Companies that want to win in the future of work know they need to architect how they do business around a combination of artificial intelligence, data, the cloud, and intelligent automation. According to a Harvard Business Review survey, in fact, 60% of respondents say the future of their organization depends on the successful implementation of machine learning.
One reason for the popularity of cognitive technology is that even in 2019, with a decade of digital transformation in the rearview, many business-critical processes remain manual and labor- or skill-intensive. While automation is growing in popularity, Capgemini reports that very few companies have deployed it at scale.
Organizations also still face challenges when it comes to working with data. Not only do some businesses lack data science and engineering talent, but Gartner estimates that 80% of the average business’s data is either unstructured data like documents, audio files, and even video. This wealth of unstructured data creates a new problem: mining large amounts of mixed-format data for strategic business insights requires a tremendous engineering investment that most businesses just aren’t prepared for. According to a survey by Sailpoint, 71% of enterprises are struggling with how to manage and protect unstructured data—which indicates that they’ll also have a hard time parsing it.
Machines, on the other hand, are getting better every day at tasks that used to depend on humans. While it’s true that cognitive technologies, such as AI or machine learning, can’t quite do everything a human can, they can make repetitive, data-heavy processes (like analyzing contracts or triaging support tickets) smarter and more scalable.
When it comes to implementing these technologies, business systems professionals are invaluable since they’re responsible for helping the organization scale systems, tools, processes, and integrations—while still keeping the future of work in mind. They see the technology future ahead, yet are building sensible roadmaps for their organizations to leverage these emerging technologies today. Here are three ways that business systems professionals at leading companies are ushering in the future of work with AI.
Lighten your reading list with OCR
Optical character recognition (OCR) is a form of cognitive technology that uses a computer to “read” a block of text and extract useful information. Using intelligent software to parse long, dense documentation is faster and more accurate than relying on humans—which is why OCR is already a popular component of contract-based workflows like vendor renewals and hiring.
From a systems perspective, incorporating AI into contract workflows makes a lot of sense, especially if you’ve already optimized your content management.
“Once a contract repository is digitalized, contract administrators need to be able to sift and search through high volumes of information—often scanned from hard copies—efficiently. Sophisticated OCR enables computer software to locate and pinpoint details from party names to renewal dates instantly, with the click of a button,” notes Jason Martinez of G2Crowd.
Processing invoices for accounts payable is a key focus for many business systems leaders who are ready to use
“Invoice processing, auditing, and digitization of documents is a common OCR driven automation used by our customers,” explains Tridivesh Sarangi, a Product Lead at Workato. “We see them using tools like Docparser, Ephesoft, Abbyy, and Captiva. Of course, these OCR technologies vary in capabilities, accuracy, scale, performance/throughput, cost and, most importantly, their ability to use Machine Learning/Artificial Intelligence to adapt quickly. Internally we use Google Cloud Vision APIs for image analysis as it’s easy to use APIs via Workato, but it is not a packaged product.”
OCR is also excellent for minimizing the amount of manual data entry required to complete a process. A leading retailer, for example, uses a form of OCR to parse job postings from competitors. By pulling out relevant data—like the position title, salary being offered, and job location—and analyzing it, the retailer can offer more competitive wages to prospective employees.
Some business systems leaders have even had success deploying AI for large-scale audits. For organizations that generate a lot of expense reports, for example, auditing is often an entirely manual process, which limits the sample size of audited reports. Smaller sample sizes can potentially lead to compliance violations and even abuse of organization resources. Cognitive tech can automate almost 100% of the auditing process, making it much easier and faster to analyze vast amounts of financial data and identify anomalies.
Welcome employees to the future of work with AI for HR
From a systems perspective, HR processes like hiring, onboarding, promotions, and offboarding can be some of the most difficult processes to scale. That’s partially because these processes are inherently dynamic: staffing needs fluctuate, as does employee performance.
AI isn’t quite ready to replace humans when it comes to functions like interviewing candidates. But cognitive technology can help push HR into the future of work by allowing them to be more data-driven.
A luxury Canadian store, for example, uses AI to inform decisions about hiring and promotion. Because cultivating good customer experiences is a top priority, the company asks patrons to fill out a survey about their time in-store and the quality of service they received. These surveys are then pushed through an AI tool that parses the data and identifies staff who dedicate themselves to consistently providing excellent service—while simultaneously streamlining the escalation process for complaints.
This helps the company make more intelligent decisions about which workers to keep on. Because the retailer frequently hires many seasonal workers during busy times, this data is especially useful in deciding which employees should be re-hired or transitioned to full-time positions.
When it comes to employee retention, AI (and its cousin, intelligent automation) can also go a long way towards making sure new employees have a great first experience with your organization. According to Stitchfix’s business systems leader Kumud Kokal—formerly of Airbnb and Intuit—using technology to improve the employee experience should be top-of-mind for all systems professionals.
“Since onboarding is the first interaction an employee has with the company, we wanted to make sure that their experience was hassle-free and intuitive,” he recalls. “This allows them to complete their new-hire tasks quickly and allowed them to get to more interesting work sooner. Again, this leads to higher employee engagement at the beginning of their journey in the company, as well as greater productivity.”
Design a more intelligent sales and marketing funnel based on real data, from awareness to close and upsell
Marketing-sales alignment and lead scoring have a huge impact on the company’s bottom line. It’s a process that should constantly be improving, because the better your lead scoring, the more deals your sales team can close.
Cognitive tech like AI can help sales be more agile and responsive. Most sales teams rely on a lead scoring system to rate leads based on their overall quality and the likelihood they’ll close. By allowing for quick analysis of multiple data points, AI can illuminate trends across seemingly incongruous lead behaviors—which is key to making sure your lead scoring system is as accurate as possible.
Some CRMs offer add-on intelligent lead scoring features, such as Salesforce Einstein. But even though most don’t offer native AI, it’s relatively easy to create a workflow that pushes new leads into a cognitive engine for scoring. These leads don’t even have to exist in your CRM yet; you can use OCR to parse a picture of a business card so that an intelligent automation platform can create a lead, enrich it using a tool like Clearbit, and score it—all within minutes. Similarly, using AI for behavioral scoring can help you better understand which of your customers are most likely to churn—and intervene appropriately before they do.
Cognitive technology is also useful for shedding light on the “dark funnel” of marketing and sales. Many research and buying signals go unnoticed and may never be logged in your marketing platform or
Additionally, AI can take the heavy lifting out of common sales and marketing processes, like
The future of work is intelligent—and anything but artificial
As business systems leaders continue to move systems and processes towards the future of work, AI will become fundamental to many organizations. It’s important for systems professionals to start identifying opportunities for cognitive tech within their companies today, before it stops being a competitive advantage and becomes just another way the organization has fallen behind.
