Enterprise Contact Centers are adopting automation at unprecedented rates. Yet, these internal teams face challenges in providing AI-driven customer experiences that meet customer expectations.
When ChatGPT launched in late November 2022 and five days later became the fastest consumer platform in history to reach 1 million users, it heralded the start of a new epoch in the long history of technology disruption. AI is changing everything about how we as humans interact with our machines.
The Generative AI revolution is already reshaping enterprises in every industry at that same breakneck pace. Generative AI's enormous potential to enhance productivity at lower cost is being realized as swiftly in the enterprise as ChatGPT exploded in the consumer market. In a market survey recently completed by C1 Edge, most enterprises report this embrace of Generative AI, with nearly all (92%) saying they are highly familiar with generative AI, and three-quarters (77%) saying there is high usage of generative AI in their organization. Enterprise Contact Centers, in particular, are adopting automation at unprecedented rates, yet they face challenges in providing excellent AI-driven customer experiences and fulfilling the potential of AI.
Bringing this capability to bear for customer experience is much more complex than most brands realize. The result is many poorly performing AI solutions for internal customer experience teams. So, what can you do to give your generative AI-based automation projects a high rate of success? Start with these three principles:
- Be Honest About Buy vs. Build vs. Partner
- Not All Partners Are Created Equal
- Bridge the AI Data “Gap” In Your Enterprise Data
Be Honest About Buy vs. Build vs. Partner
Objectively assessing the knowledge and skills of your internal teams is a critical first step to ensure your enterprise delivers effective AI. Most enterprises have some skills but often have gaps in their AI expertise, particularly in the data-driven needs of Generative AI. An objective assessment often results in needing to enlist partners who can fill these skill and expertise gaps.
Not All Partners Are Created Equal
The market for AI in customer experience is crowded with startups specializing in AI technology. However, many of these companies are primarily technology-focused and staffed by engineers who are more versed in tech than customer experience execution. Although they promote their innovative technology, they often lack a deep understanding of customer experience best practices.
On the other hand, many contact center vendors with extensive customer experience expertise are now offering tools to develop AI solutions. Unfortunately, these tools frequently place the burden on enterprise teams to implement both the technology and integrate AI best practices necessary for creating effective AI solutions for the customer experience.
The largely negative AI solutions that frustrate us all reveal this problem and speak volumes.
To prove this point, ask an AI start-up about AHT, FCR, and QM, and you’ll often get a blank stare, revealing that they do not know that these are acronyms for Average Handle Time, First Contact Resolution, and Quality Monitoring. But knowing the acronym is just the bare minimum because, as we know in customer experience, each of these and hundreds more topics require subject matter expertise and have their own best practices that an AI design must consider.
Similarly, ask a contact center AI tool vendor about their approach for AI-driven safety and PII, PCI or HIPAA compliance controls for AI and leading prompt engineering approaches for few-shot prompting of a fine-tuned model, and you’ll likely get blank stares again.
You need a partner who is both an expert in customer experience and an expert with proven expertise in deploying AI-based customer experience solutions.
Ideally, this partner would provide AI for customer experience ‘as a service’, complementing your team's existing skills and helping to develop AI solutions that enhance how customers can get things done with your company. Such a solution should not only focus on reducing costs but also aim to significantly improve customer satisfaction and reduce the burden on your already overtaxed human contact center agents.
Bridge the AI Data “Gap” In Your Enterprise Data
Data is the fuel for the engine of Generative AI solutions. Yet, many enterprises struggle to evaluate if their data is sufficiently robust and free of content gaps. Unlike Conversational AI technologies developed over the past five years, Generative AI can reveal previously unnoticed gaps in your data. Identifying and filling these gaps is crucial when deploying Generative AI solutions. It is essential to choose a partner with robust 'data gap detection' capability within their AI platform. Without this, your Generative AI projects risk encountering 'model hallucination,' where the model generates incorrect or fictional responses. This is particularly problematic in customer experience settings, where customers may pose queries that fall into these data gaps. Without a partner technically equipped to detect and manage these gaps, the AI may generate erroneous answers, compounding the issue of content gaps.
Gap detection is a feature that few Generative AI vendors currently offer in the market. Those vendors that provide it have systems that can identify gaps in data and instruct the AI to withhold answers rather than fabricate an answer. Crucially, your partner should include an AI expert who works alongside your data teams to identify and fill these gaps. This collaboration helps ensure that, over time, the gaps are systematically addressed. Once you have a partner with robust gap detection capabilities and AI consultants who assist in filling these content gaps, your enterprise can confidently deploy Generative AI solutions. Such ‘gap detection’ systems will wisely refrain from answering when they detect a gap, instead flagging it for your data and content teams to address gradually as the project operates in the market.
In our market survey, C1 discovered that only 52% of respondents feel prepared to meet the data requirements for AI solutions. Additionally, 58% use some combination of purchasing and developing data models for generative AI implementations. The concern over potential data gaps has led many enterprise security teams to insist on overly cautious AI data policies, which slow down the rollout of AI projects and can significantly postpone the cost savings and enhanced customer satisfaction that typically result from well-executed AI automation projects.
Enterprises require an AI data partner with 'gap detection' capabilities to move forward effectively. A good partner in AI ensures that projects can advance confidently, mitigating the risk of incorrect responses and allowing the enterprise to proactively identify and address content gaps. Most importantly, this can be done while operating the solution in the market, knowing that the vendors ‘gap detection’ approach will mitigate incorrect responses for the AI.
How Organizations Acquire/Implement Data Models Used for Generative AI Implementations
Effective Generative AI can deliver an incredibly customer-satisfying experience on any channel: voice, chat, text message, smartphone apps with embedded messaging, and any digital channel your marketing team might wish to deploy. Teams that try to ‘go it alone’ face a steep uphill climb and the perils of “trial and error’ to discover problems like model hallucination and content gap detection, not to mention the complex issues of customer data protection and reducing any potential model bias.
A partner with deep expertise in customer experience and a full-time team of AI Architects and project AI Business Consultants and Designers can significantly accelerate your time to value for AI and allow you to deploy solutions with the confidence that the learning curve has already been climbed by your partner vendor.
Elevating Connected Human Experiences
At C1, we understand that trust, value, responsible development, and future-proof flexibility are paramount for success. We have spent thousands of hours building, testing, and intrusion probing our platform, developing industry-leading AI strategies that set C1 apart as an AI solution partner with deep customer experience expertise that you can trust. In addition, we’ve taken our deep expertise in customer experience and ‘baked it in” to our AI platforms. This allows C1 to build solutions that you inherit when you choose C1, which have already been quantified through robust data governance. As such, C1 can empower your teams to move confidently into AI's undeniable business value proposition for customer experience. C1 has championed responsible AI development and can ensure your teams start the race with seamless integration with emerging technologies through our “as a Service” and “AI and CX vendor agnostic” solution set. By partnering with C1, you gain access to best-in-class yet adaptable AI-powered customer experience solutions, perfectly designed to address the evolving needs of modern contact centers and propel your brand experience beyond what customers expect.
