Top 3 Challenges in Scaling Conversational AI

Top 3 Challenges in Scaling Conversational AI

Artificial intelligence (AI), especially conversational AI, represents the new frontier in contact center technology, presenting countless opportunities to improve both internal operations and the customer experience. In fact, business leaders are increasingly investing in this technology with an eye on supporting the customer journey. A 2020 survey conducted by The Harris Poll revealed that more than half (53%) of respondents listed customer experience improvements as the top reason to implement AI solutions.

Many brands have already made major strides in this arena, with more organizations than ever investing in AI-based solutions to bolster their contact center operations. During our webinar, “Empower Your Business with Google Contact Center AI,”  we polled the audience to gauge their level of AI adoption. We found that 59% of respondents said they were exploring their conversational AI and virtual assistant options, while 41% were actively using AI through various platforms like Google, Microsoft, AWS, Nuance or their own homegrown solutions.

It shouldn’t come as a surprise that demand for AI-based contact center tools is on the rise. These platforms can deliver major cost savings, productivity boosts and efficiency gains — all while supporting a better customer experience. Contact centers can find applications for this technology across all touch points and channels, enhancing the customer experience at every turn.

As with any cutting-edge technology, organizations embracing AI may face a number of obstacles that stand in the way of implementing and making the most of these solutions. With contact center AI, arguably the biggest hurdle facing companies is the ability to scale these platforms to meet demand. In particular, contact centers often have three major blind spots when scaling AI that need to be addressed: user experience (UX), automation and business agility.

1. Accounting for Multiexperience Demands

Both customers and employees engage with contact centers through several different formats and channels, each with its own user experience best practices and demands. For instance, interactions that begin through a phone call and IVR system are very different than those that start with a digital interface. It’s also more common for user journeys to move back and forth across different channels. A customer may try to troubleshoot an issue through a mobile app or chatbot and then receive a prompt to call a live agent directly to resolve that problem. As such, the modern contact center is pretty complex, with various touch points that interweave and need to be completely in sync to create a seamless user journey.

Multimodal experiences bridge the gap between disparate touchpoints and channels, which is great for the customer experience. At the same time, though, they increase the need for conversational experience tools to understand user intent and find the best path forward. Contact centers also need AI solutions to drive web-first customer journeys that begin on mobile apps, brand websites and other digital platforms by combining those conversational UX tools with graphical user interfaces.

Put all of these various demands together, and contact centers will undoubtedly see their AI requirements become more complex. That increased complexity makes it more challenging to scale contact center AI platforms and solutions to support enterprise operations.

2. Automating Across Complex Systems

Contact center AI tools are built on the foundation of automation, across both customer-facing and back-office systems. Organizations need to account for a wide variety of processes, systems and rules to effectively manage automation across the entire contact center and support AI-based solutions. That may be easier said than done, considering the sheer complexity of these environments. Consider the fact that some systems will have user interfaces (UI) while other back-office platforms won’t have any graphical UIs to manage. The same holds true for APIs, which makes it more difficult to integrate disparate systems and scale sophisticated conversational AI and automation across the entire contact center. 

Your approach to automation impacts scalability as well. If your team builds out each automation individually rather than using an end-to-end contact center automation solution, it will take an enormous amount of time and effort (not to mention money) to code each of those automations at a large scale.

3. Overcoming Business Agility and Continuous Improvement Obstacles

AI is often envisioned as some kind of “set it and forget it” solution that can handle whatever task is needed without any oversight. That’s not entirely true, however. Contact center AI solutions are not completely hands-free tools; companies still need to monitor robotic process automation (RPA) bots, measure their performance and look for opportunities for improvement. After all, if AI-enabled software robots aren’t delivering tangible results, why continue investing in them?

For instance, organizations need to assess customer engagement with particular parts of user experience and journey — say a virtual assistant that looks up account information on the brand website. If most users abandon their sessions before finding the information they’re looking for, that’s a good indication that those tools aren’t getting the job done.

To fully optimize the user journey, companies should look at every step, interaction and touch point in the contact center to see where they can reduce friction and improve the customer experience. Even the most sophisticated software robot can’t do that entirely on its own — you need a knowledgeable and experienced business analyst to understand what’s happening across the entire contact center and recognize where changes should be made. Those changes may be found at different levels of the AI solution, ranging from tweaks in conversational flows to complete overhauls of the tool itself.

On top of that, companies need a comprehensive governance model to oversee every interaction and automation in the contact center so they can capture the user journey in its entirety and better understand the customer experience. With so much ground to cover when it comes to their governance models, contact centers may face a steep hill to climb when it’s time to scale their AI projects. 

Remove Scalability Barriers With Low Code Conversational Middleware

These bottlenecks and challenges may seem daunting, but there is a way forward for contact centers to scale their AI solutions to cover the entire enterprise. The key is to pair those complex conversational AI platforms with low code middleware to streamline AI implementation, management and expansion. Low code middleware reduces the costs of AI initiatives by cutting out an enormous amount of the work required to roll out advanced contact center capabilities and integrate them with existing systems. That means contact centers can bring new AI solutions online as quickly as possible without requiring a hefty investment or lengthy development process. 

Check out our follow-up blog to learn more about low code middleware and how it can help you incorporate cutting-edge AI technologies into your contact center.

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