Harnessing Generative AI use cases for business

Content creation is vital to reach customers and establish brand authority. Yet, it can be a resource-intensive task, especially if you have multiple channels that must be regularly updated.

Gen AI can streamline content creation and deliver a variety of copy by:

• Generating ideas and outlines;
• Personalizing content (i.e. product recommendations, dynamic web content, marketing messages);
• Enhancing existing content (i.e. new topics, examples, revisions, etc.);
• Producing relevant visuals (i.e. image, video, music, etc.);
• Copywriting.

Most of all, gen AI can automate the creation of copy. In essence, AI trains on your past texts and then uses this data to produce similar copy. You can power up content automation for any area, including web copy, blog, email, social media and more. It’s essential to note that in these cases, a human editor is still required to check and revise texts for the best outputs.

One of the strongest generative AI use cases is for customer support. According to industry experts, automating service tasks can save businesses about 30-45% in costs. Top generative AI business applications include:

• Powering customer self-service via chatbots and automated responses;
• Unifying customer data for agents to resolve issues more quickly and accurately;
• Personalizing service through AI-driven data insights about what a specific customer may be interested in (deals, product recs, etc.);
• Producing dynamic scripts to coach agents and provide more satisfactory customer service.

In turn, this level of automation can boost sales and enhance metrics, such as issue resolution time, agent attrition and requests to speak to a manager.

Supply chain is another high-impact area. Experts predict 43% of supply chain activities could be enhanced by generative AI. Major use cases for logistics involve:

• Demand forecasting
• Production planning
• Inventory optimization
• Predictive maintenance
• Supplier relationship management
• Smart contracts
• Logistics network optimization
• Dynamic route optimization
• Sustainable sourcing
• Fraud detection
• Risk management

In particular, the ability to forecast needs –whether for inventory stock, manufacturing or maintenance– is a huge business advantage.

Gen AI can lead to more effective and refined data analytics. It can be used for a variety of time-consuming tasks, especially when you have large datasets. For instance, it can be implemented for:

• Automating insights and reports;
• Cleaning and refining data quality (identifying missing data, outliers, etc.);
• Analyzing customer behavioral patterns;
• Generating data visualization.

Like analytics, data management can be streamlined through gen AI. Management tasks are essential to AI models, as well as business compliance. In fact, 70% of leading businesses mentioned difficulty in integrating data into their AI models.

The main applications for generative AI in management are:

• Anonymizing data
• Creating metadata labels
• Assigning lineage info
• Handling data clean-up (such as duplicate records, missing values, standardizing formats, etc.)
• Flagging data anomalies
• Staying compliant with regulations
• Creating data knowledge bases
• Setting up dynamic data access controls

Gen AI is also a powerful option for speeding up company recruitment. The average time-to-hire globally is 43 days, but you can enhance your recruitment processes through AI. For example, you can discover efficiencies in:

• Identifying candidates from different sources
• Screening resumes
• Writing job descriptions
• Streamlining outreach messaging to candidates
• Automating interview scheduling
• Preparing for in-depth interviews
• Conducting A/B testing
• Creating meeting and candidate summaries

By automating these key areas, you’ll be able to focus on core skills, such as managing relationships.