AI for Business: The Key to Increased Profits and Performance

Introducing AI to your operations is quickly becoming an essential aspect of doing business, regardless of the industry you operate in. When implemented properly, AI-driven functionalities can free up valuable time and resources so that people can focus on more strategic matters. The distinct approach that your business takes to implementing AI is summarized in your AI policy.

To adopt an effective AI policy for your business means creating a roadmap to success in today’s technology-first landscape. Before establishing a formal AI policy, business leaders must identify the specific ways in which their organizations can utilize AI and determine which tools will help them achieve their goals.

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Table of Contents:

• Predictive Analytics
• Automated Data Collection
• Predictive Modeling
• Automated Customer Service
• Natural Language Processing
• Getting Your Business Started with AI Tools

5 of the Biggest Benefits of AI in Business

Below are 5 ways that AI is making businesses more efficient and profitable—along with ideas for implementing it in your organization:

1. Predictive Analytics

Predictive analytics uses historical data to predict outcomes. It's super helpful for solving a myriad of business problems, like seizing opportunities and avoiding losses. With more and more predictive analytics software becoming available, it's easier than ever for companies to supercharge their earnings.

Here are a few ways businesses are leveraging predictive analytics:

• Demand forecasting: Retailers and e-commerce companies use predictive analytics to forecast future demand for products and services. To sufficiently meet customer demand, businesses can analyze historical sales data, market trends, and external factors like seasonality to optimize inventory management, plan production, and ensure sufficient stock levels. This capability is especially relevant for companies in verticals such as manufacturing and retail.
• Fraud detection: Financial institutions use predictive analytics to detect fraudulent activities and minimize risk. By analyzing transaction and behavioral patterns, predictive models can identify suspicious account activity or behaviors in real time, enabling proactive fraud prevention measures.
• Personalized marketing: Predictive analytics helps businesses deliver targeted and personalized marketing campaigns. By analyzing customer data—including browsing behavior, sentiment, purchase history, demographics, and preferences—businesses can segment their customer base and create tailored marketing messages, offers, and recommendations to increase the effectiveness of their marketing efforts.
• Healthcare diagnostics: Predictive analytics is used (typically by healthcare apps and software programs) to identify patients at risk of certain conditions or diseases. By analyzing patient data, medical history, lifestyle factors, and genetic information, predictive models can identify individuals susceptible to specific health issues, allowing healthcare providers to intervene early and provide targeted preventive care.

One thing to keep in mind: Before diving into predictive analytics, it's important to know what you actually want to achieve. Otherwise, you'll be like a ship without a compass—lost and going in circles. Make sure you set clear goals and benchmarks that you can measure your progress against. Don't forget to acknowledge and celebrate those successes—you deserve it!

Related Reading: Introduction to Microsoft Copilot for 365

2. Automated Data Collection

Despite all of the tools available today, many businesses still employ tedious manual processes that eat up time and resources. With automated data collection, teams can finally focus on more important tasks that elevate productivity and revenue in the long run.

The collection of forms on websites is just one of many uses of automated data capture—and it addresses one of the main sources of business pain and inefficiency: The management of unstructured information. This qualitative data isn't easily processed or used by existing systems, and the business often suffers due to inefficient operations, weaker decision-making, and a lack of insights. The ability to sufficiently manage and analyze unstructured data is one of AI's most profound benefits.

The following examples demonstrate how today’s businesses are putting automated data collection to work:

• Web scraping: Businesses use automated data collection tools to extract relevant information from websites, such as competitor pricing, product details, customer reviews, or market activity. This data helps for fleshing out market research, price optimization, and competitive analysis, enabling businesses to make informed decisions that are on par with current trends.
• Customer surveys and feedback: Automated data collection tools streamline the process of collecting customer feedback through online surveys, polls, or forms. This data provides insights into customer satisfaction, preferences, sentiment, and areas for improvement; helping businesses enhance their products, services, and customer experiences.
• Call center and customer interactions: Automated systems capture and analyze data from customer interactions, including call recordings, chat logs, and email exchanges. This data helps businesses understand customer behavior, identify recurring issues, and optimize customer support processes for improved service delivery.
• Financial data and transactions: Businesses automate the collection and processing of financial data, including invoices, receipts, and transactions. This allows for efficient bookkeeping, financial analysis, and auditing processes, improving accuracy and reducing manual effort.

