5 things to know about getting started with AI/ML


Companies around the world are investing tens of billions of dollars on artificial intelligence (AI) and machine learning (ML) and for good reason.

Alan Gibson, VP EMEA at Alteryx explains that these technologies have real business-altering potential and that’s why 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.

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 percent of CIOs have developed plans to deploy AI but just 4 percent have made the concept a reality.

The truth is that 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 Forumpredicts 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/ML, as the overall project encompasses the data collection process all the way to the data mining activities, machine learning and automation. Those who are able to engage with the data gathering, processing and training will be able to optimize 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 understandable and transparent ways and generally bridge the skills gaps across the organisation.Since AI data design requires “data speak” to help build workflows, organisations 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.

Artificial intelligence and machine learning will undoubtedly shake up the business world and life as we know it in years to come, and 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 success and true digital disruption. A smart approach to data now will guide the way for a successful AI-driven future.