For many, a standard weekday morning routine looks something like this: we summon Alexa or Siri to check on the weather to plan what to wear. Spotify serves up a personalized playlist that neatly matches the tempo of our workout. And when we leave for work, Waze seamlessly guides us to our destination, where self-driving cars can park themselves into impossibly tight spaces. Our daily lives are full of these intelligent helpers, saving us tons of time and removing complexity, so we can avoid wasting energy on many mundane mental tasks.
But when we finally sit down to begin our workday, few intelligent assistants are to be found.
Most work in the enterprise is still filled with a frustrating amount of repetitive tasks. For example, onboarding a new client requires extracting data in business documents and financial statements. Data points have to be laboriously located and collected by employees, with no help from intelligent assistants to make data easily available to get insights and take action.
Before a company can automate processes—in finance, sales, or operations—data needs to be properly structured and standardized. This is a data bottleneck that creates a poor client experience, delays new revenues, and risks employees missing data. Fortunately, this is where AI and machine learning are perfectly suited to assist overworked employees as an intelligent co-pilot that makes data easier to assemble, analyze, and use.
Despite being business-critical, there is a tendency to view these data-extraction workflows as a simple series of copy-paste tasks, and nothing could be further from the truth. The reality is that there is deep human knowledge embedded into this surprisingly complex business function, requiring situational awareness, organizational context, and lots of training. For instance, subject-matter experts have the cognitive ability to make distinctions such as:
- Is it the net or gross values for Q2 that is required for our financial reporting?
- Do we own class A or class B shares in this fund?
- Which legal entity is distributing the cash from the investment?
Answering these types of questions in a business process means that intelligent assistants like Alkymi Data Inbox are purposefully designed as a ‘human-in-the-loop’ system. The subject matter expert is always in the driver’s seat, controlling the outcome, and Data Inbox knows when data extraction requires a human mind to get involved.
When it needs help, Data Inbox gracefully calls on subject matter experts to flex their human intelligence to give the machine learning models a push across the finish line, a learning process that makes Data Inbox smarter going forward. This partnership removes manual repetitive tasks from the workflow of subject matter experts and opens up myriad new automation opportunities in every enterprise. The ROI is compelling too, with 10x faster data processing, 10x fewer mouse clicks, and 50% fewer human errors.
Unblocking the data logjam is nothing short of revolutionary: 451 Research has cited that a third of CIOs expect AI/ML to be the single most transformative technology to their business within the coming 5 years, and data extraction is a key part of this shift. Already, almost two-thirds of firms have made use of the technology in an active deployment or proof of concept, and Bain predicts companies will double data-driven automation in the next two years.
When humans aren’t constantly bogged down in their personal lives addressing mundane tasks, there’s less mental fatigue and more time spent actually enjoying the day. Likewise, human-in-the-loop designs free us to focus on customers, identify new opportunities, and generate valuable insights at work. After all, what makes life enjoyable is the time to pursue the things that matter most to us.
Take a demo to see how Alkymi can help you bring intelligent automation to your workplace.