#manual vs automated AI text conversion
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Difference Between Manually Humanizing AI Text vs Using an AI To Human Text Converter
AI-generated content has become increasingly prevalent. Whether for academic writing, blog posts, or content marketing, AI-generated text can serve as a quick starting point for various writing tasks. However, thereâs a clear distinction between manually humanizing AI text and using a reliable AI to human text converter. AI To Human Text Converter offers an efficient, free tool to convert AI-generated text into human-like content without compromising meaning, making it a must-have resource for content creators, students, and professionals.
What is AI to Human Text Conversion?
AI to human text conversion refers to transforming machine-generated content into natural, human-readable text. AI-generated text is often monotonous, lacks engagement, and reads robotically, making it necessary to humanize it for professional use.
When it comes to humanizing AI text, there are two approaches:
Manual Humanization: Manually refining the content by rewriting it, adjusting tone, and improving readability.
Using an AI To Human Text Converter: Automating the process with a specialized tool that instantly converts AI-generated text into a more natural-sounding format.
Letâs explore the differences between these two methods.
Manual Humanization of AI Text
Manually humanizing AI text can be effective but comes with its own set of challenges.
Speed: Manually editing and rewriting AI-generated content is time-consuming. It requires a keen understanding of tone, context, and structure to make the text flow naturally.
Complexity: Manually humanizing text demands a high level of effort and expertise. You need to rephrase sentences, break down robotic language, and ensure that the text makes sense while retaining its original meaning.
Security Risks: When manually humanizing AI text, especially if it's sensitive content, the risk of human error increases. Thereâs always a chance that important details could be misrepresented or misunderstood.
Limitations: Manually refining large volumes of AI-generated text is often overwhelming. This method is not ideal for those who need to process substantial content within tight deadlines.
Using an AI To Human Text Converter
Conversely, an AI To Human Text Converter automates the entire process and offers several benefits over manual humanization.
Speed: Converting AI-generated text into human-readable content is nearly instantaneous with a tool like AI To Human Text Converter. Instead of spending hours manually editing content, users can generate human-like text in seconds.
Simplicity: The toolâs user interface is straightforward, requiring no prior knowledge or expertise to use effectively. Just paste the AI-generated text, click convert, and youâre done!
Security: Since the conversion happens without the need for third-party apps or plugins, security risks are minimized. The tool allows users to safely convert any content while ensuring confidentiality.
No Limitations: With AI To Human Text Converter, there are no usage limits. You can convert as much AI text as needed without worrying about word counts, logins, or subscription fees.
Completely Free: Unlike many online tools that charge for premium access or impose restrictions on free versions, AI To Human Text Converter is entirely free with unlimited usage. No login, signup, or subscription is required.
Key Features Comparison
FeaturesManual Humanize AI TextAI To Human Text ConverterConversion of AI-Generated ContentAvailableAvailableUser InterfaceNot ApplicableStraightforwardSpeedSlowerExtremely FastSecurity RiskRisk-proneSecureUsage LimitationsLimitedUnlimited
Why Use AI To Human Text Converter?
If youâre tired of spending hours trying to manually humanize AI-generated content or need a quick, efficient way to transform robotic text into something engaging and natural, AI To Human Text Converter is the perfect solution. Hereâs why itâs the best choice for you:
Free of Cost: The tool is 100% free with no hidden charges. Convert as much text as you need without worrying about subscriptions or limits.
No Authentication Required: Thereâs no need to sign up or log in. The tool is ready for immediate use.
Unlimited Usage: Whether youâre working on a single article or hundreds of pages, AI To Human Text Converter allows you to convert unlimited content.
Quick and Accurate: Get your AI-generated content humanized in seconds, with natural flow and readability.
Ideal for Multiple Applications: Whether itâs for assignments, essays, marketing materials, or blog posts, this tool ensures your text is suitable for any professional setting.
Why Manual Humanization is Not Always Practical
While manually humanizing AI-generated text can yield good results, itâs often impractical. Hereâs why:
Time-Consuming: If you have large volumes of text, manually refining it will take too long. For students or professionals working on tight deadlines, this isnât a viable solution.
Inconsistency: Manually humanizing content introduces the risk of inconsistencies. What may seem like a minor adjustment to one part of the text might lead to inconsistencies in tone or style.
Increased Effort: Editing AI content manually requires considerable effort, especially for non-professionals who may struggle with rewriting sections effectively.
Choosing between manually humanizing AI text and using an AI to human text converter depends on your needs, time constraints, and the volume of content youâre working with. For anyone looking to save time and effort, AI To Human Text Converter is an invaluable tool. It simplifies the process, delivers human-like text instantly, and comes with no cost or limitations.
Try AI To Human Text Converter today, and discover how easy it is to humanize AI-generated text with just a few clicks!
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Digital Marketing Course in New Chandkheda
1. Digital Marketing Course in New Chandkheda Ahmedabad Overview
2. Personal Digital Marketing Course in New Chandkheda â Search Engine Optimization (SEO)
What are Search Engines and Basics?
HTML Basics.
On Page Optimization.
Off Page Optimization.
Essentials of good website designing & Much More.
3. Content Marketing
Content Marketing Overview and Strategy
Content Marketing Channels
Creating Content
Content Strategy & Challenges
Image Marketing
Video Marketing
Measuring Results
4. Website Structuring
What is Website?- Understanding website
How to register Site & Hosting of site?
Domain Extensions
5. Website Creation Using WordPress
Web Page Creation
WordPress Themes, Widgets, Plugins
Contact Forms, Sliders, Elementor
6. Blog Writing
Blogs Vs Website
How to write blogs for website
How to select topics for blog writing
AI tools for Blog writing
7. Google Analytics
Introduction
Navigating Google Analytics
Sessions
Users
Traffic Source
Content
Real Time Visitors
Bounce Rate%
Customization
Reports
Actionable Insights
Making Better Decisions
8. Understand Acquisition & Conversion
Traffic Reports
Events Tracking
Customization Reports
Actionable Insights
Making Better Decisions
Comparision Reports
9. Google Search Console
Website Performance
Url Inspection
Accelerated Mobile Pages
Google index
Crawl
Security issues
Search Analytics
Links to your Site
Internal Links
Manual Actions
10. Voice Search Optimization
What is voice engine optimization?
How do you implement voice search optimization?
Why you should optimize your website for voice search?
11. E Commerce SEO
Introduction to E commerce SEO
What is e-commerce SEO?
How Online Stores Can Drive Organic Traffic
12. Google My Business: Local Listings
What is Local SEO
Importance of Local SEO
Submission to Google My Business
Completing the Profile
Local SEO Ranking Signals
Local SEO Negative Signals
Citations and Local
Submissions
13. Social Media Optimization
What is Social Media?
How social media help Business?
Establishing your online identity.
Engaging your Audience.
How to use Groups, Forums, etc.
14. Facebook Organic
How can Facebook be used to aid my business?
Developing a useful Company / fan Page
Establishing your online identity.
Engaging your Audience, Types of posts, post scheduling
How to create & use Groups
Importance of Hashtags & how to use them
15. Twitter Organic
Basic concepts â from setting-up optimally, creating a Twitter business existence, to advanced marketing procedures and strategies.
How to use Twitter
What are hashtags, Lists
Twitter Tools
Popular Twitter Campiagns
16. LinkedIn Organic
Your Profile: Building quality connections & getting recommendations from others
How to use Groups-drive traffic with news & discussions
How to create LinkedIn Company Page & Groups
Engaging your Audience.
17. YouTube Organic
How to create YouTube channel
Youtube Keyword Research
Publish a High Retention Video
YouTube ranking factors
YouTube Video Optimization
Promote Your Video
Use of playlists
18. Video SEO
YouTube Keyword Research
Publish a High Retention Video
YouTube Ranking Factors
YouTube Video Optimization
19. YouTube Monetization
YouTube channel monetization policies
How Does YouTube Monetization Work?
YouTube monetization requirements
20. Social Media Tools
What are the main types of social media tools?
Top Social Media Tools You Need to Use
Tools used for Social Media Management
21. Social Media Automation
What is Social Media Automation?
Social Media Automation/ Management Tool
Buffer/ Hootsuite/ Postcron
Setup Connection with Facebook, Twitter, Linkedin, Instagram, Etc.
Add/ Remove Profiles in Tools
Post Scheduling in Tools
Performance Analysis
22. Facebook Ads
How to create Business Manager Accounts
What is Account, Campaign, Ad Sets, Ad Copy
How to Create Campaigns on Facebook
What is Budget & Bidding
Difference Between Reach & Impressions
Facebook Retargeting
23. Instagram Ads
Text Ads and Guidelines
Image Ad Formats and Guidelines
Landing Page Optimization
Performance Metrics: CTR, Avg. Position, Search Term
Report, Segment Data Analysis, Impression Shares
AdWords Policies, Ad Extensions
24. LinkedIn Ads
How to create Campaign Manager Account
What is Account, Campaign Groups, Campaigns
Objectives for Campaigns
Bidding Strategies
Detail Targeting
25. YouTube Advertising
How to run Video Ads?
Types of Video Ads:
Skippable in Stream Ads
Non Skippable in stream Ads
Bumper Ads
Bidding Strategies for Video Ads
26. Google PPC
Ad-Words Account Setup
Creating Ad-Words Account
Ad-Words Dash Board
Billing in Ad-Words
Creating First Campaign
Understanding purpose of Campaign
Account Limits in Ad-Words
Location and Language Settings
Networks and Devices
Bidding and Budget
Schedule: Start date, end date, ad scheduling
Ad delivery: Ad rotation, frequency capping
Ad groups and Keywords
27. Search Ads/ Text Ads
Text Ads and Guidelines
Landing Page Optimization
Performance Metrics: CTR, Avg. Position, Search Term
Report, Segment Data Analysis, Impression Shares
AdWords Policies, Ad Extensions
CPC bidding
Types of Keywords: Exact, Broad, Phrase
Bids & Budget
How to create Text ads
28. Image Ads
Image Ad Formats and Guidelines
Targeting Methods: Keywords, Topics, Placement Targeting
Performance Metrics: CPM, vCPM, Budget
Report, Segment Data Analysis, Impression Shares
Frequency Capping
Automated rules
Target Audience Strategies
29. Video Ads
How to Video Ads
Types of Video Ads
Skippable in stream ads
Non-skippable in stream ads
Bumper Ads
How to link Google AdWords Account to YouTube Channel
30. Discovery Ads
What are Discovery Ads
How to Create Discovery Ads
Bidding Strategies
How to track conversions
31. Bidding Strategies in Google Ads
Different Bidding Strategies in Google AdWords
CPC bidding, CPM bidding, CPV bidding
How to calculate CTR
What are impressions, impression shares
32. Performance Planner
33. Lead Generation for Business
Why Lead Generation Is Important?
Understanding the Landing Page
Understanding Thank You Page
Landing Page Vs. Website
Best Practices to Create Landing Page
Best Practices to Create Thank You Page
What Is A/B Testing?
How to Do A/B Testing?
Converting Leads into Sale
Understanding Lead Funnel
34. Conversion Tracking Tool
Introduction to Conversion Optimization
Conversion Planning
Landing Page Optimization
35. Remarketing and Conversion
What is conversion
Implementing conversion tracking
Conversion tracking
Remarketing in adwords
Benefits of remarketing strategy
Building remarketing list & custom targets
Creating remarketing campaign
36. Quora Marketing
How to Use Quora for Marketing
Quora Marketing Strategy for Your Business
37. Growth Hacking Topic
Growth Hacking Basics
Role of Growth Hacker
Growth Hacking Case Studies
38. Introduction to Affiliate Marketing
Understanding Affiliate Marketing
Sources to Make money online
Applying for an Affiliate
Payments & Payouts
Blogging
39. Introduction to Google AdSense
Basics of Google Adsense
Adsense code installation
Different types of Ads
Increasing your profitability through Adsense
Effective tips in placing video, image and text ads into your website correctly
40. Google Tag Manager
Adding GTM to your website
Configuring trigger & variables
Set up AdWords conversion tracking
Set up Google Analytics
Set up Google Remarketing
Set up LinkedIn Code
41. Email Marketing
Introduction to Email Marketing basic.
How does Email Marketing Works.
Building an Email List.
Creating Email Content.
Optimising Email Campaign.
CAN SPAM Act
Email Marketing Best Practices
42. SMS Marketing
Setting up account for Bulk SMS
Naming the Campaign & SMS
SMS Content
Character limits
SMS Scheduling
43. Media Buying
Advertising: Principles, Concepts and Management
Media Planning
44. Whatâs App Marketing
Whatsapp Marketing Strategies
Whatsapp Business Features
Business Profile Setup
Auto Replies
45. Influencer Marketing
Major topics covered are, identifying the influencers, measuring them, and establishing a relationship with the influencer. A go through the influencer marketing case studies.
