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“AI-driven changes will transform our work, workforce & workplace”

Rakesh Barik, Leader, Technology Consulting, Deloitte India, asserts that disruptions due to Covid-19 will bring major changes in AI and ML applications. However, he assures that this does not mean replacement of humans, but better collaboration between humans and machines for resolving complex business problems.

TPCI: In your view, how will the onset of Covid-19 and the emphasis on social distancing impact the application of AI and machine learning in industry?

Rakesh Barik: The onset of COVID-19 and subsequent lockdowns have disrupted businesses across sectors across the value chain – across customer facing areas, supply chain, manufacturing, operations, internal processes. Given these disruptions, application of AI and Machine Learning will undergo major changes with focus shifting more towards cost optimization and service continuity considerations in deciding on use cases for adoption. Use of drones and autonomous vehicles for physical delivery of goods would gain ground, both from a safety perspective as well as labour availability. Use of AI in customer service as a means of virtualization of contact centres is already gaining traction across multiple enterprises. The application of AI and Machine Learning may vary from one sector to the other. Taking the case of the BFSI sector, digital on-boarding will be enabled through use of AI based risk assessment applications.

Use of conversational UI, an application of AI (e.g. Chatbot) will increasingly be done to manage service continuity. With factories across the globe impacted, global supply chains are disrupted and industries with dependency on these supplies, would go in for cognitive supply chains that augment human capabilities with AI and Machine Learning. Irrespective of how AI and Machine Learning is leveraged, what though is certain, is that the role of AI and Machine Learning will no longer be a peripheral initiative, but rather find its way into core business areas and a part of the overall strategy.

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

Rakesh Barik: Manufacturing is one of the hardest hit activities in the COVID related lockdowns. Hence, we believe it will lead to a new normal with AI and automation coming in a big way. AI will enable robots with greater degree of learning ability and ‘sensitivity’. Thereby, robots and in some cases digital twins will be able to take over more assembly and movement-dependent activities on the manufacturing floor. Cognitive demand forecasting will increasingly be used to plan inventory and production in a predictive manner. The ability for machines to suggest better and more efficient ways of production based on past data would transform the industry post COVID-19 as well, because of the sheer scope of real-time maintenance of equipment to virtual design that allows for new, improved and customized products along with a smarter supply chain. Overall Industry 4.0 will be reality much sooner than was earlier anticipated, with renewed impetus from this disruption.

Service industry will need to focus on anytime, anywhere service as against a physical infrastructure based servicing e.g. customer service. New service operating models that are easy and pre-emptive would become imperative – AI/ML will increasingly be used to align to this new reality. The immediate focus area of AI/ML will be in self-service deflection; conversational UI’s like Chatbot and virtual assistants will shift from answering just routine queries to recommending and resolving on wide range of topics. Personalised customer campaigns using AI will help drive shift in customer behaviour, again boosted significantly by the constraints imposed during this time. In order to increase coverage, service industry may come up with conversational UI solutions in vernacular languages. Further, AI-driven B2B self-care model is likely to be the “next normal” among enterprise businesses.

TPCI: What are the current barriers to the adoption of these applications of AI in the industry?

Rakesh Barik: Major barriers to the adoption of AI at present are as follows:

• Bridging and managing the skill gap: Skill gap is one of the biggest barrier to AI adoption. Due to shortage of technology professionals with the right experience in big data, machine learning, deep learning, statistics, etc, organizations are unable to capitalize on opportunities offered by AI. Many companies are also looking beyond technical expertise, citing the need for business leaders who are able to interpret AI results, make decisions and take actions based on them. In these tough times, while organizations may believe that seeking the best external talent will provide an advantage, training their current workforce should not be overlooked.

• Managing the risks associated with AI: All across the globe organizations have recognized the importance and impact of AI, yet ethical and commercial risks remain one of the top potential concerns. Some key ethical risks include lack of transparency of AI decisions, poor accountability structures, potential bias and discrimination in AI decision-making, and use of personal data without adequate consent. Many organizations are integrating emerging tools to detect bias in AI data and algorithms, and developing remedial measures. AI adopters should be similarly proactive in addressing the ethical aspects of their AI initiatives.

• Workforce displacement and transitions due to automation: There has been insecurity in a lot of functions that if AI becomes prevalent in their area of expertise, they would become redundant, hence the adoption slows down in the organization. Every organization should have strong change management and plan how they can augment the human judgement rather than replace it, and give that comfort to the key stakeholders who can potentially drive the usage of AI.

• Lack of clarity and cost of AI programs needs to further reduce: Need for investment to kickstart an AI program with insufficient clarity on outcomes expected and lack of firepower of existing setups in organizations pose a challenge in AI implementation. The answer is to clearly draw up use cases aligned to strategic objectives from the start and use next generation of computing infrastructure, such as quantum computing or cloud infrastructure, which provides a pay-as-you-use model. However, cost of working on these platforms is coming down, thus reducing the inertia barrier of investing in an AI program.

