A Guide to Artificial Intelligence in the Enterprise

The explosion of artificial intelligence has been extraordinary. Over the past few years, new tools such as ChatGPT, DALL-E 2, and Gemini opened the floodgates of generative AI. They make tasks like drafting an email or creating an image extremely simple. However, AI is much more than a writing or image-generation shortcut. The real benefactors of AI are enterprises. Since AI can analyze vast amounts of data and draw conclusions, it may change how we work across all areas of business.

This post explores AI enterprise use cases, including how to deploy AI in the enterprise and potential challenges you may face.

First, what is enterprise AI?

Artificial intelligence refers to computer systems that are capable of performing work that’s usually handled by humans, such as identifying patterns, solving problems, recognizing speech, or making decisions. AI uses several techniques, including deep learning, machine learning, and natural language processing (NLP), to complete and resolve tasks.¹

Organizations that incorporate AI into their operations use various forms of publicly available or in-house AI enterprise software. The tools assist with specific tasks, such as automating repetitive tasks, analyzing financial data, making product recommendations or performing basic computer coding. Implementing enterprise AI tools may help organizations save time and money and reduce errors.¹

How will artificial intelligence for enterprise applications change how we work? Let’s look at a few use cases.

Customer service is one area where AI can make big waves. By implementing AI-powered chatbots, organizations can improve their customer engagement. Rather than contacting a human customer service agent, clients can interact with chatbots 24/7, without waiting to speak to an associate. The chatbots can resolve simple customer inquiries, and if someone does need additional assistance, it can route their question to the appropriate associate.²

There are numerous ways to use artificial intelligence for enterprise applications in marketing. You can use it to design marketing emails and social media posts, create subject lines, and write website copy. It’s also helpful for segmenting your audience and optimizing your blog posts to boost your rankings on the major search engines.³

Enterprise AI platforms can help with supply chain management through inventory planning, production and logistics. Such platforms can analyze massive amounts of data, helping organizations predict trends and optimize their supply chains to meet the anticipated demand. That means less unused inventory taking up warehouse space and reduced spending on manufacturing excess products.⁴

AI can handle many financial planning and analysis tasks, including ratio and variance analysis and basic reporting. Its capabilities allow it to review vast amounts of data and identify anomalies and patterns. It’s also useful for forecasting revenue, demand, expenses, and other critical financial details.⁵

AI can help with many HR tasks, such as writing job descriptions, simplifying the onboarding process, and assisting in workforce planning.⁶

AI for enterprises can assist with software quality assurance (QA) testing. It may also aid in identifying potential cybersecurity threats and writing code for software and websites.^7,8

Using enterprise AI, a company can gather market trend data to explore opportunities for new products. It can also assist in refining product designs and testing how a product works under different scenarios.⁹

Clearly, there are many use cases for AI in enterprises. Business managers can harness the technology by understanding its capabilities and identifying areas where it may be beneficial from a cost and productivity perspective.

Managers who are unfamiliar with AI may be uncomfortable incorporating it into their daily tasks. However, most AI tools don’t have quite the learning curve that some earlier technologies presented. The best way to test it is to experiment with the available tools to see how they fit into your workflow. A more advanced implementation of AI across entire departments will require additional in-depth testing and perhaps in-house development.¹⁰

In the past, significant swathes of the workforce didn’t need to understand advanced IT concepts like coding or data analysis. That will likely change as more organizations deploy AI for enterprise applications.

Some AI platforms may require knowledge of business intelligence tools, like Tableau, or basic programming knowledge using a language like Python. Businesses can prepare their teams for the future by upskilling and reskilling employees on the latest data analysis best practices. They can also encourage their employees to learn how to use efficient prompts with generative AI.¹¹

Integrating AI presents several challenges. One is a skills gap. As a new technology, many workers are unfamiliar with AI’s capabilities and how it can improve the business’s processes. Companies must encourage their employees to boost their AI skills through training and development. Another issue is identifying where AI can add the most business value. Focusing on specific problems can help a company avoid unnecessary spending on AI tools that don’t yield a positive return on investment.¹²

AI can bring significant benefits, including improved efficiency, customer satisfaction and cost savings. The following case studies illustrate that successful AI deployment in enterprises requires careful planning, effective data management and addressing the specific challenges of each use case.