46. Freelancing Projects
How to work as a freelancer
Different websites for getting projects on Digital Marketing
47. Online Reputation Management
What Is ORM?
Why We Need ORM
Examples of ORM
Case Study
48. Resume Building
How to build resume for different job profiles
Platforms for resume building
Which points you should add in Digital Marketing Resume
49. Interview Preparation
Dos and Donât for Your First Job Interview
How to prepare for interview
Commonly asked interview question & answers
50. Client Pitch
How to send quotation to the clients
How to decide budget for campaign
Quotation formats
51. Graphic Designing: Canva
How to create images using tools like CanvaÂ
How to add effects to images
52. Analysis of Other Website
Post navigatio
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Integrating AI Call Transcription into Your VoIP or CRM System
In todayâs hyper-connected business environment, customer communication is one of the most valuable assets a company possesses. Every sales call, support ticket, or service request contains rich data that can improve business processesâif captured and analyzed properly. This is where AI call transcription becomes a game changer. By converting voice conversations into searchable, structured text, businesses can unlock powerful insights. The real value, however, comes when these capabilities are integrated directly into VoIP and CRM systems, streamlining operations and enhancing customer experiences.
Why AI Call Transcription Matters
AI call transcription leverages advanced technologies such as Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) to convert real-time or recorded voice conversations into text. These transcripts can then be used for:
Compliance and auditing
Agent performance evaluation
Customer sentiment analysis
CRM data enrichment
Automated note-taking
Keyword tracking and lead scoring
Traditionally, analyzing calls was a manual and time-consuming task. AI makes this process scalable and real-time.
Key Components of AI Call Transcription Systems
Before diving into integration, itâs essential to understand the key components of an AI transcription pipeline:
Speech-to-Text Engine (ASR): Converts audio to raw text.
Speaker Diarization: Identifies and separates different speakers.
Timestamping: Tags text with time information for playback syncing.
Language Modeling: Uses NLP to enhance context, punctuation, and accuracy.
Post-processing Modules: Cleans up the transcript for readability.
APIs/SDKs: Interface for integration with external systems like CRMs or VoIP platforms.
Common Use Cases for VoIP + CRM + AI Transcription
The integration of AI transcription with VoIP and CRM platforms opens up a wide range of operational enhancements:
Sales teams: Automatically log conversations, extract deal-related data, and trigger follow-up tasks.
Customer support: Analyze tone, keywords, and escalation patterns for better agent training.
Compliance teams: Use searchable transcripts to verify adherence to legal and regulatory requirements.
Marketing teams: Mine conversation data for campaign insights, objections, and buying signals.
Step-by-Step: Integrating AI Call Transcription into VoIP Systems
Step 1: Capture the Audio Stream
Most modern VoIP systems like Twilio, RingCentral, Zoom Phone, or Aircall provide APIs or webhooks that allow you to:
Record calls in real time
Access audio streams post-call
Configure cloud storage for call files (MP3, WAV)
Ensure that you're adhering to legal and privacy regulations such as GDPR or HIPAA when capturing and storing call data.
Step 2: Choose an AI Transcription Provider
Several commercial and open-source options exist, including:
Google Speech-to-Text
AWS Transcribe
Microsoft Azure Speech
AssemblyAI
Deepgram
Whisper by OpenAI (open-source)
When selecting a provider, evaluate:
Language support
Real-time vs. batch processing capabilities
Accuracy in noisy environments
Speaker diarization support
API response latency
Security/compliance features
Step 3: Transcribe the Audio
Using the API of your chosen ASR provider, submit the call recording. Many platforms allow streaming input for real-time use cases, or you can upload an audio file for asynchronous transcription.
Hereâs a basic flow using an API:
python
CopyEdit
import requests
response = requests.post(
    "https://api.transcriptionprovider.com/v1/transcribe",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={"audio_url": "https://storage.yourvoip.com/call123.wav"}
)
transcript = response.json()
The returned transcript typically includes speaker turns, timestamps, and a confidence score.
Step-by-Step: Integrating Transcription with CRM Systems
Once youâve obtained the transcription, you can inject it into your CRM platform (e.g., Salesforce, HubSpot, Zoho, GoHighLevel) using their APIs.
Step 4: Map Transcripts to CRM Records
Youâll need to determine where and how transcripts should appear in your CRM:
Contact record timeline
Activity or task notes
Custom transcription field
Opportunity or deal notes
For example, in HubSpot:
python
CopyEdit
requests.post(
    "https://api.hubapi.com/engagements/v1/engagements",
    headers={"Authorization": "Bearer YOUR_HUBSPOT_TOKEN"},
    json={
        "engagement": {"active": True, "type": "NOTE"},
        "associations": {"contactIds": [contact_id]},
        "metadata": {"body": transcript_text}
    }
)
Step 5: Automate Trigger-Based Actions
You can automate workflows based on keywords or intent in the transcript, such as:
Create follow-up tasks if "schedule demo" is mentioned
Alert a manager if "cancel account" is detected
Move deal stage if certain intent phrases are spoken
This is where NLP tagging or intent classification models can add value.
Advanced Features and Enhancements
1. Sentiment Analysis
Apply sentiment models to gauge caller mood and flag negative experiences for review.
2. Custom Vocabulary
Teach the transcription engine brand-specific terms, product names, or industry jargon for better accuracy.
3. Voice Biometrics
Authenticate speakers based on voiceprints for added security.
4. Real-Time Transcription
Show live captions during calls or video meetings for accessibility and note-taking.
Challenges to Consider
Privacy & Consent: Ensure callers are aware that calls are recorded and transcribed.
Data Storage: Securely store transcripts, especially when handling sensitive data.
Accuracy Limitations: Background noise, accents, or low-quality audio can degrade results.
System Compatibility: Some CRMs may require custom middleware or third-party plugins for integration.
Tools That Make It Easy
Zapier/Integromat: For non-developers to connect transcription services with CRMs.
Webhooks: Trigger events based on call status or new transcriptions.
CRM Plugins: Some platforms offer native transcription integrations.
Final Thoughts
Integrating AI call transcription into your VoIP and CRM systems can significantly boost your teamâs productivity, improve customer relationships, and offer new layers of business intelligence. As the technology matures and becomes more accessible, now is the right time to embrace it.
With the right strategy and tools in place, what used to be fleeting conversations can now become a core part of your data-driven decision-making process.
#AI call transcription#VoIP integration#CRM integration#Speech-to-text software#Call transcription software#Real-time transcription#VoIP call recording#CRM automation#Customer call insights#Voice analytics#AI transcription for sales calls#Transcription in customer support#CRM call log automation#Automatic call summary#AI speech recognition tools#Sales call transcript analysis#Customer service call transcription#AI voice to text CRM#Call center compliance tools#Conversation intelligence software
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Is Lemlist Worth It? A Complete Breakdown of Pricing, Features, and Benefits
If youâre looking for an email outreach tool, chances are youâve heard of Lemlist. It promises higher engagement, better deliverability, and automated follow-upsâbut is it really worth the price? Or are there better options available?
In this guide, weâll break down Lemlistâs pricing, features, and benefits to help you decide if itâs the right tool for your business.
1. What is Lemlist?
Lemlist is an email outreach and sales engagement platform that helps businesses send personalized cold emails, automate follow-ups, and improve deliverability. Unlike traditional email marketing tools, Lemlist focuses on cold email outreachâmeaning itâs designed for reaching out to new leads rather than sending newsletters to existing contacts.
2. Who is Lemlist For?
Lemlist is ideal for: â Sales teams looking to book more meetings â Marketers who want to generate leads â Recruiters searching for top talent â Agencies running cold outreach campaigns
If your goal is to convert cold leads into warm conversations, Lemlist could be a great choice.
3. Key Features of Lemlist
Lemlist stands out from other tools because of its personalization, automation, and multi-channel outreach. Here are some of its standout features:
â Email Personalization
Lemlist allows you to add custom images, dynamic text, and even videos in your emails. This makes messages feel more personal and engaging.
â Automated Follow-Ups
Instead of manually tracking responses, Lemlist automatically sends follow-ups based on recipient actions.
â Multi-Channel Outreach
Lemlist doesnât stop at email. You can integrate LinkedIn and phone calls to create a more comprehensive outreach strategy.
â Email Deliverability Booster (Lemwarm)
One of Lemlistâs biggest selling points is Lemwarm, a tool that warms up your email domain to prevent emails from landing in spam.
4. Lemlist Pricing Plans
Lemlist offers three pricing plans: PlanPriceBest ForEmail Outreach$39/monthBasic cold email outreachSales Engagement$69/monthMulti-channel outreach (email + LinkedIn)AgencyCustomLarge teams and agencies
While Lemlist isnât the cheapest tool, its unique features might justify the cost.
5. Lemwarm: Does It Really Improve Deliverability?
Lemwarm is an AI-driven email warm-up tool that gradually builds your sender reputation. It does this by automatically sending and engaging with emails from different accounts, signaling to email providers that your domain is trustworthy.
đ Does it work? Yes! Many users report fewer emails going to spam after using Lemwarm for a few weeks.
6. Lemlistâs Personalization Features
Unlike most email tools that use basic templates, Lemlist lets you: â Insert dynamic text (e.g., first name, company name) â Add personalized images with text overlays â Embed custom videos for a more human touch
This makes your emails stand out in crowded inboxes, increasing the chances of replies.
7. Ease of Use: How Beginner-Friendly is Lemlist?
Lemlist has a modern interface, but it does come with a learning curve. Beginners might need some time to get used to features like automation sequences and personalization options. However, once you get the hang of it, itâs a powerful and intuitive tool.
8. Lemlist vs Competitors: How Does It Compare?
Letâs see how Lemlist stacks up against Mailshake, Reply.io, and GMass:
| Feature | Lemlist | Mailshake | Reply.io | GMass | |---------|--------|----------|---------| | Personalization | â
Advanced | â
Basic | â
AI-driven | â No | | Automated Follow-ups | â
Yes | â
Yes | â
Yes | â
Yes | | Multi-Channel Outreach | â
Yes | â No | â
Yes | â No | | Email Warm-up | â
Yes | â No | â No | â No | | Pricing (Starting) | $39/month | $44/month | $49/month | $19.95/month |
đ Verdict: If personalization and deliverability are your top priorities, Lemlist is the best choice. However, if you want a cheaper alternative, GMass or Mailshake might be better.
9. Pros and Cons of Lemlist
â Highly personalized email campaigns â Automated follow-ups for better engagement â Lemwarm improves email deliverability â Multi-channel outreach (email + LinkedIn + calls) â Higher learning curve for beginners â More expensive than some competitors
10. Best Alternatives to Lemlist
Mailshake â Best for beginners
Reply.io â Best for AI-powered automation
GMass â Best for budget users
Woodpecker â Best for B2B outreach
11. Who Should and Shouldnât Use Lemlist?
â Use Lemlist if:
You need highly personalized emails
You struggle with email deliverability
You want a multi-channel outreach tool
â Avoid Lemlist if:
Youâre looking for a cheap tool
You need a simple email marketing tool instead of cold outreach
12. Final Verdict: Is Lemlist Worth It?
Lemlist is one of the best cold email outreach tools available today. Its personalization, automation, and email warm-up features make it a top choice for businesses serious about cold outreach.
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The Process of Professional Thai Transcription Services
Thai transcription services involve the process of converting spoken Thai language from audio or video recordings into written text. This service is crucial for businesses, content creators, legal professionals, healthcare providers, and other industries requiring accurate and reliable transcriptions of conversations, interviews, meetings, and other spoken material in Thai.
These services cater to a variety of needs, from converting podcasts and webinars into readable formats to transcribing court hearings or medical consultations. Thai transcription requires a high level of proficiency in the language, including understanding regional accents, cultural nuances, and industry-specific terminology.
There are two primary methods for Thai transcription: manual transcription, where skilled human transcribers listen to the audio and transcribe it word-for-word, and automated transcription, which relies on software and AI algorithms. While automated tools can offer speed, human transcriptionists ensure higher accuracy, especially for complex or noisy recordings.
Overall, professional Thai transcription services provide invaluable support for businesses looking to enhance accessibility, improve SEO through transcribed content, and ensure legal and medical accuracy in their records.
Step 1: Receiving the Audio or Video File
File Submission: Clients submit their audio or video recordings in various formats, such as MP3, WAV, or MP4. Files can be uploaded via secure portals or emailed, depending on the service provider's preferences.
File Requirements: Clients specify any special instructions, such as whether the file includes multiple speakers, background noise, or if timestamps are needed.
Step 2: Choosing the Right Transcriptionist
Expertise Matching: The transcription provider selects a native Thai speaker with expertise in the relevant industry (e.g., legal, medical, or business) to ensure that specialized terminology is accurately transcribed.
