As we approach 2024, the discussion around the possibilities and practical applications of artificial intelligence (AI) for business continues. Organizations want to understand what AI can do for them today and how to apply it in their nuanced environments. The transformative power of AI and machine learning (ML) is evident in their capacity to adeptly interpret and streamline vast quantities of data. This proves invaluable in the context of business, where copious amounts of information are intricately linked to the products and services being offered. Effectively distilling and conveying actionable and succinct information to customers becomes a challenging task amid this wealth of data. The value, however, of AI when applied correctly cannot be denied. 

Today, large-language model (LLM) and natural language processing (NLP) AI enables machines to ingest text-based information.This has led to the creation of data models where machines can understand and interact with the data specific to a company. An example of this is an interactive chat-bot interface using extractive or generative AI. Extractive AI provides snippets of existing results on the topic—comparable to using a search engine like Google. A generative response combines the most relevant answers found in the available data to create an automated response.

With this in mind, here are three use cases for AI and ML that can provide impactful business results and drive services and solutions today:

1.    AI-Assisted Product FAQs
The more common types of AI-assisted responses to frequently asked questions are completed by virtual assistants or chatbots. These tools can provide 24/7 customer support with an ability to address common product questions in real-time, reducing the number of hours required of human customer support.

It is important to remember that the initial education of the AI will come from the product data and manuals first entered into the model. To enhance customer satisfaction and reduce costs, inputting historical client interactions and feedback is an important step. This allows the AI to evolve and improve, absorbing new products and additional client interactions into its learning process.

2.    AI-Assisted Selling Platforms
‍Businesses continue to navigate the challenges of effective sales and marketing in today’s market. AI-assisted selling platforms have become an impactful strategy that allows businesses to curate data-driven solutions that can increase sales and improve customer engagements. Gartner predicts that by 2025, 75% of B2B sales organizations will augment traditional sales playbooks with AI guided selling solutions.

A first step to AI-assisted selling is the inclusion of an AI-powered search platform with a generative AI chat-bot interface, providing sales teams with a more intuitive way to locate relevant product information that can provide their clients a 24/7 outlet to address product questions.

Another platform is an AI-powered recommendation engine. This can consume large amounts of product and customer data including browsing history, purchase behavior, and demographic information.This will curate personalized product recommendations for customers, increasing the likelihood of a conversion. In addition to driving revenue, the inclusion of a recommendation engine can enhance a company’s pricing strategy by predicting when consumers are most likely to purchase its products and an ability to automate email marketing campaigns based on individual preferences.  

3.    AI-Assisted Mergers and AcquisitionsDocument Review 
Mergers and acquisitions (M&A) include complex processes that involve an extensive review of documents and data from both parties. M&A specialists, like our partners at MorganFranklin Consulting, can leverage AI to streamline due diligence and reduce risks in M&A transactions – significantly reducing the number of hours required for employees to physically navigate files.

ML algorithms using NLP are able to quickly scan and extract relevant information from documents including contracts and financial records and flag potential issues or discrepancies, accelerating the review process and mitigating the risk of overlooking critical details essential to the deal. In this scenario, it is important to acknowledge the potential complexity, as there is typically a data vault not directly connected to the company's data sets.

In a single company or private equity firm, the system can identify common issues the team is dealing with and provide quicker access to a resolution – while also suggesting integration activities by leveraging past M&A data sets 

AI-powered solutions are revolutionizing business operations today, but can require even more precision than what generative AIs are currently able to provide. In these instances, utilizing a reasoning engine can deliver verifiable results by removing data discrepancies.  

Interested in learning how BUILT’s digital transformation experts can help you get started with AI-assisted solutions? Connect today: https://www.builtglobal.com/contact.