I had the pleasure of attending Enterprise Connect in late March. The event confirmed its status as the mecca of the CX industry, with the entire ecosystem— technology providers, solution providers, channels, and analysts — gathered in Orlando. It provided a fantastic opportunity to take the pulse of the industry and identify the key trends shaping technology adoption.
AI has become ubiquitous across the customer experience landscape. It was nearly impossible to attend a presentation or visit a vendor's booth that did not prominently showcase AI. Features such as automated summarization and agent assist are rapidly becoming table stakes.
While the widespread adoption of AI is undoubtedly exciting, it has also led to a certain level of confusion in the market. Product descriptors like "Copilots" or "CX Platforms" have become commonplace, making it challenging for customers to truly understand the specific capabilities and differentiators of various solutions. It's time for providers to refine their positioning, better articulating how their AI capabilities can be put to work, and sharpening how their offerings stand out in this crowded landscape.
Encouragingly, the industry is also recognizing the critical importance of data in driving successful AI initiatives. Several announcements made at the industry event highlighted the increased focus on data management and integration:
Five9 introduced GenAI Studio, which allows users to create prompts leveraging specific context data.
NICE unveiled Enlighten Experience Memory, a solution that stitches together customer interactions to provide a unified view.
Twilio announced its Unified Profiles capability to consolidate customer data.
Verint released the integration of its Unified Data Hub into the Cisco Webex Contact Center platform.
One particularly insightful moment came during the CX Keynote Panel on Generative AI, where Danielle Joiner McPherson, a practitioner from Delta, offered a reality check on actual AI adoption. Her commentary shed light on the growing disconnect between the AI capabilities touted by vendors and what customer service organizations have actually been able to successfully deploy.
Enterprises often have limited resources and can, at best, pursue a select few AI experiments at a time. To bridge this gap, technology providers must up their game and help customers prioritize the most impactful AI use cases and chart step-by-step paths to effectively test and scale these AI-powered applications.