Most importantly, this disruption offers an opportunity to advance this agenda. Organizations should invest wisely into this area during this time so as to be future ready once there is full restoration of normalcy and urgent demand / orders increase from a customer perspective.

TPCI: In what ways can AI help medical teams in prediction, detection and cure of the Covid-19 disease? What are the insights emerging in this area at present?

Rakesh Barik: Solutions and tools are being developed by epidemiologists and data scientists using big data, and machine learning across different areas from predicting hotspots to diagnosis to even drug synthesis. Take for example, forecasting infection numbers and who will contract the novel corona virus in order to be able to manage medical infrastructure.

Research centres are also working on upgrading AI system that can accurately predicts COVID-specific outcomes. The technique is known as Outbreak Analytics, which collects data on an epidemic, including confirmed cases, test results, maps of population densities, etc. and using various algorithmic models can predict the number of new cases that are likely to be infected by virus. Hospital using AI Systems detect feverish visitors with a simple facial scan. In some countries, AI is also helping doctors to predict complications such as respiratory failure or sepsis in COVID-19 patients.

AI has hugely impacted healthcare and pharmaceuticals and now AI/ML is also being deployed to help find a vaccine against COVID-19. The systems search current literature on disease, study the DNA and structure of virus and then consider the suitability of various drugs. In our country, with the launch of Aarogya Setu app, citizens can identify their risk of contracting COVID-19 and this helps take immediate action.

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

Rakesh Barik: AI driven changes, we believe, will transform our work, workforce and workplace; this does not necessarily mean replacement of human intelligence by AI, as it has been framed in many discussions. It means more collaboration between humans and humans/humans and machines, bringing together their intelligence to solve complex business problems. This is already happening across industries with the push expected to happen soon, as we are in the “age of with” – humans working with machines to continue pushing innovation for businesses and individuals.

All AI initiatives fall into 3 categories:

Assisted intelligence – e.g. a car warning a driver when changing lanes
Augmented intelligence – e.g. a car warning a driver when changing lanes and if not acted in time, AI takes decision on the driver’s behalf
Autonomous intelligence – e.g. a car driving itself from point A to point B

We believe that majority of the areas where applications would be built would fall in the first two areas of assisted and augmented intelligence. That means that while AI would help humans take decisions, ultimately the controlling factor would be human judgement and creativity. Therefore, it does not render the humans who have deep understanding of process, domain, industry redundant, but would change the way they interact and work with each other and with systems/machines.

Some areas where the push for AI would directly contribute to more and newer forms of employment generation are in the fields of strengthening digital infrastructure, better monitoring using IoT and big data, drug development, more online shopping, digital events, and e-learning.

In summary, humans and AI would work together seamlessly – centred around human experience, and driven by the power of AI to together achieve more than was possible by either individually.

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

Rakesh Barik: In the coming few years we expect most of the sectors leveraging power of AI and making investments.  Some sectors like Healthcare, Agriculture, Education, consumer/retail, FS, Smart Cities & Infrastructure, and Smart Mobility & Transportation are envisioned to benefit the most from AI in solving societal needs. Thus major focus areas of investment and innovation in AI in next five years are expected to be:

1. Healthcare workflows will one of the key areas. AI-based real time data access and recommendation across multiple hospitals pertaining to electronic health records, emergency department admissions, equipment utilization, staffing levels, etc. which will enable a wide range of efficiency and care enhancing capabilities.

2. AI-based solutions will be able to monitor and detect cellular drug interactions on a massive scale, which is a tedious process and takes years with many researchers to discover single drug at high cost. AI-based algorithms will speed up the process of experimentation and data gathering in the overall drug discovery process.

3. In the manufacturing industry, from automated shop floors to quality control, everything would be touched. Assembled individual product and component level specs, managing deadlines for rush orders as well as form automated workflows, where quality needs to be managed as well. Integrated AI solutions as a part of workflows, will be able to augment and address these challenges at scale.

4. The call centre industry is facing with a big challenge of providing excellent customer service experience at lower cost at scale. AI will play a major role in moving from deterministic based IVR conversation to an IVR with natural conversation helping companies to serve high customer experience at lower cost.

5. In the coming years, AI will drive an agriculture revolution and help in meeting the increased demand for food, particularly in crop selection and crop monitoring.

6. Urban planning, efficient utility distribution, waste management, and improving public safety are some of the areas where AI will devise a decision support channel for establishing smarter cities.

7. Autonomous technology in trucking, Intelligent Transportation Systems & Travel route/flow optimisation would be some of the major applications of AI with respect to smart mobility & transportation.

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