Change is inevitable, especially in business. Innovating and embracing change is what sets leaders and successful businesses apart. Part of that change in the modern business landscape is adopting Artificial Intelligence (AI) technologies—from operations to marketing and customer support processes. While there’s no question that high-performing companies can and have been integrating AI extensively in 2023 and beyond, the question is, how can your business benefit from implementing AI tech? In this article, we’ll explore the core differences between two different AI models, and look at five ways AI can revolutionize your business processes.
Contents
AI models explained
Businesses can use several types of AI models for their insights or to upgrade their processes. Here are two major branches, including their benefits, and shortcomings:
Natural language processing (NLP)
Natural language processing models (NLP) are designed to enable AI to connect with humans through simple language. These models employ sophisticated algorithms and vast amounts of text data to process and analyze text-based information. They’ve revolutionized various applications, including chatbots, language translation, sentiment analysis, and content generation. They work by learning the intricacies of language, capturing context, and contextually generating human-like text responses.
General NLP has several shortcomings when it comes to business-case applications. Among the most common missteps of the technology are:
- Lack of domain specificity: General NLP models may not have specialized knowledge in specific industries or domains, making them less effective for tasks requiring industry-specific jargon or context.
- Training data bias: These models are trained on diverse datasets, which can introduce bias and inaccuracies when applied to specific business contexts, especially if the training data doesn’t adequately represent the domain in question.
- Fine-tuning requirements: Adapting general NLP models to specific business tasks often requires extensive fine-tuning, which can be resource-intensive and time-consuming.
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Contextual Machine Learning
While general NLP is practical in various use cases, there’s a better way of doing things without reinventing the wheel. Contextual machine learning (Contextual ML) is at the core of what Idiomatic offers. Contextual ML understands your industry through the data you provide to it. It addresses many of the shortcomings of NLP such as:
- Domain specificity: Contextual ML uses specialized knowledge from a particular industry as the basis of its training. As such, it’s well-versed in the inner workings of the field.
- Training data: Data used for contextual ML is precise, based on real-life cases within the industry. Using real-life, specific data sets means that contextual MLs provide a far more accurate picture of the domain.
- Iterative fine-tuning: Contextual ML data sets already start from a conglomeration of data and knowledge within the domain. With each iterative success or failure the AI becomes more adept at delivering good results. It requires far less iterations to get something usable than if a business were using a General NLP model.
| Contextual Machine Learning | Natural Language Processing (NLP) | |
| Context Handling | Capture and understand the context in which data is generated, allowing them to make context-aware predictions. | Considers the linguistic context within text data, such as the meaning of words and the relationships between them. |
| Data Types | Text, images, audio, and sensor data. | Primarily text data, but can incorporate audio and video for language-related tasks. |
| Use Cases | Recommendation systems, sentiment analysis, chatbots, and personalized content generation. | Machine translation, speech recognition, text summarization, sentiment analysis, and question-answering systems. |
Revolutionizing business processes through AI implementation
Now that we understand the difference between AI models, here are 5 ways AI tools can positively improve business processes—specifically for product, marketing and customer support teams:
1. Actionable customer insights
Artificial intelligence allows you to delve deeper into what your consumers are doing and saying. By gathering and analyzing feedback data from all your sources (including phone transcripts, email tickets, surveys, app reviews, and more), you can learn your customers’ preferences and pain points—and act on them, fast.
For example, an AI-driven platform like Idiomatic gathers all your business’s customer feedback sources. We then create our own training data and use supervised contextual machine learning to train custom models to label this feedback. This allows us to identify trends and turn customer feedback into actionable next steps. For example, with Idiomatic, you don’t have to guess why there are fewer logins to your platform. We can tell you that there’s a spike in complaints from customers not receiving password reset emails, leading to a decline in logins overall.
2. Personalized marketing
Personalized marketing leads to more customer engagement. In fact, 97% of marketers report an increase in business outcomes through personalization. AI offers several different support services that you can use to help with personalized marketing campaigns. Some of these include:
- Customer segmentation: AI can analyze your customer data to identify different segments based on demographics, behaviors, and preferences.
- Predictive analytics: AI can use historical data to predict future customer behavior, such as purchase patterns or product preferences.
- Personalized content: AI-driven algorithms can generate personalized content, such as product recommendations, email subject lines, or website content, tailored to each individual’s interests and browsing history.
- Dynamic pricing: AI can adjust pricing in real-time based on factors like demand, customer behavior, and competitor pricing, offering discounts or incentives to maximize conversions.
- Chatbots and virtual assistants: AI-powered chatbots can engage with customers in real-time, answer questions, and provide personalized product recommendations or assistance with the buying process.
