How easy is it to Implement an AI driven Chatbot?

Writing the Cheque is the Easy Part...

Deploying an AI-driven chatbots requires a combination of technical, infrastructural, and design components making the ease of implementation dependent on many factors. While it's not all "doom and gloom", like anything else it will depend on the degree of complexity you require along with risk tolerance; driven also by the degree of due diligence you feel is justified.

Here are some observations and considerations: 

1.  Call/Workflow

• Call & Workflows need to be documented ensuring that they meet all of the stakeholder business requirements​

• Change management process is in place

2. Technical Expertise

• High: Requires strong knowledge of AI, NLP, and programming.

• Medium: Easier if using pre-built platforms like Dialogflow or Microsoft Bot Framework.

• Low: Low-code or no-code platforms can simplify the process significantly.

3. Use Case Complexity

• Simple Use Cases: Basic Q&A bots or simple conversation flows are relatively easy to implement, often taking days or weeks.

• Complex Use Cases: Bots requiring custom NLP models, integrations with various systems, or handling sensitive data will be more challenging and time-consuming, potentially taking months.

4. Tools & Platforms

• Pre-Built Platforms: Tools like Dialogflow offer templates and drag-and-drop interfaces, making it easier to deploy a basic chatbot with minimal coding.

• Custom Development: Building a chatbot from scratch provides flexibility but requires more time and resources, as you’ll need to manage everything from model training to infrastructure setup. In most cases, this should not be necessary.

5. Infrastructure Setup

• Cloud Services: Using cloud services like AWS, Google Cloud, or Azure can simplify the setup because these services typically come with built-in services for AI and NLP.

• On-Premises: More complex and time-consuming, as you’ll need to manage servers, databases, and security.

6. Integration Requirements

• Minimal Integrations: Easier to implement if the chatbot doesn’t need to connect to many external systems.

• Multiple Integrations: Complex integrations with CRMs, databases, and other software can increase the complexity and implementation time.

7. Customization Needs

• Out-of-the-Box Solutions: If you’re fine with the standard features provided by chatbot platforms, implementation is quicker and easier.

• Custom Features: Developing custom features will require more development time and specialized skills.

8. Maintenance and Updates

• Minimal Maintenance: If you choose a managed service or a platform that handles updates automatically, maintenance is easier.

• Ongoing Maintenance: Custom solutions will require ongoing attention to bug fixes, updates, and data model retraining, which can be resource-intensive.

9. Setting up & Managing a Knowledgebase

• Content Collection: Identify the information required

• Organize Content

• Content Sourcing

• Data Structuring: Using tables, databases and spreadsheets, Structured vs. Unstructured data and designing NLP techniques for the chatbot to understand

• Contextual Relationships between concepts and topics

• Integration with the chatbot

• Content Refresh and Version Control

• Ongoing Quality Audits

10. Implementation Timeline

• Simple Chatbots: A few days to a couple of weeks using pre-built platforms and templates.

• Moderately Complex Chatbots: A few weeks to a couple of months, particularly if custom integrations or AI model training is required.

• Highly Complex Chatbots: Several months, especially if building custom solutions from scratch and requiring extensive testing and iterations.

11. Cost Considerations

• Low-Cost: Using a free or low-cost platform with basic features.

• Medium-Cost: Paid platforms with more advanced features or hiring developers for custom work.

• High-Cost: Full-scale custom development with ongoing maintenance and scaling.

12. Overall Ease

• Easy: If your requirements are straightforward and you leverage existing platforms.

• Moderate: If you need some customizations and integrations.

• Challenging: If building a highly tailored solution with specific AI needs, requiring significant technical expertise and resources.

Summary

Implementing an AI-driven chatbot can be relatively straightforward, however this technology requires a structured knowledge source and/or knowledgebase in place with the appropriate resources whom are committed to updating and performing ongoing Quality audits.

Eric Young
President
Tele-Centre Assist Inc.
www.telecentreassist.com