in the first article, I set a foundation for the need for digitization in the aged care industry. This article discusses the future of Smart AI, drivers, and barriers to this technology, and how Infosys leverages its industry experience, relationships with AI innovators, and a suite of tools to enable healthcare organizations to transition to AI-driven businesses.

The Future of Smart AI Adoption

Globally, Smart AI in the healthcare market is projected to grow from $13.82 billion in 2022 to $164.10 billion by 2029, at a CAGR (Compound Annual Growth Rate) of 42.4%. It is forecasted that by 2028, the size of the market for smart home automation will reach $187.8 billion, which is an increase of 15.2% compound annual growth rate from 2022 to 2028 (Research and Markets, 2022). According to Statista data, more than 130 million homes own at least one smart speaker. This statistic is expected to increase to as many as 335 million over the next five years. Furthermore, statista analysts predict that spending on IoT globally will reach $1.1 trillion in 2023 (Armstrong, 2022). As such, there is a promising future for Smart AI in the healthcare sector.

Drivers to Smart AI Adoption

Smart AI growth is driven by factors such as improved security and safety. This is evidenced by technology like Smart homes, where IoT is used to complete tasks ranging from the most basic (i.e., closings curtains) to the most complex (i.e., lighting up evacuation routes in the event of an emergency). Moreover, the same electrical power lines are used to transmit data and electricity making the need for separate networks for power and data redundant. This enables more flexibility and reduced costs for installation and retrofitting.

IoT enables convenient tracking of equipment, machinery and tools using sensors and connectivity, which subsequently allows businesses to locate problems easily and quickly in the assets and perform preventative maintenance to enhance asset utilization.

As Smart AI is integrated with apps, businesses can receive alerts in an automated manner for any anomalies that are detected by remote devices. This provides visibility to businesses in a timely manner allowing for enhanced safety of individuals and equipment.

Barriers to Smart AI Adoption

The technological barriers to Smart AI are challenges to compatibility and issues of interoperability. As the market for Smart AI is fragmented, manufacturers are developing technology using varying systems. This makes it easy to integrate devices built by the same manufacturer, but difficult to link and integrate those made by different vendors, leading to lower reliability of services, limitations in functionality, and compatibility challenges. Security challenges such as verification, authorization, system configuration, access control and information storage come along with Smart AI. This is because Smart AI technology is constantly evolving, which means that varying communication protocols and platforms are used, and standards are not well-defined. Privacy issues can also result from Smart AI and include leakage of personal and sensitive customer information.

Smart AI Adoption Framework

To overcome the abovementioned barriers, five steps are required for Smart AI adoption as displayed in Figure 1 (Eslami, 2020) and described in steps below.

Figure 1: Framework for Smart AI Adoption

  1. Market research is required to understand the target market’s pain points. This involves working closely with customers and having a comprehensive understanding of their challenges. It is also essential to use this opportunity to have customers brainstorm ideas for Smart initiatives that would be useful for their everyday living. Building technology that will provide value to prospective customers will support adoption rates.
  1. Affordability and Scalability – Smart technology should be affordable and scalable. As this technology can assist businesses in cutting operating and maintenance costs and enhance resource utilization providing real-time diagnostics and predictive analytics, these benefits should be considered when pricing customers for the upfront cost, installation, and ongoing maintenance of the technology.
  1. Delivery of actionable insights – Smart AI should deliver actionable insights. This can be done when data is real-time, is analyzed in an automated manner, and visualized the end user that al, lows for straightforward decision-making in the form of apps or digital dashboards.
  1. Usage of existing technology – Smart initiatives that use existing technology are more likely to be accepted by end-users and be more cost-effective and reliable. This includes utiutilizingisting cloud systems such as Amazon Web Services to store data from the Smart AI. It also means using security software and tools that are already available in the market to protect customer information from security breaches. The use of existing technology will require fewer upgrades and installations, making the Smart AI a more viable option.
  1. Adaptability to changing direction – As Smart AI is constantly evolving, organization need to adapt to smart AI adoption. This involves introducing change management in the business where the benefits of Smart AI adoption are well-understood by staff and end-users. It is more difficult for individuals to grasp new concepts without having a comprehension of why change is required. Moreover, resistance to change can be reduced by ensuring that the Smart initiatives are easy to use, intuitive, and even fun.


Infosys is driving Successful Adoption of Smart AI

As the aged care sector undergoes digital transformation, healthcare organizations unify fragmented systems, policies, processes, and large amounts of data. Due to the drivers and barriers to Smart AI adoption, IT Consulting firms like Infosys combine their deep industry experience, service offerings, and frameworks into a strategic vision that enables healthcare organizations utilizing their tools, systems, and data to drive better experiences for both elderly and healthcare staff, subsequently supporting the end-to-end data value chain (see Figure 2).

