Comparison of the Best AI Chatbots in 2025: Find the Ideal Solution

How to create an AI chatbot: step-by-step method

Creating a successful AI chatbot requires a methodical approach. Here's how to do it without getting lost in the technique of a computer program.

🎯 Step 1: Define precisely the goals of your chatbot

Before any development, ask yourself these questions:

What problem do you want to solve with generative AI? Answers to the most frequently asked questions:

• Reduce customer service wait time (from 10 minutes to 30 seconds)
• Automate 70% of recurring questions
• Qualify leads before sales
• Offer 24/7 support in French or English without hiring
• Increase my conversion rate
• Answer questions with cutting-edge machine learning (Machine learning)

Concrete example: An e-commerce received 200 emails/day on “Where is my order?”. Their chatbot now processes 85% of these requests automatically by interviewing the delivery system directly.

📋 Step 2: Map your conversation flows

Fundamental difference between these artificial intelligence technologies and traditional chatbots:

Plan your system integrations:

• CRM (to retrieve customer data)
• Product database (for recommendations)
• Ticket system (for escalating to humans)
• Payment tools (for simple transactions)

🍽️ Step 3: Feed your AI with the right data

Essential data sources:

Conversational history (if available)

• Customer service emails from the last 6 months
• Phone call transcripts
• Social media posts

Existing documentation

• Updated FAQ
• Product user guides
• Standardized internal procedures

Data preparation example:

❌ Raw data: “Furious customer - order late - refund requested”
✅ Structured data:
Intent: Claim delivery
Emotion: High frustration
Action: Suggest follow-up + commercial gesture
Escalation: If refused → human counselor

🔧 Step 4: Choose your development platform

No-code solutions (recommended to get started)

Solutions with development

• DialogFlow (Google) : Powerful but technical
• Microsoft Bot Framework : Perfect Office integration
• Rasa (Open Source) : Full control, dev team required

🧪 Step 5: Continuously test and optimize

Beta testing phase (2-4 weeks):

• Deploy on a limited customer segment (20% of traffic)
• Watch for conversations where the bot is failing
• Measure the satisfaction rate (objective: > 80%)
• Collect user feedback

Key metrics to track:

• Resolution rate :% of conversations resolved without human intervention
• Average response time : < 3 seconds ideally
• Escalation rate :% of transfers to human advisors
• Satisfaction score : Rating given by users

Best practices for optimizing your AI chatbot

Prioritize the user experience above all

Conversational design rules:

• Natural language and consistent personality: Instead of: “Error 404 - Request not included"Prefer: “I am not sure I understand your request. Can you rephrase it?”‍
• Clear options without overwhelming: Offer a maximum of 3-4 choices at a time, with clickable buttons rather than free text when possible.

Successful omnichannel strategy

Cross-channel consistency: Your bot should recognize a user who switches from the site to WhatsApp and continue the conversation without repetition.

Transparency and smooth human transition

Clear bot identification:

• “Good morning! I'm Emma, [Company]'s virtual assistant.”
• Never a false human identity
• Distinctive avatar (no real person photo)

Smart escalation to humans:

• Automatic detection of frustration
• Trigger keywords: “emergency”, “complaint”, “not satisfied”
• Transfer with full context of the conversation

📊 Data-based personalization

Automatic user segmentation:

• New customers → Onboarding journey
• Premium customers → Direct access to dedicated experts
• Recurring Users → Personalized Recommendations

Example of advanced customization:

Customer identified: Marie Dupont
History: 3 textile orders, average budget 80€
Bot: “Hello Marie! I saw that you might be interested in our new fall collection. Do you want to discover what's new in your style?”

Limits and risks of AI chatbots to anticipate

🔒 Security and confidentiality issues

Data leak risks:

Mandatory GDPR compliance:

• Right to be forgotten: possibility to delete all data
• Explicit consent for collection
• Data processing documentation
• Appointment of a DPO if necessary

Managing AI biases and errors

📈 Monitoring and continuous improvement

Alert indicators to watch out for:

• Sudden drop in satisfaction
• Increase in transfers to humans
• Recurring complaints on the same subject
• Increased response time

Recommended maintenance schedule:

• Everyday : Verification of main metrics
• Weekly : Analysis of problem conversations
• Monthly : Knowledge base update
• Quarterly : AI model optimization

Solutions and platforms: detailed comparison

🏆 Market-leading platforms

For businesses with a large budget

IBM Watsonx Assistant

• Price: Starting at 140€/month
• Strengths: Proven Watson AI, robust enterprise integrations
• Ideal for: Key accounts, banking/insurance sector
• Customer example: Société Générale processes 40% of its after-sales service requests via Watson

Microsoft Bot Framework + Cognitive Services

• Price: 3-15€ for 1000 transactions
• Strengths: Seamless integration with Microsoft ecosystem
• Ideal for: Businesses using Office 365/Teams
• Use case: Internal HR bot for leave/training management

Accessible SMEs and VSEs solutions

🔍 AI business search solutions

Problem : Your employees waste 2 hours a day looking for scattered internal information.

Emerging technologies:

• Microsoft Viva Topics: Automatic mapping of expertise
• Elasticsearch + AI: Semantic search in documents
• AI concept: Integrated artificial intelligence knowledge base

Typical ROI : Gain of 30 min/day/employee = €2,500/year saved for an employee at €50K per year.

🛠️ Criteria for choosing your solution

Practical evaluation grid

Evaluate your needs according to this matrix:

Test before invest approach

Recommended pilot phase (2 months):

1. Week 1-2: Basic installation and configuration
2. Week 3-6: Tests with a small internal team
3. Week 7-8: Deployment on 10% customers/users
4. Final evaluation: Go/no-go decision based on metrics

Typical pilot budget: €500-2,000 depending on the solution chosen.

This approach makes it possible to validate the solution/needs adequacy without long-term commitment or massive initial investment.

Final verdict

At the end of this overview, one thing is certain: AI chatbots are emerging as new essentials of the customer experience and operational efficiency.

Thanks to their natural language processing capabilities, their personalization of interactions, and their 24/7 availability, these new generation conversational agents streamline the user journey.

Support, conversion, commitment... Their use cases are constantly expanding for meet the needs of each business.

Of course, to get the most out of these tools, you still need to choose a solution adapted to your challenges and set up quality human support.

Because if AI excels at dealing with repetitive tasks and the analysis of large volumes of data, nothing replaces a dedicated team to supervise and optimize your chatbot.

The future therefore belongs to companies that will successfully combine artificial intelligence and the human emotional intelligence of an AI chatbot to offer a ever more fluid and personalized customer experience.

Other recommended reading:

• Numerous business intelligence tools are also using AI to ensure that the right decisions are made.
• In addition, the detection of AI-generated content, the content generation tools Or the customer data platforms use the power of AI to gather specific customer information and consolidate it in one place.

FAQS

How do AI chatbots work?

AI chatbots use natural language processing (NLP) and machine learning to understand user requests. They analyze messages to extract key information and determine intent in order to provide relevant responses.

What is an AI chatbot platform?

An AI chatbot platform allows businesses to manage several chatbots in the same place, on different channels (messaging, SMS, emails, website). This makes them easier to manage and makes it possible to improve their efficiency thanks to AI.

Is Siri an AI Chatbot?

Siri is considered to be a basic chatbot. Its natural language processing (NLP) and conversation capabilities are limited compared to more advanced AI chatbots.

How can chatbots help me save money?

AI-based chatbots automate repetitive tasks that are usually done by humans. By answering frequently asked questions instantly, they free up time for support teams. Businesses thus optimize their resources while maintaining excellent customer service.