Leveraging the Untapped Potential of AI in High Tech Industry

AI in high industry solves a plethora of business problems. Tech giants like Google and Apple are effectively using AI technology.

Over the last few years, advances in Artificial intelligence (AI) have transformed every sector. Large organizations are spending billions of dollars to create AI solutions, algorithms, and models to solve a plethora of business problems. As per a study by Analytics Insight, the high-tech industry leads the adoption of AI with a 37% share and growing.

Tech giants like Google and Apple have invested billions of dollars to procure powerful computers and build sophisticated algorithms to support their AI journeys. Likewise, digital-native companies such as Netflix, Amazon, and Uber have successfully leveraged AI to emerge as industry leaders in their respective sectors.

In the entertainment sector, AI allows OTT platforms such as Netflix, Amazon Prime, and Hot Star to offer their viewers personalized experiences, thereby encouraging them to consume more content on their platforms.

Similarly, integrating AI into 5G networks can not only improve customer experience through faster and personalized services, enhance network performance, and reduce cost, but it can also accelerate the push towards Industry 4.0 by enabling smarter factories.

Tesla has used advanced AI image recognition and neural networks to deploy autonomy at scale, disrupting the auto industry. The world's largest mining group, Rio Tinto, is using hundreds of autonomous trucks across its iron ore operations. Companies such as Amazon and Microsoft are exploring ways to bring AI to every application, business process, and employee to enable large-scale digital transformation.

Our daily lives are constantly being assisted with AI-enabled features on our smartphones, laptops, and other digital devices. From finding our way on unfamiliar streets to making purchase decisions based on smart recommendations, AI has made life convenient and secure with advanced security features.

Overcoming AI Adoption Constraints

While there is widespread consensus about the immense potential that AI adoption brings, it presents several challenges. As a result, organizations often fail to adopt AI or struggle to scale AI across the enterprise to accrue its full benefits. Effective deployment of AI at scale requires organizations to invest adequate time in upfront planning before implementation.

Here are some of the factors that typically hinder AI adoption at scale:

• Availability of Right Data

Availability of the right datasets to train AI models are crucial to the success of AI projects. Building AI responsibly to ensure fair and non-discriminatory outputs is key to success. Any lapse in usage and treatment of data can create legal hurdles. Organizations need to ensure the security and integrity of data, as well as adhere to data privacy regulations. Ensuring ethical use of AI can prevent financial loss or reputational damage. Organizations have had to face lawsuits because of the way data was used.

• Compelling Business Case

Often, AI implementations fail to deliver expected results simply due to the lack of a well-articulated business objective. Therefore, it is critical to identify the right set of AI use cases that have the potential to deliver significant business value whether it is to lower costs, improve customer experience, or accelerate faster time to market.

• Effective Change Management

Large-scale deployment of AI across the enterprise requires careful planning in terms of the role of people and future impact. For instance, some roles may become redundant, making it necessary to upskill and reskill people to enable them to pick up new roles within the organization. Therefore, organizational change management plays a big role in making AI adoption at scale a successful endeavor.

Also, given the quantum of change and investments required, AI adoption at scale needs the right internal sponsorship and willingness to embrace transformation. Some organizations have successfully created new C-suite roles like CAIO, CAD, and CDO to drive such initiatives top down.

In the post-Covid-19 world, the high-tech industry is poised to lead the trend of AI adoption to transform business growth. By combining advanced technologies such as artificial intelligence, edge computing, AR/VR and metaverse kind of technologies, organizations can be well-placed to build solutions more quickly and effectively.

While some organizations are still lagging when it comes to optimum adoption of AI, the right approach can help step up on the digital transformation for enterprises and set them apart in the market.

Author:

Ashok Panda

Associate Vice President & Global Head – AI and Automation Service at Infosys

Ashok has over 24 years of professional experience in IT and Business consulting. He has delivered multiple AI and Automation led digital transformation programs for many of our key clients across different geographies and industry verticals. In his current role, Ashok heads AI and Automation service offerings, building competency and capability, amplifying our existing Infosys service offerings with AI and automation so as to deliver differentiated value to our clients. He also has a mandate to democratize Automation and AI across Infosys and drive profitability.