Infosys has been ranked in the top five in AI by Forbes (Marr, 2022). Clients in over 50 countries have been expertly steered by the firm in navigating their cloud-powered digital transformation, with Infosys enabling these businesses with an AI-powered core and Agile ways of working to drive on-going improvement through continual learning and transfer of skills, expertise, and ideas from Infosys’ innovative ecosystems.

Figure 2: Infosys Consulting data analytics offering supports the end-to-end data value chain to enable Smart AI adoption

Infosys Consulting comes with deep industry knowledge with over 500 data scientists globally that have relationships with worldwide innovative AI providers. This strong combination of knowledge and partnerships enables Infosys in assisting companies with successful transition to Smart AI and positions them for enduring, long-term success (Alex Blount, 2017).

Infosys has demonstrated its commitment towards being a global leader in next-generation digital services and consulting through the acquisition of BASE life science, a leading business in the European life sciences sector. BASE brings to Infosys the capability to bridge and integrate business logic and technology; consequently, driving insights for improved health outcomes (Infosys, 2022).

Due to its network and experience, Infosys has its own approach to Smart AI adoption that has been implemented in the healthcare sector. This is illustrated in the next few subsections.

Infosys’ Approach to Smart AI

Infosys Consulting has been assisting healthcare organizations in navigating the transition to Smart AI by working with businesses from the initial phases of design thinking (including data analysis) to implementation of AI driven systems and tools, which enables companies to implement and sustain a competitive advantage that is AI-driven (see Figure 3).

Figure 3: Infosys Consulting’s approach to drive immediate value

Healthcare use cases

Infosys’ Experience in IoT

With the influx of data from IoT sensors and wearables, there is a demand from the aged care sector to develop processes, infrastructure, and products that convert this data to insights. Infosys Consulting has assisted healthcare organisations by leveraging its IoT solution stack (see Figure 5) along with key partnerships, to provide multiple services across the IoT value chain, and not just the connectivity.

Figure 4: Infosys Consulting IoT solution stack and key partnerships across the stack

The Infosys IoT project framework consists of four key phases (see Figure 6).

Figure 5: Infosys IoT project framework

Figure 6 shows a list of IoT customer use cases implemented across industries, not just limited to healthcare. When applying a lens to the healthcare sector, however, the “OEM’s” (Operational Enterprise Management) section in Figure 7 is applicable, where IoT devices provide real-time data on the elderly that can then be used to make knowledge-based decisions or carry out predictive analytics using ML. Furthermore, in the previous paper, we discussed examples where AR (Augmented Reality) and VR (Virtual Reality) have been used for treatment and rehabilitation for the elderly.

Figure 6: Example IoT use cases across industries

The value realization from Infosys’ IoT clients across industries is depicted in Figure 8. Focusing on the healthcare sector only, the value-adds highlighted in the “Consumer IoT use cases” quadrant in Figure 8 are applicable such as improved patient care reach, enhanced personalization, and smart and connected products. These benefits are likely to contribute to increased elderly satisfaction and improved productivity and efficiencies across asset, people, processes, and systems, leading to reduced costs.

Figure 7: Value realization from IoT solutions for Infosys clients

Infosys’ Experience in Digital Portals

Infosys Careplus was developed as a next-generation care platform as a service on top of Salesforce. It provides 360 degrees view to the consumer, by consolidating health records, treatments, claims, and other information sources. The platform connects personal health-tracking tools and devices and can be extended for insurance claims and other permitted third parties. It has an intuitive and user-friendly interface, which is device-agnostic and scalable to any screen size. The solution is analytics-driven, supporting healthcare staff decision-making.

Figure 8: Infosys Careplus

Another example is the Skava platform, which is a micro-services-based healthcare digital platform, where each capability is provisioned in a micro-service that can be customized to suit given needs. It is a mobile-first, insights-driven solution hosted on the cloud, with loosely coupled architecture.

Figure 9: Skava platform


One of the factors contributing to the growth in healthcare expenditure is the ageing population. Some of this load can be relieved via the use of Smart AI initiatives within the healthcare sector. Though there are technological barriers to Smart AI adoption, this paper proposed a five-step framework that can enable its implementation. Several examples of Infosys Consulting’s global experience in leveraging its suite of tools and technology to enable Smart AI digitisation in the healthcare sector were provided, demonstrating that the firm is well-equipped in supporting the demand for Smart AI in the growing market.

Sheenal Srivastava

Sheenal Srivastava

Principal Consultant

Sheenal is a Principal in the AI&A practice in Singapore with a focus on the financial services sector. She has extensive consulting experience within the health, government, retail, oil & gas, and financial services domains. As a former Data Scientist, Sheenal has a deep interest in ML and AI and has worked on predictive asset maintenance models, customer segmentation analyses, churn prediction models, customer lifetime value models, and optimisation problems. She holds a Doctorate in Statistics and an undergraduate honours degree in Computer Bioinformatics.

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