4 Critical Phases to a Successful Implementation
In a recent blog post, What is Conversational AI and How is it Better than a Chatbot?, I discussed the value of Conversational AI and how businesses are adopting the technology to solve real business challenges. Once implemented, Conversational AI has enormous potential to improve operational efficiency and increase customer satisfaction. However, like anything else, it must be implemented correctly, guided by a strategically designed roadmap and plan. A successful implementation of Conversational AI will follow the following four phases:
Assess & Discover
The first (and perhaps most important) phase is assessing your current state and prioritizing the processes that are best suited for automation. This prioritization should include the following criteria:
- Business Value
- Organizational Readiness
- Ease of Implementation
By identifying which processes and use cases to focus on, This phase will provide the most immediate ROI—while also taking into account the level of change management required, and the level of effort needed to implement. It’s critical to identify the required solution architecture and integrations during this phase as well.
Anexinet’s 3-week Conversational AI Strategy & Roadmap Kickstart ensures your organization takes the right strategic approach to adopting Conversational AI.
Once the highest-priority use cases have been identified, the next phase centers on creating a detailed design for automating those processes with Conversational AI. The in-depth design will include the following details for the identified use cases:
- Solution Architecture
- System Integrations
- Infrastructure Requirements
- Recognition Architecture for Entitities, Intents, FAQs
- Conversational Diagrams
- Compliance/Regulatory Review
- Change Management Requirements
- Project Governance
This design phase will create the framework upon which to build a production pilot solution that will provide business value and set your organization up for success.
Build & Test
After completion of the design phase, the focus shifts to building and testing the solution for the highest priority use cases. This includes building the systems integrations that are required, processing training data to train the system, and optimizing the conversation flow to create a great user experience.
During the initial build, we create a test plan and begin functional and stress testing. After the initial testing, user acceptance testing begins and business users provide sign-off of the solution.
The build and test phase is where the hard work from the first two phases starts to payoff. The solution is now ready for production and implementation to solve the initial use cases.
Optimize & Maintain
By the last phase, clients are ready for full organizational adoption of Conversational AI. The use cases have been identified and prioritized, a pilot has successfully been designed and launched, additional use cases have been built and tested, and ROI has been realized for the initial high-priority use cases. Now, the framework that has been developed must be maintained as data sources and integrations may change or be updated. As more data is fed into it, the Conversational AI platform continues to improve via supervised learning; processes will need to be adapted to leverage those improvements. Additional processes that have been slated for automation (or new processes that were revealed) are now also ready for implementation.
Conclusion
Ensuring a successful implementation requires taking a very strategic approach to avoid pitfalls and mistakes. By following these four phases, companies will be able to truly realize the financial and customer experience benefits of adopting Conversational AI. Our partnership with Amelia—the industry’s leading conversational AI platform—enables us to offer end-to-end Conversational AI solutions, including strategy, design, implementation and managed services. Our Conversational AI Strategy Kickstart provides a well-defined plan for adopting cognitive, Conversational AI to maximize efficiency, boost accuracy, reduce costs, and provide consistently amazing customer experiences in just three weeks. Please reach out to us at any time for more information. We’d love to help you get started.
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Brian Atkiss
Director, Omni-Channel Analytics
Brian Atkiss is a Director of Analytics, and a ListenLogic Product Manager, focused on omni-channel and unstructured data analysis at Anexinet. Brian has nearly a decade of experience building analytics solutions that generate actionable insights for the Fortune 500, and has an extensive background in social listening and advanced analytics solutions around data integration, machine learning, and artificial intelligence.
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