As organizations continue to adapt to the impact of the pandemic, a large part of Indian consumers have embraced digital technologies. India remains one of the largest growing markets for digital consumers and companies are trying to acclimatize quickly. This is an excellent opportunity for organizations to concentrate on digital transformation by recognizing its necessities and roadblocks.
With the prospect of automating systems and workflows in organisational setups as well as delivering individualized value to customer, benefits of the digital era are endless. There are many advantages of coupling digitization efforts with the AI capabilities – one should look at exponential technologies like AI and ML as enablers, adding fuel to the digital transformation journey. AI enables better use of data collected by companies, providing data-driven customer insights, increased agility, efficient operations and predicting uncertainties, it makes the digital experience “intelligent”.
The catapult effect of digital transformation projects in India are driven by a few key factors. The government is playing a proactive role in creating a digitally empowered society. ‘Digital India’ is the government’s flagship program, aimed at reducing the digital divide in the country. Massive investments in AI and Quantum technology have further boosted the digital economy.
Another key enabler is the consumer, with a very large digital consumer base. There is a high receptiveness to digital solutions across the population, this provides unique opportunities for companies to capture, create and deliver value from data. In further support, the emergence of new digital ecosystems and private-sector innovation has brought AI-enabled services to a larger consumer base. It has created substantial opportunities in vital sectors of healthcare, banking, and manufacturing.
Digital transformation is helping healthcare providers take a patient-focused approach, in turn helping streamline operations, build trust and offer a better experience. Moreover, AI is aiding digital transformation in healthcare by reducing medical errors using deep learning in object detection and recognition. Banking and financial services companies are spearheading digital transformation and quickly adapting to the new normal. Real-time capturing and recording of customer touchpoints including messages help companies to dynamically map the interactions of banks with customers.
AI is being utilized to detect unauthorized access to accounts. Banks are driving cost optimization efforts by utilizing predictive analytics and reaching the right customers. In manufacturing, plants are transforming into more autonomous factories with very few people required on-site. AI models and IoT are helping manufacturing plants to suggest best course of action for workers, establish preventive maintenance programs with real-time monitoring and generate behavior models for risk prevention.
Investment in AI is helping organisations realize value faster and accelerate their digital journey, but also comes with some risks. As businesses push to tackle long-standing and newly found AI challenges, they must ensure timely development and deployment of AI solutions. Unlike conventional SDLCs, AI projects are centered around identifying and collecting data. AI applications are only as good as the information fed to them. Finding high-quality information requires streamlining collection process and pre-processing available data. Organisations must also bridge the gap in understanding among teams working on these projects. AI implementation calls for the management to understand the opportunities and limitations of AI.
With a clear understanding of the business problem, identifying quality data sources and aligning technical teams, organisations can scale to production with ease and draw actionable insights for business consumption. It is far easier for organizations to develop POCs to show value of an AI solution but productionizing the solution is a completely different ball game. It Involves leading a strategic change management exercise where traditional silos of IT, analytics and business need to be broken and a new agile construct created coupled with infrastructure up-gradations to ensure that the new infrastructure can handle the variety and volume of the data required in a sustainable way. This is where the market is evaluating and slowly making the transition to newer frameworks like MLOps which makes the productionizing of AI much smoother and easier.
Today, consumers have become increasingly concerned about how companies access and use personal information. The key to overcoming data privacy challenge is visibility and segmentation. Having a transparent and ethical AI implementation framework helps organizations build trust with all parties involved. Companies must monitor how AI algorithms use data at all stages. Being transparent about data collection policies also help relieve customer concerns over AI.
Emerging technologies are rapidly flowing into businesses worldwide. It is no more a question of ‘when’ digital transformation will arrive but ‘what’ technologies are indispensable in the journey. AI will drive resurgence and its adoption will spur developers and users alike to push through challenges. As a result, the coming decade is a promising one for an AI-fueled digital future.
(The author Prashanth Kaddi is Partner and Vishesh Tewari, Director, Deloitte India and the views expressed in this article are his own)