Siemens implemented AI-driven predictive maintenance in their manufacturing processes to reduce downtime and increase efficiency. By using sensors and advanced analytics, the AI system can predict equipment failures before they happen.¹³

Deployment Strategy:

• Data Collection: Sensors were installed on machinery to collect operational data in real time
• AI Model Development: Siemens developed machine learning models that analyze historical and real-time data to predict failures
• Integration: The AI solution was integrated with existing maintenance management systems to automate scheduling and alerts for upkeep activities

Challenges:

• Data Quality: Ensuring the data collected from sensors was accurate and relevant
• Change Management: Training employees to trust and use the new AI-driven maintenance schedules
• Scalability: Ensuring the AI models could be scaled across different plants and machines

Outcomes:¹⁴

• Reduced Downtime: Machine downtime was reduced by up to 50%, resulting in significant cost savings
• Increased Lifespan: The lifespan of machinery increased due to timely maintenance
• Enhanced Efficiency: The overall operational efficiency saw a noticeable uptick

H&M deployed AI to enhance its customer service by implementing chatbots and virtual assistants that handle customer inquiries across multiple channels, including their website and social media.¹⁵

Deployment Strategy:

• AI Training: Chatbots were trained using historical customer service data to understand common queries and appropriate responses
• Pilot Testing: The AI solutions were first tested in low-risk scenarios to gauge performance
• Full Implementation: Following the successful pilot, the chatbots were rolled out across multiple customer touchpoints

Challenges:

• Language and Context: Ensuring the AI could understand and respond accurately in various languages and contexts
• Customer Acceptance: Managing customer expectations and promoting the use of AI chatbots
• Continual Learning: Incorporating a feedback loop for continuous improvement of the AI responses

Outcomes:

• Improved Response Time: Customer inquiries were addressed faster, cutting wait times by 70% and enhancing customer satisfaction¹⁵
• Cost Savings: Reduced the need for human agents, enabling the reallocation of resources to more complex tasks
• Scalable Support: The AI system can handle an exponentially larger number of inquiries during busy periods compared to human staff

Capital One used AI to provide personalized marketing and customer engagement strategies. They could deliver highly targeted marketing messages and offers by analyzing customer transaction data.¹⁶

Deployment Strategy:

• Data Analysis: Leveraged big data and machine learning to analyze customer behavior and transaction histories
• Segmentation: Customers were segmented based on patterns identified by the AI models
• Personalized Outreach: Personalized marketing campaigns were developed for each segment

Challenges:

• Privacy Concerns: Ensuring customer data is handled responsibly and complies with regulations
• Algorithm Bias: Avoiding biases in the AI models to ensure fair and equitable marketing practices
• Integration: Seamlessly integrating AI insights with existing CRM systems

Outcomes:

• Increased Engagement: Higher engagement rates for marketing campaigns due to personalization
• Enhanced Customer Experience: Customers received offers and communications that were relevant to their needs
• Revenue Growth: Improved campaign effectiveness translated to a measurable increase in revenue

As companies implement AI enterprise tools, monitoring their effectiveness is critical. Start by defining several key performance indicators (KPIs) for each task involving AI. The metrics will vary by use case but may include evaluation for accuracy, productivity improvements, and cost savings. You can also request feedback from employees to learn their thoughts about the tools. Such input can help you refine your existing business processes and find new ways to implement the technology.¹⁷

As businesses deploy new AI technologies, it’s more important than ever for managers to understand how AI can add value. Obtaining an Online MBA from William & Mary can help you acquire the skills to evaluate these enterprise AI opportunities. Learn on your own schedule from anywhere you have an internet connection. Our expert faculty will help you grow into a skilled business leader while you develop connections that will last your entire career.

To learn more, schedule a call with an admissions outreach advisor.