#EffectiveUse Archives

With most innovative technologies, at some point in the solution evolution there’s a tipping point from “this is cool” to “there are real business outcomes to gain.” We are now seeing that shift with artificial intelligence (AI) and machine learning (ML), especially in three major use case patterns:

1. Customer care

When it comes to customer care, many companies face a similar set of challenges: Customer service representatives (CSRs) typically need to view multiple windows on multiple screens to find answers to a caller’s questions. This results in calls taking a long time to handle, frustrated customers, lower CSR productivity and overall poor customer satisfaction.

AI and ML can help change these outcomes. One insurance customer implemented AI and ML to transform its contact center, which responds to approximately 5 million voice calls per year. It also introduced an intelligent voice agent to respond directly to 50-70% of all customer inquiries. The power of AI and ML extended and enhanced the customer experience and the productivity of CSRs, who are now able to deliver more accurate and consistent answers, at lower cost and greater speed. As a result, we have also seen reduced attrition, as the CSRs are happier in their roles and require less training time to become self-sufficient. Overall, by using AI and ML, the contact center is able to provide a high level of customer service, enhanced customer satisfaction and more efficient engagement.

In another example, a customer service center handling 60M service requests per year is implementing AI “smart chat” to transform customer service, drive better quality, improve consistency and lower costs.

The transformation of an operation as complex as a customer call center is a multi-step journey:

Organizing and visualizing the data required to support typical workflows is a critical early step and can include authenticating/validating the caller, providing easy access to the data from different back end systems, initiating transactions and closing out the call.
A second step adds natural language processing (NLP) to allow the CSR to ask more complex questions. NLP uses a trained ML model to determine the most likely answers from a collection of large, unstructured documents.
A third step includes self-service for external callers, using voice and natural language to respond directly to client inquiries without requiring interaction with a CSR.

While the steps themselves remain the same, the sequencing may vary to align to the specific outcomes the organization desires.

2. Human capital management

By applying cognitive capabilities to human resources, companies are able to transform their human capital management (HCM) processes.

One valuable use case improves the employee experience by leveraging intelligent natural language conversation services to integrate data in operational HR data systems, such as Workday, and in knowledge management systems that explain policies, procedures and guidelines. Benefits include improved employee engagement, higher productivity and lower costs.

Other potential HCM use cases that are ripe for AI and ML include recruiting, selection and on-boarding of candidates. AI and ML models are already being used to help analyze and match job descriptions and candidate resumes, and to recommend potential jobs for existing employees.

3. Process rethinking/reimagination

There are also multiple examples of leveraging AI and ML for rethinking and reimagining processes.

In one example, an insurance company reduced claim settlement time and costs by incorporating AI and ML to improve First Notice of Loss (FNOL) initiation and data collection, as well as claim classification/assignment processes.

Companies can improve the speed and accuracy of FNOL by enabling claimant self-service using natural language voice/chat to initiate a claim. They can integrate external data sources such as weather and employ a fraud scoring model to facilitate claim classification and corresponding assignment to the correct processing alternative. Processing alternatives can include:

Straight-through processing without human involvement
Sending appropriate claims to the legal department for potential early intervention to reduce lawsuits
Rejecting claims because the deductible is higher than the claim
Assigning claims to a human analyst/adjustor

Predictive modeling to triage injured worker claims has been developed and applied to help determine “return to work” outcomes, along with typical treatment plans for various types of injuries (NOT replacing doctors) and estimated treatment costs. This technique optimizes case manager resource assignment and helps injured workers get back to work, improving service quality and lowering costs.

Applying an ML model to extract information from unstructured documents and forms helps to provide insights from previously unanalyzed sources. This capability works by accelerating processing of information buried in those large unstructured documents and drives new insights, lowers costs and improves processing efficiency.

In a fourth example, Anteelo is automating the underwriting process for home inspection to lower costs and improve scalability and overall quality. This use case applies visual recognition to highlight details in home inspection photos and use those details to consult the underwriting guidelines and then augment the underwriting decision.

As you can see from these use cases, the ability to improve the customer experience and gain operational efficiencies through AI and ML are real and very achievable. What other use cases can you think of for your industry? I’d love to hear your thoughts!

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The future of work is bringing new challenges for Human Capital Management (HCM) practitioners, especially as the skills shortage becomes a day-to-day reality. In the near future, the people responsible for people in your organization — from CHROs to payroll specialists, will need more time to strategize, analyze, and innovate. With truly innovative HCM technology, not only will employees have more time, employee productivity and engagement will increase.

Workday Payroll is designed to help employees increase efficiencies, eliminate errors, and anticipate changes, particularly around compliance or tax related items—a competitive advantage in a fast-changing regulatory environment. Plus, Workday Payroll not only saves employees time, but perhaps most critically, reduces questions to the payroll department and increases insight around the complex process of compensation.

Workday Payroll best practices

Our Workday experts at Anteelo recommend starting with the following simple steps to drive user engagement and adoption to get the most out of Workday Payroll dashboards:

Share—and share often.

For employees involved in the day-to-day work of payroll (or any HCM work), new technology requires that management does their due diligence in communicating the change. But we’re all bombarded with hundreds of messages every day; so this requires creating messaging that has meaning:

Empathize with the difficulties of change (even positive change is stressful!).
Make it personal: Share how Workday Payroll dashboards will benefit both employees and administrators in day-to-day work. For example, teach employees how to easily compare pay periods and make updates, such as tax elections, from one place.
Show payroll partners and administrators how the dashboard gives them instant visibility into the status of each payroll, retro differences to be paid, and employees affected by regulatory changes (such as tax rates).

Follow-up with non-users.

It’s simple: Ask employees who aren’t using Workday Payroll dashboards why they aren’t. Engage employees by providing more training, enabling one-on-ones with power users, giving employees extra time to learn about the dashboards, and/or scheduling frequent check-ins to answer questions and provide encouragement.

Invite existing users to share why they love Workday Payroll dashboards.

Ask employees who are using Workday Payroll dashboards what they love about it. And then ask them to share this with their peers. Happy users are the best advocates for new technology. Product evangelists—your employees who love payroll dashboards—can greatly influence others to get on board.

Make sure updates go smoothly.

Build a repeatable test plan for updates and always communicate any upcoming changes to Workday Payroll dashboards far in advance. If you need help, our experts can help you develop a solid testing plan that is both repeatable and not overly taxing on resources.

Plan ahead—and communicate frequently—about big events, such as audits.

One of the advantages of Workday Payroll dashboards is that they enable users to start the audit process early. Help employees understand that auditing early and often, using the tools in payroll dashboards, can make the end of the year far less stressful—and even reduce the need for overtime around the holidays.

Creating a positive, engaging user experience is the first step to getting the most out of Workday Payroll.

It’s imperative that employees doing the day-to-day payroll work need to know how Workday Payroll dashboards help them personally do their jobs better. Organizations can drive greater adoption and engagement by actively sharing the employee-centric benefits of payroll dashboards, encouraging existing users to advocate for the technology, and making it easier for non-users to become engaged users.

Tag: #EffectiveUse

AI and ML Advancement leading to new Application Emergement

1. Customer care

2. Human capital management

3. Process rethinking/reimagination

Workday Payroll dashboards: How to Get the Most Out of Them