Human vs. AI: While AI transcription tools may be used in some cases for faster turnaround, human transcriptionists are preferred for complex projects, especially for content that requires contextual understanding or nuanced language.
Step 3: Transcription Process
Listening & Understanding: The transcriptionist listens carefully to the audio or video file, paying attention to accents, background noises, and dialects. Thai transcriptionists must be familiar with regional variations in pronunciation and vocabulary.
Word-by-Word Transcription: The transcriptionist types out every spoken word in Thai, ensuring accuracy and coherence. In cases of unclear speech or difficult accents, the transcriber may pause, replay, or research terms to ensure the best transcription.
Handling Multiple Speakers: If the recording includes multiple speakers, the transcriptionist distinguishes between them, marking each speakerâs dialogue clearly.
Step 4: Quality Control and Editing
Proofreading: Once the initial transcript is completed, it undergoes a thorough proofreading process. The transcriptionist reviews the text for spelling, grammar, and punctuation errors.
Verifying Accuracy: The transcript is checked against the original audio to ensure no words, phrases, or important details are missed. The transcriptionist cross-references challenging parts of the audio for clarity.
Ensuring Consistency: Industry-specific terms, jargon, and proper nouns are verified for consistency and accuracy.
Step 5: Final Review and Formatting
Formatting for Readability: The transcript is formatted to ensure it is easy to read. Common formats include standard text documents (Word or PDF) or more structured formats with timestamps for video or audio recordings.
Timestamps (if required): If the transcript needs to be time-coded, timestamps are added at specific intervals, such as every time a new speaker begins or at regular time markers (e.g., every minute).
Client Specifications: The transcript is customized according to the client's specific needs, whether itâs for subtitles, legal documentation, or content marketing.
Step 6: Delivery and Client Review
Delivery of Transcripts: The final transcript is sent to the client through their preferred method, typically via email or a secure file-sharing platform.
Client Feedback: Clients review the transcript to ensure that it meets their expectations. If there are any issues or revisions needed, the client can provide feedback.
Revisions: If required, the transcription provider makes any necessary revisions based on the clientâs feedback and sends the updated document.
Step 7: Final Approval
Client Satisfaction: Once the client is satisfied with the final transcript, the service is marked as complete.
Archiving: The final version is stored for future reference or can be deleted upon the clientâs request for privacy and security reasons.
Why Professional Thai Transcription Services
Explore the importance of accuracy in transcriptions and how professionals ensure that no detail is missed.
Discuss the benefits, such as high-quality results, confidentiality, and time efficiency.
Illustrate how outsourcing transcription helps businesses and individuals focus on core tasks.
Examine why professionals in these industries require precise transcription for records and communication.
Highlight the limitations of automated transcription software and the advantages of human transcriptionists.
Explain how transcriptions make audio or video content accessible to a wider audience, including those with hearing impairments.
Show how transcriptions can improve SEO, engagement, and the reach of online content.
Discuss why companies prefer to outsource transcription for high-quality and reliable results.
Focus on how accurate transcription ensures that content stays consistent and professional across channels.
Explore how accurate transcription supports companies working in global markets with Thai-speaking customers or partners.
Discuss how transcriptions play a crucial role in research, interviews, and academic documentation.
Examine the need for professional transcription to create precise subtitles or captions for films, videos, and webinars.
Highlight the security measures that professional services take to ensure sensitive information is kept confidential.
Discuss how accurate transcription prevents costly errors and the need for revisions.
Explain how understanding the cultural context of the Thai language helps transcribers capture the full meaning of the conversation.
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AI Meeting Notes: Transforming the Way We Capture Meeting Insights.
Business operations rely on meetings, but keeping track of notes has been considered a tedious chore. AI meeting notes come into play there to help us capture, organize, and use the information from discussions in a way that is easier to understand. Fortunately, with automation in control, these tools minimize the variables and prevent any of this from getting lost in translation. In this article, letâs start with how AI meeting notes are transforming our working environment.
Understanding AI Meeting Notes
What Are AI Meeting Notes?
You may refer to AI meeting notes as meeting summaries or even detailed transcriptions created through artificial intelligence during a meeting. These tools are powered by technologies such as voice recognition, natural language processing (NLP), and machine learning, enabling them to listen, transcribe, and format conversations in real time.
The ability to capture key points of a conversation, highlight action items, and even assign responsibilities to enhance accountability makes AI tools an indispensable asset for any modern team during meetings.
How Do AI Tools Capture and Organize Notes?
Basically, AI tools use algorithms that convert audio into text. They identify speakers, summarize discussion, and categorize the content into concrete action items. Even some tools are integrated with platforms such as Zoom, Google Meet, or Microsoft Teams and work seamlessly.
The Benefits of Using AI for Meeting Notes
1. Improved Accuracy and Efficiency
Humans are prone to error, especially in fast-paced meetings. AI eliminates the risk of missing critical information, providing precise and structured notes.
2. Real-Time Transcription and Organization
AI tools offer real-time transcription, so team members can follow along as the meeting progresses. This feature ensures immediate access to notes without any delays.
3. Accessibility and Collaboration
With cloud storage and integration capabilities, AI meeting notes are easily accessible to all team members. This ensures better collaboration, especially for remote or hybrid teams.
Key Features of AI-Powered Meeting Note Tools
Voice Recognition and NLP
The backbone of AI meeting tools lies in their ability to recognize different voices and languages while analyzing context. This ensures accurate summaries.
Summarization Capabilities
AI tools donât just transcribe; they summarize. By focusing on key points, action items, and takeaways, these tools save users from sifting through pages of raw text.
Industries Leveraging AI Meeting Notes
1. Corporate Environments
From board meetings to team huddles, AI meeting notes help executives stay focused on strategy rather than scribbling notes.
2. Education and Training
Professors and trainers use AI tools to record lectures or training sessions, providing accurate transcripts for later reference.
3. Healthcare and Legal Fields
Doctors and lawyers rely on AI meeting notes to record patient interactions or case discussions while focusing on client care.
How AI Meeting Notes Work
Step-by-Step Process of Note Generation
Audio Capture: The AI listens to the conversation.
Transcription: It converts speech to text in real time.
Analysis: Context and sentiment are analyzed for clarity.
Output: A structured summary with key points is delivered.
The Role of Machine Learning
Machine learning algorithms adapt over time, learning from repeated usage to improve accuracy and relevance in note-taking.
Comparison Between Manual and AI Meeting Notes
Manual Methods vs. AI-Driven Solutions
Manual note-taking has been the norm for decades, requiring intense focus and multitasking. This traditional approach often leads to incomplete notes or missed information. In contrast, AI meeting notes offer automated transcription and real-time summaries, significantly reducing the workload for participants.
Time, Effort, and Quality Metrics
AI tools excel in saving time by providing instant documentation and eliminating the need for post-meeting revisions. Quality also improves as AI systems ensure consistent and precise records, free from human biases or errors.
How AI Meeting Notes Impact Productivity
Saving Time for High-Priority Tasks
By automating the note-taking process, employees can focus on brainstorming and strategic discussions, driving innovation.
Streamlining Workflows
AI tools bridge gaps between meetings and execution by directly exporting action items to project management systems.
Common Misconceptions About AI Meeting Notes
1. AI Will Replace Human Judgment
While AI tools assist with documentation, human oversight remains essential to interpret and apply meeting insights effectively.
2. AI Tools Are Hard to Use
Contrary to belief, most AI meeting note solutions offer intuitive interfaces and require minimal technical expertise.
Conclusion
The most prominent: AI meeting notes are democratizing how we do meeting documentation and the movement is only changing what and how others document. If you are a small business or a large enterprise, these tools are going to save you time, increase productivity and facilitate collaboration. But with increased advancement of AI technology, the workplace will also be influenced, making better decisions and being more efficient.
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Digital Marketing Course in New CG Road Ahmedabad
1. Digital Marketing Course in New CG Road Ahmedabad Overview
2. Personal Digital Marketing Course in New CG Road Ahmedabad â Search Engine Optimization (SEO)
What are Search Engines and Basics?
HTML Basics.
On Page Optimization.
Off Page Optimization.
Essentials of good website designing & Much More.
3. Content Marketing
Content Marketing Overview and Strategy
Content Marketing Channels
Creating Content
Content Strategy & Challenges
Image Marketing
Video Marketing
Measuring Results
4. Website Structuring
What is Website?- Understanding website
How to register Site & Hosting of site?
Domain Extensions
5. Website Creation Using WordPress
Web Page Creation
WordPress Themes, Widgets, Plugins
Contact Forms, Sliders, Elementor
6. Blog Writing
Blogs Vs Website
How to write blogs for website
How to select topics for blog writing
AI tools for Blog writing
7. Google Analytics
Introduction
Navigating Google Analytics
Sessions
Users
Traffic Source
Content
Real Time Visitors
Bounce Rate%
Customization
Reports
Actionable Insights
Making Better Decisions
8. Understand Acquisition & Conversion
Traffic Reports
Events Tracking
Customization Reports
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Comparision Reports
9. Google Search Console
Website Performance
Url Inspection
Accelerated Mobile Pages
Google index
Crawl
Security issues
Search Analytics
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10. Voice Search Optimization
What is voice engine optimization?
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Why you should optimize your website for voice search?
11. E Commerce SEO
Introduction to E commerce SEO
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How Online Stores Can Drive Organic Traffic
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Importance of Local SEO
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How can Facebook be used to aid my business?
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Importance of Hashtags & how to use them
15. Twitter Organic
Basic concepts â from setting-up optimally, creating a Twitter business existence, to advanced marketing procedures and strategies.
How to use Twitter
What are hashtags, Lists
Twitter Tools
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Your Profile: Building quality connections & getting recommendations from others
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How to create YouTube channel
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YouTube Keyword Research
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YouTube Video Optimization
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YouTube channel monetization policies
How Does YouTube Monetization Work?
YouTube monetization requirements
20. Social Media Tools
What are the main types of social media tools?
Top Social Media Tools You Need to Use
Tools used for Social Media Management
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What is Social Media Automation?
Social Media Automation/ Management Tool
Buffer/ Hootsuite/ Postcron
Setup Connection with Facebook, Twitter, Linkedin, Instagram, Etc.
Add/ Remove Profiles in Tools
Post Scheduling in Tools
Performance Analysis
22. Facebook Ads
How to create Business Manager Accounts
What is Account, Campaign, Ad Sets, Ad Copy
How to Create Campaigns on Facebook
What is Budget & Bidding
Difference Between Reach & Impressions
Facebook Retargeting
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Text Ads and Guidelines
Image Ad Formats and Guidelines
Landing Page Optimization
Performance Metrics: CTR, Avg. Position, Search Term
Report, Segment Data Analysis, Impression Shares
AdWords Policies, Ad Extensions
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How to create Campaign Manager Account
What is Account, Campaign Groups, Campaigns
Objectives for Campaigns
Bidding Strategies
Detail Targeting
25. YouTube Advertising
How to run Video Ads?
Types of Video Ads:
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Non Skippable in stream Ads
Bumper Ads
Bidding Strategies for Video Ads
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Account Limits in Ad-Words
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Schedule: Start date, end date, ad scheduling
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Ad groups and Keywords
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Text Ads and Guidelines
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Performance Metrics: CTR, Avg. Position, Search Term
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Types of Keywords: Exact, Broad, Phrase
Bids & Budget
How to create Text ads
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Image Ad Formats and Guidelines
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Performance Metrics: CPM, vCPM, Budget
Report, Segment Data Analysis, Impression Shares
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Automated rules
Target Audience Strategies
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How to Video Ads
Types of Video Ads
Skippable in stream ads
Non-skippable in stream ads
Bumper Ads
How to link Google AdWords Account to YouTube Channel
30. Discovery Ads
What are Discovery Ads
How to Create Discovery Ads
Bidding Strategies
How to track conversions
31. Bidding Strategies in Google Ads
Different Bidding Strategies in Google AdWords
CPC bidding, CPM bidding, CPV bidding
How to calculate CTR
What are impressions, impression shares
32. Performance Planner
33. Lead Generation for Business
Why Lead Generation Is Important?
Understanding the Landing Page
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Landing Page Vs. Website
Best Practices to Create Landing Page
Best Practices to Create Thank You Page
What Is A/B Testing?
