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There was a time in the not so distant past, when clients would ask why they needed two fields to accommodate the office and mobile telephone numbers for their employees. Why we would ever need to know that much about anyone and why that data would ever be valued, was actually questioned.
Uttering something like that now is of course completely arcane. The cloud has democratized computing power, and we have what would seem to be an unlimited ability – and a business imperative — to collect, unearth, and pull together as much data as we can, as we advance our quest for information.
Advances in democratizing information have of course permeated our lives outside of work faster than they have inside the proverbial cubical. If we want to take the dog out for a walk, we can glance at an app that has pulled together weather data in near real-time, displaying it in an irresistibly user-friendly manner to get an up-to-date forecast. If we see that it’s going to rain, we can put on a coat that was purchased and influenced by a recommendation engine on a favorite brand’s website. And we can quite literally ask our phone (or even our watch) as we walk to make recommendations on a dog-friendly spot we can pop into to dry off and get a cup of coffee (or tea, if you’re English like me).
But that reality has yet to pervade the enterprise. We know the data is there, and we want to leverage it. But we simply can’t get to it—at least, not easily. It often requires people who are particularly good at searching the innermost recesses of the database to uncover the information we need, and subject matter experts to turn data into meaningful insights. By the time we get our hands on it, the opportunity has passed, and the information is too out-of-date to lend any real relevance to the decisions we were trying to make.
So the data sits there, in the dark, never becoming the insight we need to make critical business decisions quite as informed as the simple act of walking the dog. All the while, the volumes of data grow, threatening to shroud dark data in deeper, permanent obscurity.
It’s for this reason that I feel artificial intelligence (AI) is the most important enterprise technology since the cloud. AI-enabled ERP holds the potential to positively affect every single business process. And the changes AI will bring won’t be incremental; they will be extraordinary.
Let’s define the potential of AI in an enterprise context. AI refers to the ability of computers to perform tasks usually associated with human intelligence, extending beyond business analytics—even predictive analytics. AI doesn’t simply present you with solutions, it remembers the best solutions, and lends the ability to incorporate them into the business process. AI gives us both access to information and the time to leverage it for business advantage.
Just think about any business process, and how it might be accomplished when the system is not waiting for you to figure out what to do next; instead, it’s making key recommendations on how to best accomplish the task. It’s at that juncture that AI makes a difference.
In retail, for instance, artificial intelligence is often most discussed in the context of personalized marketing. But the back office stands to benefit just as much, if not more, than areas we traditionally consider customer-facing. And those improvements on the back-end will have ripple effects across the organization.
Let’s consider an example. In my work, I talk with a lot of CFOs. And across the board, one of the processes that finance teams are continually trying to optimize is the month-end close. It’s a process that still, after years of investment, isn’t fully automated and can suffer from a lack of quality data, which requires manual intervention.
Artificial intelligence has the ability to accomplish the equivalent of continuously running month-end close in the background – such that when it’s actually time to complete the process, it takes a fraction of a minute, rather than days.
The essence of AI is learning from an abundance of data and building models that work in the specific business function and industry. Crucial to that is deep functional and domain knowledge to interpret the findings and further fine-tune the model. The system alerts you to exceptions, and remembers each time you address those exceptions so that it can offer a solution the next time a similar event occurs. If you keep accepting the recommended solution, the system will learn. It will get to a point where it asks whether you want to do it manually anymore, and configure the business rules to let the system make a decision. The system is anticipating, and it’s learning from its own mistakes.
The people in your finance organization devoted to chasing information will be freed up – and empowered with new insights — to do other things.
One of the most important steps you can take as an enterprise leader is learning how artificial intelligence can augment your ERP systems and unearth dark data. An AI strategy that builds on your most strategic technology – your enterprise systems – to empower your biggest asset – your people — will help you transform your organization to meet the demands of today’s digital imperative.
