Is your data ready for AI? 

AI has the power to transform your business, but your data needs to be ready.   

 

What is AI and why is data so important? Surely, it’s just state of the art technology that matters?  

 

AI technologies can solve problems and perform tasks commonly associated with human cognitive functions. For example, engaging in ‘conversations’, writing stories, proposing ideas, crafting sophisticated arguments and identifying complex solutions to difficult problems. To operate, AI systems ingest massive amounts of labelled training data, analysing the data for correlations and patterns, and using these patterns to make predictions. Over time, AI systems learn, improving the accuracy of their predictions.   

 

Whilst AI technology is maturing fast, it is only as good as the data it has access to. We only need to look how organisations with good data have used Machine Learning to drive top-line and bottom-line performance while those with poor data have been left behind to see how important data is. Accurate, high quality, comprehensive data is essential. AI can’t make accurate predictions on poor data, no matter how sophisticated the technology is.    

 

 

Why invest in AI? 

 

AI is already ubiquitous in consumer IT and enterprises are not far behind. Why? Because, when implemented successfully, these technologies radically reduce the cost, and improve the quality and efficiency of, most core enterprise functions including planning, analysis, reporting, customer service, IT support and Enterprise Resource Planning (ERP). They also improve the speed and quality of complex decision making.   

 

 

Where to start? Getting your data ready for AI 

 

Most enterprises will purchase AI products rather than try to build them so their key AI enabler will be improving the data their AI will use. The aim: accurate, high-quality, and complete data that is AI ready:   

 

  • Treat data as a strategic asset. By default, functions will tend to create data that works for them to address the problem immediately in front of them. From an enterprise perspective, this creates data silos (data that only works for one function) and undermines data quality (data that is only ‘good enough’ for a specific purpose). Fixing this requires senior leaders to think about data as a potential source of strategic value when managed correctly; pivoting from a siloed short-term approach to a mode of long-term decision-making that prioritises the enterprise over domain or function specific requirements.   

 

  • Plan the right investment. Our experience shows under-funded data governance and architecture functions and programmes are unlikely to deliver value. Often, these cost lines are categorised as support or overhead, undermining the case for benefit driven investment. For most, getting their data AI ready is a high bar and requires appropriate levels of investment, commensurate with the potential benefits of AI. This investment is likely to cut across new systems capabilities, increasing specialist resource and re-engineering business processes.    

 

  • Recruit and empower the right people. Data governance and architecture are specialist disciplines. While generalists and domain subject matter experts are invaluable, getting data right is a complex problem that requires people with deep expertise in the area. Having recruited people with the expertise, it is important to ensure they have the right senior management support and decision-making authority and can steer the enterprise to the right outcomes.  

 

  • Build data understanding and capability. As well as bringing expertise into the organisation, understanding of data management principles across your business is critical. The majority, if not all, of a modern organisation’s workforce touches its data in some way and it’s increasingly important these users understand and follow principles such as consistency, ownership and re-use over duplication to name a few. Such pervasive data skills and understanding support a foundation of ongoing data value for an organisation, in turn increasing the potential benefit opportunity for AI.  

 

  • Prepare to move at pace. It is important to recognise that AI for enterprises is reaching an inflection point. The market is maturing rapidly and, like cloud and ‘as a Service’ before it, AI is set to become mainstream. In the near-term, organisations will be using AI to automate core enterprise functions and make better decisions, reducing costs, improving customer interactions and simplifying operations. The key to gaining early mover advantage, or keeping up with the competition, is data. No matter how sophisticated it is, AI will only ever be as good as the data it has access to.   

 

 

Do you need change expertise? 

 

Project One has been helping organisations prepare for, and realise the benefits of, disruptive technologies for 25 years. Supporting leadership teams shape, enable and deliver complex digital change. Want to get ahead of the AI adoption curve and gain early mover advantage? We can help.   

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