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34. Conversion Tracking Tool
Introduction to Conversion Optimization
Conversion Planning
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35. Remarketing and Conversion
What is conversion
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Remarketing in adwords
Benefits of remarketing strategy
Building remarketing list & custom targets
Creating remarketing campaign
36. Quora Marketing
How to Use Quora for Marketing
Quora Marketing Strategy for Your Business
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Growth Hacking Basics
Role of Growth Hacker
Growth Hacking Case Studies
38. Introduction to Affiliate Marketing
Understanding Affiliate Marketing
Sources to Make money online
Applying for an Affiliate
Payments & Payouts
Blogging
39. Introduction to Google AdSense
Basics of Google Adsense
Adsense code installation
Different types of Ads
Increasing your profitability through Adsense
Effective tips in placing video, image and text ads into your website correctly
40. Google Tag Manager
Adding GTM to your website
Configuring trigger & variables
Set up AdWords conversion tracking
Set up Google Analytics
Set up Google Remarketing
Set up LinkedIn Code
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Introduction to Email Marketing basic.
How does Email Marketing Works.
Building an Email List.
Creating Email Content.
Optimising Email Campaign.
CAN SPAM Act
Email Marketing Best Practices
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Setting up account for Bulk SMS
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Advertising: Principles, Concepts and Management
Media Planning
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Whatsapp Marketing Strategies
Whatsapp Business Features
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Auto Replies
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Major topics covered are, identifying the influencers, measuring them, and establishing a relationship with the influencer. A go through the influencer marketing case studies.
46. Freelancing Projects
How to work as a freelancer
Different websites for getting projects on Digital Marketing
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What Is ORM?
Why We Need ORM
Examples of ORM
Case Study
48. Resume Building
How to build resume for different job profiles
Platforms for resume building
Which points you should add in Digital Marketing Resume
49. Interview Preparation
Dos and Donât for Your First Job Interview
How to prepare for interview
Commonly asked interview question & answers
50. Client Pitch
How to send quotation to the clients
How to decide budget for campaign
Quotation formats
51. Graphic Designing: Canva
How to create images using tools like CanvaÂ
How to add effects to images
52. Analysis of Other Website
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Microsoft Rebrands Flow Service to Power Automate in Ignite 2019
In this digital era, you will find several cloud services or applications to do just about everything. But whatâs the actual point, if we canât couple it together and work?
Disconnected applications and services are much like a cell phone device that has no internet accessibility. In this state, you cannot make any progress. But, Microsoft Power Automate allows your apps, services, and employees to work together seamlessly. If technology and your team are coupled together and started working together, then you can work smarter, work less, and do more.
What is Microsoft Power Automate?
Microsoft Power Automate, formerly known as Microsoft Flow or MS Flow. It is Microsoft Power Platformâs one of the newest services. With Power Automate, you can streamline time-consuming tasks and paperless processes. Automate is a workflow automation engine that focuses on Business Process Management (BPM). It aims to optimize, enhance, and automate your business processes.
Why Did Microsoft Rebrands Flow?
At the Ignite 2019, Microsoft announced the rebranding of Microsoft Rebrands Flow as Power Automate, the latest additions, and features to its Power Platform family. It brings in line with the rest of the platforms.
As a part of Power Platform alongside Power BI and PowerApps, the rebranding aligns with those other services. Moreover, Microsoft is adding support for RPA â Robotic Process Automation. With the integration of RAP, the app can support UI flows and become an end-to-end automation solution. It allows you to do both API-based automation and UI-based automation.
In the case of what Power Automate offers there are a few changes from the overall Microsoft Rebrands Flow experience. With this service, users can create automated workflows across applications. Itâs known as an enterprise-oriented IFTTT competitor. Users can gather data collected, file synchronization, and notifications using automated chains.
With the help of AI and bots, RPA automates business processes, repetitive tasks, and freeing up staff to be more productive. You can easily create UI flows in Power Automate, which requires minimal coding knowledge. This means anyone from your organization can be a developer now.
Power Automate vs Flow
Microsoft Rebrands Flow always had limitations such as starting a second flow as a continuation of the primary one, not capable of reordering the steps in each flow, the complexity of new flow recreation, or reconnecting it to new lists, approved email formation, and so on.
Now, Microsoft has eliminated these limitations and added new features that empower business through its new capabilities â Power Automate Platform.
Before we proceed further, let us tell you a quick note on rebranding. Microsoft has to rebrand one of its products every quarter or itâs not allowed to call itself Microsoft. Microsoft Flow launched out of preview in October 2016. The competitor, IFTTT was always more about automation than workflows â it just took Microsoft three years to realize it.
Other Features by MicrosoftPower Automate goes RPA (Robotic Process Automation)
Having said earlier, Microsoft revealed a new feature RAP in Power Automate, which is known as UI flows. UI flows are now available in public preview.
The RPA eliminates the manual processes involved in automated workflows that record and playback human-driven interaction with software systems that donât support API automation. Since Power Automate comes up with pre-built connectors for more than 275 apps and services that do support API automation, Microsoft claims it now has an end-to-end automation platform that can reinvent business processes for workload across industries.
Power Virtual Agents and AI Builder
If the requirement of needing some coding experience for UI flows concerns you, you will be relieved to hear about the Power Virtual Agents. Itâs now available in public preview.
Power Virtual Agents allow the different functions in your company such as customer service, sales, marketing, finance, HR, and so on, to create bots with a guided, no-code graphical interface. Think of it as democratizing bot creation. There is no need for any data scientists, developers, coding, or AI expertise. Because Power Virtual Agents are part of the Power Platform. You can easily use the pre-built connectors to allow your bot to communicate with your backend or other systems or call and API.
As a matter of fact, Microsoft has revealed more no-code options in the form of AI Builder on Power Platform. Additionally, the Power Platform also has new prebuilt models in public preview:
Key Phrase Extraction â analyzes the main talking points from your text.
Language Detection â analyzes the predominant language from your text.
Text recognition â extracts embedded printed and handwritten text from images into machine-readable character streams.
Sentiment Analysis â detects positive, negative, neutral, or mixed sentiment in social media, customer reviews, or any text data.
With AI Builder, organizations can tailor AI scenarios for their specific business.
Security Improvements and Teams Integration
As per the above statement, Power BI is getting more security features with the following new capabilities.
Able to analyze and secure user activity on sensitive data in real-time with alerts, session monitoring, and risk remediation through Microsoft Cloud App security.
Allow security administrators who are using data protection reports and security investigation capabilities to leverage Microsoft Cloud App Security to enhance organizational oversight.
Moreover, with Power Platform, Microsoft is trying to collaborate its workplace tools and Microsoft Teams. Now, Power Platform dashboards, app & Power automation are available within Teams. And bots can be accessed through conversations. Additionally, PowerApps developers can also publish their apps directly into their companyâs app library within Teams. New Power Automate triggers and actions are now available for common team and personal tasks.
Impact of Power Automate on Your Business
Using Power Automate in your business will surely take it to the next level. It will change the way you interact with your business.
Letâs go through some of the advantages of how your business can benefit from Power Platform.
Enhances Productivity
Your team members can create time-saving workflows for everything while using hundreds of pre-built connectors. This workflow includes individual tasks to scalable systems You can save time, improve your organization, and work more efficiently by automating repetitive tasks.
Streamline Repetitive Tasks
Power Automate notifies you if you receive any high-prioritized email or tweet or message from the receiver. It also sends a template to those you donât need to review. This ensures that you never miss any single update and stay in touch with your clients while you are engaged in your personal life.
Easy and Quick Data Sharing
By connecting apps and creating flows to copy data from one folder to another, you can easily share files. Even, if you integrate Power Automate with Common Data Services, it will help you store and manage data used by business applications.
How Can You Reap the Benefits?
If you are implementing Power Automate, there are endless possibilities for your business. To reap the benefits, you must create the necessary workflows that keep the track of email attachments, get action items approved quickly, stay on top of relevant emails, and monitor brand-related posts and events on social media.
So, if you are also planning to transform your business with the help of Microsoft Power Platforms such as Power BI, PowerApps, and Flow, then hire PowerApps developers from us. Contact us and will discuss your specific business needs.
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Customer Service Automation: Human-Robot Collaboration Best Practices
Zinnov sits down with Scott Merritt of Jacada for an insightful, two-part interview on the current state, and future of RPA.
Did you know that enterprises spent more than $2.3 billion on RPA in FY19? And that this number is expected to grow exponentially by 35-40%, to reach more than $11 billion by 2024?
RPA and Intelligent Automation have not only become mainstream conversations but are being implemented across enterprises at a rapid pace. Further, with attended and unattended scenarios making visible inroads in enterprisesâ automation journeys, there is a renewed focus on Intelligent Automation. Based on Zinnovâs research and analysis, we estimate that attended RPA (bots working together with humans) accounts for approximately 30% of the market share and is growing at a steady pace.
Given the attention that Intelligent Automation is receiving across companies, and irrespective of industry vertical, Nischay Mittal, Engagement Manager at Zinnov caught up with Scott Merritt, Vice President, Global Head of Automation and Marketing at Jacada, to gather his perspectives on the key trends within the RPA space, especially the growing focus on attended RPA and Customer Service Automation (CSA).
Hereâs an excerpt from the interview.
Zinnov: The RPA space has been growing at a rapid pace over the last 2 years. What are the top 2-3 trends you are witnessing within RPA?
Merritt: One of the biggest trends that we are witnessing is customers struggling to understand what or where to automate next. Customers often get through the proof of concept (POC) stage, start accepting the technology as a viable part of their architecture, but they fail to go beyond those initial use cases and pilots. This challenge to scale RPA for early adopters has quickly accelerated the industry from say RPA 1.0 to RPA 2.0, which is bringing to light a much-needed focus on a broader âintelligent automationâ toolset rather than just an RPA only bot operation.
As a result, RPA companies are investing in additional capabilities to expand their offerings in order to drive meaningful business outcomes instead of just delivering a somewhat commoditized back-office RPA bot that can navigate a UI on behalf of a user.
Another trend stemming from this scale gap in RPA is the growing focus on Attended RPA. Formerly known as âdesktop automationâ, Attended RPA is a 10+ year old automation approach that grew out of a need in the contact center to integrate non-API ready applications and automate the many manual microtasks that bog down agents during a call, impacting handle time and customer experience (CX). At Jacada, we often find two dozen or more solutions on the agent desktop and they are a combination of these API-ready and non-API ready legacy applications. Automating for this type of environment, however, requires a different type of discovery approach and supporting RPA framework, one that is built for human collaboration vs the one built for robots (unattended).
Zinnov: We are witnessing immense traction within Attended RPA scenarios (or automation of front office processes) over the past year. Even some of the bigger RPA tool vendors are now increasingly focusing on Attended RPA use cases. What do you think are the key reasons for that?
Merritt: There are many reasons but one I often site is âback office blindersâ or too narrow a scope. Let me explain. The original entry point for RPA unattended vendors and SIs was to target high volume / low complexity processes that could be automated end-to-end in back office departments like IT, Finance, Accounting, HR, etc. While these groups do have a need for RPA, they donât always represent the ideal use case environment for RPA⌠or at least not after the low hanging fruit is picked off during the first few projects. What you often find here are smaller departments of people working on many disparate, more complex tasks; which often require human discretion at some point in order to complete the process. For example, one regional bank I worked with in the past had 80 back office workers in their card services division working on 160 different exception-based processes in any given month. Going into this environment with an unattended RPA only approach, itâd be difficult to support an ROI that would justify the project but several companies like this bank have moved forward based on limited options and poor guidance. We are now a few years into this approach and over 50% of companies have deployed < 10 robots and havenât scaled their RPA programs beyond their original list of projects. This represents a flawed RPA strategy at the foundation and unfortunately one that was pushed by many of the new players in the market along with many of the SIs.
To counter this misstep and open up new use cases for RPA, vendors are addressing the more complex back office use cases with add-on capabilities like OCR, ML, Computer Vision, etc. However, to unlock RPAâs full potential, you have to add Attended bot capabilities to your intelligent automation stack allowing you to expand into areas of the organization where a much higher volume of people and tasks exist. A strategy that often brings you to the customer service operations arena.
As some of the larger unattended players realized this new fertile ground, they pivoted strategy and began to market this new type of bot with âattendedâ and âcustomer serviceâ banners flying on their websites. These vendors are quickly finding that successfully managing this automation triangle of Customer-Robot-Employee is very different (in approach and supporting RPA technology) than building automation solutions for robots.
Zinnov: Currently, there are a lot of RPA tool vendors in the industry. How does Jacada differentiate itself vis-Ă -vis some of the other players? Can you elaborate on Jacadaâs positioning within RPA? What would you say is your USP?
Merritt:Â Jacada is a niche player in the RPA space but at the same time, I would argue that all RPA vendors are niche players based on their core use cases and technology strength. Jacadaâs niche targets end to end customer service interactions by focusing on human and robot collaboration use cases in both digital self-service and customer assisted service scenarios. We have a strategy and supporting technology stack that has been transforming customer interactions for almost 30 years now. The space is unique compared to back office RPA as it involves an additional layer of complexity which is that all automations involve real-time interactions with agents and/or customers. As a result, we go to market with a Collaboration-First approach where capabilities like UX design, conversational AI, guidance, and automations have to be designed and orchestrated together in order to deliver a successful solution at scale for both customers and agents. All capabilities that reside within our low code intelligent automation platform, a unique offering compared to other RPA players in this space who often have to bring together multiple technology partners to deliver this kind of end to end customer service value proposition.
