Conversational AI Explained—What is it and Why is it Important
Conversational AI—What is it?
Conversational AI is a unified collection of technologies that automates conversations with self-service and augments human-to-human conversations with turn-by-turn guidance. These technologies include natural language understanding, dialog management, reasoning, Machine Learning for intent inference, omnichannel integration, connectivity with backend systems, personalization, and analytics. Also important are tools to capture, design, and author content and process knowhow. Examples of conversational AI include customer-facing chatbots or virtual customer assistants and virtual assistants for contact center agents.
Conversational AI—Why is it important?
Customers are demanding more intelligent self-service. As self-service gets smarter, agents are left to handle more complex queries. With the disruption of traditional training programs and agent dislocation, triggered by the recent pandemic, agent performance management and retention in a hybrid work environment has become a big concern for businesses. Moreover, the uncertain economy is putting pressure on operations and CX executives to control or even cut costs with automation. Conversational AI and modern knowledge management can very well be a panacea for these challenges.
The Gartner Market Guide for Customer Service Knowledge Management Systems, Sep 2022
Conversational AI—Examples of transformational value
Virtual customer assistants are a win-win for the customer and the business. They allow businesses to scale customer service cost-effectively while providing customers 24×7 access to service. Here are stunning self-service success stories from eGain clients:
- A mammoth federal government agency deflected up to 70% of incoming calls to virtual assistance, reduced case handling time by 25%, and improved form-filling with granular assistance within forms, all powered by knowledge and conversational AI. No wonder these powerful capabilities elevated their agent engagement to 92% versus their industry benchmark of 67%.
- Hypergrowth online retailer in the UK deflects up to 90% of phone calls with the eGain Virtual Assistant, powered by conversational AI.
- Leading US omnichannel retailer is automating and augmenting customer service, leveraging digital self-service, messaging, chat, and IVR deflection to digital service, all backed by eGain knowledge and conversational AI. The retailer handles over 9 million customer contacts per year, deflecting 45% of phone contacts and 30% of IVR contacts with digital self-service and chat messaging, delivering joined-up omnichannel service with context-aware escalation to agent-assisted service.
- A multinational financial services provider is leveraging eGain Conversational AI and knowledge for customer service, elevating First Contact Resolution (FCR) by 36%, while cutting training time by 40%.
- A UK-based telco giant achieved a 30-point improvement in NPS (Net Promoter Score) and a 37% improvement in FCR (First-Contact Resolution), while reducing agent time to competency by 50% across 30,000 contact center agents and 600 retail stores and enabling any agent to handle any call, by service automation and agent augmentation with eGain’s conversational AI and knowledge.
Conversational AI—What to look for in a provider
Make sure that the technology supports conversational AI across multiple channels in a unified way, allowing for seamless transition from one to another—chatbot to live agent chat without losing context, for example. Beyond channel integration, does the technology include natural language processing across multiple languages, intent inference, refined by ML, integration with voice, AI reasoning for conversational and process knowhow, search methods, and analytics? Also remember that the end-goal of conversational AI is to help a customer or employee achieve a specific goal with content and next-best process steps. Without robust content management capabilities, integrations with existing trusted content sources, and best-practice-based content strategy, conversational AI initiatives will fall short.
2. Rich functionality out of the box
Does the system offer breadth and depth of capabilities out of the box for quick time to value? Watch out for toolkits that will require huge investments in time, money, and effort to build best-in-class functionality. Also, watch out for point products that do not have all the essential technology building blocks for conversational AI success—you will then have to acquire multiple point products, which will create technology and knowledge silos and you will be struggling perpetually with integrations and content and knowhow synchronization.
3. Domain expertise
Does the vendor have domain expertise and offer best practices for success end to end—from initial deployment to value creation and expansion? Does the vendor offer expertise specific to your industry sector? You do not want to roll the dice with a newbie, who will be learning at your time and dime!
4. Time to value
In a world operating at the speed of digital, conversational AI initiatives often die a quick death if they don’t show business value in a matter of days or weeks. Ask for examples where the vendor has shown quick value, and ask how long it took.
5. Security, scale, and compliance
Is the solution compliant with privacy and security standards such as PCI, NIST SP 800-53, HIPAA, HITRUST, and FedRAMP? Can it scale to tens of thousands of agents? Ask for success stories at scale.
Does the vendor have a method for modeling and measuring the business value of conversational AI and knowledge management? Do they have a strategy for content management—what to mirror, federate, migrate to their system, or eliminate? Does the vendor have a systematic engagement and collaboration methodology to report progress on an ongoing basis and work towards short-term and long-term goals in concert with your business?
7. Implementation and support services
Does the vendor offer comprehensive services and a proven implementation methodology, informed by domain expertise and best practices? You want a provider, who has been in the trenches and even solved corner cases, guiding implementations to success. Can the vendor bring partners to the initiative, where needed? Here are the services to look for:
- Managed services
- Knowledge creation
- Berichte und Analysen
- Education and certification
- Online and in-person education, training, and certification at scale
8. Connectors and extensibility
Conversational AI systems cannot function in isolation. To be effective, they need to integrate with existing enterprise systems and contact center infrastructure as well, in the case of the customer service use-case. Furthermore, forward-looking organizations want to extend the capabilities of packaged SaaS solutions by innovating on their platform. Check if the provider offers the following enablers for enhancing the solution:
- Pre-built integrations with ECM, CRM, and other systems of record for 360 context
- Rich API library
- Developer portal and enablement
- Marketplace for complementary solutions
9. Risk-free innovation
Does the vendor put skin in the game by offering a production pilot at no charge? Do they make it a Wow pilot or just shoot for an MVP trial, or worse yet, give you a toy sandbox and walk away? Do they provide best-practice guidance to success? Piloting the solution with project stakeholders is critical to adoption and success.
10. Client success
Does the provider have a track record of success with Global and Fortune 500 companies? What were the scale and business value from those deployments? What was the time to value? How do clients rate them on a trusted client review site like the Gartner Peer Insights?
Vendor selection is the critical first step in any conversational AI initiative. Using the above checklist will maximize your odds of success.
Conversational AI—How to get going with us
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Conversational AI thought leadership
- Consulting: Conversational AI: Five vectors of progress (Deloitte)
- Analyst: Gartner Predicts Conversational AI Will Reduce Contact Center Agent Labor Costs by $80 Billion in 2026 (Gartner)
- Book: Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots (Synthesis Lectures on Human Language Technologies), Michael McTear
- Technical Paper: Conversational AI: The Science Behind the Alexa Prize (Ashwin Ram & 17 others)
- Conversational AI for Sales: The Missed Opportunity to Boost Revenue
- 12 Reasons Why Customer Service Chatbots Fail
- The Essential Complement to Agent Training in the Modern Contact Center: Knowledge and AI
- Cost Avoidance with Artificial Intelligence
- Digital Experience Transformation Needs AI and Knowledge
- Whitepaper: AI in Customer Service Contact Center