AI Grows to Create a Hyperpersonal Customer Experience

Companies are increasing their use of artificial intelligence (AI) and machine learning (ML) as new use cases emerge and AI deployment has gotten easier. Particularly retail and creative sectors such as media and entertainment are using AI to create hyperpersonal experiences for their customers. Organizations are finding these experiences easier to create with supervised learning models that allow business leaders to govern data inputs and identify desired business outcomes. Use cases in the metaverse and improved energy efficiency for AI could help continue to drive adoption in the next few years. However, enterprises should plan with care as ethical issues around some AI uses have not been resolved.

These trends, and others, are covered in our Applied AI and Advanced Analytics Services 2022 RadarView™. The report is a comprehensive study of AI and advanced analytics service providers, featuring top trends, analysis, and recommendations. It takes a close look at the market’s leaders, innovators, disruptors, and challengers.

We evaluated 37 service providers across three dimensions: practice maturity, partner ecosystem, and investments and innovation. Of those 37 providers, we recognized 23 that have brought the most value to the market during the past 12 months.

The report recognizes service providers in four categories:

• • Leaders: Accenture, Capgemini, Cognizant, HCL, IBM, Infosys, TCS, and Wipro
  • Innovators: Atos, Genpact, LTI, NTT DATA, and Tech Mahindra
  • Disruptors: DXC, EXL, Mindtree, Mphasis, Sutherland, and Zensar
  • Challengers: Coforge, Persistent Systems, Quantiphi, and UST

Figure 1 from the full report illustrates these categories:

“In the last 12 months, AI emerged as a pivot point for enterprises in their digital transformation journey,” said Anupam Govil, partner and digital practice lead at Avasant. “Interestingly, digital leaders are not just implementing AI, but converging AI with other next-gen technologies such as the metaverse, non-fungible tokens, and digital twins.”

The full report provides a number of findings and recommendations, including the following:

• • The number of AI and advanced analytics initiatives in the production stage has increased by 20% in the last 12 months, led by creative industries. AI applications in the media and entertainment sector and real-time decision-making in automobiles (especially EVs) has also gained momentum. Retail and CPG continues to lead in terms of AI implementations, primarily for hyperpersonalization, frictionless commerce, and superior product design. Manufacturing, banking, and healthcare and life sciences sectors are also gaining in AI adoption.
  • Innovative AI/ML model training techniques, such as reinforcement learning and unsupervised learning, have gained popularity over the years. However, about 50% of AI/ML models are trained using supervised learning, because business leaders want to govern the data inputs and model parameters leveraged to deliver the desired business outcomes.
  • With an increasing enterprise interest to adopt the metaverse for immersive experience and visualization, there is an urgent need to deliver high-performance computing and real-time data analytics. Hence, the hyperconvergence of AI with augmented reality/virtual reality, edge computing, 5G, and quantum will become fundamental to the metaverse.
  • Enterprises are looking toward sustainable and energy-efficient use of AI as training highly complex models often requires staggering energy consumption. This has put pressure on AI hardware providers such as Google, Nvidia, and Samsung to develop memory chips with computing capabilities to increase computational efficiency and optimize data storage. The race to build AI supercomputers that can reduce model training time is also picking up steam among tech companies such as Meta and HPE as well as enterprises such as Tesla.

“AI is a double-edged sword. While it can deliver profound business insights and efficiencies, many enterprises have encountered ethical issues with AI, such as implicit bias.” said Chandrika Dutt, research leader at Avasant. “Business leaders need a governance strategy for AI, not just engineering talent.”

The full report also features detailed RadarView profiles of 23 service providers, along with their solutions, offerings, and experience in assisting enterprises in their AI and advanced analytics journeys.

This Research Byte is a brief overview of the Applied AI and Advanced Analytics Services 2022 RadarView™ (click for pricing).