Automated data collection helps businesses to effortlessly gather, process, and analyze massive amounts of data with precision and ease. PWC research found that automating data analytics can result in efficiency improvements of up to 40 percent for most organizations. These findings imply that automated data collection is a simple yet incredibly efficient process that is a game-changer for businesses.

3. Predictive Modeling

By utilizing the predictive modeling process, businesses can identify patterns or relationships in data that are essential for predicting future events. These models—such as regression analyses, decision trees, neural networks, support vector machines, and random forests—are incredibly valuable pieces of the business intelligence (BI) toolkit. They help companies identify potential risks and opportunities that can improve profits, reduce costs, and enhance customer satisfaction.

BI can even help identify at-risk customers that may churn or discontinue their relationship with the company. With targeted retention campaigns and personalized offers, they can keep customers loyal and increase their lifetime value.

Here are some examples of how businesses are using predictive modeling tools:

• Sales forecasting: Businesses use predictive modeling to forecast future sales based on historical sales data, market trends, seasonality, and other relevant factors. This helps in demand planning, inventory management, and resource allocation, enabling businesses to optimize operations and maximize profitability.
• Customer lifetime value (CLV) prediction: Predictive modeling helps businesses estimate the potential value of a customer over their lifetime. Businesses can identify high-value customers by analyzing past customer behavior, purchase history, and engagement metrics—and then tailor their marketing and retention strategies accordingly.
• Risk assessment and underwriting: Insurance companies and financial institutions use predictive modeling to assess risk and make data-driven decisions in underwriting processes. By analyzing various factors such as demographics, credit history, and claims history, predictive models help in pricing policies accurately and managing risk effectively.
• Demand planning and supply chain optimization: Predictive modeling enables businesses to forecast future product demand and optimize their supply chains accordingly. Businesses can improve their inventory management, production planning, and logistics by considering historical sales data, market trends, and other external factors to meet customer demand and ensure efficient operations.

These examples demonstrate how businesses across different industries leverage predictive modeling to make informed decisions, optimize operations, mitigate risks, and deliver personalized experiences. Tapping into the potential of predictive modeling enables businesses to remain competitive in today's data-driven world.

Related Reading: Introduction to Microsoft Copilot for 365

4. Automated Customer Service

Let's face it—waiting on hold for hours and being transferred from one agent to another without getting a solution to your problem is enough to make anyone lose their cool.

For this reason (amongst others,) businesses are turning to AI process automation to cut down on wait times by handling those repetitive customer tasks. This not only reduces frustration for current customers but also sets future ones up for satisfaction and success. 

Here’s how businesses are leveraging automated customer service to improve response times and boost customer satisfaction:

• Interactive voice response (IVR) systems: IVR systems automate phone interactions with customers using pre-recorded messages and voice recognition tech. This approach offers self-service options like order status checks, payments, and appointment scheduling, reducing response times and improving satisfaction.
• Chatbots and virtual assistants: Chatbots and virtual assistants provide 24/7 support by answering customer queries, recommending products, tracking orders, and troubleshooting. They reduce wait times and enhance customer experience across websites, messaging apps, and social media platforms.
• Automated email responses: Businesses use automated email response systems to provide timely and personalized replies to customer inquiries. By utilizing predefined templates and dynamic content, these systems can automatically send relevant information, acknowledge receipt of emails, or provide status updates. This measure ensures prompt responses, reduces manual effort, and improves customer communication.
• Social media automation: Businesses leverage automation tools to manage customer interactions on social media platforms. These tools can automatically categorize and prioritize social media messages, respond to common inquiries, route complex issues to appropriate teams, and provide consistent messaging across different social channels.

