base is a sacred place for the company, where all essential documentation and valuable data are stored. Despite all the efforts to keep the information neatly structured, searching through the wealth of documentation is highly time-consuming.
A large biopharmaceutical company, Pfizer, shared that the development of a single drug can generate approximately 20,000 documents. Their scientists used to look through that amount of documents manually to find the data they needed, which slowed down the process of drug development. However, the company found a solution — they now use GenAI capabilities, making the process quicker and more efficient.
Pfizer's scientists can now search information using chatbots or voice commands, asking questions in natural language and getting accurate responses. This solution is expected to save scientists up to 16,000 hours in data searching annually while cutting infrastructure costs by 55%.
Intelligent document processing
Employees spend hours to process the documents manually, extracting needed information. As the business grows, the volume of documents to be processed increases as well. At the same time, the number of employees usually remains the same, so it is hard for them to keep up with the pace and ensure the error-free result.
For example, Anthem, one of the largest US-based health insurance providers, noticed that extracting sensitive information from one medical claim takes around 20 minutes. The volume of claims employees must process daily makes the process prone to errors. This manual workload also drives costs up — some health insurance providers can spend millions of dollars just to manage the claims processing.
One of the most common generative AI use cases for enterprises is intelligent document processing (IDP). Intelligent document processing makes information extraction, summarizing input data, and highlighting important sections easier. With an AI-driven IDP solution, Anthem can now effortlessly extract printed and handwritten text from scanned documents. As a result, the company managed to streamline 80% of its claim processing workflow, significantly reducing manual workload.
Enhanced quality control
As customers' expectations are always high, businesses must be meticulous about their products' quality. Most traditional quality control methods prove to be effective. However, as production volumes increase, it becomes harder to ensure compliance with quality standards.
One of the largest online retailers, Amazon, uses generative AI technologies to process multimodal data, like images from Amazon fulfillment centers and textual customer feedback, to enhance its quality control. Using artificial intelligence tools, the company can link images and text feedback to track the batch with defective products and remove them from the production line.
The Amazon team also uses generative AI models to create reports describing product damage in plain language. With these reports, the company can work more proactively with manufacturers to enhance the quality of the products. By reducing the number of faulty items, Amazon minimizes the costs associated with quality failures and improves manufacturing efficiency.
Speech transcription and sentiment analysis
AAMC, a motor vehicle insurance provider in Australia, faced the dilemma of organizing its call centers while providing remote work setup for the employees. The company realized the need for the proper infrastructure to monitor the quality of customer service in remote call centers.
AAMC found a way to implement a solution with generative speech transcription using artificial intelligence. GenAI can understand spoken natural language and transcribe it into written text in real-time, distinguishing the voice even against the background of noise. Then, generative AI performs sentiment analysis to understand how the communication with the customer went and whether there were any problematic moments.
As a result, company managers can look through the GenAI analysis without the need to listen to the whole recording and assess the quality of employees’ performance. At the same time, company team members can work fully remotely, keeping the work-life balance. This way, the company saved 54% of infrastructure costs and reduced employee turnover to 2%.
Accelerated design and prototyping
The product development process is complex, and with countless iterations, it takes a lot of time and money to roll the product into the market. Although generative AI for enterprises is still in the stage of active evolution, it can offer solutions to speed up development, especially in the product design stage. For example, physical prototyping demands a lot of resources, and any changes to design can further delay the launch date, increasing the company's spending.
Text-to-image capabilities of generative AI provide a quick and cost-effective solution for generating high-quality designs. The GenAI processes the relationships between words and generates the image based on the description. It also allows designers to experiment with different design options, making quick adjustments to the prototype with only one textual prompt.
That’s exactly what an iconic sports car manufacturer, Ferrari, did. They implemented generative AI tools to test more design variants of their Formula 1 cars and thus reduced the time to market for their vehicles. With cloud-based virtual simulations, they run thousands of design tests without investing as much money as they would with physical prototyping. With the GenAI solution, the company can run vehicle simulations up to 60% faster than before, accelerating product development.
Final thoughts
From automated report generation to enhanced quality control, generative AI for the enterprise offers plenty of ways to streamline repetitive tasks and cut operational costs. But, as with any new tool, there are a lot of details to consider before implementation. Companies need to strategically approach their data architecture and educate their team to leverage generative AI effectively. With the trusted data science partner, you can unlock the full power of GenAI to streamline your business operations and reduce costs.
