Understanding Generative AI: Exploring benefits, challenges and use cases

What is Generative AI?

Generative AI (also called GenAI)is a technology that emulates aspects of human communication and creates content including text, images, videos, audio and more by learning patterns from existing data.

Generative AI benefits

• Higher efficiency
• Better customer experience
• Better agent experience
• Improved agent productivity
• Consistency and scalability
• Lower operational costs
• Improved data acquisition
• Data-driven decision-making
• Multicultural and multilingual capabilities
• Proactive support
• Immediate feedback
• 24/7/365 accessibility
• Upsell recommendations
• Ongoing operational improvement

Challenges to implementing Generative AI

Generative AI vs AI

	AI	Generative AI
Roles	Multiple capabilities	Producing new audio and visual content
Tasks	Performs multiple tasks (e.g., delegation, queries, forecasts)	Simulates authentic-sounding reactions to human requests
Independence	Different levels of human control and management	Independent or minor amount of oversight
Competencies	Leverages advanced machine learning algorithms	Uses large language models to comprehend and respond with human-like speech and text
Inputs	Structured data, defined rules and algorithms	Taught from large, unstructured datasets to detect patterns and produce new responses
Incorporation	Necessary to combine with incumbent call center procedures, hardware and software	Must be configured to recognize and generate appropriate and human-like responses to customer queries
Flexibility	Modified and adjustable based on customer interactions and business requirements	Needs ongoing calibration to ensure accuracy and applicability
Transparency	Subject to programming, algorithms and other inputs	Must be maintained and adjusted to avoid unnecessary complexity and lack of clarity

Generative AI Use Cases

Generative AI in e-commerce

Product and service recommendations: GenAI makes suggestions based on behavior, purchase history, browsing history and other factors per customer affinities and likely needs.

Product and service descriptions: In response to requests and other prompts, descriptions of offerings that are accurate, up-to-date and engaging are given to customers, with more details upon request.

Chatbots and virtual assistants: Providing 24/7/365 customer support and purchase guidance, product information and other assistance.

Personalized content: SMS (texts), email and social media posts that are customized and personalized as well as web content, banners, landing pages and more, based on customer history, preferences and other data.

Visualization: Product shots, procedures and more can be depicted in the form of images, 3D depictions and simple animation.

Managing inventory: Based on past purchases, trends, patterns, seasonal variations and other inputs, recommendations for future needs are made to reduce or expand inventory or shift product mix.

Categorizing products By sorting, classifyingand tagging products, customers locate them faster and shops have less waste in their inventories.

Generative AI in banking

Virtual assistant: Personalized banking advice, responses to customer questions, pointing them to suitable products, services, tasks and strategies.

Credit processing: Provide real-time direction and support for loan applications, forms, credit processing, recommendations based on need and history, and other support and assistance.

Loss prevention: Scrutinize transactions, customer searches, history and other data to protect against questionable or illegal actions.

Counseling and advice: Tailored financial advice and investment recommendations, helping customers achieve their financial goals.

Reports: Generate transaction summaries, account activity, customer profiles and more.

Personal banking: Simulate authentic human actions, questions, solutions and more with generative AI.

Generative AI in retail

Similar to e-commerce, Gen AI can assist with (link internally to e-commerce section above and individually to each of the following): Product and service recommendations; Product and service descriptions; Managing inventory; Categorizing products and more.

Generative AI is also especially useful in retail to assist with:

Store layout conception: Optimize floor space, traffic patterns, checkout stations, displays and more

In-store promotions: Plan special sales and promotions based on historical sales data, calendar (holidays, festivals, etc.), paydays, tax holidays and other events

Pricing: Calculate profitable and appropriate prices based on cost, overhead, scarcity, need, seasonal variances, past success and other data

Hours of business: Determine optimum hours of operation based on the calendar, employee availability, season and other factors

Generative AI in life sciences

Research and development: By generating and predicting drug and molecule combinations and structures, Gen AI economizes and accelerates the process of creating new medicines.

Disease detection: Using Generative AI tools, biomarkers for diseases are recognized for prompt detection, patient identification and customized treatment.

Clinical trial candidate selection: Generative AI tools identify and analyze patient records to match them with relevant tests and trials.

Treatment: Gen AI reviews patients’ medical histories and profiles to devise treatment plans that include drug dosage recommendations and other therapeutic strategies.

Image evaluation: X-rays, MRIs and other medical imaging technologies are analyzed by Generative AI tools for diagnostic, monitoring and treatment planning purposes.

Evidence models: Through Generative AI, facsimiles of specific conditions and population segments are studied and leveraged for analysis.

Content creation Gen AI excels at assisting with building text and images and with the proper prompts and inputs, produce medical papers, models, blogs, articles, audio and animation.

Generative AI in customer service

Advanced call center technologies with Generative AI

Startek^® Agent AI

Intelligent automation and self-service with Generative AI

Integration of Generative AI with other technologies

Chatbots and virtual assistants

Gen AI is used to simulate human conversation and provide automated responses to user input and is used for customer service, virtual assistants and other applications where users need assistance with tasks or information. Designed to simulate conversations with human users through text or messaging.

Journey Mapping and CX design

Identifies patterns across customer touch points and suggests improvements tailored to your goals by using Generative AI to develop personas, spot pain points and opportunities and set metrics.

Voice assistants

Understand and respond to natural language commands, often used in call centers and customer service environments.

