AI in the contact center: the promise, the reality, and the future
Companies across all industries are experimenting with a suite of artificial intelligence (AI) solutions in the contact centre to improve customer care, increase operational efficiency and enhance security. And that experimentation will continue to grow. In fact, MIT found that by the end of 2020, 97% of large companies surveyed will deploy AI.
In a recent webinar, we sat down with Claire Beatty, a leading analyst from MIT, and Aarde Cosseboom from TechStyle to learn about the driving forces behind AI adoption, current AI use cases, challenges and trends. It also explores how COVID-19 pandemic has changed the trajectory of AI in the contact centre.
Here are some of the highlights from the event.
How to Deliver Personalised Customer Experiences at Scale
In the global report, MIT surveyed over 1,000 senior executives of leading brands to identify how they’re using AI today — and how they plan to use it in the future. When we took a closer look at the results, we found that AI is playing a variety of different roles across all business sectors. One of the top use cases is customer care efforts.
TechStyle, a leader in online retailer, implemented AI to stand apart from the competition. With 5 million members, 6 million phone calls per year and 3 million chats per year, communication is core to its business. And the company leverages AI to help with that communication back and forth. By integrating AI, TechStyle:
- Reduced average handle time by 45 seconds
- Saved $1.1 million in the first year in operations costs
- Achieved a score of 92% in its member satisfaction survey
Genesys AI gives agents the ability to go beyond efficiency and effectiveness to be more empathetic and earn loyalty through stronger connections and better results. By leveraging insights from historical, third-party and behavioural data, AI unifies and filters this data to provide agents with the complete context of the customer. It then turns these mounds of data into real-time insights and actions.
Three Best Practices for Integrating AI
While AI is deployed widely across industries, MIT found that implementing and scaling technology is difficult for many organisations. Existing technology limitations, process and culture change, and talent shortages are all constraints to using AI.
This is why Genesys built a platform with robust AI capabilities that empower organisations to integrate and successfully use AI across marketing, sales and service. Here’s how Genesys AI overcomes common implementation challenges.
- Data quality or availability issues: Genesys AI seamlessly integrates CRM systems, native AI capabilities and machine learning algorithms, as well as third-party technologies. For example, Entel uses Genesys technology and multiple third-party solutions to understand and best serve its customers.
- Unable to demonstrate business value with AI: Genesys offers proof of concepts that show business value quickly. For example, African Airlines saw a 14% increase in conversion rates in two weeks and a 49% boost in six weeks by using predictive engagement capabilities.
- Shortage of AI developers/data scientists: With Genesys AI, business and IT users can easily build workflows, quickly create bots and easily integrate with third-party technology and data. As an example, TechStyle deployed the Lex bot in two days.
To hear more about AI use cases, best practices for implementing and scaling AI, and market trends in the adoption of AI in the contact centre, watch the on-demand MIT webinar.
And request a demo to learn more about how Genesys AI can help you deliver personalised customer experience at scale talk with one of our AI experts today.
Digital Strategy Specialist, Genesys