Future of work through AI enabled innovations.

Future of Work - AI enabled Innovation

Globally businesses have benefitted from technological innovations which has accelerated the pace of organizational change. Yet for digital innovation to deliver long-term value, it must become embedded in the core enterprises DNA.

Several themes/ models have already been implemented Successfully in the past years like business model innovation, digital Engagement, Advanced Analytics and now artificial Intelligence / automation, machine learning and robotics.

Advances in AI has enabled algorithms / machines learning to assist humans in operations, but the task is still led by humans (and the final decision-making rests with humans). AI innovations have enabled technology to replace humans on many functions from data collection and analysis, down to the final decision making.

Currently within traditional businesses or the so called “Brick & Mortars”, a lot of tasks are achieved by humans:

• Gathering data / information
• Analyzing this data by running a model or just by using personal experience
• making a decision/ actions

“Artificial Intelligence enables most of the innovations that dominate current conversations about the future of work “

This continuous growing transformation could be understood by the numerous innovations propelled by AI in some major industrial functions:

Operational efficiency / Intelligent automation

Unlike traditional automation solutions, AI automates complex tasks that require adaptability, agility, and self-learning.

• Robotic Process Automation (RPA): The application of technology that allows employees in a company to configure software or “robots” to capture and interpret existing applications for processing a transaction, manipulating data, triggering responses and communicating with other digital systems;
• Computer Vision / Image Recognition: Analyzing images to derive information and analyze objects;
• Video Content Recognition: Analysis of a video sample to identify the content;
• Speech Recognition: Process clips of human speech, identify the words and derive meaning from them;
• Speech to Speech Translation: Recognize and translate human speech from one language into another automatically.

Robotics / Human interactions:

The development of AI will progressively lead to a replacement of humans on some certain tasks.

• Gesture Control: Enable interaction and communication with computers through gesture;
• Natural Language Processing: Process human language input and convert it into representations;
• Smart Robots: Build robots that can learn from their experience and act autonomously based on their environment;
• Context Aware Computing: Program software that automatically becomes aware of its environment and adapts its behavior accordingly;

Customization / Chatbots:

One of the least-discussed benefits of Artificial Intelligence is its ability to propel innovation as it diffuses through the economy.

• Personalized Recommendation Engine: Predict the preferences and interests of users and deliver personalized recommendations;
• Chatbots / Virtual Assistants: Program software agents that perform everyday tasks for an individual based on feedback and commands;
• Autonomous vehicles: Build cars with the ability to analyze road & obstacles and drive without a human’s intervention.

Artificial Intelligence across industry segments.

1.Insurance sector.

Artificial Intelligence can provide significant benefits to the insurance industry in different ways:

a). Customer centricity

First of all, it can help increase customer engagement and retention/anti-churn. The abundance of data can be used to refine the customers’ segmentation and provide personalized offers based on personal features.

• Customer needs identification & Customer Engagement
• Client relationship (Virtual Agents / Chatbots)
• Sales & marketing
• Claims processing
• Predicting a customer’s lifetime value
• Increasing customer segmentation accuracy
• Customer shopping patterns
• Optimizing a user’s in-app experience.

b). Value-added services 

Secondly, AI can help incumbent reinsurance companies to develop value-added services for their clients. From a Life perspective, the objective is to leverage intelligent data analysis to provide to the user new types of information/advice a human couldn’t obtain (symptom predictions, etc.).

c). Risk analysis / Underwriting

AI can enable reinsurance companies to combine public content with private 

Currently within traditional industries, a lot of tasks are achieved by humans:

• Gathering data / information
• Analyzing collected data by running a model or just by using personal experience
• Decision making to generate breakthrough approaches to assess, manage and price risk.
• The ability to ingest vast volumes of data (reports, documents, forecasts, ratings and financial and medical histories) enables to render meaningful insights, patterns and confidence-weighted recommendations far faster than humans can do.

Furthermore, AI will enhance risk assessment and refine pricing by analysis of more granular data.

d). Back-office automation

AI can allow to rapidly automate some of the current middle / back-office tasks and replace Employees. Once implemented, the software drives operational efficiency benefits, along with improvements in quality, scalability and resiliency in a cost-effective way.

Enterprises can automate current tasks as if a real person was doing them across applications and systems, achieving benefits similar to those of extended workforce support.

e). Fraud detection

AI understands past behavioral patterns of a customer and predicts the actions he or she will make, thus flagging unusual activities with a high probability to be fraudulent.

AI systems are also given real examples of fraudulent and legitimate behaviors so that the models can learn to recognize new patterns and evolve as fraudsters alter their tactics and helps reduce the number of false red-flags generated, thus saving time for the fraud handler

2.Banking 

• AI empowers banks to provide individualized, frictionless customer experiences, drive customer loyalty and profitability, and automate processes, enabling automated decision making to the banking industry.  
• AI and machine learning capabilities could either be through an easy-to-use interface or through APIs delivered on-premise, in the cloud or as a SaaS offering. 
• AI / machine learning integration could also enable new Use cases that focus on creating seamless customer journeys and automating manual processes with self-learning capabilities.
• Integration of AI / machine learning could give banks a real-time, end-to-end Smart Data Lake, offering higher quality and richness of data through multiple sources. This means that banks can make faster, more accurate and explainable decisions driven by AI algorithms

AI could transform banks to realize its value across the entire enterprise to provide individualized customer experiences and maximize straight-through processing with limited or no human intervention in all areas of the bank.

3.Healthcare

• Using predictive models to reduce drug discovery/ medicine production time.
• Identifying eligible patients for clinical trials
•  diagnosing diseases more accurately, improving personalized care, and assessing health risks.

4.Telematics

• capturing driving behaviors and habits as well as calculating fuel efficiency.
•  User Experience within vehicles, displaying information in such a way that the driver does not get distracted.
• Connected ecosystem - data insights, patterns, assisted operations and predictive modelling.

5.Data Insights / Actions

• Combining machine learning and data to provide insights that could help brands drive savings, optimize spend, and reduce risk – using all of their relevant data.

Caveat: (The views expressed in this post are personal views of the author, some of the trends mentioned in the above post might also be available in the public domain)

Please share your views and thoughts on ongoing innovations and the future of AI enabled transformations and the impact on the future of work across industry sectors

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