Say goodbye to the days of relying solely on customer service reps, consultants, and in-house strategists for decision-making. With automation in customer support, the possibilities for automated decision-making are endless.

5. Natural Language Processing

Natural language processing (NLP), or the ability to comprehend human speech and text, can feel like trying to decipher a foreign language without a translator—it's one of the trickiest parts of artificial intelligence. Luckily, employing NLP in your business operations doesn’t have to be as complex as learning an entirely new language.

There are a number of online NLP tools that can make it easy to try your hand at this field. For example, ChatGPT is regarded as the superstar of natural language processing tools because it’s able to answer questions, generate creative writing, and even hold conversations.

ChatGPT is based on large language models (LLMs), which means it's constantly learning and improving its abilities based on continuous feedback. This tool, and other AI tools like it, is being used to help businesses make sense of unstructured data, such as customer support tickets, social media posts, product reviews, and online surveys. For reference, an estimated 80-90 percent of business data consists of the unstructured variety, according to MIT.

The list of business uses for ChatGPT and its counterparts includes:

• Content generation: Companies are using ChatGPT to generate content for various purposes, including blog posts, social media updates, product descriptions, and email marketing campaigns. ChatGPT can assist in brainstorming ideas, providing suggestions, and even generating drafts—enhancing productivity and creativity.
• Market research: ChatGPT can engage with users to gather feedback, conduct surveys, and perform sentiment analysis. Businesses leverage this capability to gain insights into customer preferences, opinions, and behavioral trends, enabling data-driven decision-making and product or service enhancements.
• Personalized recommendations: By analyzing customer data and preferences, ChatGPT can provide personalized product recommendations and suggestions. This enhances the customer experience, increases cross-selling and upselling opportunities, and boosts sales and revenue.
• Training and onboarding: ChatGPT is commonly utilized for employee training and onboarding programs. It can provide interactive modules, answer common questions, and assist new employees in understanding company procedures, facilitating a smooth transition into the organization.

AI-powered software solutions can work wonders for businesses by automatically processing information and providing valuable insights. This can save time and money, and ultimately improve business outcomes.

But bear in mind, just like other types of AI, ChatGPT and other LLMs have their quirks. In other words, it can take some time for the program to fully understand the context and underlying meaning of a question or input. Often referred to as “hallucinations,” these inaccurate or inappropriate responses can lead to potential misunderstandings or misgivings on behalf of the user, illustrating the essential nature of AI-human synergy in your AI policy. To avoid this, always fact-check responses and verify accuracy of statements made by the LLM—blind reliance on LLM-generated responses is not recommended.

To help you get started with ChatGPT and the "science" of prompt engineering, check out this cheat sheet for best practices and tips:

(Download a copy of the cheat sheet here.)

Getting Your Business Started with AI Tools

Many companies struggle with how to implement AI, particularly when it comes to deciding which areas of the business can benefit most from its use. In fact, 2024 research from Boston Consulting Group states that only 26 percent of respondents who used AI reported measurable business outcomes from their deployments. These high performers, coined “transformers,” had many deployments and made significant changes to how they operated.

A few words of advice to generate transformer-level results? Leverage strategic IT planning to ensure your AI has the right infrastructure to capture data and work efficiently. Furthermore, vet your training data carefully and support ethical AI efforts—including diversity and inclusion initiatives to combat bias and misinformation. Adopting the right system and garnering the proper support for it can help you save money, make smarter decisions, and even boost your revenue.

Enacting a flexible and intuitive AI policy and plan for your organization can be challenging, which means it often requires insight from a managed service provider (MSP) or other experienced IT professional. Don’t hesitate to reach out to Ntiva for guidance on transforming your business with artificial intelligence.