Companies around the world are investing tens of billions of dollars on artificial intelligence (AI) and machine learning (ML) and for good reason. These technologies have real business-altering potential – the recent Gartner’s Enter the Age of Analytics report predicts that by 2023, AI and deep-learning techniques will be the two most common approaches for new applications of data science.

In the wake of the fourth industrial revolution, governments and businesses across the Middle East are beginning to start the journey towards AI infused process automation to drive operational efficiency and improve financial growth. According to PwC report on the potential impact of AI in the Middle East, the largest gains are expected to accrue to Saudi Arabia where AI is expected to contribute over $135.2 billion in 2030 to the economy, equivalent to 12.4 per cent of GDP.

All three data rich applications – AI, ML, and analytics – offer enterprises one crucial benefit: valuable data insights. This statement may seem obvious, but it’s the truth. Today, with vast amounts of structured and unstructured data sitting across entire organisations, almost everything has become a data problem – preparing employees for the return to work, designing retail stores to meet social distancing requirements, meeting consumer demand where it emerges, and even ensuring profit once pubs and bars re-open. Analytics can make a huge impact by not only unearthing the insights, but ML and AI based automation can significantly improve an organisation’s ability to make timely and contextual decisions that lead to faster and more competitive outcomes.

But despite the promise, few companies have been able to successfully implement and deploy this technology as part of their overall data and analytics strategy. According to Gartner, 46 per cent of CIOs have made plans to deploy AI but just 4 per cent have made the concept a reality.

Ghanem: AI alone cannot solve any crisis

The truth is that without amplifying the human intelligence first by upskilling employees to better understand the insights derived from the data and to empower them for analysing the data in order to contribute with well-informed business decisions, it will take years before many organisations realise the true potential of AI and ML. But it is never too early to lay the groundwork now for an AI-driven future. In fact, if an organisation is not already thinking about what an AI strategy looks like, its competition is likely one step ahead. There’s no time to waste, so here are five important points to consider when getting started with AI and ML.

• Ask the right questions. There are four things organisations need to be thinking about when it comes to a future-proof data strategy. What data is available within the walls of my organisation? What data do we need to acquire externally to drive differentiation? Is our data available in a way that can be readily available for machine learning and AI? And perhaps most importantly – where can we upskill our line-of-business, what requires pure data science and AI know-how and what can IT manage? The answer to these questions should serve as the foundation to your strategy.

• Take a multi-year approach. Successful AI/ML implementation does not happen overnight. The smartest organisations take a multi-year approach to data acquisition and strategy, focused on compiling data from different sources and silos—often built around a Center of Excellence (CoE)—and investing in the right technologies and people to lay the foundation. At the same time, these organisations look to cloud-based offerings from companies like Amazon, Microsoft and others to create intermediate data storage that can support diverse use cases as strategies progress over time.

• Always put humans at the center of the strategy. A recent study from ZipRecruiter found that “the most successful applications of AI have been when used in partnership with humans, rather than as a replacement.” That’s why, according to the study, AI has created three times as many jobs as it killed last year – and companies are continuing to invest in talent with data skills despite the advancement of automation technologies. The World Economic Forum (WEF) predicts that data-related jobs will be the most in demand within the next four to five years, along with AI and ML specialists.

• Build a multidisciplinary team. A diverse team that incorporates AI experts, data scientists and line-of-business analysts presents a more holistic approach to AI/M. Those who are able to engage with the data gathering, processing and training will be able to optimise their contribution to their organisations, and seriously enhance their individual or corporate ability to achieve goals.

• Bridge the skills gaps. There is increased demand for any data worker, regardless of technical acumen, to do more with data, and organisations need to look for ways to up-level skillsets, build models in transparent ways and bridge the skills gaps across the organisation. Since AI data design requires `data speak’ to help build workflows, organizations must implement technologies such as augmented analytics that automate data prep, insight discovery and data science (i.e. autoML) all while communicating actions to roles with less AI know-how.

AI and ML will undoubtedly shake up the business world and life as we know it in years to come. It’s here that we must remember that AI alone cannot solve any crisis. And, as WEF reminds us: human input is key. A capable community of statisticians, analytics experts and data workers across the world have been using AI to find useful patterns and meaningful correlations for all sorts of Covid-19 related use cases over the last year. For this to be possible, however, data is needed to feed and educate the AI.

There’s an adage that says power isn’t what you know, power is what you share. Each one of society’s pressure points -- healthcare risk, workforce management and supply chain -- are all best approached with shared data and strong analytical technology, wrapped in a community where use cases are learned and business outcomes socialised. For AI to evolve, organisations need to empower each and every human member of their business to be thinking about how to leverage the technology. No matter how AI and ML evolves, data will always be at the forefront and one of the most important drivers of true digital disruption. A smart approach to data now will guide the way for a successful AI-driven future.