All in Favor Say AI: Driving Innovation through AI and Data
Originally Posted: Toolbox
When you look at the fourth industrial revolution and the technologies that have made a big splash, it is undeniably – Big Data and Artificial Intelligence (AI). Big Data is the bloodline or the core of every business and the technology used to garner insights, while AI is the heart and is crucial in extracting meaning from data. Laxminarayanan G, Sr. Vice President & Global Delivery Head at Polestar Solutions, talks about how AI and data can help businesses drive actual value and innovation.
Given the challenges in today’s VUCA world, organizations need reliable next-gen technology solutions in-line with their business goals and objectives to come out strong and stay ahead of the competition. Organizations such as Netflix, Alexa, and Amazon are already using data analytics and AI technologies to a considerable extent to fuel their growth.
The Current State of Data Analytics & AI
There are so many cases of applied AI in our personal lives. Think of Alexa performing tasks on voice commands or Instagram anticipating our purchasing interests. With applications such as smart housing, personal assistant, fraud detection and prevention, and drones, the technology is already growing in use and acceptance.
An intelligent data and AI framework can enable organizations to become agile, pivot to preemptive decision making, uncover greater value and capitalize on emerging or unrealized market opportunities. With innovative approaches to data management, enterprises can truly realize their potential.
Today, businesses generate an abundance of data and data sets which, with the use of AI, can help create actionable insights. Now with datasets getting more voluminous and the variety of data increasing with new data types such as emails, messages, images, and videos, conventional data architectures require a re-visit to an IT-led transformation for their existing processes.
The Backbone for Innovation
Data and analytics are evolving quickly with new processes, technologies, solutions, Withplatforms and tools that periodically keep coming up. So, a business needs to identify what actually serves its purpose and goals in the long term and then look at implementing the same. Additionally, beyond fitment, it is important to consider the commercial feasibility before deciding what might work best for your business.
For example, look at a retail giant like Gap, and understand how they actively invest in data analytics and AI to fuel its success. One will see that they have moved from leveraging traditional business intelligence (BI) capabilities to accelerating their data journey to make AI a part of their growth journey. Today, Gap has built powerful capabilities to solve its key challenges, such as forecasting demands regarding what products will sell and where and when they will sell. It uses predictive analytics, voluminous datasets, sales and marketing data, etc., to understand positioning, pricing, inventory management, thus improving product availability and product margins.
Existing Challenges for Business
According to a Gartner report, just 53% of AI and ML projects make it to production, and more than 80% of the lot typically fail to deliver on their intended results. If you look at it this way, data analytics and AI have embedded themselves into products and processes in virtually every industry — it has created several opportunities for businesses with use cases such as predictability, agile inventory optimization, market-basket analysis, etc. However, organizations often face challenges in harnessing the full potential of these technologies. Common challenges include identifying the correct data set, data security, infrastructure, integration into existing systems and training AI models. Furthermore, implementing AI at scale also continues to pose a big challenge.
Businesses need to understand the impact of data and AI, be ready to embrace automation, and dramatically increase the value of their data. A lot of the time, there is hesitation towards adopting digital transformation. For this, businesses have to familiarize themselves with AI and understand how it works.
Hype to Impact
In the race to scale AI and create meaning, companies are beginning to realize that it is all about smartly implementing and unlocking the full potential of AI. Now, as data quality improves, investments in AI will also increase. Gartner predicts that more than 75% of organizations will finally shift from piloting AI projects to operationalizing them by 2024, which means that organizations must implement data analytics and AI to stay ahead in a competitive market.
AI has genuinely shifted from hype to impact. Adopting a step-by-step approach will help simplify the process of AI implementation and understand how the technology can help streamline the business process, drive better value for customers and employees and accelerate growth. Now is the time for IT decision-makers to understand the changing times and their role in enabling the faster adoption of technologies to drive growth. After all, data and AI will be the key differentiator going forward.