Has the Pandemic changed status of artificial intelligence adoption from "would like to have" to "must have"?

Before the pandemic, while artificial intelligence adoption had gained interest among the masses, people still weighed the pros and cons of AI adoption, its impact on business as well as the initial cost of adoption. However, in a world reeling from COVID and its aftermath, artificial intelligence adoption is on the way to becoming a mainstream reality. At an unprecedented speed, more and more industries are experimenting and adapting artificial intelligence, be it health, education, retail or manufacturing.

According to a recent survey by Mckinsey, robotic process automation, computer vision and machine learning are facets of AI most commonly adopted by organisations. Various features of AI can be broken down into basic functions and be utilised as a microservice. One such example includes data cleansing services which profiles data and generates statistics.

Before COVID hit

The phrase, “would like to have,” perfectly defines most organisation’s pre-pandemic artificial intelligence adoption ideology. Despite the technology being relatively new, they had already seen and experienced the return on investment with AI, one way or another. Be it operational efficiency, electronic wastage reduction, or AI-powered computer vision for healthcare and organisational safety, artificial intelligence has gained traction across all verticals. Yet, the adoption took its due time, spanning over months and, possibly, years.

One of the key factors in technology adoption is its traction and adaptability in industries with proven KPIs. Another factor is the return-on-investment metrics, which is yet to gain the industry's faith but steadily getting there. Between last year and this one, it has been noticed that the risks of implementing AI are not merely better understood but also consistent. It was no longer viewed as a facility eating away jobs, it was steadily on the way of becoming a necessity even before the pandemic hit.

The Transition Saga

When COVID-19 was declared a global pandemic and countries started nationwide lockdowns, business continuity became the primary goal for all organisations. With restricted access to resources, facilities and a limited number of employees, the focus shifted from efficiency and productivity.

Companies turned to artificial intelligence technology for various aspects in the manufacturing and production, distribution, supply chain, retail and workspaces, to name a few. Not just R&D and IT, there is an increasing scope of AI integration. The amalgamation within the existing frameworks of customer service, marketing, operations, finance and other domains, is a particularly interesting aspect.

In the face of global lockdowns happening across geographies, getting essentials became a major challenge. With lockdown and social distancing in full force, the E-commerce boom led to an enormous crack in customer service. However, it became quintessential for them to cater to an ever increasing demand from consumers. E-commerce organisations increased the use of AI to provide customers with faster and better services. With customer-centric search and suggestions to offer a better experience, AI chatbots helped smoothly resolve issues and complaints real-time by creating alerts and checkpoints so as to combat the demand-supply gap.

Computer Vision Artificial Intelligence

The computer vision AI and its use in organisational health and safety saw an uptick since the COVID-19 Pandemic. The use of AI solutions ensure the safety of its employees by monitoring safety compliance transformed into a must-have. Use cases like social distancing, face mask compliance, thermal screening, group formation and gathering limit helped organizations safely reopen and ensure their employee's safety.

Most organisations are taking the solutions for COVID-19 safety compliance while simultaneously planning for the post-COVID world. Use cases and applications of AI post-COVID have already been designed, with project roadmaps scheduled for 2-3 years when additional AI use cases will be added-in.

However, the road to growth is paved with challenges. For one, digitisation is a critical prerequisite for optimum utilisation of AI technologies. For several organisations, it can bring a transformational change in the core business processes. It is important to note that without a strong digital backbone in an organisation, the AI system lacks the training data necessary to build better models and the ability to transform superior AI insights into behavioral changes at scale.

While the adoption of AI is taking place at a rapid pace, the survey propounds that many organisations lack core enablers such as top-management sponsorship, development of an enterprise-wide portfolio view of AI opportunities and the implementation of a sophisticated data strategy. Not only is it required to derive value from AI at scale, all of it requires a strategic thinking around AI programs and agendas. Business and technology leaders must work quickly to establish key AI enablers. Otherwise, they risk missing out on the current - and future - AI opportunity.

Conclusion

AI practices are maturing, despite several production use cases appearing primitive. Enterprises adopting AI are also taking steps to control for the most common risk factors. They are experimenting with modern techniques to build their AI products and services. Adopters are using a variety of ML and AI tools to create their own AI products and services. However, the AI adoption journey has a long way to go. Between addressing serious topics like data governance and conditioning, AI expansion and adoption across industries is inevitable.

By Kunal Kislay, Co-Founder, Integration Wizards Solution IRIS tech.