The future is digital: AI and ML in a post COVID-19 world

Sudipta Ghosh, Partner and Leader, Data and Analytics, PwC India, believes that post-COVID, AI-enabled proactive deployment of automation technologies like collaborative robotics and autonomous material movement to decrease worker density without impacting output or productivity will become more prominent. 

TPCI: In your view, how will the onset of COVID-19 and the emphasis on social distancing impact the application of artificial intelligence (AI) and machine learning (ML) in industries?

Sudipta Ghosh (SG): In the wake of the COVID-19 crisis, a shift in workforce dynamics has brought AI rapidly to the forefront of creating business resilience. Adhering to social distancing norms is one of the effective measures that can help flatten the curve, and many countries and organisations are using AI-based social distancing detection tools to monitor the implementation. Multiple online education start-ups have started using AI in their products and services, which helps in designing personalised/customised courses based on the needs and specific requirements of students.

Going forward, we can expect that healthcare professionals will also be dependent on technology for providing regular check-ups, remote treatment and virtual telemedicine sessions. In such scenarios, AI will be immensely helpful for healthcare professionals to analyse past medical records and share diagnostic details with others.

TPCI: Please cite some use cases where AI can take over some work roles in major manufacturing/services industries in the immediate future

SG: AI-enabled proactive deployment of automation technologies like collaborative robotics and autonomous material movement to decrease worker density without impacting output or productivity will become more prominent. PwC has analysed the potential impact of the COVID-19 crisis on industrial manufacturing.

The COVID-19 crisis has led to substantial changes in consumer behaviour and preferences. AI models can predict the supply and demand scenarios based on such variations and point out supply chain or product-related changes in the current economic environment.

In the services sector, AI-enabled chatbots and virtual assistants will be used to address complex customer queries.

TPCI: What are the current barriers to the adoption of the above-mentioned applications of AI in the industry?

SG: In our opinion the key barriers to adoption of AI in the industry are:

1. Explainability of the AI application: It is crucial to understand the reason behind a recommendation made by an AI engine in simple business terms. This is particularly relevant when dealing with customers or employees who would be potentially affected by the decisions prescribed by AI-enabled algorithms. In the absence of this explainability, the adoption of such solutions will be a challenge.

2. Availability of relevant skills: AI-enabled solutions need to be maintained after being built, and this requires constant intervention by data scientists to keep the algorithm relevant and accurate. Lack of dedicated focus on supporting and maintaining such applications may result in reduced relevance of such solutions and their subsequent adoption in the future.

3. Availability of reliable and unbiased data: The foundational element that makes AI efficient and accurate is the data that it learns from. Feeding data sets with implicit biases into AI algorithms may result in undesirable outcomes that have the potential to reduce trust and adoption of such AI solutions.

TPCI: In what ways can AI help medical teams in predicting, detecting and finding a cure for COVID-19? What are the insights emerging in this area at present?

SG: AI-powered predictions at an individual level can be a possible approach to tackle pandemics. ML models can be trained on a multitude of data sources to assess an individual’s clinical risk of being diagnosed with COVID-19. This would also help in allocating scarce resources like medical equipment and hospital beds more efficiently.

As the COVID-19 crisis intensifies despite the lockdown, clusters can be classified on the basis of the outbreak’s severity and some of the restrictions can be further relaxed through the usage of data and AI model based predictions related to the potential spread of the pandemic.

TPCI: How is the possibility of increasing emphasis on AI in the post COVID-19 world expected to transform the employment landscape in the coming years?

SG: The post COVID-19 world would shift more towards digitisation of products and services. This shift will increase the reliance on analysing data for taking critical decisions. Hence it is likely that the demand for data analysts, data engineers and data scientists will increase in the future.

The model of remote work/work from home (WFH) will find increased acceptance even after the crisis has passed. Employees will find it beneficial to avoid long commutes to workplaces and companies will realise the benefits of hiring a global workforce without geographical restrictions. AI-enabled applications will help organisations to simulate work environments and manage their remote workforce effectively. 

TPCI: What are the major focus areas of investment and innovation with regard to industrial applications of AI for organisations in the coming five years? What key transformations can we expect?

SG: One of the primary focus areas in the coming five years will be to increase digitisation of products and services in the following areas:

1. Digital operations and smart manufacturing: An AI-enabled digital solution will help increase productivity, reduce downtime of assets and minimise costs incurred due to poor quality of operations.

2. Supply chain transformation: AI-based technologies will enable organisations to sense and detect disruptions in supply chains. This will enhance the effectiveness and accuracy of end-to-end integrated planning.

3. Front office transformation: AI-enabled solutions will help in the upskilling of sales personnel as well as in designing appropriate channels and campaigns for end consumers.

4. Finance transformation: AI-enabled process automation will help in increasing the efficiency of finance processes like procure to pay and order to cash, reduce the risk of non-compliance and ensure the scalability of operations in future.