Having not addressed the topic of AI in some time now, though in recently doing some research around AI, I came across a very relevant and interesting article. Naturally, my mind starting working overtime wading through my thoughts and opinions here, and so I decided to put them to paper and share some of my views.
There is no denying that AI is a very exciting topic and one that is fast taking over technology priority focus areas for any business on a structured digital transformation journey that puts the customer front and centre. In fact, the article I refer to above shares insights into some research undertaken that highlights nearly all of the mid-to-high level executives surveyed (99%) reported that at least one area of their company is currently utilising AI technology. And when looking forward, 89% expect the use of AI technology, across their company, to increase over the next two to three years.
While this certainly shows a keen appetite for AI, which I agree rightly exists, the article also notes that some people remain a bit sceptical to make the move. Having experienced this myself, and in reviewing the great insights shared from this research piece, I wanted to provide my take around some areas of AI that should be clearly defined, to allow a business to determine if they should invest in AI or not.
Decision-making around the investment in AI
Although AI is a technology and therefore naturally the technology division of the business tends to drive decisions over AI need and implementation, this should not necessarily be the case. A technology view alone could lead a business down a path that does not necessarily show good return on investment (ROI) on AI and therefore can result in AI being perceived as a costly task.
Although the technology department of the business are the users of AI, the insights delivered by or from AI (and how these are utilised in business processes and business strategy) are managed by the business decision makers. And this is where the real business value lies.
As such, I believe a two-pronged approach should be taken to AI implementation in business – an approach that sees IT and business working extremely close together to determine the need and use case for AI. The business decision makers alone cannot make decisions on which technology to use (and how) and neither should IT decide on which business problems/challenges or opportunities should be tackled using AI. These matters should be discussed holistically, and decisions made based on business objectives and purpose. Such an approach will only garner better AI decisions, results and ROI for a business.
The critical role of data to AI success
Some businesses are still slightly nervous about introducing AI into their systems and processes, for the fear of AI failure and the possible lasting impact this can have on customer experience and subsequently brand reputation. The research noted above highlights that 89% of respondents agree that if an AI solution doesn’t work well, it could hurt customer experience.
A business that wants to avoid this must take into account the fundamental role business data plays to the AI process, outcome and overall success. Data and AI are closely linked where the quality of the data directly affects the useability and effectiveness of the AI insights. An appetite for AI should never surpass the readiness of a business to implement AI. And if a business does not have its data house in order, the readiness for AI is simply not there. In my experience, a bad AI experience within the business creates a lot of mistrust in information and is a very hard hurdle to overcome.
AI can be a highly beneficial and critical tool for the modern digital business to gain a competitive advantage and build onto strategic growth. However, it is not a plug and play technology and the data component to its success must not be underestimated. Careful thought and consideration must be given to AI to ensure there is a clear need for it, it meets a business purpose and will produce a result the business requires. It is only through this understanding can a business decided whether it should AI or not.
