Generative AI is transforming the customer experience (CX) industry at a faster rate than ever before. AI technologies like large language models (LLMs) have the capability of processing vast amounts of data, putting it in context, personalizing it, and providing answers to questions or resolution to problems in natural language. It’s like having access to the best “next action machine,” empowering brands to deliver stronger CX outcomes by enabling them to deliver exceptional care at every interaction.
With functions such as reading, writing, categorizing, transcribing, translating, analyzing, scoring, and generating text, code, images, videos, and sounds, AI is now (or will be) part of roles across most businesses. But, implementing AI at scale is not like flipping a switch. It’s complex and requires an ecosystem approach and governance.
Getting Started
Each company will need to decide what the best use of AI technology is to meet their business goals, weighing the opportunities and risks involved. For opportunities, we estimate a phenomenal enhancement of quality, speed, and cost-effectiveness from most digital services and solutions. Organizations centered on technology will do best, as they are ready to provide infrastructure and scale to those needing AI services for themselves—or offering services to others.
Traditional CX self-serve and digital channels will see the most impact, as AI becomes content auditor, writer, editor, and moderator across search, sites, apps, emails, documents, chats, surveys, dashboards, and media. Meanwhile, human-assisted channels will see AI help advisors become stronger communicators, faster at solving problems, and more effective—and therefore more valuable to your customers and your business.
On the risks side, there are new challenges for most businesses across many areas. Strategies should include risk tolerance and benefits in regard to legal and regional issues, and evolve to include uses cases where there’s a mix of people and AI services. Native limitations in these new AI models also can cause headaches for organizations implementing new services. For instance, there are still risks around accuracy with generative AI (e.g., hallucinating responses) that require businesses to review all AI outcomes, potentially creating dissatisfaction or compliance issues. Misunderstanding the AI’s capabilities might cause end-users to look for help for features not currently deployed across solutions. But most of all, organizations are still working to ensure security around customer data is solid and impenetrable.
Implementing an AI Governance Framework
A strong framework that helps you successfully navigate this unfamiliar territory is critical to sussing out the opportunities and risks to your business and your customers. And everything begins with understanding the rules of the game—legal, compliance, privacy, security, accessibility, sustainability—all led by governance.
With all the conflicting news reports and media articles about AI technology (the good, the bad, the ugly—and the scary), it’s important for organizations to start by communicating the dos and don’ts of AI across security, privacy, and compliance. For example, your employees need to understand that some AI interfaces, such as ChatGPT, are not private. Data processed through its website is reviewed and used for research purposes, which could break confidentiality agreements with customers.
AI governance is the foundation of this business transformation, as it ensures that businesses deploy AI technologies responsibly and ethically. It involves developing a framework with principles, guidelines, technology controls, and regulations that address issues such as fairness, accountability, transparency, and privacy. By implementing AI governance, you can create a more trustworthy and inclusive CX environment, fostering customer loyalty and enhancing your brand reputation.
To ensure effective AI governance, multiple internal and external stakeholders must collaborate and contribute, as each play a unique role in designing, building, and running AI transformation:
- Leadership: Sets the tone and direction for AI adoption, ensuring that ethical considerations are at the forefront of AI decision-making and governance.
- Strategy: Develops a roadmap for AI implementation and outlines how AI will contribute to CX goals and objectives.
- Marketing: Identifies opportunities to use AI to enhance the customer experience and develops strategies to communicate the benefits of AI to customers.
- Sales: Incorporates AI into the sales process and ensures that AI-enabled solutions meet customer needs and expectations.
- Finance: Determines the ROI of AI initiatives and ensures that budget is allocated appropriately.
- Engineering: Develops and implements AI solutions that align with ethical and governance guidelines.
- Operations: Manages the day-to-day operations of AI systems, monitors their performance, and ensures compliance with regulations and guidelines.
- Support: Provides customer support for AI-enabled solutions and ensures that customer feedback is incorporated into AI system improvements.
- Government: Establishes AI guidelines, regulations, oversight, ethical considerations, data privacy, and security standards to foster customer trust and level the playing field for businesses.
- Industry: Prioritizes ethical AI principles, invests in research and development, and actively participates in industry forums to share knowledge and collaborate on AI governance initiatives.
- Training: Promotes the responsible and ethical use of AI and shapes the AI talent pool.
AI Governance Best Practices
To build a robust AI governance framework, organizations should consider the following best practices:
- Establish principles: An effective AI governance framework should encompass key components such as ethics, accountability, transparency, and privacy. Develop guidelines and policies that address these aspects and ensure that your AI systems align with your core values and industry standards.
- Involve stakeholders: Engage various stakeholders, including leadership, employees, customers, partners, information security experts, and regulators, in the development and implementation of the AI governance framework. By involving diverse perspectives, you can create a more comprehensive and inclusive framework that addresses a wide range of concerns.
- Implement frameworks: Put mechanisms in place to ensure adherence to your AI governance frameworks. This includes establishing a reporting structure up to senior leadership, training employees on AI ethics, communicating results, conducting regular audits, and establishing clear lines of responsibility for AI system management.
- Evaluate progress: AI systems evolve over time, making it vital to monitor their performance and impact continuously. By conducting regular assessments, you can identify potential biases, privacy concerns, or other governance issues, and take corrective action when necessary.
- Foster collaboration: Actively participate in industry forums and collaborate with other organizations, academia, and government agencies to share best practices, learn from each other, and contribute to the development of AI governance standards. Establishing strong partnerships will help you design, build, and run your AI initiatives in the most effective and cost-efficient way.
As AI continues to transform the CX industry, governance is essential to ensure that AI technologies are deployed responsibly and effectively. Learn how Concentrix can help you unlock new CX possibilities with AI.
Jennifer Winter
Executive Vice President, Strategy and Design
Guillermo Zuniga
Senior Director, AI Solutions
