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As customer and employee experiences become central to business success, front office automation technologies like Conversational AI are gaining traction in enterprises. Organizations are increasingly adopting AI-powered virtual assistants to streamline routine interactions with customers and employees. If you're considering integrating a new technology into your business operations, several key parameters should be evaluated.

Recently, we had a dynamic conversation with two seasoned Conversational AI automation leaders: Saima Shafiq (VP Cognitive and Centre for Excellence Manager at PNC), Kamal Parasuarm (Manager of Automation at Signify), and the team at Kore. This blog encapsulates their insights and advice on evaluating, implementing, and driving the adoption of Conversational AI within large enterprises.

What is Conversational AI?

Conversational AI can be succinctly described as a self-service model that enables users to resolve issues quickly and seamlessly, enhancing organizational efficiency. Saima Shafiq explains, "Conversational AI empowers users and developers to handle the solution without prior training." Kamal Parasuarm adds, "If you want to make your organization more self-reliant and move towards self-service, then Conversational AI is the way forward."

Kore's virtual assistant platform supports enterprises in creating a digital-first society by automating over 80% of routine business interactions.

When and Why Should You Consider the Technology?

Conversational AI is most beneficial for high-frequency, time-consuming tasks that lead to long wait times, such as password resets. For Signify, this meant transforming a 20-minute task into a 20-second self-service action. Their strategic approach aimed to maintain their technology leadership and optimize costs by reducing repetitive and manual tasks that increased wait times.

PNC also adopted Conversational AI for strategic reasons, focusing on high-volume, repetitive requests that detract from employees' ability to add value to their workday. Identifying and highlighting the value proposition for each use case is crucial for organizational buy-in and operational benefits.

Should You Buy or Build Your Solution?

This common dilemma can be addressed by asking key questions:

  • Is this our core business and strength?
  • Can we commit the resources and time to build and maintain the solution?
  • Will the solution solve our problem or create more challenges?
  • Can we scale it?

Both PNC and Signify opted to "buy from the expert," allowing them to focus on their core business while leveraging the vendor's expertise. Saima Shafiq emphasizes, "We did not want to reinvent the wheel."

How Should You Select the Platform?

When selecting a Conversational AI platform, consider these six key aspects:

  • Go-to-market speed
  • User simplicity
  • Accuracy of intent recognition
  • NLP training
  • Deployment options
  • Data reporting capabilities

Signify prioritized creating a seamless user experience, ensuring the virtual assistant could respond contextually and support agent transfers. PNC highlighted the importance of security, NLP accuracy, and involving business users in the development process.

What is at Stake if You Buy the Wrong Conversational AI Platform?

Choosing the wrong platform can lead to frustrated customers and low adoption rates. A platform without robust NLP technology can fail to support various contexts, making users feel like they're interacting with a static webpage. Ensuring a positive user experience is crucial to avoid escalations to live agents.

What Should Your Team Composition Look Like?

A successful Conversational AI team should include:

  • Solution architect
  • Business analyst
  • Process owner
  • Conversational flow designer
  • Developers skilled in NLP

Additionally, testing and QA teams are vital to ensure the product meets standards before launch. Building a team with the right attitude and a willingness to drive digital transformation is equally important.

How Can You Start Working on the Solution?

Starting with a small pilot project and scaling up is advisable. Choose a simple use case, such as a password reset, and gradually expand to more complex scenarios. A realistic timeline for deployment, considering integration and language functionality, is crucial for success.

What Are the Typical Use Cases?

Businesses should focus on high-volume, high-wait-time areas. Signify started with password resets and expanded to over 65 use cases, including ITSM, installation, service request queries, and more. PNC suggests starting with common customer inquiries or internal processes like HR and procurement.

What Results Can You Expect?

Success in Conversational AI is measured by adoption rates, containment rates, and user feedback. Key metrics include improved productivity, data accuracy, and expanded automation across business lines. The ultimate goal is to enhance customer satisfaction and maintain enthusiasm for ongoing digital transformation.

The experiences shared by PNC and Signify illustrate the journey from exploring Conversational AI to achieving successful deployment. Whether you are in the exploratory, evaluation, adoption, or scaling stage, we hope this discussion provides valuable insights. We invite you to share your questions and experiences with Conversational AI implementation.

Kore’s Virtual Assistant platform enables large organizations to automate up to 80% of routine interactions, reducing costs and enhancing experiences. Contact us to discuss specific use cases.

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