Artificial Intelligence has steadily evolved from a metaphorical character in science allegories to a real-world game-changer technology. The exponential increase in computing capabilities coupled with proliferation in data from multiple realms has been the catalyst in the adoption of AI-based tools across industries. These AI tools are allowing organizations to use the data they already have to adapt to new challenges and solve the existing ones more efficiently.
As per a survey by PWC conducted among Global CEOs, 8 out of 10 CEOs agreed that AI is bound to change the way businesses operate in the next five years, irrespective of the fact that they adopt it or not.
Banking on Artificial Intelligence development, diverse industries have found new ways of operating, optimizing costs, innovating new products or services, and streamlining the delivery of existing offerings. This paradigm shift has been primarily brought about through AI-derived insights and institutionalization of data-driven culture in the organizations. Moreover, accelerated adoption of peer technologies such as Automation, Cloud Computing, and Data Mining has facilitated building a tech ecosystem for bracing challenges of tomorrow.
A survey conducted by Gartner among global CIOs revealed that between 2018 and 2019 the number of organizations that have deployed AI-based solutions grew from 4% to 14% and Conversational AI remains at the top of corporate agendas.
Now when a sizable number of organizations have graduated from the pilot phase in Artificial Intelligence development to stabilized AI-augmented processes registering positive ROI, more and more businesses are turning to AI development companies for seeking solutions to their business challenges. To adopt AI-enabled tools, many organizations have begun to stake up their processes with streamlined data management and enhanced computing capabilities. In fact, this prepping for AI-enablement has itself helped organizations to realize that the opportunity is plenty and they have just begun the ascent.
In a survey conducted by McKinsey, companies that have adopted AI registered an increase in revenue from the processes that have deployed AI-enabled tools along with a 44% reduction in cost.
While AI-powered success stories are abundant with divergent use-cases, the end goals have been accelerating product development and personalizing service delivery. The key-enablers driving AI-led business transformation revolve around three major themes:
1.Predictive insights for decision-makers
The C-suite needs to deal with the exceedingly difficult task of making the decisions for the whole organization. In the era of disruption when the waters are murky with multifactorial problems at play, AI-based solutions help decision-makers to arrive at data-backed insights. Especially, when data space is enormous for any traditional analytics method to sift through, the Machine Learning models facilitate the generation of insights uncovering patterns that were not known to be existing.
For example, AI-powered predictive analytics has enabled Manufacturing companies to predict demand and calibrate their raw material supply accordingly.
Another exemplary use-case is from the Financial services industry that are leveraging AI development services to gauge the future market for efficient portfolio management, predict asset performance through historical data, and roll personalized products.
2.Customer engagement across multiple channels
Customer engagement strategy has seen probably the biggest structural shift due to AI-enablement. As per a news feature by Microsoft, in five years from now, 95 percent of all customer interactions would be relayed through channels supported by AI. There has been a surge in AI-powered tools that identify the patterns of services consumption, purchase history, and media preference to serve customers a highly-personalized content and product inventory. Personalization of offerings not only addresses their needs but also nurtures them through the customer journey.
ECommerce industry has already started to benefit from personalized recommendations that are generated through complex AI algorithms. Coalescence of omnichannel consumer data, real-time insights, and search context is churning relevant suggestions to consumers through Chatbots and Virtual Assistants, increasing time spent on the platform and in the end, purchases.
3.Collaborative and Streamlined workflows
The historic challenge of most industries has been a lack of synchronization between different processes. When the processes operate in silos, workers spend a substantial time searching for the information that they need to do the actual work. As per a study by McKinsey, improving communication and collaboration among teams can yield a productivity increase of 20 to 25 percent.
IaaS (Integration as a Service) model has enabled AI tools to automatically surface the required information at the right time, irrespective of the worker’s location, the platform they are using, and the app they are currently working on.
In fact, the most common enterprise project that AI development services are working on is related to building tools that intelligently interpret the employee intention and populate the information that he/she might need in close conjunction to the present query.
As more and more successful AI use-cases surface with statistically significant increment in ROI, an increasing number of organizations, including small businesses, are contemplating to deploy AI. Moreover, several AI development companies have come up with ready-to-integrate AI-enabled accelerators to shorten the development time. In a nutshell, the trend to use Artificial Intelligence is about to become a norm, an essential technology for businesses across domains.