Think about where AI can deliver value in the enterprise.

The history of Artificial Intelligence (AI) technologies can be measured in decades, but only recently have they evolved into fully mature enterprise solutions like 1Desk and Amelia. Corporate decision-makers are increasingly looking to AI systems to help their operations become more efficient and build business value. If you find yourself wondering how (or if) AI is the right choice for your company, here are three questions to consider.

Does your company run on data?

If your employees make decisions based on data culled from multiple sources, chances are an AI system can automate many routine decision-making tasks. For example, a bank’s decision to approve a customer’s credit increase request is less of an art than it is a data-driven science – the bank will agree to an increase if the customer meets the appropriate threshold in predetermined areas (e.g. annual income, employment history, credit rating etc.). Modern AI technologies are able to automate the information-gathering process and independently apply it to a predetermined algorithm – all with little-to-no human mediation.

A bank that uses AI to automate this process can execute credit limit increases far quicker and more efficiently than any purely human-powered department (or non-AI-enabled competitor), while reducing overhead following the initial investment. AI also allows companies to be nimble for business or compliance reasons – for example, should the aforementioned bank decide to make it make it harder or easier to raise credit limits, it could tweak the algorithm and instantaneously apply it to all future applicants. The important takeaway is how business processes can be accelerated with limited human interaction through AI.

Do you make decisions based on predictions of future events?

AI systems don’t only react to static data from past events, they are able to make decisions based on events that haven’t happened yet. Machine learning (ML), a sub-field of AI, enables systems to discern patterns in data and forecast future events. This is, for example, how your phone’s weather app knows whether it will rain tomorrow with a high degree of accuracy – by processing data from surrounding regions and historical weather patterns from your location and time of year, along with other factors.

Business processes can be accelerated with limited human interaction through AI.

To apply ML to the above credit limit scenario, an AI platform could discern patterns from historical and observed data. For example, the data might quantifiably show that women with full-time jobs in specific zip codes have proven particularly dependable in paying their credit cards on time. The system might then recommend to a human colleague that the algorithm use this data point to award these customers higher credit limits compared to others. Or, depending on the autonomy granted to the system, the AI platform could even independently tweak the system without any human input.

Does your company have multiple, cloistered systems?

AI allows companies to bridge disparate business areas into a unified whole. These digital connections can transform enterprise systems into “living” autonomic systems in which AI automates all the steps that require one business area to speak with another.

For example, an AI system in charge of supermarket inventory could observe which products customers are purchasing at check-out and then automatically prompt employees to re-stock those items. An even more advanced system would independently order more product from vendors when inventory is low. One step beyond that, the system would anticipate rushes on certain items and purchase additional supplies beforehand (e.g. people like to grill outdoors in the summer, so that means ensuring the market keeps up its supply of hamburgers, hot dogs, etc. during those months).

Digital colleagues like Amelia can take things a step further by applying a natural-language interface to these complex autonomic enterprise systems. That means any human – regardless of technical prowess – could input high-level commands to the system, which would automatically be carried out.

The next big paradigm

Throughout the industrial revolution, physical tasks that were once purely the domain of humans were increasingly taken on by machines (e.g. steam hammers). By adding automations to the manufacturing process, production overheads decreased and output soared, resulting in a rapid rise in wealth generation. In the information age, routine transactional tasks became automated by computers (e.g. ATMs) and resulted in similar accelerated efficiencies. And now, as we enter the age of AI, dynamic software is further automating value chains by taking over cognitive tasks – and opening up nearly limitless potential.

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