Demystifying the black box: IBM on how to get started with AI

IBM's Rob Thomas talks through to get started with AI, and some common pitfalls.

According to many analyst forecasts, Artificial Intelligence (AI) technologies represent one of the biggest economic opportunities in history. AI has a tremendous potential to transform a business, allowing it to enhance a firm's capacity for predictions, as well as revenue gains through the automation, and optimisation of core business processes. However, while adoption is increasing rapidly in the business sphere, most companies either haven't employed AI or are still in their preliminary or pilot stages of implementing the tech. 

According to the Wall Street Journal's 'State of AI Adoption' survey, 47% of organisations have employed at least one AI capability within their standard business processes, and 30% of organisations are conducting AI pilots. However, only 21% report using AI across multiple business functions, while 58% report that less than one-tenth of their digital budgets go towards AI. This is despite only 1% of respondents - who have employed AI capabilities - reporting that they have experienced none or negative value from AI implementations. 

To cap this off, there seems to be a genuine fundamental misunderstanding of AI amongst business decision-makers in general. According to research from MIT Sloan Management Review and Boston Consulting Group, 50% of surveyed organisations were either ‘passives' or ‘experimenters', meaning they either had little understanding of AI or lacked any kind of ‘deep' understanding. A further 30% (investigators), displayed knowledge of AI and applications, but had not deployed the technology beyond the pilot stage. Overall, only 20% of surveyed organisations both understood and had adopted AI technologies, who were classified as ‘Pioneers'.

Noting this, it's fair to say that AI still needs a fair bit of demystification from a strategic perspective, especially in terms of how and when to implement it. IBM, an organisation that is regularly part of these AI-based conversations with its Watson offering, has looked to address this through its AI ladder report, which aims to guide organisations through their AI journey depending on their level of maturity. The report outlines a method for assessing where an organisation is placed in terms of their maturity and getting started with the next step.

There are four rungs to the ladder, starting with ‘Collect' (collecting all types of data), ‘Organise' (organise data into a business-ready foundation), ‘Analyse' (building and scaling AI), and ‘Infuse' (operationalising AI throughout the business). This is all underpinned by a ‘Modernise' layer, which stipulates that firms update to a more agile data architecture, preparing it for synergy with an AI-driven/ multi-cloud mentality.

To speak more about how organisations should be thinking about AI today, we spoke to Robert Thomas, General Manager, IBM Data and Watson AI. As the author of IBM's AI Ladder, Thomas is well positioned to provide expertise into the mystification of the AI arena and how to cut through this. He also speaks about how organisations can work to solve ‘the data problem', as well as why he thinks DataOps is an essential ethos to adopt if firms want to get the most out of their AI deployments.

 

To continue reading this article register now