Free tools such as ChatGPT and Google Bard can provide basic marketing support but are limited in what they can do. More marketing-centric tools such as Peak, Zapier, and ChatSpot are better suited to business applications. These tools are relatively easy to implement for a marketer and reduce the time between getting an insight about a customer and acting on that insight.
3. Automation and efficiency
Efficient operation stems from automation. 50% of companies are already using AI in at least one business unit, with the highest application of AI being automation. These businesses have specific roles for AI to adopt within their organization, such as:
- Quantifying qualitative data: AI can automate data extraction from various sources, including emails and surveys, converting unstructured, qualitative feedback data into structured formats for faster analysis.
- Ticket routing: AI-powered ticket processing systems can automatically categorize, prioritize, and route tickets to the appropriate departments or individuals.
- Workflow automation: AI can design, implement, and manage complex workflows, automating repetitive tasks and ensuring that processes are executed efficiently. It can also route tasks to the appropriate personnel, based on predefined rules.
- Marketing automation: AI can optimize marketing campaigns by segmenting audiences, personalizing content, and automating email marketing, social media posts, and ad placements. It can analyze campaign performance and make real-time adjustments.
- Customer relationship management (CRM): AI-enhanced CRMs can automatically track customer interactions, segment leads, and provide insights to sales and marketing teams. They can also automate follow-up tasks and reminders.
Automation on a large scale reduces a company’s expenses by requiring less personnel on the ground. Some tools that achieve this include Process Street, and UiPath.
4. Competitive advantage
Competition within an industry determines who rises to the top and who gets buried under pressure. More than 60% of customer experience leaders believe that AI offers them a significant competitive advantage against others in their industry. Upgrading your processes to make them more efficient leads to several tangible competitive advantages, such as:
- More time for strategic thinking: AI can handle routine and time-consuming tasks, allowing employees to focus on more strategic, creative, and value-added activities. It can also process large volumes of data quickly, reducing human analysts getting bogged down.
- Data-driven insights: AI can analyze historical data to predict future trends, helping businesses make informed decisions and stay ahead of market shifts. It can also assess customer feedback and social media conversations to gauge customer sentiment and adjust strategies accordingly.
- Price optimization: AI can adjust pricing strategies in real-time based on competitor pricing and market demand.
- Cost reduction: AI can automate tasks across various departments, reducing labor costs and minimizing errors. Additionally, it can reduce equipment downtime and maintenance costs by predicting when machines need servicing.
This competitive advantage becomes more pronounced for early adopters. Businesses first on the scene to integrate AI into their processes outpace their competitors who are still trying to figure out the best way to use AI.
👉 Unlock the competitive possibilities with Idiomatic’s AI-driven platform. See all our integrations.
5. Scalability and growth
Automating tasks, improving efficiency, and enabling data-driven decision-making are all ways that AI aids in a business’s scalability and growth. Compared to general NLP, contextual machine learning has the tools, data, and analytics necessary to provide insights into how a business needs to take action, such as:
- Deep learning models that capture context specific to the business.
- Automated machine learning tools that streamline model development and deployment.
- Distributed computing frameworks for parallel processing of large datasets and models.
Growth and scalability are based on a business’s ability to adapt to changes quickly. When expansion occurs, businesses will need to shift their resources around. AI can be used to give these growth predictions, allowing businesses to redirect their resources to make growth and scalability more sustainable.
Here’s an example of how this works. A fast-growing e-commerce retailer uses AI-powered demand forecasting to predict product demand. This enables them to allocate resources and inventory efficiently, focusing on products with high anticipated sales. By doing so, they reduce overstocking in low-demand items and allocate resources where they are needed, leading to increased scalability, profitability, and overall business growth.
AI is already here. Harness its power to improve your business.
Doing things the old-fashioned way might work for now, but it also takes so long that your business is at a disadvantage while you adapt. By the time you get to where you want to be, the finish line has moved further down the road. For example, a spike in issues with your checkout process leads to increased abandoned carts and lost sales until you’ve identified the issue and fixed it.
With Idiomatic’s AI-driven customer feedback platform, you can rapidly fix issues like these in a fraction of the time it would usually take with manual analysis. We use machine learning to analyze all your customer feedback data at scale, interpreting it according to your business’s context to generate human-quality, actionable insights. No more manual tagging, time-consuming analysis, or guesswork. With real-time alerts, custom tags and filtering, and engineering-free integrations, you can include customer feedback from anywhere, and turn qualitative feedback into tangible insights and results.
Moving quickly when new things present themselves can be crucial to staying ahead of the competition. All it takes for a competitor to take the lead is to invest in the right technology before you do.
See how Idiomatic takes your business into the future, firsthand. Schedule a free custom demo to see our contextual machine learning at work.