Zinnov: Is it safe to say that more complex processes would require more intelligence or leverage of cognitive tech?
Merritt:Â It all depends on how you define âcomplexityâ quite honestly as process complexity can come in different forms when it comes to RPA.
Sometimes complexity is defined by the complex desktop environment that bots must navigate relative to the automation triangle I mentioned earlier. One of the exciting areas where we use AI to enhance RPA is in real-time speech to intent trigger. For example, when a customer calls a contact center, we leverage AI to power real-time speech analytics to orchestrate different bots throughout the live interaction. A speech bot can listen to the conversation and wait until a trained intent is recognized and then trigger a new guidance flow or bring back the answer to a customer question using Attended RPA without the agent having to touch the keyboard. This is a situation where a combined RPA and AI strategy can deliver on more complex use cases to enhance both the agentâs and customerâs experience.
Process complexity can also be defined by the complexity of the applications or application types that a bot needs to work with. Perhaps the number one challenge we see in the market is a result of a vendor platformsâ inability to integrate and automate with all of the necessary application types needed to automate the desired use cases. Known in the industry as âapplication coverageâ, those vendors who lack broad and deep coverage of applications will require more investment in their RPA tech stack, not necessarily more AI to solve this problem. Unless of course, you include computer vision into the mix but that would be a different conversation altogether.
Another form of process complexity could come from the need to automate processes that involve converting unstructured data from forms or documents as a first step to automate a process end to end. Once again, the vendor would first need to have enhanced capabilities within their RPA platform to support OCR, computer vision, or other methods that can be used to access the unstructured text. When that is achieved, AI/ML can provide a more scalable way to identify and structure this data thereby removing a significant amount of human effort from the process.
Look, the future of AI within automation is exciting and all of us are focusing on R&D to further enhance our platforms with it. However, it is not always the best first choice when trying to solve for process complexity, despite what vendors may say to convince you. Very often I see organizations jumping too quickly into the AI vortex hoping for that quick fix before they have established a proper RPA foundation.
What is your focus on â Intelligent Automation, or leveraging cognitive tech such as AI/ML, NLP, CV, etc.?
Merritt:Â Our focus is on bringing together the relevant customer service automation capabilities into a common intelligent automation platform in order to automate customer service interactions. This customer outcome-driven approach guides our investment in AI and other cognitive technology. Take Natural Language Processing (NLP/NLG) for example. Our use cases and supporting capabilities for conversational AI focus on leveraging intent and sentiment recognition to understand in real-time a customer request regardless of channel. To support these different use cases, we have developed in-house AI capabilities to enable automation design while offering an intelligent automation and AI hub that allows clients to use their own preferred conversational AI platform whether itâs Google, Facebook, Watson, or other best-in-class providers. This open AI platform approach offers our clients complete flexibility while at the same time provides a plug-and-play environment for them to tie the entire customersâ interactions together in one low code design environment.
This is similar but yet different from the AI investment approach you typically see in back office unattended RPA use cases which often target data structuring to automate things like a document or email processing. The point here is that every RPA platform provider is investing in some form of cognitive/AI/ML technology to expand the number and type of outcomes they can deliver to their customers. As a result, RPA buyers really have to understand what outcomes they are trying to achieve and select the right vendor(s) accordingly â if they are trying to match invoices by leveraging AI/ML, there might be a certain vendor path for them; if they are looking to understand intent and deploy a virtual assistant for employees, that might result in a different vendor selection. Today there is not one vendor who covers use cases across all verticals despite the vendor claims (going back to my point of view that all RPA vendors are niche players right now). I think itâs about finding the vendor who has the relevant expertise and experience to match the outcomes, use cases and application integration requirements as per the customer demand.
Zinnov:Â Thank you, Scott.
Even though itâs the new-age disruptive technologies that form the spine of any successful automation initiative, organizations often struggle with the implementation aspect of Customer Service Automation. Read the second part of this interview blog to get Scottâs insights on the challenges and key drivers of RPA implementation.
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12 Best Online Advertising and Content Management Tools for Businesses in 2020
1)Â Â Â Â Â Wask
Wask has created to maximize your adsâ efficiency for you. You can connect all your different accounts into one platform. You can make special ads for your needs with the help of our experienced advisors. Always updated unique AI program that Wask use to manage your ads is provide you to manage your budget and time in the most efficient way. Compare your results, control your ads automatically daily/weekly/monthly, start/stop/delete your ads at any time with Performance Comparison, AutoPilot and Scheduler to increase your efficiency.
https://www.wask.co/
2)Â Â Â Â Â Quuu
Quu is a hand-curated content suggestions platform for social media, Select from over 300 interest categories to receive suggestions that matter to you and your audience, hoose how many suggestions you'd like to send to your social profiles via your Buffer or HubSpot account and Let Quuu handle everything, or manually approve suggestions yourself.
https://www.quuu.co/
3)Â Â Â Â Â Planable
Planable is a platform that allows agencies and social media managers to collaborate with their clients and within marketing teams. We created a tool that speeds up the way social media campaigns are managed and makes planning, visualizing and approving social media posts easy and fun. We are the easiest way to preview your social media content in a simple interface and giving a familiar feel and look for every social media manager, teammate and colleague. After that, you can easily schedule or publish the post immediately to social media knowing that everyone is happy with the campaign. One workspace is one brand or one of your clients you are an agency or You can one page of each social network in one workspace, up to people on the free plan. Click the green button new post and type your content, select the emojis, upload an image or video, select a date and click add button. Now, invite your clients or teammates into the workspace and collaborate on content youve just created. The right side of the post is created to exchange ideas, share a text, upload an image, video or gif, suggest a correct a typo, check for legal issues and just send a high five kudos!
https://planable.io/
4)Â Â Â Â Â Word Swag
Have you wasted time and money hiring a social media graphic design artist, only to notget exactly what you want?Word Swag is an awesome application for your smartphone or tablet that will allow you toeasily create social media graphics. The toolset in Word Swag is amazing because it easily allows you to import some great photos from their library, Pixabay, or from your own photos. You will get an overview of the interface and how the program I will show you how to import images to begin creating right away. After that we will take a look at some of the best text and fontediting tools ever seen in a smartphone application. Finally, I will show you how to use custom colors and filters in your design before exportingit out as a final image. So if you want to start creating amazing images and graphics today, then go ahead and enroll and I will see you in the first lecture.
http://wordswag.co/
5)Â Â Â Â Â Waaffle
New tools have recently emerged to meet the evolving needs of busy social media marketers. In this article, youâll discover six tools that will improve your social media marketing workflow. Waaffle is an useful social media tool. Create Content Feeds With Waaffle According to Yotpo, ads based on content can get 4x higher rates and a 50% lower cost per click than average. Waaffle simplifies the process by creating aggregate custom feeds based on any account or Waaffleâs current âearly birdâ pricing starts at per campaign.
https://waaffle.com
6)Â Â Â Â Â Contently
In November Contently was named by Advertising Age as one of the best places to work in media and. Contently has a lot of clients , and we rely on freelancers like yourself to staff projects for them. We have an algorithm that combs through usersâ portfolios and pushes forward relevant candidates. So when youâre setting up your portfolio, make sure you focus on the kind of things algorithms like: namely, keywords that highlight your expertise and skills. In the headline section, be sure to specify whether youâre a writer, videographer, designer, and so on. Weâd love to provide assignments for everyone who signs up for a Contently unfortunately, we are not an open marketplace and cannot supply work for the roughly 80,000 users now using a portfolio.
https://contently.com/
7)Â Â Â Â Â Keyhole
Keyhole is a real-time hashtag tracker for Twitter, Instagram and Facebook. Its visual dashboard is simple, beautiful and shareable! Keyhole's real-time dashboard shows how many people posted with your hashtag, along with the number of Retweets, Likes and Impressions your campaign is generating. Keyhole tracks the most influential people engaging with your brand. Reach out to them to promote your content and increase your brand's reach.
http://keyhole.co/
8)Â Â Â Â Â Revealbot
RevealBot Paid social media tactics have a much more limited pool of choice for reporting tools. You can automate bid strategies, get alerted to changes in status and review performance directly from the messaging client. Snapchats business model is based around attracting pound campaigns, but its become a popular tool for influencer marketing . It allows users to review the performance of your influencers snaps via open rates, completion rates and estimated reach, which arent accessible through the Snapchat app itself.
https://revealbot.com
9)Â Â Â Â Â Instagram for business
Use relevant, popular hashtags Engage by following others and liking their photos selected images to your Facebook page with a hashtag that aligns with your campaign or brand image to help people who donât know youâre on Instagram to find you there. Debut Videos Instagramâs recent Video on Instagram has given Twitterâs Vine a serious competitor to contend with. Jordan Crook charts the differences between Instagram and Vine in the image below: Jordan Crook charts Instagram vs. Embed Instagram Video in Your Blog or Website Last month, Instagram released a new embed feature for its desktop web browser version. Weâll talk more about this in Generate a Flexible Posting Plan Carley Keenan offers the following advice on the frequency of sharing on Instagram: âYou donât need to post on Instagram every day. If you start posting a lot, you might saturate your followersâ feeds, and you donât want to force yourself into the noise too often. There are apps that let users print images, search tags and keywords, subscribe to Instagram profiles via email, download all Instagram photos in a single archive folder, plus many more. Inspire Potential Customers Anna Colibri suggests you post photos that are relevant to your brand and potential customers. Whole Foods Market posts representative photos to promote healthy, wholesome food products, store events, sustainability and their active community of customers and employees. A study conducted by Simply Measured earlier this year found percent of the worldâs top brands are now active on Instagram.
https://business.instagram.com/
10)Â Statusbrew
Statusbrew is a social media management platform that combines the power of internet and technology to empowers businesses, brands, marketing agencies, and organizations to discover and manage customer experience across various social touch-points and drive growth. With a simple to use interface for the planning of social publishing of marketing and PR campaigns on multiple social networks, Statusbrew is a trusted partner for Teams.
The publishing feature helps you to schedule content across different social platforms. With Engage on Statusbrew, never miss out any conversation about your brand or business on social. Reply to DMs, Mentions, and Comments from all the profiles in a single unified social inbox. With real-time sync, receive and assign replies to specific teammates for them to work on it as soon as a prospect talks about you and never miss out on any lead. Slack Integration will change the way Brands, and Agencies can bring in their teams to collaborate in Social Publishing and Brand Monitoring right from their workspace. When you connect your Statusbrew with one or more Slack channels or workplaces, you will receive instant Slack notifications for all the activities you choose are necessary for your Business. Signup on Statusbrew for a free 14 days trial to gain a competitive edge and build strong social connections! show less
https://statusbrew.com/
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AI Meeting Notes: Revolutionizing How We Document Meetings
Business operations rely on meetings, but keeping track of notes has been considered a tedious chore. AI meeting notes come into play there to help us capture, organize, and use the information from discussions in a way that is easier to understand. Fortunately, with automation in control, these tools minimize the variables and prevent any of this from getting lost in translation. In this article, letâs start with how AI meeting notes are transforming our working environment.
Understanding AI Meeting Notes
What Are AI Meeting Notes?
You may refer to AI meeting notes as meeting summaries or even detailed transcriptions created through artificial intelligence during a meeting. These tools are powered by technologies such as voice recognition, natural language processing (NLP), and machine learning, enabling them to listen, transcribe, and format conversations in real time.
The ability to capture key points of a conversation, highlight action items, and even assign responsibilities to enhance accountability makes AI tools an indispensable asset for any modern team during meetings.
How Do AI Tools Capture and Organize Notes?
Basically, AI tools use algorithms that convert audio into text. They identify speakers, summarize discussion, and categorize the content into concrete action items. Even some tools are integrated with platforms such as Zoom, Google Meet, or Microsoft Teams and work seamlessly.
The Benefits of Using AI for Meeting Notes
1. Improved Accuracy and Efficiency
Humans are prone to error, especially in fast-paced meetings. AI eliminates the risk of missing critical information, providing precise and structured notes.
2. Real-Time Transcription and Organization
AI tools offer real-time transcription, so team members can follow along as the meeting progresses. This feature ensures immediate access to notes without any delays.
3. Accessibility and Collaboration
With cloud storage and integration capabilities, AI meeting notes are easily accessible to all team members. This ensures better collaboration, especially for remote or hybrid teams.
Key Features of AI-Powered Meeting Note Tools
Voice Recognition and NLP
The backbone of AI meeting tools lies in their ability to recognize different voices and languages while analyzing context. This ensures accurate summaries.
