By: Ash Patel, Chief Information Officer, IRI
Worldwide revenues for the artificial intelligence (AI) market, including software, hardware, and services, are forecast to grow 16.4 per cent year over year in 2021 to $327.5 billion, according to the latest figures from the IDC Worldwide Semiannual Artificial Intelligence Tracker. By 2024, the market is expected to break the $500 billion mark with a five-year compound annual growth rate (CAGR) of 17.5% and total revenues reaching an impressive $554.3 billion.
Despite a year dominated by the global pandemic, these are impressive growth figures for a technology that has started making waves over the last few years.
The role of AI continues to grow, transforming how businesses operate and go to market. We are seeing developments across a number of sectors, including automotive, education, finance services, and healthcare, while it’s also helping to drive digital transformation in FMCG and retail, an industry that has faced huge challenges during the pandemic. The opportunity to leverage AI, big data and analytics has never been more relevant today for retailers and brands.
Key to unlocking the potential of data
Data is at the heart of all of this however. AI and complementary technologies like machine learning (ML) are helping to deliver new ways to unlock the potential of the data that organisations (retailers and manufacturers most notably) collect, store and analyse.
Take data analytics, which is rapidly moving from human to machine-driven analysis. Until recently, developing strategies for growth involved a query-based, ad-hoc approach to test a variety of hypotheses. But this has reached its limits. With the amount of data today, it’s just not possible to use this methodology to identify the optimal strategy for a product or business.
Powered by AI, new analytics solutions allow computers do a lot of the heavy lifting. Algorithms can analyse trillions of data points and provide forward-looking insights that maximise ROI. But can AI, which promises to deliver benefits on many levels, reach its full potential?
Change management fundamental to success
In my opinion, organisational change management is key to success. Businesses that are operating within an environment that is not prepared to embrace change are set to fail when it comes to disruptive technologies.
This doesn’t just apply to AI, but any new technology that’s likely to disrupt a business. It’s clear that some are attempting to fit AI-based solutions into a traditional organisational structure, or perhaps thinking too narrowly about how to take advantage of its true capabilities.
Stuck in their comfort zone, they are happy to rely on intuition and subjective decision-making based on experience or previously acquired knowledge. Some will almost certainly distrust AI. They might be concerned that without human intervention it will lead to inaccurate decisions. For others that have based decisions on gut instinct and don’t fully trust the technology, running contests or games internally to prove that AI and ML can consistently outperform human decisions in the traditional way, provides a real-life, real-time test environment.
But to do this effectively, it involves measuring return on investment (ROI) of the decisions that were made, as well as the ROI of the recommendations the AI/ML produced that the human did not follow or execute. Then using the quantified opportunity cost of ‘the road not taken’ to further support any change management and adoption.
Tackling a shrinking talent pool
More than four out of 10 enterprises now use AI in a serious way, up one-third in just two years, according to a recent survey of 1,000 executives by RELX. But at the same time, AI talent is in short supply and is one of the main reasons why more companies are not using it.
Even mature AI adopters are challenged by this skills gap, with advanced AI adopters seeing the most acute shortages of talent, according to a 2020 Deloitte survey. The type of talent most in demand is for AI developers and engineers, AI researchers, and data scientists.
Mature or otherwise, any organisation can take advantage of the power of AI technologies, creating a level playing field in many ways. There’s the opportunity to transform existing roles of information workers from collating and preparing data and information for others, to creating algorithms and automation that produces accurate and actionable recommendations, instead of just information for further interpretation.
The challenge is in combining domain expertise and real world experience with AI and ML brute force algorithms and compute. So the need for solutions like IRI Neuron that emphasise sector knowledge and gray matter capture and automate the data science algorithm building without the need for an army of data scientists has never been more relevant or timely.
To truly take advantage of AI, organisations must first overcome resistance to change and see themselves as a data democracy where information is transparent and available to all. They need to move towards a more collaborative approach and away from siloed decision-making. Only then can AI become a powerful tool to help give them the edge and deliver its potential.