The current artificial intelligence (AI) movement has turned into a rocket ship for enormous transformative changes that are set to accelerate new opportunities, despite the business landscape of the past few years being terrified by challenges posed by geopolitical and economic uncertainties. Research indicates that the amount of data being created globally will surpass 180 zettabytes by 2025, coinciding with the current AI craze and the exponential growth of data within every organisation. But, in the intelligence era, the question isn’t how much data a company can produce, rather, it’s how they use it to inform decisions and train their employees to ask the right questions in order to receive the right answers.
Enterprises nowadays gather data from various sources, ranging from outdated databases and applications to contemporary cloud data warehouses and platforms. This can be done by a CFO seeking to shorten the time it takes to close a quarter or a Head of Supply Chain seeking to streamline intricate logistics.
As more businesses work to realise the full potential of these innovations, it appears that AI-driven automation will continue to be a crucial feature of future businesses. This will create a perfect storm for data- and AI-related skills and will also change the roles and skill sets needed for the workforce of the future. The World Economic Forum echoes this in its “The Future of Jobs Report 2023.” Stressing that two of the top ten jobs predicted to grow at the fastest rate between 2023 and 2027 are “AI and Machine Learning Specialists” and “Data Analysts and Scientists.”
Whether met with anticipation or trepidation, AI is certain to change the face of business in the coming three years. According to the latest Alteryx research, around 82% of businesses believe AI is already influencing how they will define and shape the future enterprise, and 57% of business leaders believe AI will be widely used in all industries and functional areas. Whatever the future holds, generative AI needs to be successfully incorporated into every aspect of the company’s operations through a business-wide strategy for data-driven decision-making that enables every employee to fully utilise the technology. Tech and business leaders need to start planning for the future now. Organisations can leverage both present and future AI capabilities—all driven by data—by collaborating with people managers to build the skills stack that supports the tech stack.
AI-infused future
A growing amount of dirty data is available everywhere. The heightened volumes and varieties of data cannot be turned into business opportunities by investments in the tech stack ecosystem on their own. It isolates the process to a small group of people rather than enabling value extraction from data at the speed and scale required for real-time intelligence. This limits the ability to obtain the decision intelligence required to meet changing business objectives by making it difficult to extract meaningful insights from data at scale.
All businesses, though, have a large pool of untapped data talent waiting to be realised and used to the fullest extent possible. The way that AI operates and performs in the future is expected to be greatly impacted by the skills gap that currently exists. If this gap is not closed, this progress will come to a halt. The development of non-technical soft skills—which should not be limited to traditional data analysts—is essential to preparing for this increasingly complex and data-driven future. These skills allow a wider range of people to participate in thoughtful decision-making.
Global AI market
The size of the world market for artificial intelligence was estimated at $136 point 55 billion in 2022, and it is anticipated to rise rapidly in the years to come due to rising investments in AI technologies, digital disruption, and competitive advantage in the rapidly expanding global economy.
According to market research, the global artificial intelligence market is expected to experience significant growth from 2023 to 2030. The compound annual growth rate (CAGR) is projected to be 37.3%, reaching a total value of $1,811.8 billion by 2030. This promising technology has the potential to boost the world economy, and some analysts predict it will have a greater global economic impact than the combined output of China and India.
By 2030, it is expected that the world economy will benefit from artificial intelligence by $15.77 trillion, which is more than the combined GDP of China and India at present. The countries that are expected to gain the most economically from AI are China, where GDP is expected to rise by 26% by 2030, and North America, where GDP is expected to rise by 14.5%. These two regions are expected to account for nearly 70% of the global economic impact, which comes to $10.7 trillion.
Meanwhile, by 2027, it’s predicted that the size of the global AI chip market will be $83-225 billion. For the years 2019 through 2027, it is anticipated to grow at a CAGR of 35%. A lot of industries use AI chips, including telecommunications, IT, healthcare, and automotive. The market for AI chips is dominated by North America, with Asia-Pacific said to be growing at the quickest rate. Among AI chips’ advantages are their extremely high bandwidth memory, quick computation, and faster processing in parallel.
The market for self-driving cars is predicted to grow, from 20.3 million in 2021 to 13.7 billion. By 2030, 10% of automobiles are anticipated to be driverless. It is projected that by 2030, fully automated vehicles will bring in roughly $13.7 billion. The most common application for driverless cars is expected to be robo-taxis. Also, according to Gartner, by the end of 2023, about half of US-based healthcare providers intend to implement AI technologies like robotic process automation, or RPA, in their medical facilities.
Building a data-literate workforce
Businesses are adopting AI and Large Language Model (LLM) technologies at an accelerating rate, so it is imperative that everyone learn how to use these cutting-edge tools to extract insightful data. As per Gartner’s 2025 projections, the most in-demand skills in the data and analytics talent market will be analytical and soft skills.
The cornerstones of developing the next generation of data science talent are encouraging data curiosity and analytical thinking. But equally important are transferable soft skills like communication, curiosity, teamwork, and creative problem-solving.
According to research by Alteryx, 72% of companies believe that having employees with a broad skill set is more important than having them specialise in a particular area. Additionally, 61% of respondents identified creativity as the human skill that will be most valuable in a market environment that is being shaped by artificial intelligence, followed by empathy and critical thinking.
Although their value may not always be immediately apparent, employees who possess both technical and soft skills are extremely valuable to businesses. In this category are professionals in the middle of their careers, regardless of age or educational background. It also includes individuals considering returning to the workforce or expanding their skills to progress in their professions. Their most valuable asset is their distinct understanding of the larger business context.
Individuals can utilise this skillset to showcase their ability to generate relevant enquiries, execute effective data procedures, and generate valuable outcomes. Moreover, the same ability can also be used to convert data into insights that play a vital role in making informed business decisions. Even though this expertise may not align with the conventional definition of data scientists’ skills, it is crucial to gain insightful information.
Upskilling in an AI world
For AI to help businesses understand the “what” and “why” behind important business decisions, it must be combined with high-quality data, a variety of human intellect, and business context. When data is used alone, it cannot offer the insights required to address business problems, and when AI is used without domain knowledge to ask intelligent questions, it cannot produce results that are dependable, secure, and trustworthy.
As a result of the AI wave, new paradigms for data interaction will be created, and patterns and insights that have business value will be found in data more quickly. Companies that equip their domain experts with analytical, data-literate, critical thinking, and domain knowledge will prosper in the AI-driven intelligence era.
Without a doubt, data-driven decision-making will continue to be essential to businesses in the future. Businesses can only successfully transform and be prepared to use generative AI by supporting the upskilling and reskilling of their current employees, from knowledge workers in business lines to those in more technical roles.