Summarization Capabilities
AI tools donât just transcribe; they summarize. By focusing on key points, action items, and takeaways, these tools save users from sifting through pages of raw text.
Industries Leveraging AI Meeting Notes
1. Corporate Environments
From board meetings to team huddles, AI meeting notes help executives stay focused on strategy rather than scribbling notes.
2. Education and Training
Professors and trainers use AI tools to record lectures or training sessions, providing accurate transcripts for later reference.
3. Healthcare and Legal Fields
Doctors and lawyers rely on AI meeting notes to record patient interactions or case discussions while focusing on client care.
How AI Meeting Notes Work
Step-by-Step Process of Note Generation
Audio Capture: The AI listens to the conversation.
Transcription: It converts speech to text in real time.
Analysis: Context and sentiment are analyzed for clarity.
Output: A structured summary with key points is delivered.
The Role of Machine Learning
Machine learning algorithms adapt over time, learning from repeated usage to improve accuracy and relevance in note-taking.
Comparison Between Manual and AI Meeting Notes
Manual Methods vs. AI-Driven Solutions
Manual note-taking has been the norm for decades, requiring intense focus and multitasking. This traditional approach often leads to incomplete notes or missed information. In contrast, AI meeting notes offer automated transcription and real-time summaries, significantly reducing the workload for participants.
Time, Effort, and Quality Metrics
AI tools excel in saving time by providing instant documentation and eliminating the need for post-meeting revisions. Quality also improves as AI systems ensure consistent and precise records, free from human biases or errors.
How AI Meeting Notes Impact Productivity
Saving Time for High-Priority Tasks
By automating the note-taking process, employees can focus on brainstorming and strategic discussions, driving innovation.
Streamlining Workflows
AI tools bridge gaps between meetings and execution by directly exporting action items to project management systems.
Common Misconceptions About AI Meeting Notes
1. AI Will Replace Human Judgment
While AI tools assist with documentation, human oversight remains essential to interpret and apply meeting insights effectively.
2. AI Tools Are Hard to Use
Contrary to belief, most AI meeting note solutions offer intuitive interfaces and require minimal technical expertise.
Conclusion
The most prominent: AI meeting notes are democratizing how we do meeting documentation and the movement is only changing what and how others document. If you are a small business or a large enterprise, these tools are going to save you time, increase productivity and facilitate collaboration. But with increased advancement of AI technology, the workplace will also be influenced, making better decisions and being more efficient.
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Opportunities for AI in Content Marketing Easily Explained
Until recently, the closest Iâve come to understanding artificial intelligence is knowing that it powered tools in my martech stack (e.g., marketing automation, predictive lead scoring, etc.).
Beyond that, I found the concept hard to grasp until Chris Pennâs presentation at Content Marketing World, How to Use AI to Boost Your Content Marketing Impact.
Chris, co-founder and chief innovator at Trust Insights, covered several real-world applications of AI. His examples helped transform abstract concepts into tangible use cases.
Chris implemented these examples himself via hands-on coding in the R programming language, using a deep understanding of mathematics, data science, and machine learning. But most marketers donât have data science and computer programming skills. Later in this article, I share Chrisâ advice about how marketers can apply these AI concepts.
Here are several of Chrisâ experiments.
Driver analysis: What results in profitable action?
When you have a bunch of data but youâre not sure what matters to the outcome you want, driver analysis is an effective tool, Chris says.
Machine learning software excels in this case. You feed in all the data and it tells you what matters in it. Chris explains that the analysis concludes with something like, âHey, this combination of variables seems to have the strongest mathematical relationship to the objective you want.â
AI in #contentmarketing: Driver analysis to show what factors drive the most leads. @cspenn Click To Tweet
Chris performed driver analysis on the popular PR and marketing blog, Spin Sucks, where the primary business objective is lead generation.
â(It) determined that organic search was the third most powerful driver. The team focused a lot of time and energy on it, and they should, but email was the No. 1 driver,â Chris says.
By understanding better what drives leads, the Spin Sucks team could decide to shift more of their time to email marketing because it was the most effective source.
Whether your objective is page views, social shares, leads, or revenue, a ranked list of drivers can help you plan resources, priorities, and budgets more effectively.
Implementation detail: Chris used the R programing language to implement Markov chain attribution. For a detailed look at one such implementation, read this post by data scientist Sergey Bryl, which will give you a good sense of how much mathematics and data science is involved.
HANDPICKED RELATED CONTENT: Why Marketers Need to Think Like Data Scientists (And How to Do It)
Text mining: Reveal topics, keywords, and hidden problems
Text mining is an application of AI that ingests content (e.g., text) to classify, categorize, and make sense of it.
Chris notes that text mining uses vectorization, which transforms words into numbers. It looks at the mathematical relationship among those numbers and determines how similar those words are. It is a form of deep learning.
Reverse engineer Google to reveal key topics and terms
The Google algorithm, which uses a heavy amount of AI itself, is an example of a deep-learning system. âGoogleâs search algorithm is so complex now that no one knows how it works, including Google,â Chris said. âThey have very little interpretability of their model.â
You can use text mining to reverse engineer the Google algorithm for your targeted topics. âWe can deploy our own machine learning models to say, âOK, for a search term like content marketing, what words do the top 10 or 20 pages all have in common?ââ
AI in #contentmarketing: Find #SEO-friendly #content topics via text mining. @cspenn Click To Tweet
Hereâs a sample output from reverse engineering Google:
The resulting lists hint at what words or categories to cover when developing new content around your reverse-engineered keyword. Having this set of common words gives you a higher chance of success with organic search than simply saying, âLetâs write a really good article about content marketing.â
Implementation detail: Chris implemented text mining and topic modeling via the R programming language, extracting related topics from a corpus of text (e.g., the contents of articles found in the search engine results pages).
HANDPICKED RELATED CONTENT: How to Make Your Content Powerful in Eyes of Searchers (and Google)
Extract hidden insights via text mining
In 2014, Darden Restaurants, the parent company of Olive Garden, replaced its board. The new group implemented changes, including enforcing its existing but mostly ignored breadstick policy (serving one per person plus one extra).
As Chris explains, employees then spent their time enforcing the policy by counting the number of breadsticks in the basket based on the number of people at the table.
Chris used text mining on 2,500 publicly available reviews written by the companyâs employees on Glassdoor. Hereâs a glimpse of the results:
Text mining surfaced breadsticks as a problem. If Olive Garden was looking to repair low employee morale and a poor customer experience, a manual review of its Glassdoor reviews, where the usual restaurant worker complaints like low pay and long hours abound, may have led them down the wrong path.
AI use in #contentmarketing: Use text mining on reviews to reveal hidden problems. @cspenn Click To Tweet
Text mining revealed the breadstick problem. (After intense public pushback, Olive Garden returned to its previous breadstick approach.)
Text mining of unstructured data can be applied in many useful marketing contexts: customer reviews, poor/high performing blog posts, transcripts of customer success phone calls, etc. Extracting that hidden gem of insight can point you to courses of action with a high ROI.
Implementation detail: Similar to the reverse engineering Google example, Chris implemented text mining via the R programming language.
HANDPICKED RELATED CONTENT: Scale Your B2B Content With Artificial Intelligence: Ideas and Tools Marketers Can Try
Time-series forecasting: Analyze competitorsâ brand searches
Letâs combine math, statistics, and AI to create a Magic 8-Ball.
âWouldnât it be great to know whatâs going to happen,â Chris asked. âIt would be so much easier to plan, to set budgets, to staff, to have an editorial calendar.â
Chris did an exercise of predictive time-series forecasting for Cleveland hotel search data. He looked at more than 12 months of branded searches â where searchers named specific hotels (e.g., Hilton Cleveland, Holiday Inn Cleveland, Hyatt Cleveland, Marriott Cleveland).
The results predicted when search volumes go up and down for each hotel:
âIf you (worked at) the Cleveland Marriott here, you now know that right around the end of September you have more search interests than your competitors. You could be running campaigns against them to take even more market share away from them,â Chris said.
Any brand could benefit from predictive time-series forecasting â analyzing brand searches for your company vs. your competitors. You can search for when your brand underperforms, for example, and use that data to bid on your competitorsâ brand names with a relevant content asset or promotional offer.
AI in #contentmarketing: Use time-series forecasting to predict lead-gen and revenue. @cspenn Click To Tweet
âImagine search topics, conversations, social media. You can forecast more than search volume,â Chris said. âYou can forecast lead generation from your marketing automation software. You can forecast revenue from your CRM or your ERP. Anything that is regular data over time you can forecast forward.â
Implementation detail: Chris used R to process five years of Google search data, then implemented a statistical method called autoregressive integrated moving average (ARIMA).
How content marketers can try these AI uses
I know what some of you must be thinking about now:
âWait, really?â
âThe data science and probability are over my head.â
âIâm too busy and canât possibly learn to do this myself.â
These reactions are understandable. The good news is that you have options. And you donât need to learn the deep nuts and bolts covered earlier.
Chris offered three recommendations for marketers thinking about approaching AI.
Do it yourself. This approach fits for the small percentage of marketers who have a genuine interest in data science and machine learning. You should be interested in going deep with math, statistics, and probability â and comfortable writing code.
If you decide to go this route, Chris suggests checking out Googleâs Machine Learning Crash Course, available free online, which takes you through 40-plus exercises, 25 lessons, real-world case studies, and lectures from Google researchers.
Chris notes that IBM Watson Studio has an intuitive, drag-and-drop user interface. While Watson does enable programmers to write code on its platform, the UI can be useful for marketers who are not inclined to write code.
For those interested in coding, Chris recommends learning the R and Python languages, which form the basis for a lot of AI tools and libraries. Be prepared to spend six to 12 months to learn the programming language and another six to 12 months to learn the data science.
If youâre just getting started with coding, the Dummies franchise has books that may be useful: R for Dummies and Python for Dummies.
Tap your staff data scientist. The second option applies to larger organizations that employ data scientists (e.g., Google, Facebook, and Uber). âStaff with data science skills are quantitatively inclined and know how to use the technology properly, so they can be of great help,â Chris said.
Think back to the use cases I mentioned. For text mining or time-series forecasting, in-house data scientists will understand your objectives and goals, build the right models, then implement the necessary codes.
Outsource. This option works for organizations that donât have AI and data science talent in-house. The answer is to outsource to the experts: people or agencies with the necessary AI know-how and experience.
Chris puts it this way: âAgencies and consultants can help you use the methodologies. You can do small projects on a one-off basis. If the need is ongoing or more frequent, they can help you build software that runs when you need it to.â
Next steps
No matter which of the three options makes sense for you, thereâs one thing I urge all marketers to do: Learn about AI and understand the role it plays in marketing technology.
While you donât need to understand Markov chain attribution or how to program in R, you need to know enough to determine where and how AI can help your marketing. Basic AI knowledge will also help you better evaluate vendor solutions and claims.
Think about the kind of knowledge you need to buy a computer. You donât need to be a chip designer, but you need to know the difference between a 32-bit and 64-bit processor and whether a 1.5 GHz processor is better than a 2.7 GHz processor. With AI, when a vendor says, âOur predictive analytics solution uses the latest AI techniques,â you need to know how to question the claim and how to distinguish fluff from reality.
HANDPICKED RELATED CONTENT: Are You Really Smart About How AI Works in Marketing?
Since AI is a topic often covered in business, marketing, and technology publications, Iâm soaking up as much as I can. Next, Iâll probably enroll in some free, online courses in machine learning.
What about you? Whatâs your interest level in AI for marketing and how are you staying informed and educated?
Hereâs an excerpt from Chrisâ talk:
youtube
Further your tech skills in 2019 by attending ContentTECH Summit in April. Register today using code BLOG100 to save $100.Â
Cover image by Joseph Kalinowski/Content Marketing Institute
from http://bit.ly/2VHL0sx
0 notes
Text
Opportunities for AI in Content Marketing Easily Explained
Until recently, the closest Iâve come to understanding artificial intelligence is knowing that it powered tools in my martech stack (e.g., marketing automation, predictive lead scoring, etc.).
Beyond that, I found the concept hard to grasp until Chris Pennâs presentation at Content Marketing World, How to Use AI to Boost Your Content Marketing Impact.
Chris, co-founder and chief innovator at Trust Insights, covered several real-world applications of AI. His examples helped transform abstract concepts into tangible use cases.
Chris implemented these examples himself via hands-on coding in the R programming language, using a deep understanding of mathematics, data science, and machine learning. But most marketers donât have data science and computer programming skills. Later in this article, I share Chrisâ advice about how marketers can apply these AI concepts.
Here are several of Chrisâ experiments.
Driver analysis: What results in profitable action?
When you have a bunch of data but youâre not sure what matters to the outcome you want, driver analysis is an effective tool, Chris says.
