AI Can Play A Big Role In Smarter Decision Making


There has been a veritable explosion of data over the past several years, leading to a virtual spawning of new technologies such as Artificial Intelligence (AI) and Big Data Analytics. Sophisticated Big Data Analytics, working in tandem with Cloud-based enterprise tools, can lead to a more focused, collaborative and explorative understanding and leveraging of business data. No wonder, these new-age technologies have been radically transforming the way organizations do business.

For example, IBM has unveiled a supercomputer Watson that can crunch data at the speed of 67 million pages per second, transforming them into actionable insights. The data tsunami is coming from millions of interconnected devices as part of the Industrial Internet of Things. The innovation is aimed at establishing a question and answer dialogue between it and humans—Watson will analyze huge amount of data and respond verbally and in real time.

“The use of new tech such as AI in enterprise technology has fascinated industry experts. This is the era of AI and its scope is widening. With its powerful capabilities, the technology is optimizing system operating models as well as transforming business processes for enterprises across the world,” said Shashank Dixit, CEO, Deskera, a global leader in cloud-based business software.

AI can help enterprises take better decisions

Artificial intelligence has introduced novel ideas such as real-time and interactive dashboards, improved end-user experience, etc. Combined with Predictive Analytics tools, which are basically extensions of data mining, AI is also aiding organizations forecast business opportunities as well as helping them understand products, partners, customers, and stakeholders.

Sophisticated neural networks and the industrial internet of things (IoT) together would be able to detect discrepancies and notify users through real-time alerts. Such Prescriptive Analytics eventually helps enterprises assess the impact of future decisions prior to them being made. With data becoming increasingly accessible to businesses, AI and explorative data analytics tools will occupy center stage for enterprises as well as end users. Moreover, visual data discovery and visualization tools will help businesses discern relevant and useful patterns and structures, eventually helping organizations identify prominent trends in the market as well as locate shortcomings in business strategies.

Organizations will be able to reduce risks by analyzing and absorbing data as the analytics would push for quick turnaround times for decisions to be taken so that short-lived opportunities can be capitalized on and profit margins can be boosted. Organizations would also be able to perform more advanced pattern-oriented data analytics. Statistical models, sophisticated algorithms and automation will assist enterprises tackle unexpected problems. No wonder, many organizations and end users are making substantial investments in data discovery.

Sky is the limit for the rapid expansion of AI tools

According to the PwC report, ‘Artificial Intelligence and Robotics – 2017: Leveraging artificial intelligence and robotics for sustainable growth’, the cumulative economic impact of artificial intelligence could be between $1.49 trillion and $2.95 trillion by 2025. The study highlighted that AI tools would be up for mass adoption in several domains, including data science (9.6%) and business intelligence (7.8%) in industry. Apart from providing cutting-edge business intelligence, AI involvement can also see an upward trend in other areas such as manufacturing, financial services, defence and security, logistics, and agriculture among others.

Why the reluctance of industry to go for AI

Currently, the roadblocks that the technology faces basically stems from the fact that companies are not able to properly execute the AI in their business processes because the field needs research, innovation, and critical and creative thinking to be fruitful. This will also require significant investments upfront prior to the AI products becoming commercially viable.

“Earlier advanced AI functionalities were expensive and exclusive due to undeveloped tools. AI algorithm constructions required highly skilled workforce, generally not available at a number of software companies. CPU-intensive computing and huge amounts of data storage were needed for pattern recognition with respect to the complex algorithms,” added Shashank.

Research into AI tools needs to take into consideration several factors including engineering and production specifics including capacity planning, material requirements, infrastructure requirements and costs, as well as product design. In fact, many Indian technology startups have started providing AI solutions, on the back of investors’ willingness to see the technology through.

Currently, many Indian organizations are unable to envisage the benefits of AI tools and systems. However, the Indian government can adopt proactive measures to overcome such obvious initial hiccups and boost the adoption of AI in the country. All said and done, SMEs and corporates should approach the technology with an open mind so that they are not ‘also-rans’ in the race of technology and development.

[Disclaimer: The views expressed in this article are solely those of the authors and do not necessarily represent or reflect the views of Trivone Media Network's or that of CXOToday's.]