Seven steps to a successful AI and machine learning implementation
Prentiss Donohue, senior vice president, professional services, OpenText outlines in Information Age the seven key steps to help AI and machine learning deliver on its full potential.
Artificial intelligence (AI) and machine learning (ML) are shifting from being business buzzwords toward wider enterprise adoption. The efforts around strategies and adoption are reminiscent of the cycle and tipping point for enterprise cloud strategies four years ago when companies no longer had the option to move to the cloud and it only became a question of when? And how? AI and ML strategies are in the same evolution mode as companies build their approaches. Below are some thoughts around the how.
Forrester recently reported that almost two-thirds of enterprise technology decision-makers have either implemented, are currently implementing, or are expanding their use of AI. The exercise and effort is driven by the enterprise data lakes that reside within companies which, thanks to compliance and low-cost storage, are sitting mostly idle. Tapping into these rich repositories to have AI answer the questions which we are not asking, and may not know to ask, is the bounty which enterprises need to understand… before someone else does it before them.
With enterprise spending on AI technologies expected to hit over $47 billion in 2020, up from $8 billion in 2016, according to International Data Corp, the juice needs to be worth the squeeze – and the squeeze needs to be done properly.
Organisations across all sectors will continue to embrace AI and ML technology over the coming years, transforming their core processes and business models to take advantage of machine learning systems for enhanced operations and greater cost efficiencies. As business leaders start drawing up plans and strategies for how to make the best use of this technology, it’s important for them to remember that the road to AI and ML adoption is a journey, rather than a race. Organisations should begin by considering the following seven steps.
It’s important for business leaders and their project managers to start by spending time on clearly defining and articulating the particular problems or challenges they would like AI to solve; the more specific the goal is, the better chance of success for their implementation of AI.
Stating that the organisation would like to ‘increase online sales by 10%’, for example, is not sufficiently specific. Instead, a more defined statement such as ‘aiming to increase online sales by 10% by monitoring the demographics of site visitors’ is much more useful in articulating the goal and ensuring it is clearly understood by all stakeholders.
The next step, once the use case has been clearly defined, is to ensure the processes and systems already in place are capable of capturing and tracking the data needed to perform the required analysis.
A considerable amount of time and effort is spent on data ingestion and wrangling, so organisations must ensure the right data is being captured in sufficient volumes and with the right variables or features such as age, gender, or ethnicity. It’s worth remembering that, as the quality of the data is as critical to a successful outcome as its volume, organisations should make data governance procedures a priority.
It may be tempting for a business to leap headfirst into a model building exercise, but it is crucial that it first carries out a quick data exploration exercise in which it can validate its data assumptions and understanding. Doing so will help to establish whether the data is telling the right story based on the organisation’s subject matter expertise and business acumen.
