Imagine this, if you will. You’ve just bought a new car, it’s the latest model with all the frills. All of these extras make parking a doddle, your emissions reduced and your journeys smoother. You use your car nearly every day from that point onwards. You ignore the engine light when it comes on and don’t get that rattling noise checked out.
Now imagine that your organisation has adopted Artificial Intelligence (AI) tools but doesn’t adopt tools and procedures to care for the data. New cars and AI adoption may be miles apart, but for both to function optimally, correct procedures and routine care are essential.
As AI adoption permeates across industry, so too must understanding of the challenges and issues that this revolution will bring. In understanding where gaps are likely form, the enterprise is better placed to tackle them head on and ensure that small issues don’t become bigger challenges.
The AI age
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% 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.
Finding the blind spot
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 this 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% 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. Conversely, companies in the evaluating stages of AI adoption may not be aware of the extent of any data issues.
Yet, despite data quality issues being so frequently cited and clear patterns in when they typically occur, the very fact that year-on-year it remains a top concern indicates that many companies do not consider data governance a priority.
Leveraging assets
Of the nearly 1,400 respondents in our survey, just over one fifth stated that they have implemented governance procedures and/or tools to support and complement their AI projects. It is evident, then, that many businesses are yet to initiate measures to control common risk factors and embed formal processes for data governance and conditioning – a task made more arduous the longer it is postponed.
In this case, then, AI “immaturity” may well work in a business’s favour. The benefit of nearly half of companies identifying as being at the early stages of AI and ML adoption is that the introduction or implementation of proper governance and risk management may be less of a culture shock and, therefore, should more easily be adopted. More good news is that over a quarter of companies reported that they have plans to introduce formal data governance by 2021.
However, the question still remains – why aren’t companies building data governance into their AI projects from the outset? Data governance, like any type of business governance, should be embedded within corporate strategy. Particularly in recent years, with many companies now adopting digital business models, data management should be optimised and championed.
Data is the driver for AI and digital transformation. Yet time and time again, we see instances where it is not leveraged in a way that reflects its value. Of course, it is never as easy as we want – data governance and conditioning take time and resources. However, it must be viewed in terms of the benefits it will bring: observability, reproducibility, efficiency and transparency.
Day to day data governance
With AI increasingly integral to business operations, the enterprise must respond accordingly to data governance requirements. For companies at the early stages of AI adoption, awareness of challenges allows for early introduction of good data practices. Those with more developed AI programmes must self-reflect and embed procedures. One thing rings true for any organisation using or evaluating AI: proactivity wins. Addressing issues early has a big impact.
As the enterprise digitalises and companies integrate artificial intelligence into business models, the need for proper data governance will grow. Best practice will see a place carved out for it within operating models.
AI is nothing without data and good data is not possible without data governance. To go the distance with AI, the enterprise must create cultures and practices that allow for scalability and success.
