Unlocking AI’s hidden potential
When it comes to AI tools, it’s not all about generative AI. CIOs can combine gen AI with conventional AI types to improve EX and CX.
The use of generative AI has spread faster than any other technology in history, including the Internet and personal computers. Roughly 3 out of 4 companies this year are using generative AI in either testing or limited production, and 25% have fully deployed it, at scale.
There’s no denying gen AI’s immense potential. But the adoption race presents a risk: Some businesses may be overlooking traditional AI tools that would be better suited to specific needs.
Read also: Employees want more AI—and leaders are listening
An array of preexisting AI types—collectively known as analytical AI— aren’t just powerful and proven, they often work in tandem with new gen AI applications. For example, conventional machine learning models can analyze customer sentiment and behavior; gen AI tools can use those insights to produce targeted marketing content and art.
Here’s a quick run-down of popular forms of analytical AI, and how they are commonly applied in two key areas of practice: customer experience (CX) and employee experience (EX).
Machine learning models
One of the foundational tools of AI, machine learning (ML) models analyze vast amounts of data for a wide range of use cases, from detecting cyberattacks to understanding customer behavior.
Applications in CX:
Predictive customer service: Use historical data to anticipate future behavior, including anticipating issues before they arise
Customer segmentation: Identify interests and preferences based on demographics and behavior
Applications in EX:
Performance management: Assess performance through a range of data, then provide real-time actionable insights
Employee retention: Build historical models to predict and address factors leading to employee churn
Robotic process automation (RPA)
RPA uses bots to automate repetitive, rule-based tasks that are usually done manually.
Applications in CX:
Order processing: Automatically process orders, generate invoices, update customer records
Customer data management: Extract, validate, and update customer data across multiple systems to ensure customer profiles are always current and accurate
Applications in EX:
Payroll and benefits administration: Calculate salaries, process deductions and update benefits information
Routine HR processes and routine data entry: Automate tracking and approval of vacation requests, sick leaves, attendance records, and the like
Multimodal AI
AI tools that can synthesize and analyze multiple types of data—such as text, images, audio, and video—to create a richer understanding of a situation that a single modality cannot.
Applications in CX:
Enhanced customer support: Better understanding of a customer situation by going beyond text to include images, documents, audio
Sentiment analysis: Analyze mixed media content to gauge sentiment more accurately
Applications in EX:
Streamlined onboarding: Automated onboarding procedures, tailored according to the employee, using a range of content formats
Personalized learning: Customized training programs tailored to their skills and interests, based on insights from a range of modalities
Federated learning
Federated learning is a decentralized approach to machine learning that enables multiple parties to collaboratively train models without sharing their raw data.
Applications in CX:
Personalized recommendations: Using only the data on a customer’s own device, FL can make relevant product or content recommendations
Fraud detection: FL algorithms trained locally on a device can detect suspicious patterns and anomalies
Applications in EX:
Productivity analysis: Analyzes work habits such as email response times and task completion rates to make productivity recommendations
Customized training models: Since FL keeps data at the local level, companies can create training models based on sensitive data that would otherwise be inaccessible
Search
Advanced retrieval models that help businesses give customers and employees access to relevant information among vast amounts of data.
Applications in CX
Knowledge base and self-service: AI-powered search engines pull in relevant information for customers’ questions
Recommendation systems: Enhanced user experiences from suggestions based on visual similarity or textual descriptions
Applications in EX:
Enterprise search: Aggregated numerous internal documents, enabling employees to find relevant information quickly
Collaboration and communication tools: Embedded search in workplace applications to access critical information
Natural language processing (NLP)
Specialized type of machine learning that can read, understand, and even respond to text or spoken words, or churn through large volumes of text data, allowing businesses to understand customer emotions and preferences.
Applications in CX:
Sentiment analysis: Understand a range of customer expressions and emotions from social media, reviews, or internal data
Chatbots and virtual assistants: Conversational AI systems that can provide 24/7 support and immediate responses to customer inquiries
Applications in EX:
Employee feedback analysis: Analysis of employee surveys, identifying pain points and areas for improvement
Virtual HR assistants: Chatbots that provide instant answers using natural language, freeing up HR teams from routine tasks and enabling them to focus on more strategic initiatives