Machine learning software excels in this case. You feed in all the data and it tells you what matters in it. Chris explains that the analysis concludes with something like, âHey, this combination of variables seems to have the strongest mathematical relationship to the objective you want.â
AI in #contentmarketing: Driver analysis to show what factors drive the most leads. @cspenn Click To Tweet
Chris performed driver analysis on the popular PR and marketing blog, Spin Sucks, where the primary business objective is lead generation.
â(It) determined that organic search was the third most powerful driver. The team focused a lot of time and energy on it, and they should, but email was the No. 1 driver,â Chris says.
By understanding better what drives leads, the Spin Sucks team could decide to shift more of their time to email marketing because it was the most effective source.
Whether your objective is page views, social shares, leads, or revenue, a ranked list of drivers can help you plan resources, priorities, and budgets more effectively.
Implementation detail: Chris used the R programing language to implement Markov chain attribution. For a detailed look at one such implementation, read this post by data scientist Sergey Bryl, which will give you a good sense of how much mathematics and data science is involved.
HANDPICKED RELATED CONTENT: Why Marketers Need to Think Like Data Scientists (And How to Do It)
Text mining: Reveal topics, keywords, and hidden problems
Text mining is an application of AI that ingests content (e.g., text) to classify, categorize, and make sense of it.
Chris notes that text mining uses vectorization, which transforms words into numbers. It looks at the mathematical relationship among those numbers and determines how similar those words are. It is a form of deep learning.
Reverse engineer Google to reveal key topics and terms
The Google algorithm, which uses a heavy amount of AI itself, is an example of a deep-learning system. âGoogleâs search algorithm is so complex now that no one knows how it works, including Google,â Chris said. âThey have very little interpretability of their model.â
You can use text mining to reverse engineer the Google algorithm for your targeted topics. âWe can deploy our own machine learning models to say, âOK, for a search term like content marketing, what words do the top 10 or 20 pages all have in common?ââ
AI in #contentmarketing: Find #SEO-friendly #content topics via text mining. @cspenn Click To Tweet
Hereâs a sample output from reverse engineering Google:
The resulting lists hint at what words or categories to cover when developing new content around your reverse-engineered keyword. Having this set of common words gives you a higher chance of success with organic search than simply saying, âLetâs write a really good article about content marketing.â
Implementation detail: Chris implemented text mining and topic modeling via the R programming language, extracting related topics from a corpus of text (e.g., the contents of articles found in the search engine results pages).
HANDPICKED RELATED CONTENT: How to Make Your Content Powerful in Eyes of Searchers (and Google)
Extract hidden insights via text mining
In 2014, Darden Restaurants, the parent company of Olive Garden, replaced its board. The new group implemented changes, including enforcing its existing but mostly ignored breadstick policy (serving one per person plus one extra).
As Chris explains, employees then spent their time enforcing the policy by counting the number of breadsticks in the basket based on the number of people at the table.
Chris used text mining on 2,500 publicly available reviews written by the companyâs employees on Glassdoor. Hereâs a glimpse of the results:
Text mining surfaced breadsticks as a problem. If Olive Garden was looking to repair low employee morale and a poor customer experience, a manual review of its Glassdoor reviews, where the usual restaurant worker complaints like low pay and long hours abound, may have led them down the wrong path.
AI use in #contentmarketing: Use text mining on reviews to reveal hidden problems. @cspenn Click To Tweet
Text mining revealed the breadstick problem. (After intense public pushback, Olive Garden returned to its previous breadstick approach.)
Text mining of unstructured data can be applied in many useful marketing contexts: customer reviews, poor/high performing blog posts, transcripts of customer success phone calls, etc. Extracting that hidden gem of insight can point you to courses of action with a high ROI.
Implementation detail: Similar to the reverse engineering Google example, Chris implemented text mining via the R programming language.
HANDPICKED RELATED CONTENT: Scale Your B2B Content With Artificial Intelligence: Ideas and Tools Marketers Can Try
Time-series forecasting: Analyze competitorsâ brand searches
Letâs combine math, statistics, and AI to create a Magic 8-Ball.
âWouldnât it be great to know whatâs going to happen,â Chris asked. âIt would be so much easier to plan, to set budgets, to staff, to have an editorial calendar.â
Chris did an exercise of predictive time-series forecasting for Cleveland hotel search data. He looked at more than 12 months of branded searches â where searchers named specific hotels (e.g., Hilton Cleveland, Holiday Inn Cleveland, Hyatt Cleveland, Marriott Cleveland).
The results predicted when search volumes go up and down for each hotel:
âIf you (worked at) the Cleveland Marriott here, you now know that right around the end of September you have more search interests than your competitors. You could be running campaigns against them to take even more market share away from them,â Chris said.
Any brand could benefit from predictive time-series forecasting â analyzing brand searches for your company vs. your competitors. You can search for when your brand underperforms, for example, and use that data to bid on your competitorsâ brand names with a relevant content asset or promotional offer.
AI in #contentmarketing: Use time-series forecasting to predict lead-gen and revenue. @cspenn Click To Tweet
âImagine search topics, conversations, social media. You can forecast more than search volume,â Chris said. âYou can forecast lead generation from your marketing automation software. You can forecast revenue from your CRM or your ERP. Anything that is regular data over time you can forecast forward.â
Implementation detail: Chris used R to process five years of Google search data, then implemented a statistical method called autoregressive integrated moving average (ARIMA).
How content marketers can try these AI uses
I know what some of you must be thinking about now:
âWait, really?â
âThe data science and probability are over my head.â
âIâm too busy and canât possibly learn to do this myself.â
These reactions are understandable. The good news is that you have options. And you donât need to learn the deep nuts and bolts covered earlier.
Chris offered three recommendations for marketers thinking about approaching AI.
Do it yourself. This approach fits for the small percentage of marketers who have a genuine interest in data science and machine learning. You should be interested in going deep with math, statistics, and probability â and comfortable writing code.
If you decide to go this route, Chris suggests checking out Googleâs Machine Learning Crash Course, available free online, which takes you through 40-plus exercises, 25 lessons, real-world case studies, and lectures from Google researchers.
Chris notes that IBM Watson Studio has an intuitive, drag-and-drop user interface. While Watson does enable programmers to write code on its platform, the UI can be useful for marketers who are not inclined to write code.
For those interested in coding, Chris recommends learning the R and Python languages, which form the basis for a lot of AI tools and libraries. Be prepared to spend six to 12 months to learn the programming language and another six to 12 months to learn the data science.
If youâre just getting started with coding, the Dummies franchise has books that may be useful: R for Dummies and Python for Dummies.
Tap your staff data scientist. The second option applies to larger organizations that employ data scientists (e.g., Google, Facebook, and Uber). âStaff with data science skills are quantitatively inclined and know how to use the technology properly, so they can be of great help,â Chris said.
Think back to the use cases I mentioned. For text mining or time-series forecasting, in-house data scientists will understand your objectives and goals, build the right models, then implement the necessary codes.
Outsource. This option works for organizations that donât have AI and data science talent in-house. The answer is to outsource to the experts: people or agencies with the necessary AI know-how and experience.
Chris puts it this way: âAgencies and consultants can help you use the methodologies. You can do small projects on a one-off basis. If the need is ongoing or more frequent, they can help you build software that runs when you need it to.â
Next steps
No matter which of the three options makes sense for you, thereâs one thing I urge all marketers to do: Learn about AI and understand the role it plays in marketing technology.
While you donât need to understand Markov chain attribution or how to program in R, you need to know enough to determine where and how AI can help your marketing. Basic AI knowledge will also help you better evaluate vendor solutions and claims.
Think about the kind of knowledge you need to buy a computer. You donât need to be a chip designer, but you need to know the difference between a 32-bit and 64-bit processor and whether a 1.5 GHz processor is better than a 2.7 GHz processor. With AI, when a vendor says, âOur predictive analytics solution uses the latest AI techniques,â you need to know how to question the claim and how to distinguish fluff from reality.
HANDPICKED RELATED CONTENT: Are You Really Smart About How AI Works in Marketing?
Since AI is a topic often covered in business, marketing, and technology publications, Iâm soaking up as much as I can. Next, Iâll probably enroll in some free, online courses in machine learning.
What about you? Whatâs your interest level in AI for marketing and how are you staying informed and educated?
Hereâs an excerpt from Chrisâ talk:
youtube
Further your tech skills in 2019 by attending ContentTECH Summit in April. Register today using code BLOG100 to save $100.Â
Cover image by Joseph Kalinowski/Content Marketing Institute
The post Opportunities for AI in Content Marketing Easily Explained appeared first on Content Marketing Institute.
from https://contentmarketinginstitute.com/2019/01/artificial-intelligence-content-marketing/
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ChatGPT vs Software Developers
In the rapidly evolving world of technology, the emergence of ChatGPT and Generative AI has sparked numerous discussions about the future of software developers. As we witness AI becoming more sophisticated, a question looms: "Is Generative AI the end of the road for developers?" In this comprehensive article, we delve into the implications of this dynamic shift, exploring the potential challenges, opportunities, and transformations that ChatGPT and Generative AI bring to the world of software development. ChatGPT vs Software Developers: Is Generative AI the End of the Road for Developers? As the software development landscape continues to evolve, the conversation around ChatGPT's impact gains prominence. Developers now find themselves at a crossroads, where they must adapt to the changing technological landscape. The question of whether Generative AI will replace developers entirely is a pertinent one. Let's dive into the various aspects of this paradigm shift. The Rise of Generative AI: Generative AI, a subset of artificial intelligence, focuses on creating content that is human-like in nature. With advancements in natural language processing, ChatGPT has become a prime example of this technology. This AI model can generate text, answer questions, and even engage in meaningful conversations, blurring the lines between human and machine interaction. Transforming Traditional Development: Traditional software development involves manual coding, testing, and debugging. However, Generative AI has the potential to revolutionize this process. Developers can now leverage AI-generated code snippets to accelerate their work, leading to increased efficiency and productivity. This transition also raises concerns about the quality and security of AI-generated code. The Collaborative Dynamics: Rather than rendering developers obsolete, Generative AI and ChatGPT can become invaluable collaborators. Developers can utilize AI-generated suggestions to enhance their creativity and problem-solving skills. This collaboration can lead to the development of innovative solutions that combine human ingenuity with AI's computational power. Redefining Skillsets: The advent of Generative AI calls for a shift in developers' skillsets. While coding remains a vital skill, developers must also embrace AI literacy. Understanding how to interact with and leverage AI models like ChatGPT becomes essential. Developers who adapt and learn to integrate AI into their workflow will likely remain indispensable. Addressing Apprehensions: Amidst the excitement, concerns about job displacement arise. Will developers lose their jobs to AI? While automation can handle repetitive tasks, the role of developers extends beyond coding. They conceptualize, design, and innovate. Moreover, AI models require training and oversight, creating new roles for developers in AI model development and monitoring. Ethical Considerations: As AI becomes more autonomous, ethical considerations come to the forefront. Developers play a crucial role in ensuring that AI systems are unbiased, transparent, and responsible. Human oversight is vital to prevent AI-generated content from propagating misinformation or exhibiting biased behavior. FAQ's: Can ChatGPT entirely replace software developers? No, ChatGPT and Generative AI are tools that can enhance developer productivity, but they cannot replace the creativity, critical thinking, and problem-solving abilities that human developers bring to the table. How can developers adapt to the rise of Generative AI? Developers can adapt by expanding their skillsets to include AI literacy, collaborating with AI models, and focusing on higher-level tasks such as innovation and ethical oversight. Will AI-generated code be as reliable as manually written code? AI-generated code has its benefits in terms of speed and efficiency, but it requires careful review and testing by human developers to ensure its quality, security, and adherence to best practices. What role will developers play in AI model development? Developers will play a pivotal role in training, fine-tuning, and monitoring AI models like ChatGPT. Their expertise is essential to ensure AI systems align with ethical guidelines and produce accurate results. Can AI models like ChatGPT think creatively? AI models like ChatGPT can generate text and responses based on patterns in the data they've been trained on. While they can simulate creativity, true creative thinking involves understanding context, emotions, and cultural nuances â areas where human developers excel. How will Generative AI impact software development jobs? Generative AI might lead to a shift in the nature of software development jobs, with a greater emphasis on collaboration between developers and AI models. New roles may emerge in AI ethics, model development, and oversight. Conclusion: In the ongoing evolution of technology, Generative AI and ChatGPT are reshaping the software development landscape. However, the assertion that they will completely replace developers oversimplifies the complex interplay between human creativity and AI's capabilities. As developers adapt and collaborate with AI, they will continue to be at the forefront of innovation, ensuring that technology serves humanity's needs ethically, responsibly, and creatively. Read the full article
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Why You Should Optimize Your Email Campaigns With Artificial Intelligence
Have you heard of artificial intelligence?
