Democratization of AI critical to bridge the skill gap: Anand Mahurkar, Findability Sciences

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Democratization of AI critical to bridge the skill gap: Anand Mahurkar, Findability Sciences

By- Mr. Anand Mahurkar

Since the early 2000s, data has played a friend and a foe for businesses. The breadth of an organization’s data repositories, which can easily reach the petabyte level, leads to information overload, even though most executives know the value of data. As a result, traditional businesses are having trouble, and terminology like “data lakes” and “data warehouses,” which are widely used in IT, is now colloquially referred to as “data dumping grounds.”

Today’s challenge is transforming these “data dumping grounds” into logical recommendations and predictions. This is where an artificial intelligence (AI) approach can help by providing a means of gathering and combining pertinent data to make it helpful and informative.

AI, for instance, may revitalize a company’s supply chain management analytics. A company must develop a broad data strategy once it starts its AI journey. This entails gathering and combining information from the internal ERP, and CRM systems and information from outside resources like news and social media feeds. Once all the data has been gathered and processed, the company may propose the best inventory based on demand estimates, receive intelligent predictions about the cost of raw materials, help automate resource planning, and more.

Giving external data merit is equally important since an enterprise can’t control numerous dependencies. For example, forecasting is heavily influenced by holidays, the weather, and socioeconomic conditions in the sales industry. These elements could have a big impact on revenue projections. A machine must learn correlations for various data to understand the cause of sales variations and provide useful insights.

It is no doubt that data is the next game-changer for AI-enabled businesses and is emerging as a critical differentiator. An analysis of data and analytics (powered by AI) reveals that organizations driven by data have a 19 times higher chance of succeeding. Intelligent digital processes are being powered by emerging technologies, allowing machines to assist humans in their work. A PwC report estimated that by 2030, AI might have a $15 trillion impact on the global economy. Only a small number of technologies have this kind of potential.

Further, AI adoption amongst mid-sized and small-sized businesses is already rising. Moreover, enterprises across industries, including banking, agriculture, food, healthcare, and environmental education, increasingly rely on AI to enhance their overall performance. For instance, AI is used for credit eligibility, financial advice, trading decisions, and fraud detection. A fantastic customer experience is ensured by intelligent chatbots powered by AI and deep learning, propelling enterprises toward digital transformation.

Only once AI is widely accessible and everyone can use it to their advantage will it realize its full potential. Thankfully, this will be simpler than ever in 2023. Regardless of one’s level of technical expertise, a rising number of apps put AI capability at the fingertips of everyone. This can be as basic as apps that let us build complex visualizations and reports with a mouse click, decreasing the typing required to search or write emails.

Ultimately, the democratization of AI will make it possible for companies and organizations to overcome the difficulties brought on by the AI skills gap caused by a lack of qualified data scientists and AI software engineers. The potential and value of artificial intelligence will be accessible to all of us by enabling anybody to become “armchair” data scientists and engineers.

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