Interaction analytics

By combining data from multiple sources, including phone calls, chat sessions, email communications and social media interactions, interaction analytics analyzes data to reveal insights and drive better decision-making.

Robotic process automation (RPA)

Automates high-volume interactions and routine inquiries, eliminating wait times to drive cost efficiencies and customer satisfaction.

Conversational AI

From chatbots to virtual assistants, conversational AI technology enables machines to understand and respond to human language and enter into meaningful conversations with people, allowing for seamless communication between humans and machines.

Knowledge management system

By identifying, creating, organizing and sharing team members’, customers’ and other stakeholders’ information, knowledge management systems improve efficiency, productivity and decision-making capabilities.

Omnichannel communication platforms

In providing customers with a consistent experience, regardless of the channel they choose, omnichannel contact center technology improves customer satisfaction, loyalty and engagement.

Self-service technologies

Customers access information and perform tasks on their own without human agents, taking control of their own experience — and reducing costs by automating repetitive processes.

Gamification

AI provides insights and a real-time view of individual performance to drive employee engagement while gamification modules incentivize desired behaviors, leading to operational efficiencies.

Speech analytics

Identify words and analyze audio patterns to detect emotions, monitor agent performance and assess call quality to uncover deep actionable insights to improve customer interactions in real-time.

Startek® Generative AI ensures intuitive experiences for agents and customers across self-service, workforce management and analytics

The future of Generative AI

Extrapolating current and projected technological advancements and ongoing call center needs, here are several possible tools and functionalities that GenAI could make possible in the future:

Customized journeys: Based on resources, needs and types of issues, Generative AI can compose custom scripts and personalized journeys that may involve multiple channels and communication styles, as required.

Proactive diagnosis: Autonomously troubleshoot problems, pinpoint issues and give directions to resolve them.

Variable personas: Virtual customer and agent identities for agent and manager training and testing of other AI processes, including chatbots and other virtual tools.

Conversational fluidity: Virtual call center agents will be able to switch immediately between several languages and cultures during an interaction.

Empathetic support: Though current tools may employ understanding and empathetic language, a more human-like expression of sympathy and consideration can be powered by improved GenAI.

Resource allocation: GenAI tools will consider multiple factors including type of issue, customer history, agent availability, competencies and more to devise the best and most efficient way to assist the customer.

How to implement Generative AI In your organization

As with all new process and technology implementations, integrating Generative AI into a business’s operations requires planning and preparation in the form of a change management plan. Every organization is different but here are some high-level suggestions for successful implementation and deployment.

Identify goals: What are you trying to improve? Cost, efficiency, CX, AX? It’s best to begin with the end in mind so every step aligns with what you are trying to achieve.

Develop use cases: Though every business is unique, you share many challenges with others in your industry or sector.

Evaluate current (and future) state: Inventory technology already in use, business conditions and expectations for future circumstances to determine if the required investment in resources is justified.

Identify stakeholders: Ascertain internal and external supporters (and detractors) to facilitate integration and avoid obstacles. Ensure at least one executive supporter is on the financial side to assist with budgeting and help with projections and reporting.

Determine which tools you need: Must-haves, nice-to-haves, immediate needs, later add-onsall must be budgeted for.

Establish your budget: As with all capital purchases, determine ROI, leveraging use cases and data to launch and sustain the Gen AI tools. Be sure to include training beyond what may be provided during set-up by the vendor. It helps to know how much you have before shopping but there’s no shame in “kicking the tires” first so you have a better idea of what you must spend. Research and due diligence are essential.

Establish governance: Who’s in charge? Who has a voice and a vote? What are the roles and responsibilities? You cannot just make it up as you go along — though flexibility and agility are super-powers.

Training and coaching: Ensure proper on-site instruction is provided but plan to conduct it — and coaching — on an ongoing basis.

Identify vendors Match use cases with functionalities but ensure the ones provided by vendors closely match yours. Be sure to speak to current users (if possible) to evaluate their satisfaction (or lack of same). Your technology partner is an important choice so don’t be afraid to ask tough questions.

Test, test again and iterate: Be prepared by conducting trials and stress tests. Make changes as necessary.

Watch the calendar: Do not introduce new tools in busy times or during holidays. Find a “sweet” spot and ensure your backups are in place in the event of failures.

Plan for the future: What’s next? Listen carefully to agents and associates, managers and customers to determine what’s working and what is lacking. Stay current with industry developments and innovations.applications, transportable body-based uses and more general-use open-domain conversational AI tools.

Boost efficiency & delight customers! Startek optimizes call centers with Generative AI technology.

Generative AI FAQs

How does Generative AI enhance customer experience?
Generative AI improves customer experience by personalizing recommendations, generating tailored content and enabling interactive experiences such as virtual assistants or chatbots.

What role does Generative AI play in customer engagement?
Generative AI enables businesses to engage customers more effectively by providing personalized product recommendations, creating targeted marketing campaigns and offering interactive experiences through chatbots and virtual assistants.

Does Generative AI help in understanding customer preferences?
Yes, Generative AI analyzes customer data to identify patterns and preferences, which is used to personalize product recommendations and marketing messages.

Is Generative AI used in customer service?
Yes, Generative AI powers virtual assistants and chatbots to provide instant responses to customer inquiries, improving response times and overall customer satisfaction.

What are the potential challenges of implementing Generative AI in customer experience?
Challenges include ensuring data privacy and security, mitigating biases in generated content and maintaining transparency in AI-driven interactions to build trust with customers.