Today, itâs a favorite topic among email marketers.
Experts from different areas get excited about the possible applications of AI. On the other hand, most marketers donât really understand how to use AI solutions in practice.
AI has its specifics, it requires proper investment and resources. At the same time, some simple solutions are nevertheless effective and capable of improving your marketing campaigns dramatically.
[The effectiveness of different means of marketing]
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According to eMarketer statistics, more than 80% of retailers think that email is the most effective way to get new customers and to keep existing ones. More than 90% of Americans check their emails every day, and the purchase rates of such consumers are 17% higher than in the case of social media promotions.
However, the success of email campaigns directly depends on the chosen strategy. You have to make sure that you target the right audience. In addition, itâs important to know that they actually interact with your emails, responding by buying your products or services. Fortunately, machine learning and AI makes such overwhelming tasks as email verification, targeting, and statistics, as well as scheduling, much simpler.
AI is capable of predicting the content and special offers that will bring you the best conversions, making your audience more engaged. AI tools can easily determine the right frequency and the best time of day so that your emails can reach out to the right audience at the right moment.
How AI is changing email marketing
Even though artificial intelligence is a relatively new technology, its influence on email marketing is already apparent. AI tools are all about optimization and making cumbersome tasks simpler. For example, all marketers know that the success of emails to a large extent depends on the time they are sent. People from one part of the world are less likely to open emails optimized for another time zone. AI solves this problem, along with many others.
[How companies use AI solutions]
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AI is capable of personalizing subject lines which are 95% more effective than those written by humans. Many experts also notice that cognitive content written by machines gets constantly improved, and making it more effective than human-written content is just a question of time.
Machine learning allows marketers to forget about large segments, as machines can learn when any particular recipient is most active. Itâs impossible to do manually, but for AI, itâs a simple task.
Timing is not the only advantage of AI tools. Adobe has already integrated its AI called Sensei into Adobe Campaigns. Sensei writes personalized subject lines and even improves other content, such as images. This algorithm can calculate how customers react to a certain image and find alternatives based on a particular customerâs preferences.
Artificial intelligence and machine learning are also great solutions for A/B testing. Even though itâs nothing new in the world of marketing, A/B testing has never been as effective as now, when it can be optimized by using machine learning and AI solutions. Marketers can make predictions and detect trends faster than ever, taking into account minor details that otherwise would remain unnoticed.
The best AI tools to optimize your email campaigns
Of course, the main advantage of AI solutions for email campaigns is an opportunity to significantly improve customization. Creating custom email campaigns manually would be unbelievably expensive and time-consuming. Fortunately, now business owners donât have to choose between the effectiveness of their campaigns and budget.
Tool #1. Phrasee
Phrasee is a great example of a tool that can increase clicks and conversions by just choosing the best subject lines for your emails. Smart algorithms of this tool analyze your content, predict the responses of a contact, and choose the appropriate writing style to reflect the specifics of your business. The best thing about Phrasee is what makes all the AI solutions so good â the more you use it, the better it understands your business, constantly learning something new.
AI-based tools can also make your email campaigns more engaging, selecting better images and optimizing text content. Itâs important to understand that the actions of your recipients depend on whether or not they are engaged. Even though the substantial improvement of the content may seem to be a time-consuming task, AI is able to deal with it much faster and more effectively than people.
Tool #2. Boomtrain
Boomtrain is an AI solution designed for different types of content. It personalizes your content for each user, increasing engagement and clicks. This tool can increase your sales by choosing the most relevant content that fits the preferences of your audience. It can also analyze user behavior, taking into account activity on your website, in mobile apps, or emails, building a profile for each customer and increasing engagement.
[Human vs AI personalization ]
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As weâve already mentioned above, timing is very important if you want your emails to actually work. If all your subscribers receive emails at the same time, only a few of them will actively interact with your emails and bring you profits. Everyone has specific preferences and habits, and thereâs no marketer who has enough time to record and analyze this sort of data. Fortunately, AI can make it.
Tool #3. Seventh Sense
Seventh Sense is a tool that will be appreciated by all Marketo and HubSpot users. Seventh Sense is tracking your customersâ activity and calculates the perfect delivery time for every email. It can also personalize the number of emails and their frequency so that your recipients wonât be annoyed by your emails and wonât mark them as spam.
According to statistics, more than 50% of consumers switch brands that donât customize their communications. Multivariate testing and A/B testing can help you adjust your campaigns for different segments, but AI tools turn out to be more effective than any traditional approach.
Tool #4. Optimail
Optimail is a tool that automatically adjusts the content and timing of your emails. It shows better results than traditional A/B testing. It leads your subscribers towards your business goals, analyzing their online activity. It also monitors shares on social media and purchases.
Tool #5. Siftrock
Siftrock is a very useful tool which can be integrated with your automation platform and help you manage the replies to your emails. Any new replies are recorded and new profiles are immediately transferred to your database. Using this tool, you can also know when someone leaves your company. Moreover, this tool will immediately notify a responsible member of your team to quickly take action.
Wrapping up
Email marketing has always been time-consuming and complicated. It required manual adjustment and analysis of large amounts of data. Such technologies as artificial intelligence and machine learning make all the overwhelming marketing tasks simpler, showing results that would be impossible to achieve without AI.
Simply put, artificial intelligence makes you a better marketer, expanding your capabilities and making you able to take into account all the details about your subscribersâ behavior and preferences.
Guest author:Â Berta Melder is a content strategist and co-founder of the Masterra. Specializing in brand management, she cooperates with different education courses and helps participants develop the brand management skills. Enjoys creative writing and blogging. Follow her on Twitter.
The post Why You Should Optimize Your Email Campaigns With Artificial Intelligence appeared first on Jeffbullasâs Blog.
Read more: jeffbullas.com
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Ultimate.ai nabs $1.3M for a customer service AI focused on non-English markets
For customer service, Ultimate.aiâs thesis is itâs not humans or AI but humans and AI. The Helsinki- and Berlin-based startup has built an AI-powered suggestion engine that, once trained on clientsâ data-sets, is able to provide real-time help to (human) staff dealing with customer queries via chat, email and social channels. So the AI layer is intended to make the humans behind the screens smarter and faster at responding to customer needs â as well as freeing them up from handling basic queries to focus on more complex issues.
AI-fuelled chatbots have fast become a very crowded market, with hundreds of so called âconversational AIâ startups all vying to serve the customer service cause.
Ultimate.ai stands out by merit of having focused on non-English language markets, says co-founder and CEO Reetu Kainulainen. This is a consequence of the business being founded in Finland, whose language belongs to a cluster of Eastern and Northern Eurasian languages that are plenty removed from English in sound and grammatical character.
â[We] started with one of the toughest languages in the world,â he tells TechCrunch. âWith no available NLP [natural language processing] able to tackle Finnish, we had to build everything in house. To solve the problem, we leveraged state-of-the-art deep neural network technologies.
âToday, our proprietary deep learning algorithms enable us to learn the structure of any language by training on our clientsâ customer service data. Core within this is our use of transfer learning, which we use to transfer knowledge between languages and customers, to provide a high-accuracy NLU engine. We grow more accurate the more clients we have and the more agents use our platform.â
Ultimate.ai was founded in November 2016 and launched its first product in summer 2017. It now has more than 25 enterprise clients, including the likes of Zalando, Telia and Finnair. It also touts partnerships with tech giants including SAP, Microsoft, Salesforce and Genesys â integrating with their Contact Center solutions.
âWe partner with these players both technically (on client deployments) and commercially (via co-selling). We also list our solution on their Marketplaces,â he notes.
Up to taking in its first seed round now it had raised an angel round of âŹ230k in March 2017, as well as relying on revenue generated by the product as soon as it launched.
The $1.3M seed round is co-led by Holtzbrinck Ventures and Maki.vc.
Kainulainen says one of the âkey strengthsâ of Ultimate.aiâs approach to AI for text-based customer service touch-points is rapid set-up when it comes to ingesting a clientâs historical customer logs to train the suggestion system.
âOur proprietary clustering algorithms automatically cluster our customerâs historical data (chat, email, knowledge base) to train our neural network. We can go from millions of lines of unstructured data into a trained deep neural network within a day,â he says.
âAlongside this, our state-of-the-art transfer learning algorithms can seed the AI with very limited data â we have deployed Contact Center automation for enterprise clients with as little as 500 lines of historical conversation.â
Ultimate.aiâs proprietary NLP achieves âstate-of-the-art accuracy at 98.6%â, he claims.
It can also make use of what he dubs âsemi-supervised learningâ to further boost accuracy over time as agents use the tool.
âFinally, we leverage transfer learning to apply a single algorithmic model across all clients, scaling our learnings from client-to-client and constantly improving our solution,â he adds.
On the competitive front, itâs going up against the likes of IBMâs Watson AI. However Kainulainen argues that IBMâs manual tools â which he argues ârequire large onboarding projects and are limited in languages with no self-learning capabilitiesâ â make that sort of manual approach to chatbot building âunsustainable in the long-termâ.
He also contends that many rivals are saddled with âlengthy set-up and heavy maintenance requirementsâ which makes them âextortionately expensiveâ.
A closer competitor (in terms of approach) which he namechecks is TC Disrupt battlefield alum Digital Genius. But again theyâve got English language origins â so he flags that as a differentiating factor vs the proprietary NLP at the core of Ultimate.aiâs product (which he claims can handle any language).
âIt is very difficult to scale out of English to other languages,â he argues. âIt also uneconomical to rebuild your architecture to serve multi-language scenarios. Out of necessity, we have been language-agnostic since day one.â
âOur technology and team is tailored to the customer service problem; generic conversational AI tools cannot compete,â he adds. âWithin this, we are a full package for enterprises. We provide a complete AI platform, from automation to augmentation, as well as omnichannel capabilities across Chat, Email and Social. Languages are also a key technical strength, enabling our clients to serve their customers wherever they may be.â
The multi-language architecture is not the only claimed differentiator, either.
Kainulainen points to the teamâs mission as another key factor on that front, saying: âWe want to transform how people work in customer service. Itâs not about building a simple FAQ bot, itâs about deeply understanding how the division and the people work and building tools to empower them. For us, itâs not Superagent vs. Botman, itâs Superagent + Botman.â
So itâs not trying to suggest that AI should replace your entire customers service team but rather enhance your in house humans.
Asked what the AI canât do well, he says this boils down to interactions that are transactional vs relational â with the former category meshing well with automation, but the latter (aka interactions that require emotional engagement and/or complex thought) definitely not something to attempt to automate away.
âTransactional cases are mechanical and AI is good at mechanical. The customer knows what they want (a specific query or action) and so can frame their request clearly. Itâs a simple, in-and-out case. Full automation can be powerful here,â he says. âRelational cases are more frequent, more human and more complex. They can require empathy, persuasion and complex thought. Sometimes a customer doesnât know what the problem is â âitâs just not workingâ.
âOther times are sales opportunities, which businesses definitely donât want to automate away (AI isnât great at persuasion). And some specific industries, e.g. emergency services, see the human response as so vital that they refuse automation entirely. In all of these situations, AI which augments people, rather than replaces, is most effective.
âWe see work in customer service being transformed over the next decade. As automation of simple requests becomes the status-quo, businesses will increasingly differentiate through the quality of their human-touch. Customer service will become less labour intensive, higher skilled work. We try and imagine what tools will power this workforce of tomorrow and build them, today.â
On the ethics front, he says customers are always told when they are transferred to a human agent â though that agent will still be receiving AI support (i.e. in the form of suggested replies to help âbolster their speed and qualityâ) behind the scenes.
Ultimate.aiâs customers define cases theyâd prefer an agent to handle â for instance where there may be a sales opportunity.
âIn these cases, the AI may gather some pre-qualifying customer information to speed up the agent handle time. Human agents are also brought in for complex cases where the AI has had difficulty understanding the customer query, based on a set confidence threshold,â he adds.
Kainulainen says the seed funding will be used to enhance the scalability of the product, with investments going into its AI clustering system.
The team will also be targeting underserved language markets to chase scale â âfocusing heavily on the Nordics and DACH [Germany, Austria, Switzerland]â.
âWe are building out our teams across Berlin and Helsinki. We will be working closely with our partners â SAP, Microsoft, Salesforce and Genesys â to further this vision,â he adds.Â
Commenting on the funding in a statement, Jasper Masemann, investment manager at Holtzbrinck Ventures, added:Â âThe customer service industry is a huge market and one of the worldâs largest employers. Ultimate.ai addresses the main industry challenges of inefficiency, quality control and high people turnover with latest advancements in deep learning and human machine hybrid models. The results and customer feedback are the best I have seen, which makes me very confident the team can become a forerunner in this space.â
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