At the end of every year, I take a few minutes to think back on the most disruptive technologies of the previous year and consider what tomorrow might have in store. This past year will undoubtedly be remembered as the year of Artificial Intelligence and chatbots. However, when it comes to developing technologies that truly change the way organizations operate, the hype of the simplistic call-and-response chatbots we saw this year will likely be just a small blip on a much longer journey towards true AI. As we approach 2019, I believe that technologies that will truly impact business operations and ROI may not garner as much hype but will bring greater change. This includes trends such as digital transformation, Deep Learning, and low-code/no-applications.
In the 1980s, the television program Knight Rider gave a glimpse into a world where artificial intelligence could learn, communicate, and make independent decisions. The star of the show, a self-aware computer, was housed in a 1982 Pontiac Trans Am, and it intrigued viewers. They imagined how the technology could change lives, handling dull or dangerous tasks with a simple spoken command. At the time, such software sounded like a creation of science fiction. Now, less than four decades later, artificial intelligence and machine learning technology are our reality, communicating with users through natural language interfaces.
When the average person thinks of bots entering the workforce, scenes from X-Men: Days of Future Pastleap to mind, but actually, bots are not the future, they are already adopted and deployed among us. The reality of bots in day-to-day life is less “dominating Sentinel” and more “helpful and productive assistant.”
A bot is a piece of software that is designed and created to automate the kinds of tasks we would usually do on our own. Powered by a set of simple rules and varying degrees of artificial intelligence (AI), we can now create bots that can hold natural-sounding conversations with human beings with the aim of accomplishing tasks, such as answering questions or enabling product purchases.
In the 1980s, the television program Knight Ridergave a glimpse into a world where artificial intelligence could learn, communicate and make independent decisions. The star of the show, a self-aware computer called KITT (Knight Industries Two Thousand), was housed in a 1982 Pontiac Trans Am, and it intrigued viewers. They imagined how the technology could change lives, handling dull or dangerous tasks with a simple spoken command. At the time, such software sounded like a creation of science fiction. Now, less than four decades later, artificial intelligence (AI) and machine learning technology is a reality, communicating with users through natural language interfaces. Everyday jobs will soon be transformed as this technology advances.
The first week on any college campus is hectic, but what if there was a digital assistant available 24/7 to help new students and their families navigate the college process?
With chatbots rapidly infiltrating every aspect of our personal lives, it’s no surprise that student technology is an area ripe for bot intervention. As both students and faculty embrace bots in their personal lives for things like checking the weather, ordering pizza, or finding a cab, it’s about time that campus tech caught up.
Four major enterprise management trends are maturing simultaneously, and their imminent fusion has only begun to transform the way we conduct business. They are big data, mobile access, user experience, and computer intelligence.
Individually, each of these breakthroughs is game changers. Together they will combine into a new, vast and complex enterprise software. Mastering it will require a new type of software capable of autonomously emulating how people perform tasks and make decisions. By operating in the background on a massive scale, it will free employees from mundane tasks and empower them to spend their time on high-level decision-making and oversight. It is called self-driving software.
By using advanced technologies within pattern recognition, machine learning and computer aided decision support systems, as well as new IoT devices, software can make automated decisions on behalf of the user, or provide guidance to allow users to make intelligent decisions based on smart patterns. This is achieved through self-learning software that assumes actions based on previous or similar scenarios and uses that to pre-populate and self-drive.
Interaction with the software is minimized to only involve people when human intelligence and experience is critical to resolving a business issue or to drive business processes in the right direction. If user interaction is required, a UI will be provided in its simplest form to accommodate that, on the users preferred device of choice, intelligently adapted to the situation.
Incredible value can be derived by limiting the amount of manual input required by users so they can focus on serving customers. This is the future of business software.
What is self-driving enterprise software really about?
In a nut shell self-driving enterprise software is literally about creating software with which the user does not need to interact, or at least interaction is minimized. The reality is that the majority of the interactions users have with their enterprise software is administrative. It’s not about doing their job well or running their business but simply maintaining the mechanics of sending invoices, doing bookkeeping or filing expense reports.
All these mechanic interactions can be tracked, analyzed and fed into algorithms so the software can learn typical usage patterns within a specific context whether that’s procurement, invoicing, bookkeeping or expenses. Through pattern recognition and statistical analysis the software can “learn” typical user actions in specific situations and automate the expected action in the future.
Analyzing past interactions will allow us to construct more semi-intelligent solutions. Solutions that dynamically alter behavior based on previous interactions, decisions and user actions. There are a number of different aspects to self-driving software.
The fundamental idea of adaptive UI is, as the name suggests, UI that intelligently adapts to the user based on previous interactions. In today’s enterprise software, forms or screens typically contain many fields that need to be filled out by the user. Some fields are linked with other fields, and there are even hard dependencies meaning that if a certain field has a specific value, others need to be populated exactly right for the system to accept the data as a whole. As a causal user simply entering a purchase order it can be a horrifying experience – regardless of how nice the UI looks. The adaptive UI records everything the user is doing, when he or she is doing it and in what order. If a user always populates a specific number of fields with the same values it can do that automatically. Even better, if some fields are always left untouched – they can be removed dynamically from the UI to make it less ‘noisy’ or confusing. By removing un-used fields, and populating everything automatically based on previous interactions – the system can complete the task on the user’s behalf. For example, if someone orders A4 binders on every second Monday from the same vendor, the system might as well just do it.
Normally business processes require user interaction for approvals. A manager needs to approve a request for a new laptop from one of his reports for example. If the manager in question always approves requests within certain criteria, most commonly the price, the system can give that approval and notify the manager it’s been done.
Historical and Current Data
What I’ve discussed above is all based on historical data; however, using aggregation of historical and current data – like your present location software has the ability to automatically populate travel expenses, field service work reports, and even suggest additional maintenance work do be done as part of a field service visit.
Some examples of where self-driving ERP will add significant value:
Expense reporting is a great example of an administrative task that lends itself to self-driving software. In the future systems will automatically create line items in an expense report based on the scanning of a receipt. By extracting the address the system can gauge what the item is and based on the time know if it’s breakfast, lunch or dinner. Essentially populating the complete line. Rather than having to store the receipt, go into the system and enter expense type, date, amount, all the user has to do is photograph the receipt.
Within field service, systems can schedule maintenance visits for equipment based on the previous maintenance of similar equipment at other customers. Also, if a visit is already planned, it may prove more economical to – pro-actively – service other equipment while already on-site.
The future is here
Previously analyzing massive amounts of data was expensive due to the amount of data storage and computational power required. However, due to ever decreasing prices on storage and CPU, running advanced algorithms becomes more accessible enabling inclusion of semi-intelligent agents into a vast majority of the functionality within enterprise software. Customers will benefit from being able to focus on running their businesses and serving their clients rather than the tedious mechanics of maintaining the ERP.
This is why we focus on developing enterprise software that allows customers to focus on their business instead of focusing on the software, by making it completely self-driving. We’re already starting to deliver this in our software.