AI adoption – Don’t leave data governance behind
Artificial Intelligence (AI) is a part of our lives, whether we like it or not, whether we think we use it or not. Somewhere along the chain of production in our daily activities – shopping, working, searching online – we encounter AI in one form or another.
However, as AI becomes an increasingly integral aspect of business operations, so too must appropriate governance. Businesses must endeavour to understand issues and challenges to AI adoption so that operational gaps do not form. In understanding its full potential, businesses will have greater control over AI application in industry. After all, better the devil you know.
To survive in today’s climate, companies must ensure transformational capacity and ability is in-built. Digital transformation may have become something of a buzzword, but for good reason. It is seen and heard everywhere because it is important to all facets of the modern enterprise. To be an industry leader, businesses need to demonstrate the capacity to adapt their processes and alter business models in line with cultural and technological shifts.
AI and machine learning represent such a shift. The technology has seen a usage boom in recent years. Notably, a majority of organisations are evaluating AI or using it in production. In fact, over half of respondents in our recent survey on AI adoption in the enterprise identified as “mature” users of AI technologies – that is, they’re using AI for analysis and/or in production. Only 15 per cent of respondents reported that they’re not using AI at all.
Unsurprisingly, across the board research and development dominate in current AI adoption trends, followed closely by applications in IT and customer service. That being said, respondents cited a widening range of industry areas in which functional parts of a company use AI. As a whole, this indicates that companies are increasingly turning to AI and machine learning as a business tool.
Obstacles are to be expected on the path to digital transformation, particularly with unfamiliar entities in the mix. For AI adoption, the most prevalent obstructions are: a company culture that doesn’t recognise a need for AI, difficulties in identifying business use cases, a skills gap or difficulty hiring and retaining staff and a lack of data or data quality issues.
With such a broad spectrum of challenges, it is worth delving into a couple of them. Firstly, it is interesting to note that an incompatible company culture mostly effects those companies that are in the evaluation stage with AI. When rephrased, perhaps it is obvious – a company with “mature” AI practices is 50 per cent less likely to see no use for AI. By contrast, in a company where AI is not yet an integrated business function, resistance is more likely. Secondly, AI adopters are more likely to encounter data quality issues; by virtue of working closely with data and requiring good data practice, they are more likely to notice when errors and inconsistencies arise.
