Digital Opportunities, fuelled by AI
We are lucky to be in the midst of an AI revolution creating a plethora of use cases in almost every industry. So whats driving this ?
1. Massive computing power availability at scale is a key driver. It takes about 2 clicks and 10 USD a month to have access to GPUs and TPUs to train complex neural models and store these model checkpoints in terra-bytes of drive space.
2. New technology and algorithms : Another key aspect is the rapid pace of development of new technology and algorithms. CNNs ( Convolutional Neural Networks ) revolutionised computer vision, and then it quickly reached new heights and capabilities with GANs ( Generative Adversarial Networks ). And if we take the NLP (Natural Language Processing) space, a massive lexical to neural transformation is underway with Transformer based models from google and facebook.
3. Democratisation of AI Education : Want to learn about any topic in AI ? It takes a few clicks to learn on online platforms.
But as we know, technology, infrastructure and education is only useful when applied in the right context for the right business need. So where do we start ? How can we frame our thinking of applying AI for business ? It helps to think by classifying what AI brings in terms of business value : Improved Digital Customer Experience fuelled by improvements in IoT, computer vision and NLP surely comes to mind.
But there is more : Digital Operations and Digital Business. So in a nutshell :
1. Digital Customer Experience: Market-Driven Growth
● Leads to Increase willingness to pay from customers
● Focus is to digitise and delight with digital customer experience
2. Digital Operations: Cost-Driven Growth
● Reduce costs, increase efficiency
● Focus: Digitise Repetitive Operations
3. Digital Business: Strategic Growth
● Digitise existing or build a new business model
● Increase willingness to pay or attract new customer types
So where do we see applications and use cases in these areas ? Lets dig in...
1. Digital Customer Experience : Primarily in web experience and customer care / contact center Experience :
1. Self-service using community, knowledge base, and powerful search in the community with semantic and contextual information retrieval. Think of expressing your complex domain specific question and the AI understanding natural language and coming up with relevant content for your query.
2. Partner/Customer portal: Lead and Opportunity Management (for partners)
3. Semantic website search: Better engagement, Better results. Search by understanding meaning and not only keyword match.
4. Marketing Automation: Predictive personalisation / Relevant content and lead scoring, nurturing and lead generation.
5. Conversational AI: Faster Answers, Reduced Research, Stronger Customer Engagement, Predictive Insights, Next Steps in the Conversation with sentiment analysis
6. Intent Prediction: For example, the technology can identify patterns that indicate a customer’s intent based on web activity or text and route the call or chat to the appropriate agent. Intent prediction enables contact centers to up their game by giving customers the assistance they need in the way they want.
7. Emotion Analytics: For example, an angry customer might be routed to the customer retention team, while a happy, satisfied customer might be routed to the sales team to be pitched a new product or service.
8. Text Analysis of customer feedback : NLP analysis also allows companies to extract product/service suggestions and complaints from text feedback in order to proactively address any issues. These technologies enable companies to gain insights on a micro-level — by understanding the emotions of each customer – as well as on a macro level, by keeping their finger on the pulse of their customer base’s opinions.
9. e-commerce / order management : Improved customer retention and sale through knowledge of previous purchases, searched products, and online browsing habits.
2. Digital Operations : Reduce cost of operations
1. Self service:
a. Community, FAQs, bots
b. Call Deflection to self-service using computer vision : Let AI do visual checks and sort out simple / routine issues.
2. Robotic Process Automation in
Quote to Cash: ML, NLP, Computer Vision, OCR
Source-to-pay: ML, NLP, Computer Vision, OCR
4. Automated Customer Onboarding
5. Data Migration, Entry, Reporting Automation
6. Human Resources : Candidate Sourcing, Employment History Verification
7. Logistics – Document processing automation in Trade Finance
3. Digital Business :
1. Service Play : Introducing a new digital service business leveraging IoT
2. Platform Play : Introducing an ecosystem of third party apps on top of a core platform
offering to increase customer preference over competition.
Hope this frame of thinking helps you in your digital and AI transformation. There is no doubt, an exciting world of possibilities is ahead of us !